Computational Investigation of the Sn2 Reactivity of Halogenated Pollutants

COMPUTATIONAL INVESTIGATION OF THE SN2 REACTIVITY OF HALOGENATED POLLUTANTS

A DISSERTATION SUBMITTED TO THE DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING AND THE COMMITTEE OF GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

Brett Taketsugu Kawakami December 2010

This dissertation is online at: http://purl.stanford.edu/cs274qq3228

ii I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.

Martin Reinhard, Primary Adviser

I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.

Lynn Hildemann

I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.

James Leckie

Approved for the Stanford University Committee on Graduate Studies. Patricia J. Gumport, Vice Provost Graduate Education

This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file in University Archives.

iii Table of Contents Abstract...... iv Acknowledgements ...... v Table of Contents ...... vi List of Tables ...... xi List of Figures...... xiii List of Abbreviations ...... xv

1 Introduction...... 1 1.1 Motivation...... 1 1.2 Objectives ...... 2 1.3 Nucleophilic Substitution Reactions...... 2 1.4 Previous Efforts to Understand Reactivity of Halogenated Pollutants...... 3 1.4.1 Limitations of Experimental Interpretation ...... 3 1.4.2 The Role of Solvent ...... 4 1.4.3 The Advent of Computational Studies...... 4 1.5 Research Overview ...... 5 1.6 Halogenated Compounds of Potential Environmental Significance...... 6

2 SN2 Reactivity of Halogenated Compounds ...... 8 2.1 Reaction Environments...... 9 2.1.1 Solution/Hydrolysis ...... 9 2.1.2 Biological Transformations ...... 10 2.1.3 Gas Phase...... 10 2.2 Previous Investigations of SN2 Reactivity ...... 11 2.2.1 Hydrolysis...... 11 2.2.2 Biological Transformations ...... 17 2.2.3 Gas Phase Studies ...... 19 2.2.4 Theoretical Studies...... 20 2.3 SN2 Reaction Profile ...... 21 2.4 SN2 Transformation Rates...... 24 2.4.1 Empirical – Arrhenius Equation ...... 24 2.4.2 Statistical Rate Theory – Eyring Polyani Equaton ...... 25 2.5 Existing Haloalkane Structure-Reactivity Understanding...... 25 2.5.1 Nature of Effects...... 26 2.5.2 Structural Features ...... 26 2.5.3 Solvent Effects...... 29 2.6 Theories of SN2 Reactivity...... 30 2.6.1 Valence Bond Configuration Mixing Model (VBCM)...... 31 2.6.2 Ab Initio Methods – Molecular Orbital Theory...... 32

3 Research Approach...... 33 3.1 Approach...... 33 3.1.1 Calculation of Activation Energies (Chapter 5)...... 33 3.1.2 Development of SRRs (Chapter 5) ...... 34

vi 3.1.3 Identification of Mechanisms (Chapter 6) ...... 34 3.1.4 Docking Studies (Chapter 7)...... 34 3.1.5 Quantum Chemical Based QSAR Models for Hydrolysis and Biological Data (Chapter 8)...... 35 3.2 Hypotheses to be tested...... 35 3.3 Relationship of Gas Phase Activation Energies to Environmental Transformation Rates...... 36 3.4 Nucleophiles Included in the Study ...... 38 3.5 Compound Set...... 38

4 Methods...... 42 4.1 Ab Initio Methods...... 42 4.1.1 The Hamiltonian ...... 42 4.1.2 Solution of the Schröedinger Equation...... 43 4.1.3 Gaussian 98 Calculations...... 44 4.2 AUTODOCK ...... 47 4.2.1 Energy Evaluation...... 47 4.2.2 Monte Carlo Simulated Annealing ...... 47 4.2.3 AUTODOCK simulations...... 48

5 Ab Initio Calculations and Structure Reactivity Relationship (SRR) Development ...... 55 5.1 Results...... 55 5.2 Structure Reactivity Relationships for Govr ...... 58 5.2.1 Leaving Group Effects...... 59 5.2.2 Chain Length...... 61 5.2.3  substituent effects...... 62 5.2.4  substituent effects ...... 65 5.2.5 Vinyl groups...... 69 5.2.6 Cyclic groups...... 69 5.2.7 Other Observations...... 70 5.3 Solvent Effects...... 73 5.4 Other Nucleophiles...... 75 5.5 Summary of Structure Reactivity Relationships...... 75 5.6 Implications for Experimental Interpretation...... 79 5.6.1 Comparisons with Experiment...... 79 5.6.2 Relationship to experimental hydrolysis activation energies...... 82 5.6.3 Understanding reactivity patterns for a set of critical environmental pollutants 86 5.6.4 Conclusions and prediction of reactivity in biotransformation and hydrolysis...... 91

6 Mechanism of Substituent Effects ...... 95 6.1 Need for Research...... 96 6.2 Proposed Theories for Mechanism of Substituent Effects...... 97 6.2.1 SN2 TS Structures in Concerted Reaction...... 97

vii 6.2.2 Bond Coupling...... 98 6.2.3 Valence Bond Configuration Mixing Model (VBCM)...... 100 6.2.4 Through Space Stabilization/Intramolecular Solvation...... 103 6.3 Hypothesis...... 104 6.4 Approach...... 104 6.4.1 TS Distortion Analysis...... 104 6.4.2 Correlations with Electron Affinity ...... 104 6.4.3 Identification of TS Stabilization/Intramolecular Solvation Effects ...... 105 6.5 TS Distortion Energies...... 105 6.5.1 TS geometries ...... 105 6.5.2 Morse potential...... 106 6.5.3 BDE Analysis...... 107 6.6 Evidence for Electron Delocalization ...... 113 6.6.1 Role of electron affinity in bond coupling delay ...... 113 6.6.2 Correlation of BDE and Govr with VEA...... 114 6.6.3 Determination of VBCM values ...... 119 6.6.4 Conclusions from VEA Analysis...... 120 6.7 Correlations of Govr with LUMO Energies...... 120 6.7.1 Relationship between VAE and LUMO ...... 120 6.7.2 Comparison of Govr to LUMO...... 121 6.8 Evidence for Through Space Stabilization/ Intramolecular Solvation ...... 125 6.9 Conclusions...... 125

7 Docking Investigation of Haloalkane Binding in the Active Site of Haloalkane Dehalogenase ...... 127 7.1 Introduction...... 127 7.2 Haloalkane Dehalogenase...... 128 7.2.1 Active Site and Mechanism ...... 129 7.2.2 Reaction Steps...... 130 7.2.3 Substrate Range...... 131 7.3 Factors Governing Enzyme Catalysis...... 131 7.4 Research Approach ...... 132 7.4.1 HAD Docking Study with Haloalkane Pollutants ...... 133 7.4.2 Hypotheses...... 133 7.5 2HAD Crystallographic Structure...... 133 7.6 AUTODOCK Evaluation criteria ...... 133 7.7 Results and Discussion...... 135 7.7.1 Validation for 12DCA docking...... 137 7.7.2 Modes of Binding ...... 138 7.7.3 Structure Effects on Docking...... 140 7.7.4 Correlation between Docking and Experimental Activity...... 140 7.7.5 Relationship with Calculated Gcent ...... 142 7.8 Conclusions...... 144

8 Quantitative Structure Activity Relationships...... 148 8.1 Background...... 149

viii 8.2 Approach...... 149 8.3 Experimental Data Set ...... 150 8.4 Descriptors ...... 152 8.4.1 Activation Energy Descriptors...... 153 8.4.2 Solvent Effects Descriptors...... 153 8.4.3 Hydrophobic Effects Descriptors...... 154 8.4.4 Enzyme Interaction Descriptors...... 154 8.4.5 Structural Variability Covered by Descriptors ...... 155 8.4.6 Experimental Data Relationship with Govr ...... 155 8.5 MLR...... 156 8.6 PCA...... 157 8.7 Overview of Results...... 157 8.8 Hydrolysis Model I...... 158 8.8.1 Principal Components Analysis – Model I ...... 160 8.8.2 Model I MLR ...... 161 8.8.3 Model I Predictions...... 163 8.9 Hydrolysis Model II...... 164 8.9.1 PCA Analysis for Model II...... 165 8.9.2 MLR for Model II ...... 166 8.9.3 Model II Cross Validation – Leave-One-Out Analysis ...... 166 8.9.4 Model II Validation – Development and Testing of a Training Set ...... 168 8.10 Hydrolysis Model III...... 171 8.10.1 PCA Analysis for Model III...... 172 8.10.2 MLR for Model III...... 172 8.11 Biodegradation Model...... 173 8.12 Conclusions...... 174 8.12.1 Model Performance and Interpretation ...... 175 8.12.2 Utility of Quantum Chemistry Descriptors...... 175 8.12.3 Common Modes of Compound Variance and Association with Mechanisms ...... 176 8.12.4 Predictive Value...... 176 8.12.5 Summary and Next Steps...... 176

9 Summary and Conclusions...... 178 9.1 Suitability of Computational Methods for Studying Haloalkane Pollutant Behavior...... 178 9.2 Categories of Structural Effects...... 179 9.3 Major Modes of SN2 Reactivity...... 180 9.4 Implications for Transformation Potential for Halogenated Pollutants...... 181 9.4.1 Comparing Experiment to Intrinsic (Gas Phase) Order of Reactivity .... 181 9.4.2 Measurable or Calculable Properties for Estimation of Reactivity ...... 181 9.4.3 Use of Quantum Chemistry Values as Descriptors in QSAR analysis... 182 9.4.4 Docking Frequency and Orientation...... 182 9.4.5 Similarity of Substrate Range Among Nucleophiles...... 183 9.5 Future Work...... 183 10 Bibliography ...... 184

x List of Tables Table 1-1: Some Environmentally Significant Halogenated Compounds...... 7 Table 2-1: Experimental Studies of Haloalkane Transformation ...... 11 Table 2-2: Experimental Hydrolysis Half Lives...... 15 Table 2-3: Substrate Structural Features and Observed Impact on Reaction Rates ...... 28 Table 3-1: Structures of Halogenated Compounds in this Study...... 39 Table 4-1: Structures Calculated in this Study ...... 46 Table 4-2: Energies calculated in this study ...... 47 Table 5-1: Energies of Halogenated Compounds for ...... 56

Table 5-2: struct = Replacement of bromine leaving group with chlorine or flourine ..... 59 Table 5-3: Bond lengths and bond formation enthalpies for carbon-halogen bonds...... 60

Table 5-4: struct = ncrease in carbon chain length ...... 62

Table 5-5: struct =  methyl group substitution...... 63

Table 5-6: struct = Shift of halogen from 1° to 2° position...... 63

Table 5-7:struct =  halogen substitution ...... 64 Table 5-8: Direct comparison of bromine to chlorine and fluorine as  halogen substituents (where struct =  halogen substitution)...... 65

Table 5-9: struct =  methyl group substitution ...... 66

Table 5-10: struct =  halogen substitution ...... 67 Table 5-11: Direct comparison of bromine versus chlorine ...... 67

Table 5-12: struct = location of  substitution ...... 68

Table 5-13: struct =  OH substitution...... 68

Table 5-14: struct =  OOH substitution ...... 69

Table 5-15: struct = Vinyl group replacement ...... 69 Table 5-16: Cyclic Halide Comparisons...... 70

Table 5-17: Comparison of Halogen Position Effect on Govr for DBCP...... 72

Table 5-18: Solvated and Gas Phase Hovr...... 73

Table 5-19: SRRs for Halogenated Pollutants Based on Govr Gas Phase Calculations.. 77 Table 5-20: Experimental Enthalpies and Free Energies...... 82

Table 5-21: Summary of Govr ...... 87

xi Table 6-1: Change in Govr from  Substitution on CA Compared...... 96 Table 6-2: Summary of Experimental and Calculated Properties ...... 107 Table 6-3: VBCM parameter values...... 119 Table 7-1: Experimental Activity for Haloalkane Dehalogenase...... 131 Table 7-2: Van der Waals and Observed Distances for bound 12DCA ...... 134 Table 7-3: Binding Distance Criteria...... 135 Table 7-4: Docking Summary for HAD ...... 136 Table 7-5: Comparison of AUTODOCK versus crystallographic results for 12DCA ... 137 Table 7-6: Effect of Structure on HAD activity...... 146 Table 8-1: Hydrolysis Experimental Data ...... 151 Table 8-2: Descriptors Used in this Study ...... 152 Table 8-3: Descriptor Components of Models...... 158 Table 8-4: Descriptors and Activity of Compounds Included in Hydrolysis Model I.... 159 Table 8-5: Predicted k and half lives using Hydrolysis Model I ...... 163 Table 8-6: Compounds in Hydrolysis Model II with Descriptors and Activity ...... 164 Table 8-7: Leave-One-Out Model Cross Validation Predictions for Hydrolysis Model II ...... 167 Table 8-8: Compounds in Hydrolysis Model III...... 171 Table 8-9: Biodegradation Model Descriptors and Activity...... 173 Table 8-10: Model Summary...... 174

Table 9-1: Major Modes of Variability in SN2 ...... 180

xii List of Figures

Figure 2-1: SN2 Reaction for Chloroethane ...... 8 - Figure 2-2: SN2 versus E2 Reactions for 12DCA and OH ...... 10 Figure 2-3: 12DCA Transformation Pathway for...... 18 Figure 2-4: Active Site of Haloalkane Dehalogenase...... 19

Figure 2-5: Gas Phase SN2 Reaction Potential Energy Surface...... 21 Figure 2-6: Free Energy versus Enthalpy Potential Energy Surface ...... 23

Figure 2-7: SN2 Reaction Solvent Phase Potential Energy Surface...... 24 Figure 2-8: Gas Phase versus Solvent Reaction Profiles...... 29 Figure 2-9: State Correlation Diagram ...... 31 Figure 3-1: Reaction Compartments Considered in this Study ...... 37

Figure 4-1: SN2 Reaction Energies ...... 45

Figure 5-1: Govr trend for a series of bromo-, chloro- and fluoro-substituted compounds...... 57 Figure 5-2: Example of Changes in Leaving Group...... 59

Figure 5-3: Comparison of Govr for analogous compounds containing bromine versus chlorine leaving groups...... 61 Figure 5-4: Changes in Chain Length...... 61 Figure 5-5: Examples of methyl group  substitution...... 62 Figure 5-6: Examples of  halogen substitution...... 63 Figure 5-7: Example of Addition of  Methyl Group Substituent (Branching)...... 65 Figure 5-8: Addition of  Halogen Substituents...... 66 Figure 5-9: DBCP Halogen Positions ...... 71 Figure 5-10: Solvated versus Gas Phase Activation Energies...... 74 - - - Figure 5-11: Cl , HS and OH vs. Acetate: Calculated Results for Gas Phase Govr...... 75 ≠ Figure 5-12: Experimental Hydrolysis H exp versus Gas Phase Calculated Govr ...... 84 ≠ Figure 5-13: Experimental Hydrolysis G exp versus Gas Phase Calculated Govr ...... 84

Figure 6-1: SN2 Reaction Path Structures...... 98 Figure 6-2: Bond Coupling ...... 99 Figure 6-3: Interaction Energies for Stabilization versus Non-Stabilization by Incoming Nucleophile for the Identity Reaction with Chloromethane...... 99

xiii Figure 6-4: State Correlation Diagram for Identity SN2 Reaction...... 100 Figure 6-5: Shallow versus Steep Descent of the Excited State ...... 102 Figure 6-6: TS for dichloromethane reaction with acetate ...... 105 Figure 6-7: C-Cl Bond Distances at the TS ...... 106

Figure 6-8: Govr versus C-X BDEs for Brominated Compounds ...... 110

Figure 6-9: Govr versus C-X BDEs for Chlorinated Compounds ...... 111 Figure 6-10: BDE versus VEA for chlorinated compounds...... 116

Figure 6-11: Govr versus VEA for Chlorinated...... 117

Figure 6-12: Govr versus VEA for chlorinated compounds ( and  substituents) ...... 118 Figure 6-13: LUMO versus VEA ...... 121

Figure 6-14: Govr versus LUMO...... 123 Figure 7-1: Xanthobacter Autotrophicus Pathway ...... 128 Figure 7-2: Haloalkane Dehalogenase Active Site ...... 129 Figure 7-3: Haloalkane Dehalogenase Reaction Steps ...... 130 Figure 7-4: 12DCA bound in the active site from the 2.4 Å crystal structure (2DHD).. 137 Figure 7-5: Major Modes of CA Binding ...... 138 Figure 7-6: Major Modes of 2CP Binding...... 139 Figure 7-7: 111TCA Binding...... 139 Figure 7-8: Experimental Halide Production Rate versus Productive Binding Frequency Determined by AUTODOCK ...... 141 Figure 7-9: Calculated activation energies versus experimental ...... 143

Figure 8-1: Half lives versus Calculated Govr...... 156

Figure 8-2: Solvent Effect on SN2 Reaction ...... 159 Figure 8-3: Predicted versus Experimental logk for Hydrolysis Model I...... 162 Figure 8-4: Hydrolysis Model II...... 166 Figure 8-5: Leave-One-Out Cross Validation for Hydrolysis Model II ...... 168 Figure 8-6: Model II Descriptors 3-D Space ...... 169 Figure 8-7: Predicted Versus Actual Hydrolysis Rates for Testing Set...... 170 Figure 8-8: Hydrolysis Model III...... 173

xiv List of Abbreviations Acronyms

BDE Bond distortion energy BEP Bell-Evans-Polyani DFT Density functional theory ETS Electron transmission spectroscopy FMO Frontier molecular orbital GS Ground state HAD Haloalkane dehalogenase HSAB Hard Soft Acid Base IMC Ion molecular complex IPCM Isodensity surface polarized continuum model IRC Internal reaction coordinate LUMO Lowest unoccupied molecular orbital MLR Multivariate linear regression MO Molecular orbital PCA Principal components analysis PDB Protein Data Bank PES Potential energy surface PRESS Predictive residual sum of squares QMRE Quantum mechanics resonance energy QSAR Quantitative structure activity relationships QST2 Quadratic synchronous transit RCSB Research Collaboratory for Structural Bioinformatics RMSD Root mean squared distance RRKM Rice-Ramsberger-Kassel-Marcus SCD State correlation diagram SRR Structure reactivity relationships TS Transition state VAE Vertical attachment energy VB Valence bond VBCM Valence bond configuration mixing model VEA Vertical electron affinity

xv Compounds

ALLYLCL Allyl chloride 12CBA 1-chloro,2-bromoethane BA Bromoethane 12CFA 1-chloro,2-fluoroethane BAACET Bromoacetamide 12DCA 1,2-dichloroethane BAOH Bromoethanol 12DCB 1,2-dichlorobutane BAOOH Bromoacetate 12DCP 1,2-dichloropropane BM Bromomethane 12BFA 1-bromo,2-fluoroethane BP 1-bromopropane 1B2MeP 1-bromo,2-methylpropane BPOH Bromopropanol 1C2MeP 1-chloro,2-methylpropane CA Chloroethane 13DB2POH 1,3-dibromo-2-propanol CAACET Chloroacetamide 13DCB 1,3-dichlorobutane CAOH Chloroethanol 13DCP 1,3-dichloropropane CAOOH Chloroacetate 13DCPENE 1,3-dichloropropene CB Chlorobutane 13DC2POH 1,3-dichloro-2-propanol CH Chlorohexane 14DCB 1,4-dichlorobutane CM Chloromethane 16DCH 1,6-dichlorohexane CP 1-chloropropane 1C11DFA 1-chloro,1,1-difluoroethane CPOH Chloropropanol 1F11DBA 1-fluoro,1,1-dibromoethane CYCBUTBR Cyclobutyl bromide 11DB1CA 1,1-dibromo,1-chloroethane CYCHEXBR Cyclohexyl bromide 11DB1FA 1,1-dibromo,1-fluoroethane 1-bromo,1-chloro,1- CYCPENTCL Cyclopentyl bromide 111BCFA fluoroethane CYCPROPCL Cyclopropyl chloride 111TBA 1,1,1-tribromoethane CYCPENTBR Cyclopentyl bromide 111TCA 1,1,1-trichloroethane 1,2-dibromo-3- DBCP 112TCA 1,1,2-trichloroethane chloropropane DCAOH Dichloroethanol 123TBP 1,2,3-tribromopropane DCAOOH Dichloroacetate 123TCP 1,2,3-trichloropropane DCM Dichloromethane 1122TETCA 1,1,2,2-tetrachloroethane EPIC Epichlorohydrin 2BB 2-bromobutane FA Fluoroethane 2BP 2-bromopropane FM Fluoromethane 2CB 2-chlorobutane 11BCA 1-bromo,1-chloroethane 2CP 2-chloropropane 11BFA 1-bromo,1-fluoroethane 2CPOOH 2-chloropropionate 11CBA 1-chloro,1-bromoethane 22DCP 2,2-dichloropropane 11CBB 1,1 chlorobromobutane 3CPOOH 3-chloropropionate 11CFA 1-chloro,1-fluoroethane 3C12PDIOL 3-chloro,1,2-propanediol 11DBA 1,1-dibromoethane 11DCA 1,1-dichloroethane 11DFA 1,1-difluoroethane 12BCA 1-bromo,2-chloroethane 12DBA 1,2-dibromoethane 12DBB 1,2-dibromobutane 12DBP 1,2-dibromopropane 12CBP 1-chloro,2-bromopropane

xvi

1 Introduction Because of the threat that halogenated pollutants pose to human health, there is a great deal of interest in understanding the fate of these compounds in the environment and describing the potential mechanisms for their removal (Alexander 1965; Vogel et al.

1987). The class of SN2 displacement reactions, in which a halogen atom (usually bromine or chlorine in the case of most halogenated pollutants) is displaced from a molecule by a nucleophile represents one such important mechanism (Schwarzenbach et

al. 1993). SN2 reactions in the natural environment can take place abiotically (Barbash and Reinhard 1989; Schwarzenbach et al. 1993), or can be mediated by microorganisms

through enzyme pathways (Alexander 1981; Janssen et al. 1987). SN2 transformation rates are strongly influenced by substrate structure (Wolfe et al. 1980; Vogel and Reinhard 1986; Haag and Mill 1988b; Schwarzenbach et al. 1993), and because there is significant structural diversity among halogenated pollutants, there is also a wide range in observed transformation rates. Transformation rates are also dependent upon the environment in which the reaction takes place, such as in the aqueous phase or within the more hydrophobic active site of an enzyme. There has been much experimental effort performed to determine reaction rates, identify trends and explain observed behavior of halogenated compounds, with a focus on those of particular environmental concern (Mabey and Mill 1978; Wolfe et al. 1980; Janssen et al. 1985; Vogel and Reinhard 1986; Janssen et al. 1987; Haag and Mill 1988b; Barbash and Reinhard 1989; Jeffers et al. 1989).

1.1 Motivation Environmental engineers are concerned with developing knowledge about the rates at which halogenated pollutants are transformed under natural conditions. The motivation for this research is to explain the various trends that have been identified through the

generation of previous data sets and to develop a comprehensive model for SN2 reactivity of haloalkane pollutants that can account for structural differences and reaction environment differences.

1 Another motivation for this research is to obtain an understanding of the reactivity patterns for haloalkane pollutants at a more fundamental point of view.

1.2 Objectives My objectives are to better explain observed reactivity trends in terms of quantifiable structure reactivity relationships, to develop a more fundamental understanding and explanation of mechanisms responsible for the trends, and to estimate transformation potential for compounds that are untested experimentally. Another objective is to demonstrate the effectiveness of novel computational approaches towards understanding environmental transformation rates and to provide insights that were not previously accessible from solely experimental approaches.

1.3 Nucleophilic Substitution Reactions Nucleophilic substitution reactions can occur between nucleophiles and polar compounds. A generalized reaction is shown below: X- + RY = XR + Y- Nucleophilic substitution can occur in molecules when there are two atoms covalently bonded that possess differing electronegativities. The atom with higher electronegativity withdraws electron density from the other atom, rendering it electrophilic (electron loving). In most cases, a carbon is the electrophile which is bonded to electronegative substituents such as halogens or oxygen. If there are nucleophiles (nucleus loving) species present, they will proceed to react with the electrophilic carbon via nucleophilic attack. The nucelophile forms a bond with the carbon and the attached electronegative atom is then displaced as the leaving group. When simultaneous bond formation and leaving group departure occurs, this reaction is called an SN2 reaction.

Reactions can also occur when the leaving group departs first without being displaced by

a nucleophile. These are called SN1 reactions. Once the leaving groups departs, a carbocation (carbon with a partial positive charge) is formed which then is attacked by a

nucleophile. In the case of an SN1 reaction, the strength of the nucleophile is unimportant

and it is the rate of carbocation formation that determines the rate. SN1 reactions tend to predominate when the central carbon is surrounded by bulky substituents that tend to

2 hinder approach of a nucleophile. For instance, experimental findings suggest that 1,1,1 trichloroethane (111TCA) may undergo SN1 type reactions (Schwarzenbach et al. 1993;

Aflatooni et al. 2000). This preference for SN1 could be explained by a higher relative

barrier for the SN2 reaction versus the SN1 reaction.

1.4 Previous Efforts to Understand Reactivity of Halogenated Pollutants One of the first efforts to understand the transformation behavior of the class of halogenated organic pollutants in the environment was completed in 1978 (Mabey and Mill 1978). This work assembled a large set of experimental hydrolysis information from a number of sources, defined distinct structural classifications and then attempted to identify reactivity patterns and explain observed rate differences in terms of structural considerations. Since that time, a wealth of empirical transformation rate data has been generated for halogenated pollutants, primarily for haloalkanes, but also for haloalcohols, haloacids, and halogenated allylic and cyclic structures. Experiments have been conducted in a variety of settings including the aqueous phase (Vogel and Reinhard 1986; Haag and Mill 1988b; Barbash and Reinhard 1989), sediment (Haag and Mill 1988a), and in bacterial cultures (Janssen et al. 1985; Janssen et al. 1987; Olaniran et al. 2004).

1.4.1 Limitations of Experimental Interpretation From the experimental data, a general understanding of the relationship between structure

and SN2 transformation activity for halogenated compounds has emerged and has been embodied in various theories of reactivity. However, the picture is still incomplete due to the inherent challenges of attempting to piece together the results of separate experiments and coalesce them into a unified model of structure-reactivity. Chief among these challenges are data gaps and inconsistency of methods between experiments. Data gaps exist as researchers have different motivations for studying certain compounds, and so there are some compounds about which a great deal is known (e.g. 1,2 dichloroethane), and others for which relatively little data is available (e.g. 1,2,3-trichloropropane). Researchers also employ different methods and use different metrics for reporting rate data, which makes it difficult to directly compare results between experiments. This is confounded by the fact that reactivity patterns can be different in different environments,

3 and so factors that influence reaction rates can be different depending on the system that is the focus of a particular study. Interpretation of rate data can suffer because without a large set of consistently generated data for a comprehensive set of compounds, it can be difficult to distinguish between the subtle effects of structure and environment on structure-reactivity patterns.

1.4.2 The Role of Solvent One aspect in particular that complicates the interpretation of experimental aqueous phase data is solvent effects (Caldwell et al. 1984; Chandrasekhar et al. 1984;

Schwarzenbach et al. 1993). Solvent can have a strong influence on SN2 reaction rates, which has been described as its ability to preferentially stabilize the more polarized reactants relative to the transition state (Ingold 1953), effectively raising the barrier to reaction and thereby slowing reaction rates. Requirement for desolvation of the substrate and nucleophile (solvent reorganization) prior to the reaction occurring has also been identified as an important solvent effect (Dewar and Dougherty 1975). In order to understand the role of solvent in SN2 reactions, gas phase studies of SN2 reactions have been employed as a means to separate out solvent effects (Olmstead and Brauman 1977). Gas phase data allows a core reactivity (reactivity in the absence of external solvent effects) to be defined for a given nucleophile/substrate reaction. Once the core reactivity is established, solvent effects are then more readily apparent through comparison to solution phase reactivity data (Caldwell et al. 1984). Gas phase data is also valuable in

understanding biological SN2 transformations, where reactions take place within the confines of the relatively solvent free (low dielectric) environment of enzymes and solvent effects are not important (Lightstone et al. 1997).

1.4.3 The Advent of Computational Studies In addition to experimental studies, theoretical ab initio computational studies have also been employed to calculate reaction energetic profiles both in the gas phase and in solution. Such studies provide additional insight not available through experiment and so are valuable adjuncts to experiment. Unfortunately, much of the previous theoretical research has been restricted to a few simple model systems such as the identity reactions with methyl chloride (Chandrasekhar et al. 1984; Deng et al. 1994; Glukhovtsev et al.

4 1996), methyl fluoride (Dedieu and Veillard 1972) and chloroacetonitrile (Wladkowski et al. 1992); and the non-identity reaction of chloride with methyl bromide. Thus, previous work has only included a small subset of the halogenated pollutants of environmental interest. Similarly, the previously discussed gas phase experimental studies have often focused on the same simpler systems. Thus, these systems have been studied in detail and much has been learned from a mechanistic standpoint, but because of the limited number of compounds, the ability to apply this knowledge to the broader set of environmentally relevant compounds is more limited than if the studies had included a larger number of compounds with more structural diversity.

1.5 Research Overview In this research, I apply computational quantum chemistry methods (Hehre et al. 1986) using the commercially available Gaussian 98 software package (Foresman and Frisch 1995) to obtain activation energies for a comprehensive set of 77 halogenated compounds in the gas and solvent phase. Activation energies determine reaction rates and provide a relative measure of reactivity (Jeffers et al. 1989; Schwarzenbach et al. 1993). Quantum chemically derived activation energies have also been used successfully as molecular descriptors of reactivity in the development of quantitative structure activity relationships (QSARs) for environmental pollutants (Eriksson et al. 1990). Analysis of this comprehensive set of results allowed me to gain an improved understanding of the reactivity of halogenated pollutants from a fundamental level of theory and provide enhanced interpretation of experimental data. I developed structure reactivity relationships (SRR) based on the observed effects of incremental changes in substrate structure such as leaving group, substituents, and chain length on activation energies. I also examined the consistency of these SRRs, and whether they applied across the compound set, or were dependent on the structural context in which they were developed.

I analyzed the results to gain a mechanistic understanding of the basis for the observed reactivity patterns. From examination of calculated transition state structure geometries, I determined that the degree of distortion of the primary C-X bond at the transition state was closely related to the magnitude of the activation energy. I also obtained correlations of calculated activation energies with experimental vertical electron affinities for a subset

5 of compounds which helped to understand the electronic mechanism underlying substituent effects, and deduce the underlying energetic factors that govern reactivity. This also validated the link between experimental observables and theoretical calculations for SN2 reactions.

To account for effects of the reaction environment, I compared differences in gas and solvent phase activation energies and quantified solvent effects. I examined the important influence of enzyme-substrate interactions through the use of the docking method AUTODOCK (Morris et al. 1996) and crystallographic enzyme structures of dehalogenase enzymes available from the Research Collaboratory for Structural Bioinformatics (RSCB) Protein Data Bank (Berman et al. 2000).

Finally, I developed two quantitative structure activity relationship (QSAR) multivariate statistical models for both hydrolysis and biotransformation that incorporated experimental and computational descriptors including calculated activation energies and values derived from an enzyme docking analysis.

My research provides a more complete picture of SN2 reactivity of halogenated pollutants based on a set of energies which were developed independently of experimental data. I have demonstrated that robust models can be developed from theoretical results that can account for the observed variability in experimental data.

1.6 Halogenated Compounds of Potential Environmental Significance Table 1-1 presents examples of halogenated compounds that are in use or had been used in the past for industrial uses. The table is not intended to exhaustive, but shows the representative compounds and structural classes that comprise halogenated pollutants. These compounds are included in my study, along with other halogenated compounds that together span the range of structural diversity present in the set of halogenated compounds.

6 Table 1-1: Some Environmentally Significant Halogenated Compounds Compound Industrial Use Halomethanes Chloromethane pesticide,fumigant Dichloromethane paint remover,solvent Tetrachloromethane solvent n-haloalkanes Chloroethane solvent, leaded gasoline Terminally halogenated alkanes 1,1 dichlorothane solvent, flotation agent 1,1-dichloro,1-fluoroethane refrigerant 1,1-difluoroethane aerosol propellant, refrigerant 1,1,1-trichloroethane solvent, degreaser 1,1,2-trichloroethane solvent, intermediate 1,1,2,2-tetrachloroethane solvent, intermediate,degreaser Secondary haloalkanes 2-bromobutane pharmaceutical intermediate 2-chloropropane solvent Halogenated ethenes Allyl chloride chemical synthesis 1,3-dichloropropene fungicide -dihaloalkanes 1,2-dibromoethane pesticide, gasoline additive 1,2-dichloroethane solvent, gasoline additive 1-bromo,2-chloroethane solvent, fumigant 1,2-dibromo, 3-chloropropane soil fumigant, nematocide Branched haloalkanes 1,2-dichloropropane solvent, fumigant, intermediate 1-bromo,2-methylpropane pharmaceutical intermediate 1-chloro,2-methylpropane solvent, intermediate 1,2,3-trichloropropane solvent, degreaser Halogenated Alcohols bromoethanol intermediate chloroethanol intermediate chloropropanol created during food production 1,3-dichloro-2-propanol solvent 3-chloro,1,2-propanediol food contaminant Halogenated acids Bromoacetate pharmaceutical intermediate Chloroacetate herbicide Halogenated epoxide Epichlorohydrin drinking water clarifier, epoxy resins production

2 SN2 Reactivity of Halogenated Compounds

The SN2 reaction is one of the most extensively studied reactions (Shaik et al. 1992; Ren and Brauman 2002). Because halogens have generally high electronegativity and create electrophilic carbon centers, halogenated compounds are generally susceptible to SN2 reactions from nucleophiles that exist in the environment. Figure 2-1 illustrates the SN2 reaction for chloroethane. In this example, Cl- is considered the leaving group.

‡

H H H - Nu- CClCl Nu C Cl Nu C + Cl + H H H H H H

Reactants Transition State Products

Figure 2-1: SN2 Reaction for Chloroethane As shown in Figure 2-1, the nucleophile Nu- approaches the electrophilic carbon in chloromethane from the side opposite the covalently bonded halogen (Cl). After passing through a high energy penta-coordinated transition state, the nucleophile displaces the Cl- resulting in an inversion of configuration where the nucleophile is now bonded to the carbon (Ingold 1953; Schwarzenbach et al. 1993; Smith and March 2007). The energy of the transition state is the key determinant of reactivity. Structural variations (often viewed in terms of “substituent effects”) can influence the structure and energy of the transition state and thus affect reactivity.

- Nucleophiles common in the environment include water (H2O), bisulfide (HS ), - - - - - hydroxide (OH ), acetate (CH3COO ), fluoride (F ), chloride (Cl ) and bromide (Br ), 2- 2- sulfate (SO4 ), and phosphate (HPO4 ) (Schwarzenbach et al. 1993). Nucleophilic functional groups are also present in enzymes that can initiate nucleophilic substitution reactions, such as aspartate, which is employed by haloalkane dehalogenase (Janssen et al. 1988). In this case, the aspartate contains a carboxylic group which serves as the nucleophile.

For environmental pollutants, this reaction is important as a pathway to transform halogenated compounds that typically carry bromine or chlorine substituents. A potential reaction is the displacement of a chlorine in chloroethane by a hydroxide ion to yield a chloride ion and ethanol.

- - OH + CH3CH2Cl = CH3CH2OH + Cl Hydroxide Chloroethane Ethanol Chloride Because of the ubiquitous presence of naturally occurring nucleophiles and their ability to transform a wide variety of halogenated compounds, the SN2 reaction represents a critical area for study for understanding the removal of halogenated pollutants by both abiotic and biological processes.

2.1 Reaction Environments

SN2 reactions of halogenated compounds have been studied in three environments - in

solution, in enzymes and in the gas phase. Each environment has different effects on SN2 reactivity patterns. A brief description of each environment is provided below and a more detailed accounting of the experiments and approaches in each environment follows in the next section.

2.1.1 Solution/Hydrolysis The most significant compartment in terms of environmental relevance are reactions that take place in solution (Vogel et al. 1987). Halogenated compounds undergo SN2 reactions in water, which is termed hydrolysis. The products of hydrolysis are alcohols. Much of

the SN2 data available on halogenated pollutants has been generated by studies that focus on hydrolysis. An important aspect about hydrolysis reactions is that they compete with elimination (E2) reactions (also termed dehydrohalogenation when it occurs with halogenated compounds), which is another reaction that occurs in the aqueous phase (Vogel and Reinhard 1986; Schwarzenbach et al. 1993). In the example E2 reaction shown in Figure 2-2, a nucleophile OH- abstracts a proton (which bears a partial positive charge) from 12DCA. The electrons in the breaking C-H bond perform a nucleophilic attack on the adjacent carbon, which displaces the Cl-, creating a double bond and

resulting in vinyl chloride, which is a toxic compound. Which reaction dominates depends on the relative activation energies for each reaction type.

Cl H

H CCH H Cl 12DCA E2 Elimination SN2 Substitution

- - OH OH - Cl- H2O, Cl

Cl H Cl H

H CCH CC

H OH H H Chloroethanol Vinyl Chloride

- Figure 2-2: SN2 versus E2 Reactions for 12DCA and OH

2.1.2 Biological Transformations

Biological SN2 transformations are also an important mechanism for transformation in the environment. Although many halogenated pollutants were once thought to be resistant to biodegradation (Alexander 1965), it has since been demonstrated that many microorganisms have the capacity to evolve enzymes to transform these compounds

(Vogel et al. 1987). Haloalkane dehalogenase enzymes mediate SN2 reactions as part of a microbial pathway (Janssen et al. 1985; Janssen et al. 1988). An important consideration for enzyme mediated reactions is that the steric constraints of the enzyme active site and substrate positioning relative to reactive groups must be considered (Kennes et al. 1995; Fersht 1999; Kuhn and Kollman 2000; Lau et al. 2000; Bohác ̌ et al. 2002).

2.1.3 Gas Phase

Study of the gas phase SN2 reaction is important for number of reasons. First of all, it allows for development of the understanding of the reactivity in the absence of external

factors (Asubiojo and Brauman 1979; Pellerite and Brauman 1980; Bohme and Mackay 1981; Gronert et al. 2001). By understanding the gas phase trends and relationships, the study of reactivity in solution and the effects of solute become much more apparent. Finally, the gas phase can be considered a reasonable surrogate of the hydrophobic environment within enzyme active sites (Lightstone et al. 1997; Lau et al. 2000).

2.2 Previous Investigations of SN2 Reactivity There have been numerous experiments studying the reaction in solution, mediated by microbes and in the gas phase. Theoretical studies have also been conducted through computational methods that allow reactions to be simulated in each of the three environments.

2.2.1 Hydrolysis A number of experiments have generated a wealth of hydrolysis data on various aspects

of environmentally relevant SN2 reactions (Mabey and Mill 1978; Burlinson et al. 1982; Vogel and Reinhard 1986; Haag and Mill 1988a; 1988b; Barbash and Reinhard 1989; Jeffers et al. 1989; Roberts et al. 1992; Barbash 1993). These experiments have varied in scope and focus. Some have achieved broad coverage across a number of compounds, while others have focused on a smaller set of compounds to study a particular aspect of the reaction or a specific compound. As shown in Table 2-2, there have been a number of

experiments that specifically address SN2 transformations of haloalkanes under a variety of different and often complex conditions.

Table 2-1: Experimental Studies of Haloalkane Transformation Experiment Reaction # Compounds

Mabey and Mill (1978) Hydrolysis 18 alkyl, allyl halides Hydrolysis Burlinson et al. (1982) 1 DBCP Elimination

Vogel and Reinhard (1986) Hydrolysis 4 brominated alkanes Elimination Hydrolysis brominated and Haag and Mill (1988) 4 Elimination chlorinated alkanes Substitution Haag and Mill (1988) 4 Bromo-,chloro- alkanes (HS-)

Experiment Reaction # Compounds

Hydrolysis Substitution Barbash and Reinhard (1989) (HS-) 2 12DBA & 12DCA Elimination (HS-) Hydrolysis chlorinated alkanes and Jeffers et al. (1989) 18 Elimination alkenes brominated and Hydrolysis Jeffers et al. (1996) 17 chlorinated alkanes and Elimination alkenes Substitution Roberts et al. (1992) 3 dichloromethanes (HS-) Hydrolysis bromo-, chloro- alkanes Barbash (1993) Substitution 7 chloroethenes Elimination Okamoto (1967a) Substitution 35 alkyl substrates Substitution Okamoto (1967b) 4 -chloroalkylbenzenes Elimination Substitution Okamoto (1967c) 1 12DCA Elimination Substitution -substituted ethyl Okamoto (1967d) 5 Elimination chlorides

Mabey and Mill (1978) provided the earliest and most extensive review of hydrolysis of organic compounds in natural waters. They examined rate constants for 12 classes of organic compounds including methyl halides, alkyl halides and allyl halides. Based on their review, they noted that the rate of hydrolysis for RX was greatest when the X was Br and least when X was F. They found that compounds with bromine leaving groups were 5-10 times higher rates than with chlorine as a leaving group. An interesting trend they identified was that the rate of hydrolysis increased when the halogen was located adjacent to the secondary carbon instead of the primary carbon, which seemed counterintuitive as the approach of the nucleophile would be more hindered. Finally, they concluded that allyl groups enhance the rate of hydrolysis by 5-100 times over a primary substituted methane

Burlinson et al. (1982) studied the kinetics and products for the hydrolysis of the pesticide DBCP. It was found that the major mechanism for DBCP was E2 elimination of

HBr, although substitution reactions by H2O did occur to a much smaller degree. By

performing experiments also at low pH, it was possible to observe the isolated H2O substitution reaction.

Vogel and Reinhard (1986) performed studies with 1-bromoheptane (BH), 1-bromo-3- phenylpropane (1BPP), 12DBA, 12DBP and 12DCA. They found that the terminal monohalogenated compounds BH and 1BPP reacted solely via substitution to the corresponding alcohols. The other compounds reacted via dehydrodehalogenation to alkene products, although the extent to which substitution did compete with dehydrodehalogenation was unknown due to analytical limitations. Subsequent experiments showed that substitution reactions do occur to a significant extent with the dihalogenated  substituted halolkanes, although elimination reactions do occur simultaneously and were responsible for approximately 9% of the products in one experiment.

Haag and Mill (1988a; 1988b) conducted 2 studies with a set of compounds that encompassed a large variety of structural features. One study was a hydrolysis study while the other was with HS- as a nucleophile. The compounds studied were 1- bromohexane, 111TCA, 2BP, 12DBA and 1122TETCA. In general the reaction rate constants were much higher when HS- was the nucleophile. It was also determined that with HS-, the presence of 3-4 chlorines on the reactive carbon center greatly inhibited reaction rates. An interesting contrast between the HS- studies and the hydrolysis studies was that elimination was not a major reaction mechanism with HS-. With HS-, elimination was found not to be a major reaction mechanism. However, in the hydrolysis experiments, elimination reactions competed strongly with substitution reactions in transformation of the substrates.

Barbash and Reinhard (1989) studied the reactions of 12DBA and 12DCA with hydrogen - - sulfide HS . They noted that HS reacts much faster with the 2 compounds than H2O and attributed this to Hard Soft Acid Base (HSAB) effects which are described in Chapter 3. Haloalkanes are considered “soft” substrates and would tend to react better with “soft” nucleophiles such as HS-. Also in accordance with HSAB theory, they noticed a - symbiotic effect where the ksoft/kh20 is larger for HS than for HPO4.

Jeffers et al. (1989) determined hydrolysis rate constants for 18 compounds that included methanes, ethanes, ethenes and propanes. This study evaluated a structurally diverse range of halaoalkanes making the data especially useful for studying the relationship between structure and reactivity. In 1996, a similar experiment was conducted to expand the data set to include brominated compounds (Jeffers and Wolfe 1996).

Roberts et al. (1992) measured rate constants and derived activation energies for the nucleophilic substitution reactions of dihalomethanes (DBM, BCM, and DCM) with HS-. They found that the activation energies for the HS- promoted reaction were lower than that of the OH- promoted reaction. It was concluded that steric effects could not explain the observed difference between BA and DBM since the methyl group substituent in BA is about the same size as the bromine substituent in BDM, but DBM had a much slower reaction rate. Roberts et al. also concluded that inductive effects could not explain the lower reactivity of DBM versus BCM, which led them to propose delocalization effects of substituents as an explanation for the observed reaction sequence.

Barbash (1993) conducted a study of the rates and pathways for both nucleophilic substitution and dehydrodehalogenation reactions of a set of chlorinated alkanes with HS- in buffered aqueous solutions. Barbash determined activation enthalpies and entropies.

He examined the competition between SN2 and E2 reactions and found that the partitioning between the two pathways (as measured by the ratio of rate constants

(kE2/kSN2) was determined by the number of halogens attached to the molecule. He also found that some effects, such as the rate retardation upon adding an  substituent, seemed related to entropic - rather than enthalpic - effects.

Okamoto et al. performed a series of studies on nucleophilic substitution that covered a series of alkyl substrates – methyl, ethyl, n-propyl, n-butyl, i-propyl, i-butyl and neo pentyl (Okamoto et al. 1967d); rates of SN2 and E2 reactions of w-chloroalkylbenzenes

(Okamoto et al. 1967b); SN2 and E2 reactions of 12DCA with various nucleophiles

(Okamoto et al. 1967c); and SN2 and E2 reactions with of -substituted ethyl chlorides with sodium acetate in aqueous solution (Okamoto et al. 1967a). These studies resulted in linear free energy relationships with varying degrees of success. One conclusion that Okamoto (1967d) arrived at was that the Taft polar and substituent constants were not able to explain the reactivity of alkyl halides.

Table 2-2 presents a compilation of hydrolysis half lives calculated from some of the experiments listed above. The compounds are listed in descending order of half life, so that the reactivity increases going down the table. This illustrates the wide range of reactivity of halogenated pollutants. What is also notable is that in some cases, there is more than one experimental value for a given compound, which points out the challenges of drawing conclusions on reactivity based on experimental data. The goal of my research is to be able to explain such variability and to be able to estimate transformation rates for compounds based on structural considerations.

Table 2-2: Experimental Hydrolysis Half Lives Half Life Compound (yrs) Reference 1,2-dichlorotrifluoroethane 6.3E+13 4 1,2-dichloroethene 2.1E+10 3 fluoro-1,1,2,2-tetrachlorethane 3.3E+09 4 hexachloroethane 1.8E+09 3 tetrachloroethene 9.6E+08 3 11-DCE 1.2E+08 3 trichloroethene 1.2E+06 3 trichloroethene 4.0E+05 4 bromoacetamide 2.1E+04 5 1-chloro, 1,1-difluoroethane 1.6E+04 4 tribromoethene 1.0E+04 4 trichloromethane 1.8E+03 3 dibromomethane 4.8E+02 6

Half Life Compound (yrs) Reference Dibromochloromethane 3.4E+02 6 Fluoromethane 2.4E+02 4 bromodichloromethane 1.7E+02 6 1,2 dibromo, 3-chloropropane 1.4E+02 1 1,1,2 trichloroethane 1.4E+02 3 1,2 dichloroethane 7.3E+01 4 1,2 dichloroethane 7.2E+01 3 1,1 dichloroethane 6.1E+01 3 1,1,1,2 tetrachloroethane 4.7E+01 3 bromochloromethane 4.3E+01 6 tetrachloromethane 4.0E+01 3 flouromethane 3.0E+01 5 1,1-dichloro, 1-fluoroethane 1.8E+01 4 1,2 dibromoethane 6.3E+00 4 1,1 dibromoethane 5.5E+00 4 1,2 dibromoethane 4.1E+00 2 1-bromo, 1-chloroethane 2.7E+00 4 chloroethane 2.6E+00 4 1,2 dibromoethane 2.5E+00 6 1,3 dichloropropane 2.2E+00 3 chloroacetamide 1.5E+00 5 1,1,1 trichloroethane 1.1E+00 2 1,1,1 trichloroethane 1.1E+00 3 hlouromethane 9.3E-01 5 1,2 dibromopropane 8.8E-01 6 Chloropropane 8.2E-01 4 1-bromo, 3-phenyl propane 7.8E-01 6 1,1,2 TBA 7.4E-01 4 1,1,2,2 TCA 4.4E-01 3 allyl chloride 1.9E-01 5 1-3 DBA 1.3E-01 6 2-chloropropane 1.1E-01 4 chloroethane 1.0E-01 5 2-chloropropane 1.0E-01 5 1,1,2,2 TCA 1.0E-01 2 bromoethane 8.3E-02 5 bromopropane 7.2E-02 5 bromomethane 5.4E-02 5 bromomethane 5.4E-02 6 epichlorohydrin 2.2E-02 5 1,1,2,3 TCP 1.7E-02 3 1,1,2,3,3-PCP 1.3E-02 3 pentachloroethane 1.0E-02 3 bromopropane 5.7E-03 2

Half Life Compound (yrs) Reference 2-bromopropane 5.7E-03 5 2,2 DCP 4.1E-03 3 allyl bromide 1.3E-03 5 1 Burlinson et al. (1982) 2 Haag and Mill (1988b) 3 Jeffers et al. (1989) 4 Jeffers and Wolfe (1996) 5 Mabey and Mill (1978) 6 Vogel and Reinhard (1986)

2.2.2 Biological Transformations One of the earliest instances of biological degradation of halogenated compounds was recorded by Goldman et al. (1968) who studied a bacteria that utilized haloacetates for growth. Soon, microorganisms were discovered that could mediate a wide array of reactions to degrade a large range of foreign (xenobiotic) compounds. In many instances, it appears that evolutionary pressures actually allowed many microorganisms to evolve enzymes to be able to utilize the myriad compounds that were entering the biosphere due to anthropogenic activities.

Haloalkane Dehalogenase Janssen et al. (1985) identified a bacteria, Xanthobacter autotrophicus GJ10, that could degrade halogenated aliphatic compounds. They determined the pathway which consisted of a series of enzymes including two dehalogenases – haloalkane dehalogenase (XaDhLa) and haloalkanoate dehalogenase (XaDhLb).

Janssen et al. 1985

Figure 2-3: 12DCA Transformation Pathway for Xanthobacter autotrophicus

For the primary substrate (12DCA), XaDHLa displaced the first chloride and XaDHLb

displaced the remaining chloride with the final product being glycolate (CH3OHCOOH), which then fed into existing metabolic pathways.

XaDHLa was eventually purified and crystallized (Franken et al. 1991), providing a structural model for understanding how the enzymatic reaction was mediated. The enzyme active site consists of a hydrophobic pocket which contains an aspartate (Asp

124) amino acid residue that conducts a SN2 attack on halogenated compounds that bind within the pocket (Verschueren et al. 1993a). The functional group of Asp124 resembles - an acetate anion (CH3COO ) and employs a carboxylate as the nucleophile. Two tryptophan residues (Trp 125 and Trp 175) are believed to have a role in stabilizing the departing halide (Krooshof et al. 1997). Other amino acids (Asp 260 and His 289) are also thought to play functional roles in facilitating the reaction (Verschueren et al. 1993c) .

Schastra et al., 1996

Figure 2-4: Active Site of Haloalkane Dehalogenase

Schanstra et al. (1996) determined kinetic parameters for XaDhLa for 18 chlorinated and brominated compounds. They found that short chain (<4 carbons) haloalkanes tended to make the best substrates, while long chain substrates and polar halogenated compounds such as haloalcohols and epoxides were poor substrates. Brominated compounds had faster enzymatic rates of transformation than their respective chlorinated analogues. Finally, it was found that 12DCA and 12DBA had the highest rates of transformation within their respective sets of chlorine- or bromine- substituted compounds, indicating that compounds with a  halogen substituent were the best suited substrates for the enzyme.

Janssen et al. (1988) conducted a kinetics study of a larger set of substrates that included cyclic compounds, substituted propanes, and alcohols. They compared the kinetics for haloalkane dehalogenase to another dehalogenase (RdDHL) from Rhodococcus rhodochrous. It was shown that RrDHL had a more extensive substrate range, including all of the cyclic compounds. XaDHLa on the other hand, could only transform shorter linear haloalkanes.

2.2.3 Gas Phase Studies

One of the first systematic gas phase studies of SN2 reactions of halogenated compounds, conducted in 1976 (Tanaka et al.), involved the gas phase reactions of halogenated methanes with a number of nucleophiles. Generally, gas phase studies have focused on simpler systems such as identity reactions, as researchers in this arena were interested in developing an understanding of gas phase dynamics, which had a reaction energy profile

much different than in solution. Olmstead and Brauman (1977) were the first to propose that the SN2 gas phase reaction had a unique double well potential energy surface, as well as pioneering the use of Rice-Ramsberger-Kassel-Marcus (RRKM) unimolecular reaction

theory towards modeling gas-phase SN2 reactions. A mainstay of gas phase research was the Cl-/CM identity reaction, although there was a variety of research on different nucleophiles and substrates (Olmstead and Brauman 1977; Asubiojo and Brauman 1979; Bohme and Mackay 1981; Bohme and Raksit 1984; Caldwell et al. 1984; Bohme and Raksit 1985; Barlow et al. 1988; DePuy et al. 1990; Wladkowski et al. 1992; Li et al. 1996; Laerdahl and Uggerud 2002).

2.2.4 Theoretical Studies An important complement to gas studies has been theoretical studies performed with the aid of computers. There have been a number of theoretical studies that have focused on improving mechanistic understanding. These have focused on small systems, in many cases with identity reactions. Theoretical methods have been use extensively to gain insight from an electronic structure level, which is less accessible from an experimental level. Gronert has stated that “important insights into the intrinsic reactivity of organic systems can be gained through gas phase studies” (Gronert 2003). Usually, theoretical studies are performed in tandem with experiment to help analyze observed experimental results. Most of these studies have focused on very select sets of compounds and simpler systems, as the goal has been to elucidate a mechanism by using a controlled system with a minimum amount of variables. Focusing on small systems has allowed for an isolation of variables, so that the fundamental nature of the reaction can be understood. .

There have been a number of previous theoretical studies that have focused various

aspects of SN2 reactivity of halogenated compounds (Dedieu and Veillard 1972; Carrion and Dewar 1984; Chandrasekhar et al. 1984; DePuy et al. 1990; Wladkowski et al. 1992; Deng et al. 1994; Wladkowski et al. 1994; Truong and Stefanovich 1995; Glukhovtsev et al. 1996; Truong and Stefanovich 1997; Craig and Brauman 1999; Gronert et al. 2001; Gronert 2003). Gas and solvent phase calculations have been proven effective as tools for

studying the SN2 reactivity of halogenated compounds. There are some trends that have begun to emerge but a more complete picture is still needed. For instance, theoretical

studies are lacking for 11DCA. Another issue with the coverage provided by the current body of computational work is that the researchers have used different methods, levels of theory and basis sets, so is not always possible to make comparisons without introducing the potential for variance in results between methods. The success and the limits of this prior theoretical work have paved the way for a more comprehensive study. The research discussed in this dissertation has derived calculated activation energies for a full set of halogenated pollutant compound structures generated with a single consistent method which has allowed for expanded conclusions for SN2 reactivity encompassing a larger set of compounds and structural features.

2.3 SN2 Reaction Profile

The SN2 reaction profile has some unique characteristics that vary from the gas phase to the solvent phase. Gas Phase Based on observations of reaction data, Olmstead and Brauman (1977) first surmised that

the basic reaction energetic profile of the gas phase SN2 reaction resembled a double well when plotted as potential energy as shown in Figure 2-5.

Reactants Products

Transition Eovr State

Energy Ecent

Ion-Molecular Complex Reaction Coordinate

Figure 2-5: Gas Phase SN2 Reaction Potential Energy Surface

In this profile, the separated reactants combine to form an ion-molecular complex (IMC) that is stabilized energetically, primarily due to electrostatic charge-dipole interactions. From the IMC, the reaction path proceeds to a higher energy transition state (TS), where a significant degree of bond making and breaking has occurred. The reaction then

proceeds to a product side IMC before proceeding to the final products. In this figure, the reaction profile for an identity reaction (where the nucleophile and leaving groups are identical) is shown so the reactants and products have identical energies. In the case of non-identity reactions where the reaction is exothermic, the energy of the products will be lower than the reactants. The Bell-Evans Polyani principle says that the higher the exothermicity, the more the transition state will resemble the higher energy reactants

(Shaik et al. 1992). Although there is a central barrier (Ecent) that exists between the TS and the IMC, the energy difference that determines the rate of reaction is the energy difference between the separated reactants and the transition state (Eovr) (Gronert et al. 2001).

Although it is possible for SN2 gas phase reactions have TS energies below the reactants, essentially translating to a barrierless reaction, the reactions still proceed at less than 100% efficiency. This is because the transition state is more highly ordered. There is a lower density of states for the TS compared to the reactants, which means that there is an additional entropic barrier (Olmstead and Brauman 1977). Olmstead and Braumann accounted for this by pioneering the use of the Rice-Ramsberger-Kassel-Marcus (RRKM)

theory for interpreting gas phase SN2 reaction rate data. As shown in Figure 2-6, when entropy is taken into account, then the free energy profile shows that the energy of the TS is higher than that of the reactants leading to a positive activation energy and a reaction that is less than 100% efficient and proceeds with activation (i.e. in accord with statistical rate theory).

Free Energy G

Enthalpy H

Figure 2-6: Free Energy versus Enthalpy Potential Energy Surface

Solvent Phase There are three categories of effects that account for the difference reaction profiles between the gas phase and solution. The main difference between the solvent phase and gas phase is the desolvation of the anion. Solvent stabilizes both the transition state and the reactants (mainly the anion), but as the transition state is in a more delocalized state, it is not pulled down as much as solvent. It has been shown that it is the high degree of solvation of the bare ion, relative to the transition state that pulls the reactants down below the IMC and creates a unimodal potential energy surface (PES) (Chandrasekhar et al. 1984) as shown in Figure 2-7. Solvent can also dampen any through space effects (field effects) which are electrostatic in nature. Inductive effects are less affected by solvent. Finally, there are entropic effects that may be different between solvent and gas phase. Thus, it would be expected that the differences between solvent and gas phase reaction profiles can be broken down by these three categories. If we understand the relative contributions of these categories, then we can utilize the gas phase reaction profiles to accurately estimate solvent phase profiles.

Transition State

Energy

Reactants Products

Reaction Coordinate

Figure 2-7: SN2 Reaction Solvent Phase Potential Energy Surface

2.4 SN2 Transformation Rates In general, reaction rates are determined by the difference in energy between the transition state and the reactants. This is known as the activation energy. The relationship between reaction rate and activation energy can take different forms as discussed below.

2.4.1 Empirical – Arrhenius Equation The basic form of the rate relationship for empirical data is the Arrhenius equation.

Ea / RT k  Ae

In this equation, k is the rate constant, Ea is the activation energy, R is the universal gas constant, T is temperature and A is the preexponential factor. As apparent from the

equation, k is exponentially related to the Ea. The preexponential factor A accounts for factors related to the probability of correct orientation upon collision (Schwarzenbach et al. 1993) and it is not dependent on temperature. The procedure for determining the kinetic parameters from experimental data using the Arrhenius equation is to determine the temperature dependence of the rate constant to determine k by the equation: d ln k  E  a d(1/T ) R

Derivation of Ea from experimental data is then accomplished by plotting ln(k) versus

1/T. The slope of the line will be –Ea/R and the y intercept is the A value (Jeffers et al. 1989).

2.4.2 Statistical Rate Theory – Eyring Polyani Equaton The Arrhenius equation is an empirical relationship. Another approach towards understanding reaction rates is at the molecular level through statistical rate theory. Here, the transition state represents a first order saddle point on the energy landscape. Basic rate theory states that reaction rates are related to statistical distributions (Boltzmann distributions) of energies among molecules. Using transition state theory to relate statistical ensembles of molecular energy to reaction rates, the Eying-Polyani equation states:

k T  k  B eG / RT h ≠ In this equation, k is the rate constant, G is the free energy of activation, kb is the Boltzmann constant, T is temperature, h is the Planks constant and R is the universal gas constant. Thus, for a given temperature, it is possible to determine the rate at which reactant molecules collide with enough energy to propel them over the barrier and into the product channel. Reaction rates are determined by the magnitude of the difference between the transition state and the separated reactants. The common variable for both the Arrhenius equation and the Eyring-Polyani equations is the activation energy. By comparing activation energies for compounds, it is possible to estimate relative rates of transformation.

2.5 Existing Haloalkane Structure-Reactivity Understanding The above discussion focused on the general factors that determine structure-reactivity relationships. In this section, discussion of how these factors have manifested themselves experimentally is provided. A number of structural features have been observed to correlate with reaction rates of halogenated compounds. Most of the structural variation responsible for variations in reactivity between halogenated compounds can be described in terms of the leaving group and/or substituents (Schwarzenbach et al. 1993). Leaving group is the type of halide that will be displaced in the reaction, in most cases a bromine

or chlorine. The leaving group determines the ease of halide displacement. Substituent types can be halides, methyl groups or one of series of functional groups that categorize halogenated compounds chemically into one of many categories including alkanes, alcohols, acetates, acetamides and butyl rings.

2.5.1 Nature of Effects The nature of the effects of structure can be categorized into inductive and steric effects on the reaction:

Steric Steric effects are caused by substituents that can interfere with the reaction mechanism, and/or lead to a more crowded transition state of higher energy due to steric strain, which would increase the activation energy (Ingold 1953; Schwarzenbach et al. 1993). It has been described as a physical blockage to the approach of the nucleophile (Smith and March 2007). Thus, larger, more bulky substituents are commonly associated with a decrease in observed rates. Although some researchers have postulated that steric factors are primarily responsible for the effects of substituents on SN2 reactions (Lowry and Richardson 1987), others have questioned this theory (Roberts et al. 1992).

Electronic Electronic effects fall into the categories of inductive, field, and electron delocalization effects (Schwarzenbach et al. 1993; Smith and March 2007). Inductive effects act through bonds and occur when electron donating or withdrawing substituents act to polarize adjacent carbon atoms. Field effects, on the other hand, act through space state and can induce similar polarizing effects. Electron delocalization refers to the smearing of electron density over many bonds (Schwarzenbach et al. 1993). If these effects stabilize or destabilize the transition state or the ground states, thereby changing activation energies, they can affect reaction rates.

2.5.2 Structural Features The following categories of structural features are associated with changes in reactivity. The strength of the nucleophile is important and for the substrate, the leaving group and substituents are the two major structural features. The effect of the nucleophile and the

leaving group on reactivity is related mainly to type. Substituent effects are manifested in terms of steric and/or electronic effects and depend on type, number and position.

Nucleophile The type of nucleophile is another important factor in determining reaction rates. Some commonly occurring nucleophiles and the order of their strength as nucleophiles is H2O < - - - CH3COO < OH < HS (Schwarzenbach et al. 1993).

Leaving Group The atom being displaced – the leaving group - is one of the most important factors governing SN2 reactivity. For halides the leaving group ability order follows the order below (Schwarzenbach et al. 1993): Br > Cl > F > I.

Thus, brominated compounds will tend to be more reactive to SN2 substitutions than chlorinated compounds. The difference reflects the relative ease at which the halide is displaced. For example, the bromine atom is larger and the electron density is more diffuse lowering the energy of the transition state. C-F bonds on the other hand are very strong, thus making fluorine displacements much less likely than bromine or chlorine displacements (Schwarzenbach et al. 1993).

 Substituents  substitution (also known as gem substitution) is substitution on the same carbon that is undergoing halide displacement.  substituents have dramatic slowing effects on SN2 reactions. Steric effects are commonly invoked to explain  substituent effects, with the proposed mechanism being a crowding of the transiton state. Some researchers have noted that this notion of physical blockage does not fit when examining the effect of methyl groups versus halogens as  substituents (Roberts et al. 1992). Other researchers have proposed that the effect is more of an electronic nature and could be attributed to electron delocalization .

 Substituents  substitution (also known as vicinal substitution) is substitution on the carbon adjacent to the carbon undergoing displacement. There is a high degree of variability of impacts

on observed reactivity depending on the type of  substituent. Halogen substituents tend to have milder rate retardation effects when occurring in a  position as compared to a  position. It has been noted that  substituents actually accelerate reaction rates in the gas phase (Roberts et al. 1992; Craig and Brauman 1999; Gronert et al. 2001; Gronert 2003).

Summary of Structural Effects By far, the most data has been collected on haloalkanes, both mono- and di-halogenated. Data is more limited with the haloalcohols and haloacids. Table 2-3 presents the basic structural categories that encompass much of the variation in halogenated compound structure, and the experimental observations of the impacts of variations within these categories on reactivity. As discussed previously, there is much variation in experimental results and findings on structural-reactivity relationships are still inconclusive, so the observations in Table 2-3 represent only a limited scope of understanding and conclusions.

Table 2-3: Substrate Structural Features and Observed Impact on Reaction Rates Structural category Experimental Observations Leaving group Rates of hydrolysis follow the order: Br > Cl > F

 Substituents Halogens attached in the position will decrease SN2 reactivity significantly, although the hydrolysis data of 111TCA does not fit this pattern completely. The  substitution of a methyl group (which can also be viewed as a substitution performed on a secondary versus primary carbon) have been found to increase rates of hydrolysis. Experimental data on the effect of cyclic groups is not available.  Substituents substituents will generally decrease reactivity, although allyl substituents actually enhance hydrolysis (relative to haloalkanes). There is a large range of effects depending on the substituent. With  halogen substituents, contrasting effects have been observed depending on the specific environment. Distance Effects The further the substitution occurs away from the site of nucleophilic attack, the less impact it will have on reactivity Chain Length The length of the carbon chain has little influence on reactivity beyond the first 3 carbons.

2.5.3 Solvent Effects

Solvent has significant effect on SN2 reaction rates. For haloalkane pollutants, the most relevant solvent is water. As discussed previously, polar solvents such as water are

thought to preferentially stabilize the reactants relative to the transition state of SN2 reactions and decrease the rate of reaction overall (Bohme and Mackay 1981; Chandrasekhar et al. 1984; Shaik 1984; Truong and Stefanovich 1997; Chen et al. 2009). Figure 2-8 provides an illustration of the reaction profile for an identity reaction (when the nucleophile and leaving group are identical) which shows that the overall reaction profile is stabilized (energies are lower) in a polar solvent due to favorable solvent interactions. The reactants are stabilized to a greater extent due to the fact that the nucleophile has a localized charge, while the transition state charge distribution is more diffuse (Ingold 1953; Chandrasekhar et al. 1985).The net result is an activation energy which is higher in solution. Another important consideration when comparing gas phase to solvent reactivity is that when field effects that act through space are involved, solvent may tend to dampen these out which, in some cases, can actually reverse the order of reaction observed in the gas phase (Gronert et al. 2001).

Gas Transition State

Energy Solvent

Reactants Products

Reaction Coordinate

Figure 2-8: Gas Phase versus Solvent Reaction Profiles

2.6 Theories of SN2 Reactivity The strength of the nucleophile, ease of displacement of the leaving group, and solvent

effects are the three factors that most strongly influence SN2 reaction rates. From interpretation of the available experimental data, a number of relationships and theories have been developed to attempt to explain observed behavior in terms of inherent characteristics of both nucleophiles and substrates and the reaction environment. Swain and Scott developed a linear free-energy relationship that described relative nucleophilic strengths in water (Swain and Scott 1953). An observation is that trends among a series of compounds can vary depending on the nucleophile. The Hard-Soft Acids Bases (HSAB) theory was developed to attempt to explain such changes in the order of reactivity for different compounds (Pearson 1963). The main premise is that reactivity is maximized when the nucleophile (Lewis base) and the substrate (Lewis acid) are most similar in terms of “hardness” and “softness”, where hard acids and bases are smaller, have low polarizability, and have lower tendency to form covalent bonds and soft acids and bases have the opposite characteristics. Frontier molecular orbital (FMO) theory has also been invoked to explain observed reactivity behavior (Kost and Aviram 1982),

however, FMO has proven inadequate to explain certain aspects of SN2 reactions (Dewar 1989).

Approaches to explain reactivity through quantum chemistry fall into two conceptual frameworks of valence bond (VB) and molecular orbital (MO) theory (Shaik 2001). At their core, both theories are essentially equivalent, but are based on different treatment and each has its own advantages. VB theory is based on the ideas of a Lewis electron-pair bond and resonance structures. As such, it explicitly includes the concept of a chemical bond. In contrast, MO theory does not directly address the breaking and making of bonds. MO theory. However, it has become more widely employed because it is amenable to computational treatment and allows direct calculation of energies. Nevertheless, VB theory has proven more versatile and intuitive in providing qualitative explanations for observed chemical behavior. The form of each theory that is most useful for analysis of

SN2 reactions are described below.

2.6.1 Valence Bond Configuration Mixing Model (VBCM)

A somewhat successful VB theory for explaining SN2 reaction behavior has been the valence bond configuration mixing model (VBCM) (Shaik 1981; Shaik and Pross 1982; Pross 1985; Shaik et al. 1992) that has been developed by Pross and Shaik. This model is rooted in a central statement of the Bell-Evans-Polyani (BEP) principle that says that a reaction profile is the result of a crossing of reactant– and product-like energy curves. The essence of the VBCM can be viewed by examining the State Correlation Diagram (SCD) shown in Figure 2-9.

X●(R  X) (X R) ● X

(Ix-ARX) Energy

EO

● ● X(R● X) (X R) X●

Reaction Coordinate Figure 2-9: State Correlation Diagram

VBCM assumes that at any point along the reaction path, there are a set of resonance structures that combined will describe the character of the reactants and products. The SCD consists of ground state (equilibrium) structures at the bottom of the diagram and charge transfer state (one electron has moved to the reactant) structures at the top of the diagram. As the reaction proceeds along the reaction coordinate, these structures undergo distortions that bring them to a point of equivalent energy, which is the crossing point. Favorable interactions between X- and RX will reduce this energy further by the amount

of the avoided overcrossing, B. VBCM theory views the SN2 reaction as a transfer of a single electron and views the formation of the activation energy barrier as a function of the distortions in structure necessary to achieve the transition state. There are a number of

alternate structure configurations available that mix to determine the transition state. Factors that tend to increase favorable interactions between the charge transfer state and the ground state will stabilize the activation energy barrier and lead to faster reactions. This is determined by the energy gap and the extent of delocalization. Underlying each of these theories is recognition that solvent effects also have an important influence on SN2 reactions. A major premise of this model is that the reactivity is related to the “looseness” or “tightness” of the transition state (Shaik et al. 1988). To aid in interpretation, a bond distance index has been developed to compare transition state structures.

2.6.2 Ab Initio Methods – Molecular Orbital Theory The above theories are important attempts to describe the basic underlying factors governing SN2 reactivity, but they suffer from providing only a qualitative or semi- quantitative capability for describing reactivity and/or an inability to distinguish among the effects of more subtle changes in structure. Often, there are a number of factors acting simultaneously in tandem or in competition that make actual quantitative predictions of reactivity using these theories difficult. Molecular Orbital (MO) theory has the advantage of being implemented through computational ab initio methods which provide the means to directly simulate reactions and derive energies. In addition, intermediate structures, such as transition states are available, which provide additional explanatory power. As discussed earlier, a number of theoretical treatments have been conducted to study

various aspects of SN2 reactions for halogenated compounds. These studies used MO theory as implemented in a variety of computational methods. Both types of theories –

MO theory and VBCM can be complementary, and both are useful in explaining SN2 reactivity (Shaik et al. 1992; Shaik 2001).

3 Research Approach Much of the knowledge regarding reactivity of halogenated compounds in the environment derives from experimental data gathered under different sets of conditions. Questions that have arisen from analysis of observed reactivity patterns of haloalkanes could be addressed if a more consistent measure of intrinsic reactivity were available across a comprehensive set of compounds. Improved analysis of factors that influence reaction rates, such as substituent, solvent and enzymatic effects, could then be performed.

3.1 Approach The core of my research was the development of a calculated set of gas phase activation

energies (Govr) using ab initio computational methods for a large set of halogenated

pollutant compounds. I analyzed the Govr results and uncovered relationships that exist between compound structure and reactivity and documented those in a series of structure- reactivity relationships (SRRs). I then examined potential mechanisms underlying the

SRRs and tested my hypothesis that two proposed mechanisms could explain the Govr results in gas phase and solution. To account for enzyme constraints that influence reactivity, I employed a docking model to explore steric interactions within an enzyme active site. Finally, I developed statistical Quantitative Structure Activity Relationships

(QSARs) for experimental activity using a set of molecular descriptors (including Govr) that account for other factors in experimental data such as solvation. The steps that I followed are described below:

 Calculate Govr  Develop SRRs  Test Mechanisms  Conduct Docking Analysis  Develop QSARs

3.1.1 Calculation of Activation Energies (Chapter 5)

Govr was calculated using Gaussian 98 for 76 halogenated compounds and four nucleophiles (Acetate, Cl-, OH- and HS-), as described in Chapter 4. Activation energies

in solvent (Hsolv) were also calculated for a subset of compounds using the Isodensity Polarized Continuum Model (IPCM) solvent model. As described in Chapter 2, activation energies are a determinant of reaction rates and provide the measure of reactivity for my work.

3.1.2 Development of SRRs (Chapter 5) I identified relationships apparent between activation energies and structure and determined the qualitative and quantitative consistency of these relationships across the set of compounds. A structure reactivity relationship (SRR) is a relationship between an observed change in chemical property and a change in some aspect of chemical structure:

act ~ structure

SRR development was accomplished through a series of pairwise comparisons of Govr between compounds. I developed 18 SRRs and evaluated how well the SRRs were able to align with experimental trends and provided possible explanations when there were discrepancies.

3.1.3 Identification of Mechanisms (Chapter 6) I identified the two proposed substituent effect mechanisms of electron delocalization and through space/intramolecular solvation and hypothesized that the range of variability in

Govr for my compound set can be quantitatively explained using these two mechanisms.

I tested whether these combined mechanisms could explain the breadth of Govr results and the SRRs through examination of TS structures and correlation with experimental vertical electron affinity data.

3.1.4 Docking Studies (Chapter 7)

In some cases, there were conflicts between the reactivity that Govr values suggested and observed enzyme activity, which I hypothesize is attributable to steric effects imposed by the active site pocket of the enzymes. To test this hypothesis, I employed a computational docking method (AUTODOCK) to study how steric constraints can affect the ability of halogenated compounds to orient in a reactive configuration within the active site of haloalkane dehalogenase (HAD) enzymes. Crystal structures were obtained from the RSCB Protein Data Bank (Berman et al. 2000) for two HAD enzymes from Xanthobacter Autotrophicus (PDB ID: 2HAD) and Rhodococcus (PDB ID: 1CQW).

3.1.5 Quantum Chemical Based QSAR Models for Hydrolysis and Biological Data (Chapter 8) For the final analysis, I used the calculated activation energies in conjunction with other molecular descriptors to develop quantitative structure reactivity relationships with historical experimental data using statistical methods. I developed a robust QSAR model for hydrolysis based on existing data and considerations of gas phase intrinsic reactivity and solvent related descriptors. I also developed a robust model of biotransformation using existing data and considerations of steric constraint and gas phase SAR. I used the gas phase SAR to evaluate biological transformation data. I employed additional descriptors that were generated from a docking algorithm to account for steric enzyme constraints.

3.2 Hypotheses to be tested My main overarching hypotheses are threefold:

 Results from ab initio calculations results provide information useful in interpretation of experimental trends  The reactivity and transformation rates of halogenated pollutants and the effect of substituents can be accounted for by the simultaneous operation of two mechanisms: electron delocalization and through space/intramolecular solvation mechanisms.

 Deviation from gas phase Govr SRR observed in experimental hydrolysis and enzymatic transformation data is due to solvent effects and steric active site constraints and can be accounted for using docking and QSAR methods

Other hypotheses include:

 The leaving group is one of the most important factors affecting reactivity. The order for rates of transformation when considering the leaving group is: Br > Cl > F

 SN2 substitutions must compete with E2 reactions in cases where there is at least one  halogen substituent.

 SN2 substitutions are favored on monohalogenated substituted compounds

  substituents decrease reactivity.   substituents will also decrease reactivity but have a lesser impact than  substituents and vary significantly depending on functional group  Halogen  substituents will have contrasting effects depending on the reaction environment.  Substituent impact decreases with distance from the displacement.  Chain length does not have significant effect on reactivity  Effect of identical structure changes on reactivity in analogous compounds will be consistent  Solvation will raise activation energies compared to the gas phase  Halide  substituents increase delocalization leading to looser and higher energy transition states   substituents will provide stabilization to the transition state in the gas phase and in enzyme environments, but solvent will dampen out this effect  Enzyme steric constraints are an important factor in determining biotransformation rates  Structure activity relationships will vary in different reaction environments

3.3 Relationship of Gas Phase Activation Energies to Environmental Transformation Rates The question arises: How do gas phase activation energies relate to environmentally relevant transformation rates? This can be answered by a discussion of the reaction compartments in which I studied halogenated pollutant transformation and the interrelationship of reactivity factors as envisioned in Figure 3-1.

Hydrolysis Gas Phase Biological (Aqueous) order of (Enzyme) order of reactivities order of reactivities (Core activities Reactivity) Solvent effects Solvent free, Pure – intrinsic stabilizing E2 competition reactivity residues

Selectivity

Figure 3-1: Reaction Compartments Considered in this Study

There are three principal compartments of interest for which experimental data has been obtained for SN2 reactions of halogenated compounds. Each compartment has a certain set of effects based on the reaction environment. The gas phase is the purest environment from which we obtain the “intrinsic” reactivity. This can be considered loosely as the “chemical” effect that exists primarily from the differences in bond breaking and bond making considerations. The other two compartments are usually where reactions of environmental relevance take place. Each of these can be compared to the gas phase to determine the effects. Any significant differences between gas phase reactivity and aqueous and enzymatic activity would be attributable to the difference in environments. Moving to the aqueous environment incurs solvent effects. In solvent, the potential for competing reactions is likely. Moving from the gas phase into an enzyme environment usually does not incur solvent effects, but steric constraints may come into play due to the interaction with the enzyme active site. In fact, many enzymes are designed to impose selectivity on compounds through steric limitations (i.e. the active site may be shaped to accommodate only certain compounds) (Fersht 1999) To truly understand structure- activity relationships and mechanisms in the relevant aqueous phase and environment, one must have an understanding of the reaction in the gas phase as the baseline case. There is data that exists for hydrolysis and enzyme activity. From calculation of gas

phase activation energies what I have established is the intrinsic reactivity free of external effects.

3.4 Nucleophiles Included in the Study - - - - Reactions were calculated with the CH3COO (acetate), OH , Cl , and HS nucleophiles.

Acetate is most representative of enzyme SN2 reactions, while OH- and HS- are important nucleophiles in abiotic transformations. The Cl- ion has been the focus of study for many gas phase reactions. Comparative analysis allowed me to determine where there were common patterns of reactivity among the nucleophiles and where there were differences. A benefit of this availability of results for all of these nucleophiles using a single consistent method is that the data and results from previous experimental and computational studies can now be related to each other.

3.5 Compound Set Halogenated pollutants contain a large degree of structural diversity. One of the reasons that halogenated compounds have become such widespread pollutants is precisely because the large degree of possible structural variety has allowed for the manufacture of many compounds with different beneficial properties that serve specific human purposes. Haloalkanes are the most common pollutant, but haloalcohols, haloacetates, cyclic halides and other compounds (e.g. epichlorohydrin) also exist. Halogenated compounds contain varying numbers of halogens at different locations and of varying type. All of these structural variations can confer different effects on reactivity.

Most of the compounds that were included in my calculated set represent known halogenated pollutants. However, some non-existent compounds were included to ensure the breadth of structural diversity. There also some compounds included that were not necessarily of environmental concern, but did possess experimental data which would be useful in developing correlations. The gamut of structural variety that exists for halogenated compounds is shown in Table 3-1. The naming convention for compounds with multiple halogens of different types was to put the letter of the halogen that is being displaced first. Thus, 1-bromo,1-chloroethane will be named as 11BCA if the bromine is being displaced in the calculated reaction.

Table 3-1: Structures of Halogenated Compounds in this Study Bromine Displacement Chlorine Displacement Flourine Displacement Monohalogenated Alkanes Bromomethane Br Chloromethane Cl Fluoromethane F BM CM FM

Bromoethane Br Chloroethane Cl Fluoroethane F BA CA FA 1-bromopropane Br Chloropropane Cl BP CP Chlorobutane Cl CB Chlorohexane Cl CH Br Cl 2-bromopropane 2-chloropropane 2BP 2CP

Br Cl 2-bromobutane 2-chlorobutane 2BB 2CB

Cl 22- dichloropropane 22DCP Cl  Halogen Substituted Alkanes 1,2- Br 1-chloro,2- Cl Dibromoethane bromoethane 12DBA Br 12CBA Br 1-bromo,2- Br Cl 1,2-dichloroethane chloroethane 12DCA 12BCA Cl Cl 1-bromo,2- Br 1-chloro,2- Cl fluoroethane fluoroethane 12BFA F 12CFA F 1,2- Br 1,2- Cl dibromopropane Br dichloropropane Cl 12DBP 12DCP 1,2- Br Cl 1,2-dichlorobutane dibromobutane Br 12DCB Cl 12DBB 1,2-dibromo-3- Br chloropropane Cl Br DBCP 1,2,3- Br 1,2,3- Cl tribromopropane trichloropropane Br Br Cl Cl 123TBP 123TCP 1-bromo,2- Cl 1,3- Cl Cl chloropropane Dichloropropane Br 12BCP 13DCP 1,3- Cl Dichlorobutane 13DCB Cl 1,4-dichlorobutane Cl 14DCB Cl 1,6- dichlorohexane Cl 16DCH

Bromine Displacement Chlorine Displacement Fluorine Displacement  Halide Substituted Alkanes Dichloromethane Cl DCM Cl Br 1-chloro,1- Cl 1,1-Dibromoethane bromoethane 11DBA Br 11CBA Br 1-bromo.1- Br Cl 1,1-Dichloroethane chloroethane 11DCA 11BCA Cl Cl 1-bromo,1- Br 1-chloro,1- Cl 1,1- F fluoroethane fluoroethane difluoroethane 11BFA F 11CFA F 11DFA F 1,1 Cl chlorobromobutane 11CBB Br Br 1-fluoro,1,1- F 1,1,1-Tribromoethane dibromoethane 111TBA Br Br Br 1F11DBA Br 1,1-dibromo,1- Br chloroethane Br 11DB1CA Cl Cl 1,1,1-trichloroethane

111TCA Cl Cl 1,1-dibromo,1- Br fluoroethane Br 11DB1FA F 1-bromo, 1-chloro,1- Br fluoroethane Cl 111BCFA F 1-chloro,1,1- Cl difluoroethane F 1C11DFA F Combined Gem and Vicinal Substituted Alkanes 1,1,2,2 Cl Cl Tetrachloroethane 1122TETCA Cl Cl 1,1,2-trichloroethane Cl Cl (vinyl trichloride) 112TCA Cl  Methyl Group Substitution 1-bromo,2- 1-chloro,2- methylpropane Br methylpropane Cl 1B2MeP 1C2MeP

Bromine Displacement Chlorine Displacement Fluorine Displacement  OH Group Substitutions (Haloalcohols) 1,3 dibromo 2- OH 1,3-Dichloro-2- OH propanol Br Br propanol Cl Cl 13DB2POH 13DC2POH Br Cl Bromoethanol Chloroethanol BAOH CAOH HO HO HO Cl Dichloroethanol

DCAOH Cl Bromopropanol HO Br Chloropropanol HO Cl BPOH CPOH 3-chloro 1,2 OH propanediol HO Cl 3C12PDIOL  OOH Substitutions (Haloacetid Acids) Bromoacetate O Br Chloroacetate O Cl BAOOH CAOOH HO HO Dichloroacetate O Cl DCAOOH HO Cl 2-chloropropionate Cl 2CPOOH O

OH 3-chloropropionate O Cl 3CPOOH HO Vinyl group Substitutions Allyl chloride Cl ALLYLCL 1,3-dichloropropene Cl Cl 13DCPENE Haloacetamide O O Bromoacetamide Chloroacetamide BAACET Br CAACET Cl NH2 NH2 Cyclic Halides Cl Cyclopropyl chloride

CYCPROPCL

Br Cl Cyclobutyl bromide Cyclobutyl chloride CYCBUTBR CYCBUTCL

Br Cl Cyclopentyl-bromide Cyclopentyl Chloride CYCPENTBR CYCPENTCL

Br Cyclohextyl-bromide

CYCHEXBR

Haloepoxide Epichlorohydrin O Cl EPIC

4 Methods This chapter provides background for the ab initio and docking methods used in my research.

4.1 Ab Initio Methods Ab initio methods determine the energy of a system from first principles by solving the Schrödinger equation (Hehre et al. 1986; Foresman and Frisch 1995):

 In this equation H, is the Hamiltonian, is the wavefunction and E is total energy of the system. The Schrödinger equation is an eigenvalue equation with as the eigenvector, H as an operator and E is the eigenvalue. This equation essentially states that the Hamiltonian is an operator on  that will produce the energy E as a function of The equation captures the fact that there are multiple solutions possible for The square of  is the probability density function for a particle.

4.1.1 The Hamiltonian The Hamiltonian contains terms for both kinetic and potential energy. T is the kinetic energy and V is the potential energy.

H = T + V

The equations below show the components of the Hamiltonian for a molecular system. In this case,  is a function of the positions of all electrons and nuclei in the molecule, so the Hamiltonian for the system sums up all the kinetic and potential energy over all space for all particles in the molecule. The kinetic energy, T, is the summation over all particles

in the molecule. In the equation above, h is Plank’s constant, and mk is the mass of the particle.

h 2 1   2  2  2    T  2   2  2  2  8 k mk xk yk zk 

The potential energy V includes the coulombic repulsions and attractions between nuclei and electrons in the system. The first term in the equation for V is electron-nuclei

attraction, the second term is electron-electron repulsion and the last term is for nuclei- nuclei repulsion. The charge on an electron is –e, while the charge for a nucleus is Z, where Z has the value of the atomic number for a given atom. r and R are the distances between the particles.

1  electronsnuclei Z e2  electrons e2  nuclei Z Z e2    I     I J  V             4 0  i I  riI  i j i rij  I J I  RIJ 

4.1.2 Solution of the Schröedinger Equation The discussion here provides an overview for solving the Schrödinger equation using ab initio methods. For a more in-depth treatment, see Hehre et al. (1986).

In order to solve the Schrödinger equation, a number of assumptions and approximations are necessary. The Born-Oppenheimer Approximation is one key assumption that the positions of nuclei are assumed fixed relative to the electrons in a system, since the mass of the nuclei is much greater than the electrons (Foresman and Frisch 1995). Electrons essentially react instantaneously to changes in positions of the nuclei. This means that electrons will be functions of nuclear position, but not of nuclear velocities. This allows the Hamiltonian to leave out the term for kinetic energy of the nuclei, greatly simplifying the solution.

To reduce the space that must be considered in determining a solution, can be decomposed into combinations of molecular orbitals. In terms of molecular orbitals, the  can be described as a Slater determinant. The Slater determinant is antisymmetric, which stems from the requirement that  must change signs when identical particles are interchanged. The antisymmetric requirement is based on the observed physical behavior of electrons. Molecular orbitals can be expressed as linear combinations of basis functions. Basis functions are one electron functions that are usually centered on atomic nuclei. The basis functions define the space r for a molecular orbital, as defined below.

i  ci  1

i is an individual molecular orbital. ci are known as the molecular orbital expansion

coefficients.  are the basis functions. Gaussian 98 uses a Gaussian-type atomic basis

function to define the molecular orbital in three dimensional space.  is a constant that determines the radial extent of the function.

n m l r 2 g(,r)  cx y z e

Once the orbitals have been defined in terms of basis functions, the solution becomes one

of solving for the molecular orbital expansion coeffecients, ci. This can be done with the application of the variational principle which states that the energy of the exact wavefunction will always be lower than calculated energies. This reduces the problem to one of determining the coefficients that will minimize the energy of the calculated wavefunction.

4.1.3 Gaussian 98 Calculations Gaussian 98 was employed to perform the electronic structure calculations. B3LYP density functional theory was used with a 6-311+g(d) basis set. The Stanford Bio-X supercomputer was used for all calculations. The creation of molecular structures for the calculations is facilitated by the Gaussview Graphic User Interface.

Density Functional Theory Method One of the most computationally intensive parts of a calculation is the accounting for electron correlation effects. Electron correlation is the explicit accounting for the interaction of electrons on eachother’s motions. The most basic method employed – Hartree-Fock - essentially ignores electron correlation and only accounts for interaction with the average electron density. However, it sacrifices accuracy. Density Functional Theory (DFT) methods allow efficient calculations while accounting for electron correlation, through the use of functionals. A functional is a function of a function. In DFT, this means that the functional is function of the electron density. Traditional DFT

methods include local exchange and correlational functionals, that only include the values of electron spin densities; and gradient-corrected functionals that include the values of both the electron spin densities and the associated gradients (Foresman and Frisch 1995) The B3LYP method (Becke 1993) developed by Lee, Yang and Parr is a well known hybrid functional that combines aspects of both types of the above methods and has proven to be superior

Basis Set A basis set is the mathematical description of the available orbitals. It is an arbitrary restriction of space that the solution can explore. In its most basic form (e.g. 3-21), the orbitals are restricted to be centered around the nuclear positions. In order to properly represent polar molecules, polarization functions are necessary to allow for non-covalent displacement of charge away from the nuclear centers. The 6-311+g(d) basis set used in my calculations includes polarization functions. This includes an inner shell of 6 s-type Gaussians, and an outer valence region divided into 3 areas consisting of 3, 1, and 1 primitives. The ‘+’ indicates the use of a diffuse function that has significant amplitude far from the center. This provides description of molecules in which a significant amount of the valence-electron density is allocated to diffuse lone-pair or to antibonding orbitals.

Critical Energy Points

For the SN2 reaction, there are a number of critical points along the reaction path as shown in Figure 4-1.

Reactants Transition State H H CH COO- CCl 3 + H H CH3COOC Cl H H

= H1 H

H - H CH3COO C + Cl - H CH3COO CCl H H H

Ion-Molecular Complex (IMC) Products

Figure 4-1: SN2 Reaction Energies

The reaction path includes four key structures, which include the pair of separated reactants, the Ion-Molecular Complex (IMC) and Transition State (TS) intermediates, and the Products. The geometries and energies of each of these structures can be determined in Gaussian 98

Structures For a given configuration of a molecule, a single point calculation will determine the energy of the system. Optimizations are also possible that will search conformational space to find the set of coordinates that provides the lowest overall energy. The TS structures were obtained using the quadratic synchronous transit (QST2) method (Foresman and Frisch 1995). Here, the initial reactant and product structures were approximated visually and QST2 searches to locate the TS, which is a 1st order saddle point between the starting structures. Once the TS structure is determined, an internal reaction coordinate (IRC) calculation is performed to trace back and connect the transition state to the IMC (Foresman and Frisch 1995). The structures determined in this study are shown in Table 4-1.

Table 4-1: Structures Calculated in this Study Structure Method Used to Determine Reactants Optimization Transition State Optimization- locate saddle point IMC Follow path down slope from transition state , then optimize.

Energy Differences From the calculated energies for each set of structures, I determine the two types of energies that are relevant to characterizing the reaction energetic profile. As shown in

Table 4-2, subtracting the energy of the isolated reactants from the TS provides Govr. and subtracting the IMC from the TS energy will give Gcent. Both values have

implications in characterizing SN2 reactivity.

Table 4-2: Energies calculated in this study Energy How calculated

Govr TS-Reactants

Gcent TS-IMC

4.2 AUTODOCK AUTODOCK (Morris et al. 1995) was developed to provide a procedure to predict the interaction of small molecules with macromolecular targets (e.g. enzymes). I used Autodock 2.4 to explore configurational space, which is how the substrate is oriented in relation to the macromolecule. There are two important aspects to a docking algorithm – the energy evaluation, or scoring method, that determines the energy of a given configuration and the search method which allows thorough exploration of configurational space. A grid based method utilizing precalculated potentials allows very rapid energy evaluation (Goodford 1985).

4.2.1 Energy Evaluation Energy evaluation is based on the 12-6 Lennard Jones potential to model van der Waals (steric) forces. Here the potential energy between two atoms is expressed as: C C V(r)  12  6 r12 r6

There is a minimum energy which occurs at the bottom of a well that represents the most favorable distance (van der Waals radius) between two atoms.

4.2.2 Monte Carlo Simulated Annealing

To search configurational space, Autodock 2.4 utilizes Monte Carlo simulated annealing. In this method, the macromolecule is static and the substrate performs a random walk. This is accomplished by generating at each step a small random displacement in each of the degrees of freedom in the substrate molecule. This includes translational and rotational movement around the center of gravity, as well as rotating of internal dihedral angles. After the displacement is applied, the energy of the new configuration is calculated. If the energy is lower (i.e. it represents a more favorable binding) then the

new configuration is accepted. If the energy is higher, then the new configuration will be accepted with a probability according to a Boltzmann probability distribution:

E - kB T P(E) = e P = Probability that configuration is accepted E = energy difference between new configuration and starting configuration kB = Boltzmann’s constant T = Temperature

E is the energy of the current configuration minus the energy of the previous configuration. According to the equation, the probability, P, will be higher at higher temperature and lower with higher E. Hence, at high temperatures almost all new configurations will be accepted. At low temperatures fewer configurations are accepted. As the simulation runs, the temperature T is progressively decreased. The reason for the difference in acceptance probabilities is a central feature of the simulated annealing technique. It is designed to allow a thorough exploration of minima without getting trapped in the local minima.

4.2.3 AUTODOCK simulations

Polar hydrogens and charges were added to the crystal structure HAD using the molecular modeling program SYBYL (Tripos International, 2000). Substrate molecules were also built using SYBYL. Non-polar hydrogens were united and charges added. Torsions were defined using AUTOTORS (AUTODOCK script). For each substrate molecule, I ran 100 runs using a linear temperature reduction schedule. Each run started with the same initial temperature and consists of a set of 50 Monte Carlo cycles, with a temperature reduction that occurred for each cycle. A maximum of 25,000 steps (each step resulted in a new configuration) were accepted or rejected for each cycle. At the conclusion of each run, AUTODOCK provides the three dimensional coordinates and the energy of the final structure for that run. This is known as a “pose”. Structure binning was performed to cluster the results where possible.

5 Ab Initio Calculations and Structure Reactivity Relationship (SRR) Development This chapter presents the results of the ab initio calculations and includes the following:

 Description of the results (Govr for 76 compounds with acetate nucleophile)  Development of structure-reactivity relationships (SRRs) for six categories of structure through comparative analysis between compounds  Analysis of solvent effects from the IPCM model - - -  Comparison of Govr for other nucleophiles (Cl , OH , HS )  Discussion of implications for interpretation of experimental data

5.1 Results All calculations were performed using Gaussian 98 on the Stanford Bio-X

supercomputer. Govr values calculated for the gas phase SN2 reaction with the acetate nucleophile are shown in Table 5-1. The values ranged from -3.7 to 45.5 kcal/mol, illustrating that structural differences among halogenated compounds can lead to significant differences in Govr. Table 5-1 represents the most comprehensive set of Govr values – theoretical or experimental – that has been obtained to date for SN2 reactions of halogenated pollutant compounds using a single consistent method. As such, it will allow a thorough investigation of the effects of small changes in structure on reactivity as measured by Govr. It will also allow a determination of the degree of consistency of the SRRs across the compound set.

In general, the entropy corrections that are included in Govr did not change the trends

from the enthalpic activation energy Hovr (not shown). Govr includes the vibrational, translational and rotational contributions to free energies for the reacting molecules (Foresman and Frisch 1995), but does not account for intermolecular entropic effects such as those related to the probability of correct orientations for collisions. Entropic factors resulted in a positive correction to Hovr so that Govr was always higher. This is because the transition state is more highly ordered and has fewer degrees of freedom than

the reactants. Many of the Hovr values are negative because the transition state has lower energy than the separated reactants, but once the calculated entropies are added, the

barrier (Govr) becomes positive. The entropic energy was fairly constant between compounds and ranged from +8-10 kcal/mol. This is consistent with the observation that barrierless reactions are not actually observed because the separated reactants are entropically favored (Olmstead and Brauman 1977).

Table 5-1: Energies of Halogenated Compounds for SN2 Displacement Reaction with Acetate

Govr Govr Govr TS-Reactants TS-Reactants TS-Reactants Compound (kcal/mol) Compound (kcal/mol) Compound (kcal/mol) ALLYLCL 6.2 DCAOOH 7.3 13DB2POH -3.7 BA 5.7 DCM 9.1 13DCB 5.9 BAACET 1.1 EPIC 7.1 13DCP 5.6 BAOH -0.3 FA 26.8 13DCPENE 1.9 BAOOH -3.7 FM 23.3 13DC2POH -0.6 BM 2.4 11BCA 9.4 14DCB 5.0 BP 5.1 11BFA 6.7 16DCH 5.9 BPOH 2.4 11CBA 15.3 1C11DFA 18.6 CA 9.8 11CBB 15.4 1F11DBA 45.5 CAACET 5.4 11CFA 11.7 11DB1CA 17.5 CAOH 3.4 11DBA 10.9 11DB1FA 16.7 CAOOH -0.2 11DCA 14.0 111BCFA 15.3 CB 9.1 11DFA 34.0 111TBA 18.8

CH 9.3 12BCA 2.6 111TCA 21.9

CM 6.4 12DBA 2.0 112TCA 7.2 CP 9.3 12DBB 4.2 123TBP 0.0 CPOH 6.5 12DBP 4.4 123TCP 3.5 CYCBUTBR 11.1 12CBP (2°) 4.9 1122TETCA 13.4 CYCHEXBR 12.7 12CBA 6.0 2BB 7.2 CYCPENTCL 13.1 12CFA 6.8 2BP 8.0 CYCPROPCL 21.4 12DCA 5.8 2CB 11.2 CYCPENTBR 7.6 12DCB 9.1 2CP 11.9 DBCP(1° Br) 0.0 12DCP 8.5 2CPOOH 6.7 DBCP (2° Br) 3.2 12BFA 3.1 22DCP 18.4 DBCP (3°Cl ) 2.4 1B2MeP 6.8 3CPOOH -1.3 DCAOH 7.9 1C2MeP 10.8 3C12PDIOL -2.6

Halogenated compounds represented in this study can be broadly classified by type as haloalkanes, haloalcohols, haloacids, epoxides, cyclic compounds and allylic halides. They can also be further described in terms of the leaving group (bromine, chlorine and fluorine), and by the type, number and location of substituents present on the molecule. Structures vary in the length of carbon chains and can also possess branching where more than one carbon chain extends from a carbon in the structure (e.g. 12DCP).

Figure 5-1 shows the general trend in Govr for representative compounds from the following groups: Haloalcohols ~haloacetates > vicinally substituted haloalkanes, monohalogenated terminal haloalkanes > secondary haloalkanes > di-terminal >cyclic halide> tri-terminal. The brominated compounds are labeled, while the unlabeled symbols above each brominated compounds are the chlorinated and fluorinated analogues (squares and triangles respectively).

30 Fluorinated Analogues 25

20 111TBA 15

(kcal/mol) Chlorinated Analogues 11DBA ovr 10 G 2BP  BA CY CPENTBR 5 BP BM BPOH Brominated 12DBA 0 123TBP Chlorinated BAOH Brominated Compounds BAOOH Fluorinated -5 Increasing Gov r

Figure 5-1: Govr trend for a series of bromo-, chloro- and fluoro-substituted compounds

The trend is consistent for analogous sets of brominated and chlorinated compounds (and for two fluorinated compounds). Brominated compounds have consistently lower Govr than their chlorinated analogues. The trends closely parallel each other with a relatively consistent difference of about 3-4 kcal/mol, which indicates that a consistent SRR

between leaving group types may exist. The pair of fluorinated compounds (FM and FA) shown in the figure have a Govr 17-19 kcal/mol higher than the chlorinated analogues,

which is consistent with the relative non-reactivity of fluorinated compounds in SN2 reactions.

5.2 Structure Reactivity Relationships for Govr I developed SRRs by conducting pairwise comparisons between compounds selected to represent the effects of single step changes in structure. An SRR is a relationship between a change in structure and a change in a measure of reactivity, which in this research is

Govr. The premise is that a change in structure will results in a change in Govr, as shown below.

struct = Govr

I defined six categories of structure for SRR development: leaving group, chain length,  and  substituents, vinyl groups, and cyclic groups. In each category, I identified

compound pairs that could best represent the structural change (struct) within the category

and then determined the Govr difference between the compounds (Govr). For example,

in the category of leaving group, a comparison between the Govr for BA (5.7 kcal/mol)

and the Govr for CA (9.8 kcal./mol) would capture the effect of changing a bromine

leaving group to a chlorine leaving group which is an increase of 4.1 kcal/mol in Govr, as illustrated below.

struct(Br to Cl leaving group) = Govr = [Govr-CA - Govr-BA] = +4.1 kcal/mol

Structurally, BA and CA are different only by a single structural change. I refer to such compounds as analogous. The purpose of this section is to identify SRRs from the calculated values for Govr and to determine the degree of consistency across the compound set. The focus in this section is not to establish the underlying basis for the effects, but only to identify the relationships. In Chapter 6, I examine the underlying mechanisms responsible for these observed trends (and outliers) by correlating changes in

Govr with changes in observable molecular properties between compounds.

5.2.1 Leaving Group Effects The leaving groups in this study are the halogens bromine, chlorine and flouring. Leaving group changes are illustrated in Figure 5-2, where the structure consists of a change in leaving group.

Br Cl F

Br Cl

Cl Cl = structure

Figure 5-2: Example of Changes in Leaving Group

The trend in calculated Govr for leaving group is fluorine > chlorine > bromine. Table

5-2 provides a comparison of Govr between analogous compounds where only the leaving group is different.

Table 5-2: struct = Replacement of bromine leaving group with chlorine or flourine

Compound Govr Compound with struct Govr Govr Br to Cl 12BCA 2.6 12 DCA 5.8 +3.2 BAOH -0.3 CAOH 3.4 +3.7 BAOOH -3.7 CAOOH -0.2 +3.9 2BP 8.0 2CP 11.9 +3.9 BM 2.4 CM 6.4 +4.0 BA 5.7 CA 9.8 +4.1 BPOH 2.4 CPOH 6.5 +4.1 11DBA 10.9 11CBA 15.3 +4.4 11BCA 9.4 11 DCA 14.0 +4.6 Br to F BM 2.4 FM 23.3 +20.9 BA 5.7 FA 26.8 +21.1 111TBA 18.8 1F11DBA 45.5 +26.7 Cl to F CA 9.8 FA 21.1 +11.3

Compounds with chlorine as the leaving group have Govr values from 3.2 to 4.6 kcal/mol higher compared to similar structures where bromine is the leaving group.

Compounds with fluorine leaving groups have a much higher difference in Govr relative to bromine leaving groups from 21.1 to 26.0 kcal/mol. The magnitude of the difference in

Govr also appears to be influenced by the type of compound. Relative to the monohalogenated compounds, leaving group related Govr differences are observed to be larger when they occur on compounds with  halogen substituents, and smaller for compounds with  halogen substituents. In these cases, the relative size of the Govr differences between analogous compounds correlates with the relative magnitudes of

Govr of the compounds. For the haloalcohols, haloacetates, and haloalkanes without  or

 halogen substituents, Govr differences between compounds with bromine and chlorine leaving groups were found to be in a tight range from 3.7 to 4.1 kcal/mol.

The underlying factors in the leaving group trend are the carbon-halogen bond strengths as shown in Table 5-3. The increasing strength of the C-X bond from C-Br to C-Cl to C-F as measured by the enthalpy of formation makes the bonds progressively more difficult to break.

Table 5-3: Bond lengths and bond formation enthalpies for carbon-halogen bonds

Length Bond Formation C-X Bond (A) Enthalpy (kJ/mol) C-Br 1.94 281 C-Cl 1.78 339 C-F 1.38 486

From Schwarzenbach, et al 1993

As Figure 5-3 illustrates, the difference in calculated Govr between a bromine and

chlorine leaving group is fairly constant, even though the absolute Govr energies span a wide range. The solid line indicates the 1 to 1 relationship and indicates that compounds with a chlorine leaving group have higher Govr values than analogous compounds with bromine as the leaving group.

20 R2 = 0.998

15 BA & CA

10 (kcal/mol) (kcal/mol)

ovr Monohalogenated G 5  alpha substitution 1:1 line alchohols, acids Chlorine Leaving Group 0 beta substitution cyclic -5 -5 0 5 10 15 20 25 Gov r (kcal/mol) Bromine Leaving Group

Figure 5-3: Comparison of Govr for analogous compounds containing bromine versus chlorine leaving groups.

5.2.2 Chain Length A change in structure from increasing chain length is shown in Figure 5-4, where the

structure consists of a progressive lengthening of the carbon chain by the successive

addition of methyl groups (CH3) to the terminal carbon opposite the leaving group.

Cl Cl Cl Cl

= structure

Figure 5-4: Changes in Chain Length

There was an initial increase in Govr in the initial step from a one carbon molecule to a

two carbon molecule, as evidenced by the ~3.5 kcal/mol increase in Govr from BM to BA and from CM to CA (Table 5-4). Beyond this, lengthening of the chain had only a mild effect. Addition of a third carbon to CA to led to a 0.5 kcal decrease in Govr. Further lengthening for the chlorinated series of compounds created small fluctuating changes in Govr.

Table 5-4: struct = ncrease in carbon chain length Bromo- Chloro-

Govr Govr Compound Type (kcal/mol) (kcal/mol) -methane 2.4 6.4 -ethane 5.7 9.8 -propane 5.1 9.3 -butane - 9.1 -hexane - 9.3

5.2.3  substituent effects I determined the trends in substituent effects for three classes of  substituents: methyl, halogen, and cyclic groups.

Methyl Group

Addition of a methyl group  substituent is shown in Figure 5-5 , where the structure consists of the replacement of a hydrogen by a methyl group (CH3) on the same carbon as the leaving group.

Br Br

Cl Cl

= structure

Figure 5-5: Examples of methyl group  substitution

The effect of substitution of a methyl group as an  substituent is shown in Table 5-5.

The overall observed effect is to increase the calculated Govr by an average of 2.1

kcal/mol. A methyl group addition caused similar (within 0.2 kcal/mol) increase in Govr for chlorinated and brominated analogues even though the Govr itself was ~ 4 kcal/mol higher for the chlorinated analogues.

Table 5-5: struct =  methyl group substitution

Compound Govr Compound with struct Govr Govr

BA 5.7 2BP 8.0 +2.3 CA 9.8 2CP 11.9 +2.1 CP 9.3 2CB 11.2 +1.9

Adding a methyl group  substituent is similar to a comparison of halogen displacement in the secondary (2°) position (i.e. in the middle of a carbon chain) versus a halogen in the primary (1°) or terminal position. Table 5-6 shows that Govr for displacement of a bromine in the secondary position in 2BP is 2.9 kcal/mol higher than displacement in the terminal position of BP, and 2.6 kcal/mol higher in the case of 2CP versus CP. A comparison between 2CP and CB shows a smaller difference of 2.1 kcal/mol. The comparison between 2CPOOH and 3CPOOH, shows a larger difference in Govr between displacement at the terminal versus secondary position when a carboxyl is present.

Table 5-6: struct = Shift of halogen from 1° to 2° position

Compound Govr Compound with struct Govr Govr BP 5.1 2BP 8.0 +2.9 CP 9.3 2CP 11.9 +2.6 CB 9.1 2CB 11.2 +2.1 3CPOOH -1.3 2CPOOH 6.7 +8

Halogen Substituents Addition of halogen  substituents is shown in Figure 5-6, for the replacement of a hydrogen by a halogen on the same carbon as the leaving group.

Cl Cl Cl Cl Cl Cl Cl

Br Br Br Br Br Br Cl Cl Cl Cl Cl structure

Figure 5-6: Examples of  halogen substitution

Halogen  substituents have significant effects on Govr.as shown in Table 5-7.

Table 5-7:struct =  halogen substitution

Compound Govr Compound with struct Govr Govr  Br Substitution BA 5.7 11DBA 10.9 +5.2 CA 9.8 11CBA 15.3 +5.5 CB 9.1 11CBB 15.4 +6.3 11DBA 10.9 111TBA 18.8 +7.9  Cl Substitution BA 5.7 11BCA 9.4 +3.7 CA 9.8 11DCA 14.0 +4.2 CAOH 3.4 DCOH 7.9 +4.5 12DCA 5.8 112 TCA 7.2 +1.4 11DBA 10.9 11DB1CA 17.5 +6.6 11DCA 14.0 111TCA 21.9 +7.9  F Substitution BA 5.7 11BFA 6.7 +1.0 CA 9.8 11CFA 11.7 +1.9 11DBA 10.9 11DB1FA 16.7 +5.8 11BCA 9.4 111BCFA 15.4 +6.0

A single  halogen substituent can raise Govr by 4-5 kcal/mol. The magnitude of the effect is dependent on the halogen  substituent type and decreases in the order Br > Cl > F, which is reverse that of the leaving group trend. An addition of a second  halogen

substituent increases Govr even further.

As with the leaving group category, the magnitude of the effect is somewhat dependent on the compound structure. The effect of a halogen addition seems to be higher when it occurs on a compound that already has two  halogens (e.g. 111TCA versus 11DCA,

Govr = +7.9) and less if there is a  halogen substituent (e.g. comparing 112TCA to

12DCA, Govr = +1.4).

Table 5-8 presents the difference between bromine, chlorine and fluorine as  substituents.

Table 5-8: Direct comparison of bromine to chlorine and fluorine as  halogen substituents (where struct =  halogen substitution)

Compound Govr Compound with struct Govr Govr Br to Cl 11DBA 10.9 11BCA 9.4 -1.5 11CBA 15.3 11DCA 14.0 -1.3 111TBA 18.8 11DB1CA 17.5 -1.3 11DB1FA 16.7 111BCFA 15.3 -1.4 Br to Fl 111TBA 18.8 11DB1FA 16.7 -2.1 Cl to Fl 11DCA 14.0 11DCFA 11.7 -2.3 11DB1CA 17.5 11DB1FA 16.7 -0.8

In conclusion, addition of an  halogen substituent will increase Govr in varying amount depending on 1) the type of halogen and 2) the degree of existing halogenation.

5.2.4  substituent effects  substituents are those that occur on the carbon adjacent to the carbon undergoing substitution or at further distance down a carbon chain. These include methyl groups, halogens, hydroxyl and carboxyl groups.

Methyl substituents

The addition of a second methyl group as a  substituent to the same carbon is presented here. As shown in Figure 5-7, the addition of a second methyl group creates a branched compound.

Cl Cl

= structure

Figure 5-7: Example of Addition of  Methyl Group Substituent (Branching)

As shown in Table 5-9, this branching causes a small increase in Govr.

Table 5-9: struct =  methyl group substitution

Compound Govr Compound with struct Govr Govr BA 5.7 1B2MEP 6.8 +1.1 CA 9.8 1C2MEP 10.8 +1

Halogen substituents

The addition of a  halogen substituent is shown in Figure 5-8, where the structure consists of the replacement of a hydrogen by a halogen on a carbon that is adjacent to the carbon with the leaving group or further down the carbon chain. As Figure 5-8 shows, there are many possible variants on  halogen substitutions possible.

Cl Cl Cl Cl Cl Br Cl . Cl Cl Cl

Cl Cl Cl Cl Cl Cl Cl Cl

= structure Figure 5-8: Addition of  Halogen Substituents

The additional of a  halogen has the effect of decreasing Govr from 1-4 kcal/mol as shown in Table 5-10. The magnitude of this effect increases in the order F < Cl

Table 5-10: struct =  halogen substitution

Compound Govr Compound with struct Govr Govr Br BA 5.7 12DBA 2.0 -3.7 CA 9.8 12CBA 6 -3.8 Cl BA 5.7 12BCA 2.6 -3.1 CA 9.8 12DCA 5.8 -4.0 11DCA 14.0 112TCA 7.2 -6.8 Fl CA 9.8 12CFA 6.8 -3 2 Halogens on both 2° and 3° carbons BP 5.1 123TBP 0 -5.1 CP 9.3 123TCP 3.5 -5.8

The effect of a  bromine substituent addition was almost identical whether it occurred on a BA or a CA. However the effect of a  chlorine substituent addition on the same compounds was much less on the BA than on CA. This also led to an inconsistent effect relative to the  bromine substituent on analogous compounds (lower in the case of addition to BA and higher for addition to CA). As with leaving group and  substituents,  substituents are also sensitive to compound type. In this case, the addition of a 

halogen to a compound with an existing substituent causes a higher reduction in Govr. The addition of 2  halogen substituents in the 2° and 3° positions as in 123TBP or

123TCP lowered Govr by larger amount.

Table 5-11: Direct comparison of bromine versus chlorine

as a  substituent (where struct =  halogen replacement)

Compound Govr Compound with struct Govr Govr 12DBA 2.0 12BCA 2.6 +0.6

I also examined the effect of the distance between the  substitution and the leaving group. Table 5-12 shows an interesting trend.  substitution adjacent to the reacting

carbon produces a drop in Govr to 5.8 kcal/mol (from 9.8 kcal/mol in CA). Substitutions

at the opposite terminal positions in 13DCP and 14DCB respectively produce small decreases in Govr that decrease with distance. For 16DCH, the trend reverses and the

Govr is slightly higher than as a  substitution at the secondary position in 12DCA.

Table 5-12: struct = location of  substitution

Compound Govr 12DCA 5.8 13DCP 5.6 14DCB 5 16DCH 5.9

Haloalcohols and Haloacids As shown in Table 5-13, the substitution of a hydroxide (OH) functional group lowered

the Govr, in some cases significantly. The magnitude of the effect varied greatly. It had the smallest decrease when the hydroxyl group was added to a propane (from -2.7 to -3.0 kcal/mol) and had a much greater effect when it occurred on an ethane (-6.0 to -6.4 kcal/mol). The addition of two hydroxyl groups on the 2° and 3° positions in

3C12PDIOL decreased Govr most significantly (by 11.9 kcal/mol as compared to chloropropane).

Table 5-13: struct =  OH substitution

Compound Govr Compound with struct Govr Govr BA 5.7 BAOH -0.3 -6.0 BP 5.1 BPOH 2.4 -2.7 CA 9.8 CAOH 3.4 -6.4 CP 9.3 CPOH 6.5 -3.0 11DCA 14.0 DCOH 7.9 -6.1 13DCP 5.6 13DCPOH -0.6 -6.2 CP 9.3 3C12PDIOL -2.6 -11.9

As shown in Table 5-14, the substitution of a carboxyl group (OOH) also significantly lowered Govr. The magnitude of the effect from this substitution was much higher when it occurred on CA versus BA.

Table 5-14: struct =  OOH substitution

Compound Govr Compound with struct Govr Govr BA 5.7 BAOOH 1.0 -4.7 CA 9.8 CAOOH -0.2 -10 CP 9.3 CPOOH -1.3 -10.6 11DCA 14.0 DCOOH 7.3 -6.7

5.2.5 Vinyl groups The allyl halides are a class of halogenated compound that possess a vinyl group. The presence of a vinyl group lowered Govr by 2.7 to 3.7 kcal/mol as shown in Table 5-15.

Table 5-15: struct = Vinyl group replacement

Compound Govr Compound with struct Govr Govr CA 9.8 ALLYLCL 6.2 -3.6 CA 9.8 EPIC 7.1 -2.7 13DCP 5.6 13DCPENE 1.9 -3.7

ALLYLCL has a Govr that is lower than CA. EPIC is essentially the substitution of an epoxide. It also has the effect of lowering Govr. There is a similar lowering of Govr with the substitution of a vinyl group in 13DCP.

5.2.6 Cyclic groups

For cycloalkanes, the impact on Govr was based on the ring size. The smaller butyl ring had a higher impact than the larger pentyl ring. Cyclic compounds have generally higher

Govrs than the corresponding ethanes. The order of Govr among the cycloalkanes is – pentyl > -butyl > hex > propyl.

Table 5-16: Cyclic Halide Comparisons Leaving Group Bromine Chlorine

Compound Type Govr Govr Cyclopropyl- - 21.4 Cyclobutyl- 11.1 - Cyclopentyl- 7.6 13.1 Cyclohex- 12.7 -

5.2.7 Other Observations There are some aspects of interest that can be explored through examination of the

calculated set of Govr. These address the questions:

 To what degree are structural effects additive?

 What are the differences in Govr for halogen leaving groups in different positions in multiply halogenated compounds?

Additive Structural Effects

I have already noted the nature of additive effects for  halogen substituents. The effects of multiple  substitutions are not linear, but are magnified for the second substitution. Similarly, I found the same trend for 22DCP and 13DCPENE.

22DCP can be broken down in two incremental changes from CA, which consists of an addition of an  methyl group and the addition of an  chlorine. The addition of the methyl group is characterized as going from CA to 2CP, which is +2.1 kcal/mol. The addition of an  chlorine substituent is characterized as going from CA to 11DCA, which is +4.2 kcal/mol. The two incremental changes add up to 6.3 kcal/mol. The actual change in Govr from CA to 22DCP is 8.9 kcal/mol, which means that the total change is larger than the sum of the two incremental changes. Thus, increases in Govr from structural changes are enhanced when they occur with other changes that also produce increases in

Govr.

A comparison between CP to 13DCPENE yields similar results in the opposite direction.

Relative to CP, 13DCPENE experiences a significant drop in Govr because of the

presence of a vinyl group, (which lowers Govr by 3.6 kcal/mol in the CP to ALLYLCL

comparison) , and the presence of a  chlorine, (which lowers Govr by 3 kcal/mol in the CP to 13DCP comparison). The actual difference between CP and 13DCPENE was a decrease in Govr by 7.4 kcal/mol, which is larger than the sum of the two isolated changes. Previously, there was no experimental data available in which to gauge the relative contributions of the vinyl group and the  chlorine. Now, the relative contributions are apparent.

The conclusion is that effects of structural changes on Govr are additive in a qualitative sense. The net results of two structural changes are greater than the change of one change, but the effects are increased beyond merely the addition of the two changes.

Halogen SN2 Selectivity – Positional Effects In haloalkanes with multiple halogens, an important question is which halogens are the most likely to be displaced. An important example is DBCP shown in Figure 5-9.

2° 3° Br 1° Cl Br

Figure 5-9: DBCP Halogen Positions

It would be expected that the most reactive halogens would be displaced preferentially.

As shown in Table 5-17, the terminal bromine in the 1° position has the lowest Govr, followed by the terminal chlorine in the 3° position and then by the bromine in the 2° position.

Table 5-17: Comparison of Halogen Position Effect on Govr for DBCP

Halogen Govr Displaced (kcal/mol) 1° Br 0 3° Cl 2.4 2° Br 3.2

This is an example where it would not be necessarily obvious what the most reactive halogen would be after the terminal bromine, and illustrates where the quantitative calculations are useful. Also notable is that the differences in Govr are relatively small, so a mixture of products with displacement at different locations is plausible.

5.3 Solvent Effects

The results from the use of the IPCM model to model the effects of solvent on the SN2 reaction with acetate are shown in Table 5-18. The model was applied to a subset of the compounds in the gas phase calculation set.

Table 5-18: Solvated and Gas Phase Hovr

Solvated Solvent Hovr Gas Hovr effect Compound (kcal/mol) (kcal/mol) (kcal/mol) ALLYLCL 8.2 -4.1 12.3 111TCA 23.9 11.2 12.7 CYCBUTBR 13.9 0.5 13.4 CA 13.1 -0.5 13.6 2BB 10.3 -3.6 13.8 CYCPENTCL 13.8 -0.1 13.9 2CP 15.9 1.6 14.3 DCM 12.8 -1.6 14.4 2BP 12.0 -2.6 14.6 BA 10.4 -4.5 15.0 12DCB 13.5 -1.8 15.4 2CB 16.0 0.6 15.4 CPOH 11.6 -4.3 15.9 13DCPENE 7.8 -8.5 16.3 CAOH 8.5 -7.9 16.4 BAOH 4.7 -11.8 16.4 1B2MEP 12.8 -3.9 16.7 CYCHEXBR 18.6 1.6 17.0 11DCB 21.3 4.2 17.1 DCOH 13.5 -3.7 17.2 DCOOH 11.4 -5.8 17.2 11DBCA 15.7 -1.6 17.2 12DCA 13.5 -4.7 18.2 14DCB 13.1 -5.5 18.7 12DBA 10.2 -8.5 18.7 12DBB 12.6 -7.0 19.6 123TCP 12.0 -7.7 19.7 21DCP 18.1 -2.0 20.1 3CP00H 9.2 -13.5 22.7 13DBPOH 10.6 -15.3 25.9 3C12PDIOL 11.2 -15.4 26.7

The Hovr values shown were obtained from the IPCM model and ordered according to increasing Hovr. The inclusion of solvent effects increased Hovr from 12.3 to 26.7

kcal/mol. The IPCM model results showed that solvent stabilized the reactants to a greater extent than the transition state (not shown). Compounds with  substituents had higher solvent effects relative to monohalogenated compounds and compounds with  substituents. 111TCA and ALLYLCL had the lowest solvent effects. Compounds with cyclic groups show mixed behavior with respect to solvent effects.

Figure 5-10 shows the relationship between solvated and gas phase Hovr. What stands out is that most of the compounds with  substituents lie above the trendline shown for the monohalogenated and  substituted compounds. This implies there is a magnified solvent effect for compounds with  substituents. The compounds with OH groups show even higher solvent effects.

30 a substituted R2 = 0.9028 25 b substituted alcohol acetate 20

15 H Solv  10

0 -20 -10 0 10 20 G Gas

Figure 5-10: Solvated versus Gas Phase Activation Energies One possible explanation for the differential trends for  substituted compounds is the loss of stabilization by  substituents. This has been observed and described by Craig and

Brauman (1999), who studied the intramolecular microsolvation of SN2 transition states.. They concluded that, in the gas phase, rate acceleration was caused by through space dipolar salvation of the ionic transition state, rather than inductive effects transmitted

through the carbon chain. Gronert (2001) came to a similar conclusion. The comparison shown here supports that theory as applied the halogenated pollutants in the study.

5.4 Other Nucleophiles I performed calculations for other nucleophiles (Cl-, HS-, and OH-) as shown below in Figure 5-11. The results show that there is reasonable correlation between the Cl-, OH- - - and HS and CH3COO , illustrating the broader applicability of these results. There are some gaps as calculations were not completed for all compounds for all four nucleophiles, however, the results show that there are consistent trends between the nucleophiles. The r2 for Cl- HS- and OH- was was 0.99, 0.94 and 0.81 respectively. This indicates that reactivity patterns of halogenated pollutants explored with the acetate nucleophile may be broadly applicable to other nucleophiles. It also means that much of the prior intensive work on Cl- may apply to more complex substrates (such as halogenated polluants) and different nucleophiles.

Cl- 30 HS- 1-1 20 OH- 10

0 -10

-20 -30 -40 Govr for Cl-,HS-, and OH- (kcal/mol) and OH- Cl-,HS-, for Govr

 -50 -10 0 10 20 30 40 Govr for Acetate (kcal/mol) - - - Figure 5-11: Cl , HS and OH vs. Acetate: Calculated Results for Gas Phase Govr 5.5 Summary of Structure Reactivity Relationships

There are discernible patterns of reactivity apparent in the calculated SN2 Govr values of halogenated compounds. Six categories of structural features, which included leaving group type and various classes of substituents, were used to group compounds and

classify structural changes. I found that these six categories were sufficient to capture the major trends and compound behavior with respect to Govr. These are summarized in Table 5-19. Table 5-19 provides a list of SRRs developed from the acetate gas phase results. The SRRs consist of a range of expected effects based on the actual results (instances), rounded to the nearest whole number (with the exception of #2b-2d). An instance is one compound pair that described the pertinent structural change. A general trend was noted that the presence of  substituents seemed to magnify the effects of other structural changes while  substituents tended to be affected to smaller degrees by other changes in structure. This indicates a possible proportionality of effect in these cases, however this proportional effect did not hold in all cases (e.g. structural changes on

haloalcohols, which have generally lower Govr as compared to haloalkanes, tended to

produce similar Govr changes as the same structural change on a haloalkanes).

Table 5-19: SRRs for Halogenated Pollutants Based on Govr Gas Phase Calculations

SRR Govr Table # & ID struct (kcal/mol) # Instances Notes #1 Change in Leaving Group Br

#1a Br - Cl +3 to +4 Table 5-2 (9) Govr increases were higher when 2  halogens were already present #1b Br - F +21 to +27 Table 5-2 (3) and lower in the presence of a  halogen substituent. #2 Chain Length Increase Me>Et~Pr~Bu~Hex #2a Me→Eth +3 Table 5-4 (2) #2b Eth→n-Pr -0.5 Table 5-4 (2) #2c n-Pr→ n-Br -0.2 Table 5-4 (1) #2d n-Br→Hex +0.2 Table 5-4 (1) #3 Add  substituent Me -butyl > -hexyl > -propyl #6 C-C → Cyclic +3 to +14

Table 5-16 (5)

5.6 Implications for Experimental Interpretation In this section, I place the observations from the results of my calculations into the context of experimentally observed reactivity trends. The gas phase calculations account for the intrinsic reactivity of the compounds in the absence of external effects such as solvent and enzyme active site constraints. Here I examine how the SRRs I have developed compare with experimental observations and offer thoughts for any differences that are encountered.

5.6.1 Comparisons with Experiment The comparisons are described below in terms of structural categories.

Nucleophiles The order of nucleophilicity based on my calculations is OH- >>HS- >Acetate > Cl-. In solution, HS- is a stronger nucleophile than OH- (Schwarzenbach et al. 1993), however, OH- becomes one of the stronger nucleophiles in a gas phase environment, once the effects of solvent are removed (Olmstead and Brauman 1977).

Haloalkane dehalogenase enzymes (HAD) employ aspartate residues which employ a carboxyl functional group, so the trends for the acetate nucleophile presented in this chapter should be most applicable for HAD data.

Leaving Groups Both the calculated gas phase and solvated trends in SRR #1 parallel experimentally observed trends in hydrolysis rates (Mabey and Mill 1978; Barbash 1993).

Schanstra et al. (1996) found that the rate of C-Br bond cleavage was faster than C-Cl bond cleavage for the HAD enzyme from Xanthobater Autotrophicus (2HAD), in agreement with expectations from SRR #1 trend. 

Chain Length

The trend in Govr for lengthening of the carbon chain in SRR #2 agrees with experimental trends. The methyl→ethyl;→n-propyl→n-butyl→i-propyl→i-butyl series has been well studied in gas phase and solution (DeTar et al. 1978; Caldwell et al. 1984;

Carrion and Dewar 1984). The distinct increase from a methyl to ethyl halide in Govr was in line with gas phase experiments. An opposite trend was noted as CA activation energy for hydrolysis was lower than CM in data compiled by Mabey and Mill (1978). However, Jeffers et al. (1996) determined an experimental value for CA that showed an increase from Mabey and Mill’s CM value, in agreement with the Govr trend, to include the subsequent drop in activation energy from CA to CP.

The activity of 2HAD drops off or is non existent after the compound exceeds a certain length, typically 3-4 carbons (Keuning et al. 1985). SRR #2 confirms there is no basis for a decrease in intrinsic reactivity with lengthening of the chain, so the rapid drop off in enzyme activity must be due to other factors, which are likely steric constraints of the active sites.

 substituents

Experimentally, it has been long known that  substituents retard SN2 reaction rates for halogenated compounds (Ingold 1953; Shaik 1983; Vogel et al. 1987; Roberts et al. 1992; Barbash 1993; Schwarzenbach et al. 1993). SRR #3 agrees with this for both methyl and halogen substituents, and provides additional insight into the quantitative effect of  substituents, especially as they occur in combination with other substituents. There has been substantial debate and conjecture on the cause of the observed slowing of transformation and about whether the underlying effects are due to steric or electronic factors (Dougherty 1974; DeTar et al. 1978; Shaik 1983; Roberts et al. 1992; Chen et al. 2009). I explore this further in Chapter 6, where I investigate the underlying mechanisms for both and  substituent effects.

In 2HAD, no activity was observed for compounds that possessed methyl groups or halogens as  substituents (2CP, DCM, 112TCA and 11DCA). According the Govr values for these compounds, it would be reasonable to expect that at least DCM (Govr =

9.1 kcal/mol), 112TCA (Govr = 7.8 kcal/mol) would be reactive since their Govr is about the same as CA (Govr = 9.8), which does get transformed by the enzyme. The fact

that there was no activity suggests that the bulky halogen substituents may play a role in hindering interactions with enzyme active site.

 substituents

 halogen substituents have contrasting effects in the gas phase versus solution, which is apparent when comparing the calculated gas phase Govr to the calculated IPCM results or to experimental trends. An example is the CA/12DCA comparison, which is one of the

instances in SRR #3c. In the gas phase, adding a  chlorine substituent lowered Govr by 4 kcal/mol. In contrast, for the IPCM modeled reaction in solvent, adding the same

substituent increased Hsolv by 0.4 kcal/mol, thus causing a reversal of reactivity order for these compounds. It is known that experimentally 12DCA has a slower rate of hydrolysis than CA, and experimental trends consistently show that 12DCA reacts more slowly and possesses higher G values. Thus, the ab initio gas phase method and IPCM model were successful at capturing the experimental effects. As described previously, the gas phase effect of  halogen substituents has been described as a stabilization of the transition state through a through space or intramolecular interaction, and the reversal in

solution has been attributed to a dampening of this interaction. My results for Govr show the strength of this effect for a variety of  substituted compounds with a variety of structures. In 2HAD, 12DCA and 12DBA are the compounds with the highest rate of transformation (Keuning et al. 1985; Schanstra et al. 1996). A proposed explanation for the high reactivity of HAD enzymes is that they provide a solvent free environment, which in addition to accelerating the general SN2 reaction, would also potentially remove the solvent dampening of the  halogen stabilization. Along with  subsitutents, I explore the underlying mechanisms of  substituents in Chapter 6.

Vinyl Groups The effect of a vinyl group agrees with experimental hydrolysis trends. Both ALLYLCL and 13DCPENE had lower Govr compared to analogous alkanes. Bach et al. (1986) discusses some potential effects of the structure of allyl chloride on reactivity. Chabinyc et al. (1998) stated that benzylic and allylic structures stabilize the transition state. I found

this to be true for ALLYLCL. This agrees with experimental results that show higher rates of hydrolysis for allyl chloride.

Cyclic Groups

I was unable to locate any experimental hydrolysis rates of these compounds. My gas phase calculations for cyclopentyl and cyclohexyl bromides agree with the trend found by

Gronert (2003) who studied SN2 reactions of alkyl bromides with dianions. Gronert

found that cyclobutyl bromide and cyclohexyl bromide were unreactive to SN2 displacement, while cyclopentyl bromide did react. The basis for the difference between cyclohexyl bromide and cyclopentyl bromide is likely that there is added strain in the cyclohexyl bromide due to eclipsing interactions during the inversion process which are not present in cyclopentyl bromide (Gronert 1994). My results agree with this trend and confirm that the energetics are responsible for the low rate of reaction.

5.6.2 Relationship to experimental hydrolysis activation energies Because this work is being used to help interpret hydrolysis data, it is of interest to understand the relationship between the calculated gas phase values for Govr and measured free energy activation energies of hydrolysis. Table 5-20 summarizes some available experimental enthalpies and free energies of hydrolysis.

Table 5-20: Experimental Enthalpies and Free Energies Experimental Calculated

Experimental G (kcal/mol) Govr Compound Reference H (kcal/mol) 25° C (kcal/mol) BM 3 24.1 26.4 2.4 CM 3 25.3 27.9 6.4 FM 3 25.6 29.2 23.3 BA 3 24.3 26.4 5.7 3 25.0 26.5 9.8 CA 2 26.5 29 9.8 BP 3 23.3 26.3 5.1 CP 2 22.6 28.3 9.3 2BP 3 24.4 27.9 8.0 2CP 3 25.0 26.5 11.9

Experimental Calculated

Experimental G (kcal/mol) Govr Compound Reference H (kcal/mol) 25° C (kcal/mol) 22DCP 1 26.6 25.2 18.4 1B2MEP 3 26.6 27.4 6.8 11DBA 2 26.5 29.4 10.9 11BCA 2 26.1 29 9.4 11DCA 2 26.2 30.8 14.0 111TCA 1 27.7 28.4 21.9 112TCA 1 28.9 34.4 7.2 1C11DFA 2 27.0 34.2 18.6 12DBA 2 25.8 29.5 2.0 12DCA 1 27.2 31.0 5.8 112TCA 1 28.9 34.4 7.2 1122TETCA 1 22.7 31.3 13.4 1 Jeffers et al. (1989) 2 Jeffers and Wolfe (1996) 3 Mabel and Mill (1978)

The most significant difference between the gas phase and hydrolysis environments is the presence of solvent. As noted, activation energies are always higher in solvent due to the preferential solvation of the nucleophile reactant. My limited set of IPCM calculations confirmed this effect.

Significant of Govr

Do the gas phase calculated results for Govr best represent experimental activation enthalpies or free energies? The question is important because activation free energies, which include both enthalpic and entropic contributions, are the values directly derived from and related to observed transformation rates. Often, activation enthalpies are reported either as Ea or H≠ along with values related to entropies in the form of the ≠ preexponential factor A or S . The Govr calculations account for vibrational, translational and rotational contributions to entropy, but do not account for intermolecular contributions to entropy (e.g. probabilities of correct orientation upon collision), which may be important. In some instances, researchers have noted that differences in entropic barriers seem to be the more significant determinants of the observed transformation rates

(Barbash 1993; Jeffers and Wolfe 1996). To investigate this, I plotted calculated Govr

values against data (Mabey and Mill 1978; Jeffers et al. 1989; Jeffers and Wolfe 1996) ≠ ≠ converted to enthalpic (H exp) and free energy (G exp) forms as shown in Figure 5-12 and Figure 5-13.

36 H

 34

30 28

(kcal/mol) 26

24 mono/alpha sub 22 beta sub HydrolysisExperimental 20

0 5 10 15 20 25 Calculated Gas Phase Govr (kcal/mol)

≠ Figure 5-12: Experimental Hydrolysis H exp versus Gas Phase Calculated Govr

G 34  32 30 28

(kcal/mol) 26

24 Mono/alpha sub 22 Hydrolysis Experimental beta sub 20

0 5 10 15 20 25

Calculated Gas Phase Govr (kcal/mol)

≠ Figure 5-13: Experimental Hydrolysis G exp versus Gas Phase Calculated Govr

The results are shown for three classes of compounds.  substituted compounds are shown separately as they have been identified as having effects that do not occur in solution. There is not a significant correlation when enthalpies are considered as shown in Figure 5-12. When experimental free energies are used, the correlation improves as shown in Figure 5-13. There are 3 groups which all parallel a similar increasing trend ≠ with G exp with Govr.

What this means is that the calculated Govr, in spite of not accounting for intermolecular ≠ effects, actually correlates better with G exp. The implications are that the calculations are capturing the important differences in compounds related to differences in the transition state, and that these enthalpic differences which are detectable in my calculations are in fact important in the experimental data. Intuitively, it is difficult to accept that the difference between brominated and chlorinated compounds is due mainly to entropic effects, as Jeffers and Wolfe (Jeffers and Wolfe 1996) concluded from their

data. My calculated Govr are showing clear trends that agree with observed trends.

What is responsible for the large variances in experimental entropy and how what does

that mean for the ability of calculated Govr to provide valid SRR information? Carrion and Dewar (1984) state that the experimentally observed large variations in entropy may be related to changes in solvation, which actually are then offset by changes in enthalpy such that the net effect of entropy differences may be small. This implies that the actual differences that are being observed are indeed due to enthalpic differences, which is what my calculations provide. Carrion and Dewar concurred that calculated activation energies should still be a good indicator of measured free energies of activation. Caldwell et al. (1984) came to a similar conclusion in terms of the agreement between gas phase and solution phase activation enthalpies, and noted that there was a large difference in trends in preexponential factors between gas phase and solution phase data. They pointed out that rates constants between gas phase and solvent reactions may agree at room temperature and below, but may deviate at higher temperatures. Since many of the aqueous phase experiments have been conducted at high temperatures to assure feasible reaction times, there may be inconsistencies introduced at the higher temperature that

may be responsible for the disagreement in the trends of some of the experimental data and my calculated enthalpies. This is an area that deserves further exploration.

I conclude that although the calculated Govr does not account for intermolecular entropic ≠ effects, it does correlate with G exp. This analysis demonstrates that the gas phase calculations do capture the main components of reactivity and that the enthalpic barriers ≠ accounted for are the drivers behind the observed activities. The wide variance in S exp is likely an artefact of solvent variation. The true distinguishing factor is the differences in transition state energies. The structural effects captured in my results represent the ones critical for developing SRR. These enthalpic effects may actually have been manifested as entropic effects in the high temperature experiments.

5.6.3 Understanding reactivity patterns for a set of critical environmental pollutants I applied the information synthesized above to explain some experimental observables and to draw some conclusions for select compound classes based on existing knowledge and with the additional insights possible from the gas phase results. I focused on the following compounds which have shown interesting patterns of experimental reactivity: 111TCA, 11DCA, 1122TETCA, 2CP, CA, 112TCA, 12DCA and DBCP. These

compounds are all of direct environmental concern. A summary of the Govr for these compounds is provided below in Table 5-21.

Table 5-21: Summary of Govr Possible Reasons for Reactivity Deviation from Expected Enzyme Com- Hydrolysis (2HAD)

pound Govr Expected Actual Actual Hydrolysis Enzyme (2HAD)

111TC 21.9 Very Low Moderate None Alternate Steric hindrance to A Reaction – active site entry or SN1 orientation

11DCA 14.0 Low Low None - Steric hindrance to active site entry or orientation

1122 13.4 Low None None E2 Steric hindrance to TETCA competition active site entry or orientation

2CP 11.9 Moderate, Moderate, Low Lower Steric hindrance to Lower Higher than desolvation active site entry or than CA CA energy orientation

CA 9.8 Moderate Moderate Moderate - -

112TC 7.2 Moderate Low None Solvent Steric hindrance to A dampens  active site entry or substituent orientation effect

12DCA 5.8 High, Low, lower High Solvent Solvent free Higher than CA dampens  environment than CA substituent effect

DBCP 3.2 High Steric hindrance to (2°Br) - active site entry or Solvent orientation dampens  DBCP 2.4 High - Steric hindrance to substituent (3° Cl) None active site entry or effect and orientation E2 DBCP 0 Very High - competition Steric hindrance to (1° Br) active site entry or orientation

111 TCA In hydrolysis experiments, the order of transformation rates is: 111TCA (1.1 years)> CA (2.6 years)> 11DCA(61.3 years). This contradicts the order of the trend indicated by the

gas phase Govr, which shows that 111TCA should have a much slower reaction. There has been speculation that hydrolysis of 111TCA could be occur via an SN1 or E2 reaction. It has also been proposed that dynamic entropic effects could be responsible because of the fact that there are many more “successful” orientations possible. My calculated result for 111TCA supports this possibility for either case, since it confirms

that SN2 reactions would proceed at a higher rate than observed.

DBCP DBCP is a soil fumigant that has been widely used and has become an environmental issue due to its persistence in the environment. It is interesting from a reactivity standpoint as there are different leaving groups in different positions as discussed in Section 5.2.7. There is also the possibility of competing E2 reactions. My calculated gas phase Govr for DBCP by acetate is small, and would indicate that DBCP should proceed

with high rates of SN2 transformation. Experimental data have shown conflicting results., Burlison et al. (1982) found that in phosphate buffer solution, the E2 reaction dominates and there was no indication of hydrolysis. However, Deeley et al. (1991) found that hydrolysis reactions did dominate in groundwater. There are two contributing factors that

could explain this deviation in the Burlison et al, data from the projected high SN2 reactivity. The first is that in phosphate buffer solution, the E2 reaction is equally or more favorable (i.e. activation energies are low) than the SN2 reaction; and the second is that solvent may dampen the stabilizing effect of the  halogens, which would increase the relatively low activation energy. Even considering the loss of the  substituent stabilization due to solvent, the terminal bromine in DBCP could be expected to be at least as reactive as 12DBA due to the similarity in structure. The half life of 12DBA has been measured at 6.2 years at 25° C (Jeffers and Wolfe 1996), so it would be expected the E2 reaction would need to proceed faster than that in order to be competitive. The measured half life for DBCP in phosphate buffer is 38 years at 25º C (Burlinson et al. 1982), while in groundwater it was determined to be 6.1 years (Deeley et al. 1991). The fact that the E2 reaction for DBCP measured by Burlison is slower than the hydrolysis

reaction for 12DBA is puzzling since my calculated Govr would indicate that DBCP is

more reactive (for SN2 transformation) than 12DBA. I conclude that the half life determined by Burlison must reflect an enhancement to the E2 reaction that occurs with

DBCP in phosphate buffer to account for the fact there is no observed SN2 reactivity. This discrepancy was also discussed by Deeley et al. (1991) who surmised that the reaction rate measured by Burlinson et al. (1982) may have been influenced by other dissolved constituents or temperature.

11DCA The observed reactivity of 11DCA agrees with the conclusion from the gas phase trend. There is a deviation from the trend in 2HAD, which indicates that enzyme steric constraints must be considered. It is possible that the Govr also may be too high. Thus, in this case there two potentially valid reasons why there is no 11DCA activity in 2HAD.

1122TETCA 1122 TETCA is not reactive in aqueous phase for hydrolysis, however it has high reactivity in gas phase. It has been postulated that 1122TETCA should be substantially less reactive because the four chlorine atoms are electron withdrawing and at the same time present steric hindrance. However, the calculated results show, that on the contrary that while it is less reactive than CA, it is still about as reactive as 11DCA. This seems to suggest while the additional terminal chlorine does increase Govr, the addition of the

first chlorine on the 2 carbon has the opposing and higher effect of lowering Govr (i.e. same as 112TCA). It is the addition of the second 2º chlorine that drives the Govr higher again. In a hydrolysis environment what occurs is that the stabilization effect from the  substituents is lost, however the destabilization effect of having 2  substituents is still maintained. Thus, it could be expected that 1122TETCA would have the same reactivity in solution as 11DCA or slower. Under actual conditions, 1122TETCA actually undergoes E2 reactions. This would make sense if the E2 reaction is more favorable. In

this case, the activation energy would need to be less than the Govr for 11DCA. In the case of 1122TETCA, the case is commonly made that steric effects contribute to the slow rates of transformation (Haag and Mill 1988a). Steric effects may have some validity

when assessing the affect of methyl groups, however, when examining electronegative substituents such as halogens, the steric argument may lose some explanatory power. It has already been noted that even though methyl groups are larger than bromine and fluorine, they do not retard the SN2 transformation rates as much as these halogens (Carrion and Dewar 1984; Roberts et al. 1992). My own calculations support the notion that there are other factors beyond steric factors that can dominate. Other factors may be significant. I will explore this effect further in Chapter 6.

2CP With 2CP, the observed reactivity is higher in solution than CA. Since the gas phase results indicate the opposite trend, this implies that there is some contribution of solvent that makes the reaction less favorable for 2CP than for CA. This could possibly be due to the need for desolvation of the compound. In the IPCM results, 2CP is reversed, or at least less well solvated, so there is some support for this theory.

CA Chloroethane has modest reactivity, as would be expected from the gas phase results. It is a structurally simple molecule, and its relative reactivity is not influenced by the environment. It is an appropriate “base” molecule for reactivity comparisons for other molecules.

112 TCA The gas phase calculation showed that 112TCA reactivity should be fall somewhere between 12DCA and 11DCA. In reality, hydrolysis of 112CA was determined to be 139.2 years versus 61.3 years for 11DCA. The difference in reactivity order between the gas phase and hydrolysis is likely because in solvent, the favorable effects of the chlorine  substituent are lost.

12 DCA The rate of transformation is relatively slower than CA in solution, whereas it is has a higher rate of enzymatic transformation. This could be due to the  substituent enhancement effect which is lost in solution but is present in the enzyme. There may also

be substrate specificity considerations in the enzyme that may account for the higher rate relative to CA.

5.6.4 Conclusions and prediction of reactivity in biotransformation and hydrolysis

Based on the SRRs developed in this section for the SN2 reaction with acetate, I can draw the following inferences and conclusions about the expected behavior of compounds in both the biological and hydrolysis reaction environments described in Section 3.3. Trends for monohalogenated compounds, compounds with  substituents and compounds with vinylic groups should likely follow the reactivity trends established by the gas phase calculations. There are some important differences in experimental enthalpies and entropies that need to be examined. From the results of the gas phase calculations there are distinct enthalpic and entropic (bond entropy) differences that warrant further study. The experimental behavior in the aqueous phase is much different than the gas phase. I have managed to capture some of the difference using a solvent model. Generally, for compounds with  substituents, the behavior is reversed, where a  substituent retards the rate of reaction. Similar behaviors can be expected. It is uncertain as to why a  substituent would actually slow the rate of reaction (as opposed to just being neutral as would be expected if the favorable effects were simply dampened out). For cyclic compounds, the trend should be consistent between the gas phase and hydrolysis as solvent would not change the steric strain implications.

The strong correlations among compounds for the various nucleophiles shows that the compounds tend to behave in a manner similar to the gas phase. Thus, the trends and observations derived from the results shown for acetate in this Chapter may also be useful in a qualitative sense for analysis of structure-reactivity for other nucleophiles. I have

confirmed that gas phase Govr can replicate experimental trends and more importantly, that the differences in structure that are driving reactivity in experiment are indeed capture in my gas phase results.

6 Mechanism of Substituent Effects In the last chapter, I identified structure reactivity relationships (SRRs) from results of the gas phase calculations. In this chapter, I establish the underlying mechanistic basis for the observed SRRs. To accomplish this, I obtained correlations between molecular properties

(experimental and calculated) and the calculated gas phase values of Govr to provide evidence for the existence of electron delocalization and through space stabilization/intramolecular solvation effects, which are two proposed mechanisms of substituent effects applicable to SN2 reactions (Shaik 1983; Craig and Brauman 1999; Gronert et al. 2001). My hypothesis is that these are the dominant mechanisms that are responsible for the SRRs, and that between them they can explain both the gas phase and aqueous behavior of the range of halogenated pollutants in this study.

An advantage of ab initio computational methods is that electronic and molecular structural information is available regarding key transient intermediates such as transition state (TS) structures (Foresman and Frisch 1995; Young 2001). Such information is not typically obtainable from experimental methods (Caldwell et al. 1984; Bachrach 2007). This allows for a more direct study of the TS structures and fundamental mechanisms underlying the observed trends in Govr, and a quantitative analysis of factors thought to contribute to the observed trends. In short they provide the means to determine causation behind the correlations. An improved understanding of the underlying mechanisms allows for more accurate estimates of reactivity and placement of trends into a proper context – for instance determining what bounds need to be placed in terms of application of the SRRs. The output from ab initio calculations provides the necessary tools to examine the fundamental nature of the relationships.

In this chapter, I show by examination of TS structures that the extent of structural distortion or “looseness” at the TS is an important contributor to Govr. In the case of monohalogenated compounds and compounds with  substituents, the degree of looseness was correlated with electron affinity of the compounds, indicating that electron delocalization was a primary effect. I found that compounds with  substituents had

consistently lower Govr values than monohalogenated and  substituted compounds for similar values of electron affinity, providing evidence for the contribution of through space stabilization/intramolecular solvation effects for  substituents. After accounting for both mechanisms, I was able to explain the reactivity of the broad set of haloalkane pollutants and account for the observed patterns of reactivity in both gas phase and solvated environments. This will allow for a more complete understanding of the fundamental factors that govern reactivity of haloalkane pollutants and will facilitate future estimation of reactivity.

6.1 Need for Research

Although the basic effects of structural variation on SN2 reactions for halogenated compounds have been widely studied experimentally, there are still conflicting views on the underlying factors that cause variation in reaction rates. For instance, the effect of  substituents has been attributed to steric hindrance (Lowry and Richardson 1987), however other researchers point to the significance of increased electron delocalization. (Roberts et al. 1992; Barbash 1993).

The underlying causes for substitutent effects on SN2 reactivity have been categorized into steric or electronic effects. However, determining quantitative SRRs based on these categories has been difficult due to the uncertainty about which molecular descriptors capture the salient structural characteristics responsible for the substituent effect.

Table 6-1: Change in Govr from  Substitution on CA Compared to Substituent van der Waals radius and Electronegativity van der Waals radius1 2 Substituent (picometers) EN Govr F 130 4.0 +1.9 Cl 180 3.0 +4.2 Br 195 2.8 +5.2 Methyl Group 200 <2.2 +2.1 1Brown et al. 2009 2Schwarzenbach et al. 1993

Table 6-1 presents the variance in van der Waals radii and electronegativity with  substituent type and shows the change in Govr upon addition of the  substituent to CA (taken from Table 5-5 and Table 5-7). The van der Waals radius is the minimum distance of approach to an atom or atomic group. The compounds are listed in terms of increasing van der Waals radii and decreasing electronegativity. Among the halogen substituents the trends seem consistent – increasing substituent effect corresponds with increasing van der Waals radius and decreasing electronegativity of the  substituent. However, the trend is not followed with respect to a methyl group substituent. The methyl group has the highest van der Waals radius and the lowest electronegativity, but causes the second least

increase to Govr. This inability to fully explain the observed Govr based on these two general descriptors indicates the need for development of a more suitable set of descriptors. It also highlights the need to understand more about the mechanism behind the SRRs.

From an environmental perspective, there is a need to understand the factors that influence reactivity - and the relative importance among these factors - in order to improve our ability to estimate transformation potential of halogenated pollutants, as well as to make proper sense of experimental data. The development of useful structure activity relationships has also been limited by gaps and scatter in the experimental data (Vogel and Reinhard 1986) and comparative analysis is often difficult due to inconsistent experimental methods and conditions.

6.2 Proposed Theories for Mechanism of Substituent Effects Understanding the basis for the SRRs requires a more detailed examination of the nature

of the SN2 TS structures for halogenated compounds and the factors that affect them. The mechanisms of bond coupling and through space stabilization/intramolecular solvation can be manifested in the TS, so clues to the influence of these mechanisms can be gained by examination of the TS structures and energies.

6.2.1 SN2 TS Structures in Concerted Reaction

The SN2 TS is a first order saddle point on the energy landscape that leads from the stable reactants to stable productsError! Reference source not found.. In the reactants and

products, bond lengths are at an equilibrium distance. As illustrated in Figure 6-1, the path along the reaction coordinate for an SN2 reaction involves the lengthening and eventual breaking of one bond between the carbon and leaving group, along with the simultaneous creation of a new bond between the incoming nucleophile and the carbon (Schwarzenbach et al. 1993). The dominant distortion is the lengthening of the C-Cl on the leaving group side and a shortening of the C-Cl bond on the nucleophile side. ‡

H H H - Nu- CClCl Nu C Cl Nu C + Cl + H H H H H H

Reactants Transition State Products

Figure 6-1: SN2 Reaction Path Structures

A key aspect of the SN2 reaction is that it is concerted, meaning that bond formation with the incoming nucleophile occurs simultaneously with bond breaking with the leaving

group. This contrasts with an SN1 reaction, where the leaving group detaches from the molecule in the first step, leaving a carbocation which is then subsequently attacked by

the nucleophile (Schwarzenbach et al. 1993). In an SN1 reaction, the activation energy is

much higher because there is no stabilization from the incoming nucleophile. In an SN2 reaction, the TS occurs well before the point at which the C-X bond completely breaks because the incoming nucleophile acts to stabilize the growing charge on the substrate and lower the overall activation energy at the TS. Thus, for an SN2 reaction the effect of substrate structure on this interaction would be expected to be an important factor in determining the variation in activation energies. Other possible substituent effects would be ones that act through space through field effects to stabilize or destabilize the TS.

6.2.2 Bond Coupling

The electronic interaction of the incoming nucleophile with the substrate in an SN2 reaction is called bond coupling and plays an important role in determining the energy at the TS (Shaik et al. 1992). Figure 6-2 shows that the odd electron in the charge transfer state (which is represented in the higher energy orbital that is part of the VBCM three

electron bond between R and the leaving group X-) will interact with the incoming nucleophile. This makes sense as the nucleophile carries an electron that will replace the electron that is departing with the leaving group.

*  CX Y

CX

● Y ● R X

Figure 6-2: Bond Coupling

In Figure 6-1, this bond coupling is illustrated in terms of structure where the nucleophile has formed a partial bond with the substrate at the TS. This concept is further illustrated in the example below in Figure 6-3.

E (kcal/mol) - I Ground State Cl: CH3Cl  ∞ Bond Breaking - - II No Stabilization Cl: ·CH3 - - - - Cl:  ∞ Bond Breaking Stabilization with III - - - Incoming Cl Cl: ----·CH3 ---- Cl: 

Figure 6-3: Interaction Energies for Stabilization versus Non-Stabilization by Incoming Nucleophile for the Identity Reaction with Chloromethane

Figure 6-3 illustrates the effect of energy stabilization when a substitution reaction is concerted versus when it is not. I shows the ground state structures where CM is at its equilibrium structure and is at an infinite distance apart from the Cl- nucleophile. In the

situation shown in II, the C-Cl bond of CM is breaking while the Cl- remains at an infinite distance from CM. In the absence of a stabilizing bond-forming interaction, the energy increases as the C-Cl distance increases because of the loss of the favorable attractive molecular interactions. In III, which represents a concerted TS configuration, the C-Cl bond is breaking as in II, but in this case, the Cl- nucleophile is forming a bond

with the carbon of the CH3 radical simultaneously, which leads to a much lower interaction energy.

6.2.3 Valence Bond Configuration Mixing Model (VBCM) The VBCM (Shaik et al. 1992) is useful for visualizing the reaction profile described above and for illustrating how electronic effects from substituents can alter bond coupling and the energy profile and lead to changes in activation energies. As described in Chapter 2, the VBCM is rooted in resonance theory, however it is complementary to Molecular Orbital (MO) theory (Shaik et al. 1992; Shaik 2001) so the insights gained from VBCM apply to the MO based calculations in my study. An example VBCM reaction profile is reproduced below in Figure 6-4.

 (X R) ● X X●(R X)

B (Ix-ARX)

Energy

EO

● ● X(R● X) (X R) X●

Reaction Coordinate

Figure 6-4: State Correlation Diagram for Identity SN2 Reaction

Reaction Coordinate

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The reaction can be described in terms of structural changes along the reaction coordinate with a resulting change in energy profile. In VBCM terminology the energy profiles are referred to as curves. At any point along the reaction path, there is a linear superposition of resonance structures with unique electronic configurations that contribute to the final configuration. The bottom corners of the chart represent the ground states and the upper corners represent the charge transfer states. The configurations associated with these states are shown in the figure. The ground state consists of a configuration with the C-X bond fully formed and the nucleophile at infinite distance, while the charge transfer state represents a configuration where one electron has been moved to the nucleophile. The charge transfer state is not to be interpreted as a representation of an electron transfer reaction, but rather is used in the VBCM model to illustrate the possible resonance structures that need to be considered.

Proceeding from left to right, the reaction coordinate consists of a progressive lengthening and breaking of the C-X bond of the departing leaving group with a simultaneous forming of a C-X bond with the incoming nucleophile (Note that Figure 6-4 represents an identity reaction where the leaving group and the nucleophile are the same). As the C-X bond lengthens, the energy curve of the ground state increases because of the loss of favorable interactions, while the charge transfer state energy curve decreases because the electron can begin to interact with the nucleophile through bond coupling. The crossing point of the curves is where the charge transfer states and the ground states are of equal energy. The TS is located at some point below the crossing point with a difference in energy. This energy difference, B, is called the avoided crossing and it is due to the mixing in of intermediate configurations that are not important for description of reactants or products, but can influence the energy and character of the TS (Shaik et al. 1992). B is also known as the quantum mechanics resonance energy or QMRE (Shaik et al. 1990).

Factors that Determine Activation Energy In Figure 6-4, the energy gap that must be overcome is represented by the difference between the ionization potential of the nucleophile (Ix) and the electron affinity of the substrate (ARX). The activation energy, Eo (in VBCM terminology) is seen to be a

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fraction f of the energy gap minus the avoided crossing and can be represented in the equation below:

Eo = f(IX – ARX) – B

Under VBCM theory, it is the steepness of the descent from the excited state that determines the activation energy. The steepness is determined by the extent of favorable interactions. In other words, the earlier and faster that favorable interactions occur, the

steeper the descent of the excited state and the lower the point of crossing and Eo will be. This is illustrated in diagram II in Figure 6-5. In the case of SN2 reactions, it is bond coupling that provides the favorable interaction. If there are any factors that hinder or

delay bond coupling, then the descent will be shallower and Eo will be higher as illustrated in diagram I in Figure 6-5. The quantity f can be taken as the bond coupling

delay index since it will directly reflect the effect on Eo from changes in the descent (Shaik et al. 1992). I II

 X●(R  X) (X R) ● X X●(R  X) (X R) ● X

(Ix-ARX) (Ix-ARX) Energy Energy

E C

E C ● ● ● ● X(R● X) (X R) X● X(R● X) (X R) X●

Reaction Coordinate Reaction Coordinate

Shallow Descent Steep Descent

Figure 6-5: Shallow versus Steep Descent of the Excited State

Shaik (1992) has proposed a significant contributor to the variation in activation energies to be variations in bond coupling. This refers to how efficiently the incoming nucleophile can begin bond formation.

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Electron Delocalization Substituent Effects Shaik et al. (1992) has suggested that electron delocalization by substituents can delay favorable bond coupling and thus increase the reaction barrier by increasing f. They found that f was positively correlated with ARX. This has the effect that although an

increase in ARX actually reduces the size of the energy gap (IX – ARX), it also increases

the fraction of the energy gap that enters into Eo with the net result usually being a

higher Eo for a higher ARX. The reason f increases with ARX is because a higher ARX also reflects a stronger tendency to hold the electron more tightly and diffuse or delocalize the electron density over the molecule. This makes it less available for bond coupling with the incoming nucleophile.

Translated, this means that as ARX of a compound increases, there will be a delocalization effect that delays favorable bond coupling leading to a higher energy (and

more distorted) TS. In summary, increased ARX has two opposing effects: it lowers the energy gap by making the substrate a better electron acceptor, but this is outweighed in

terms of Eo by the loss of favorable bond coupling at the TS.

6.2.4 Through Space Stabilization/Intramolecular Solvation Through space stabilization/intramolecular solvation is another mechanism which is proposed to influence the activation energy at the TS. Under this mechanism, electronegative substituents at one or more carbons distant from the site of the nucleophilic attack (e.g.  substituents) are thought to stabilize the TS. Such stabilization occurs through space. Gronert et al. (2001) described this stabilization as a field effect whereby the substituent dipole has favorable, stabilizing interactions with the anionic reaction center TS. Craig and Braumann (1999) explained the same phenomena in terms of intramolecular solvation, whereby the negative charge of the  halogen acts to stabilize the growing charge on the carbon undergoing displacement, in a manner similar to solvent. In the gas phase, this lowers the overall activation energy since there is no simultaneous stabilization of the ground state. Craig and Braumann also found that the magnitude of intramolecular solvation effects were determined by the type and position of  substituents

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6.3 Hypothesis I hypothesize that the reactivity of the set of halogenated pollutants can be described in terms of electron delocalization and through space stabilization/intramolecular solvation effects. The important assumptions are that 1) substituents cause changes in the electronic character of a molecule in a way that affects TS energy and 2) these changes are associated with changes in measurable molecular properties. Furthermore, I hypothesize that these effects will be observable through correlations with measurable molecular properties. Both of these mechanisms have been validated for small sets of compounds (Shaik 1983; Craig and Brauman 1999; Gronert et al. 2001), but they have not been used to quantitatively explain activity across a broad range of structures such as exists among halogenated pollutants. In this chapter, I extend these previous analyses to cover the compounds in my data set to test whether these two mechanisms can explain the observed

trends in Govr and offer greater insight on mechanisms underlying substituent effects.

6.4 Approach Structural differences between compounds brought on by electronic delocalization and/or through space stabilization/intramolecular solvation are thought to result in changes to the nucleophile’s interaction with the substrate along the reaction path, affecting the

timing and energy of the TS, which then determines Govr. Such differences should be manifested in the TS. The following steps were completed to determine these TS effects and links to mechanism.

6.4.1 TS Distortion Analysis I first analyzed the TS structures available from the calculations to verify that there were observable geometric differences in the degree of C-X bond distortion at the TS, as measured by the C-X bond length stretch. The bond distortion energy (BDE), which is the energy required to stretch the C-X bond to the TS distance, was also determined from the C-X bond stretch distances and used as the metric for bond distortion in further analysis.

6.4.2 Correlations with Electron Affinity To connect the observed differences in the degree of TS distortions to differences in

electronic affinities (ARX in VBCM terminology) caused by substituents, I utilized two

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measures of electron affinity to determine if differences in these properties explain TS differences as well as the observed patterns in Govr. These measures were vertical electron energies (VEAs) obtained from experimental vertical attachment energies (VAEs), and calculated values of lowest unoccupied molecular orbital (LUMO) energies.

6.4.3 Identification of TS Stabilization/Intramolecular Solvation Effects Finally, I examined the patterns to see if there was evidence of other effects not explained by delocalization which could point to the existence of through space stabilization/intramolecular solvation effects as described in Section 6.2.4.

6.5 TS Distortion Energies

Changes in Govr due to differences in TS stabilization through bond coupling should be manifested in structural differences at the TS.

6.5.1 TS geometries Examination of the geometry of TS structures and analysis of BDEs can yield important clues about the origin of differences in Govr. BDE is a function of the C-X bond stretching between the target carbon and the leaving group in the substrate molecule at the TS. BDE is important because it contributes to Govr. An example of the TS for the acetate reaction with dichloromethane is shown in Figure 6-6.

Figure 6-6: TS for dichloromethane reaction with acetate

The TS represents the intermediate point in the path of breaking and making of bonds in a chemical reaction. Mitchell et al. (1985) found that the primary mode of distortion for

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SN2 reactions is the C-X stretch. Although the bond angle (e.g. H-C-H for chloromethane) distortion contributed to higher TS energies as well, it did not have a direct correlation with changes in activation energy. Figure 6-7 shows the TS structures for chloroethane and 11DCA. The C-Cl distance in the ground state of each molecule was identical at 1.82 A. However the C-Cl distance in 11DCA is significantly longer than in the chloroethane TS. The more elongated 11DCA TS is referred to as “loose”.

H H H H 2.52 Cl 2.39 Cl C C H C H C H H Cl H Chloroethane 11 DCA

Figure 6-7: C-Cl Bond Distances at the TS for CA and 11DCA

As the reaction proceeds, the leaving group begins to be displaced as the C-X bond elongates. The lengthening of the bond rapidly increases the energy due to the loss of favorable interactions between the electrons and nuclei. The highest energy results in

cleavage of the bond. However, the highest energy is never experienced in SN2 reactions because it is stabilized by the simultaneous bond making with the nucleophile. Thus, the TS consists of a partially formed C-X bond with the nucleophile and a partially broken C- X bond with the leaving group.

6.5.2 Morse potential I determined the C-X BDEs using the method of Mitchell et al. (1985) To convert the bond distance stretching into BDE, I used a Morse potential, which is defined below:

2  f R  BDE = ER  D[1 exp(R)] ;      2D  Equation 6-1

D = Bond disassociation energy R=bond length change from equilibrium distance (R-Re) fR = harmonic stretching force constant

The Morse potential captures the anharmonicity present in the bond energy profile of a diatomic molecule. It also accounts for the disassociation of the bond at a certain

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distance. The Morse curve has the property that at distances smaller than the equilibrium distance Re, the nuclear repulsive forces become very strong and the potential energy

increases exponentially. At distances longer than Re, the potential energy increase because of the loss of attractive intermolecular forces.

6.5.3 BDE Analysis From examination of the calculated transition structures, I determined the C-X distances (distance between the reactive carbon and the leaving group) in the halogenated compounds at the ground state (GS) and the TS. As expected, the TS C-X bond lengths were always longer than in the GS. The change in C-X bond lengths from the GS to TS structures varied from 0.5 to 1 Å, so it is apparent that there are significant differences in the TS “looseness” among substrates. The difference between the TS and GS C-X bond lengths was then used in Equation 6-1 to determine BDE. An assumption was made that the force constants remain constant among compounds. A distortion index (DI) was calculated which normalized the bond length change by the bond length in the ground state – DI indicates the percent change. BDEs were determined using the Morse function.

The results, along with experimental VAE values and Govr, which will be used in the following analysis, are shown in Table 6-2 below.

Table 6-2: Summary of Experimental and Calculated Properties C-X C-X Lengt Length h Δ C-X BDE Exp. GS TS Length DI (kcal/mol VAE ΔGovr Molecule (Å)1 (Å) 1 (Å) 1 (-) ) (eV) 2 (kcal/mol Bromine Leaving Groups BM 1.96 2.41 0.45 23 12.1 2.40 2.4 BB 1.99 2.49 0.50 25 14.4 1.27 5.1 12DBA 1.97 2.47 0.50 25 14.3 1.06 2.0 12BCA 1.97 2.48 0.50 26 14.4 1.41 2.6 123TBP 1.97 2.49 0.51 26 14.7 - 0.0 BA 1.99 2.51 0.52 26 15.3 1.18 5.7 2BB 2.02 2.62 0.60 30 18.7 1.18 7.2 2BP 2.01 2.62 0.61 30 18.7 1.21 8.0 CYCHEXBR 2.01 2.61 0.61 30 18.8 1.30 12.7 11BFA 1.98 2.63 0.65 33 20.5 - 6.7

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C-X C-X Lengt Length h Δ C-X BDE Exp. GS TS Length DI (kcal/mol VAE ΔGovr Molecule (Å)1 (Å) 1 (Å) 1 (-) ) (eV) 2 (kcal/mol 11BCA 1.98 2.66 0.68 35 22.1 - 9.4 11DBA 1.97 2.66 0.69 35 22.3 0.26 10.9 111TBA 1.98 3.08 1.10 56 38.3 - 18.8 Chlorine Leaving Groups 13DC2POH 1.82 2.31 0.48 27 31.3 - -0.6 CM 1.80 2.30 0.50 28 32.3 3.45 6.4 EPIC 1.81 2.33 0.52 29 34.0 - 7.1 12CBA 1.81 2.34 0.53 29 35.1 - 6.0 12CFA 1.81 2.35 0.53 30 35.2 - 6.8 12DCA 1.81 2.35 0.54 30 35.3 2.01 5.8 16DCH 1.83 2.36 0.54 29 35.4 - 5.9 14DCB 1.82 2.36 0.54 29 35.4 2.07 5.0 CH 1.83 2.37 0.54 30 35.9 - 9.3 123TCP 1.81 2.35 0.54 30 35.9 - 3.5 CB 1.83 2.37 0.55 30 36.1 - 9.1 CP 1.83 2.38 0.55 30 36.3 2.40 9.3 13DCPENE 1.81 2.36 0.55 30 36.5 - 1.9 CPOH 1.82 2.38 0.55 30 36.6 - 6.5 ALLYLCL 1.81 2.37 0.56 31 37.0 - 6.2 CA 1.82 2.39 0.56 31 37.4 2.41 9.8 13DCP 1.82 2.38 0.57 31 37.7 1.91 5.6 13DCB 1.82 2.39 0.57 31 37.9 - 5.9 DCM 1.79 2.37 0.58 32 38.6 1.01 9.1 CAACET 1.82 2.40 0.59 32 39.0 - 5.4 1C2MeP 1.83 2.42 0.59 32 39.4 - 10.8 12DCP 1.81 2.41 0.59 33 39.6 1.64 8.5 12DCB 1.81 2.41 0.60 33 39.7 - 9.1 CAOOH 1.81 2.42 0.61 34 40.7 - -0.2 CAOH 1.81 2.43 0.61 34 40.7 - 3.4 2CP 1.84 2.48 0.64 35 42.7 1.97 11.9 CYCPROPCL 1.79 2.44 0.66 37 43.8 - 21.4 CYCPENTCL 1.82 2.49 0.67 37 44.5 1.93 13.1 11CFA 1.81 2.49 0.68 38 45.4 - 11.7 11DCA 1.82 2.52 0.70 38 46.3 1.36 14.0 11CBA 1.80 2.51 0.71 39 47.2 - 15.3 DCAOOH 1.79 2.53 0.74 41 48.9 - 7.3 1122TETCA 1.80 2.55 0.75 42 49.5 0.60 13.4 111TCA 1.80 2.57 0.76 42 50.4 0.64 21.9

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C-X C-X Lengt Length h Δ C-X BDE Exp. GS TS Length DI (kcal/mol VAE ΔGovr Molecule (Å)1 (Å) 1 (Å) 1 (-) ) (eV) 2 (kcal/mol DCAOH 1.81 2.63 0.82 45 53.2 - 7.9 1C11DFA 1.80 2.63 0.83 46 53.9 - 18.6 112TCA 1.80 2.78 0.98 54 60.9 0.83 7.2 22 DCP 1.83 2.83 1.00 55 61.8 1.41 18.4 1From calculations 2 From experiment 3 The following values were used in Equation 6-1: C-Br(D=73.3 kcal/mol, fR=198.2 kcal.mol-A2); C-Cl (D=84 kcal/mol, fR=639.1 kcal/mol-A2)

In all cases, the BDE for a compound was less than the respective bond disassociation energy (D), which means that the TS always occurred before the C-X bond was fully broken. BDE was always greater than Govr, the difference being the avoided overcrossing B, which represents the stabilization that occurs due to QMRE and other factors (e.g. such as possible through space stabilization/intramolecular solvation effects). DI is a useful metric as it is calculated in terms of the percent change of the bond length from the original bond. DI and BDE are essentially interchangeable in terms of a metric for bond stretch, distortion, TS looseness etc. since they are directly correlated with each other. BDE is utilized from this point forward in the discussion since it has units of

energy, which makes a straightforward comparison with Govr possible.

Govr versus BDE

The relationship between Govr and BDE is shown in Figure 6-8 for the brominated compounds. Note: In this figure and the figures that follow, compound symbols are plotted according to three different classes: monohalogenated and  substituted (diamonds),  substituted (squares), and cyclic (triangles). This is to aid in the visualization of trends, because these classes tend to behave differently.

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25 mono halogenated and alpha substituted beta substituted 111TBA 20 cyclic

15 1:1 line CYCHEXBR 2BP 11DBA 10 2BB 11BCA

Govr (kcal/mol) BA 11BFA  5 BB 12BCA BM 12DBA 0 123TBP 0 5 10 15 20 25 30 35 40 C-Br Bond Distortion Energy (kcal/mol)

Figure 6-8: Govr versus C-X BDEs for Brominated Compounds

For monohalogenated and -substituted compounds, Govr is correlated with the C-Br BDE (r2=0.96). This shows that for the monohalogenated compounds and compounds with  substituents, changes in Govr are driven by changes in BDE, which is in turn determined by the amount of C-Br bond distortion at the TS. The 1-1 line is shown. The distance from this line (i.e. BDE - Govr) can be viewed as the amount of TS stabilization that occurs. Figure 6-8 shows that  substituents cause an increase in BDE. The  substituted compounds exhibit a different behavior. The  bromine substituents in 12BCA, 12DBA and 123TBP caused a small decrease in the BDE relative to BA, which was accompanied by a drop in Govr. However, the size of the decrease varied among these compounds, and was not related to the changes in the C-Br BDE. A plot for the chlorinated compounds shown in Figure 6-9 tells a similar story.

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25 12CFA 13DCB CYCPROPCL 12CBA 13DCP 111TCA 16DCH 20 12DCA 1C11DFA 22DCP 14DCB 11CBA CYCPENTCL 15 11DCA DCM 2CP 1122TETCA 1C2MeP 11CFA CB CP CA 10 12DCB DCAOH EPIC 12DCP DCAOOH 112TCA Govr (kcal/mol) CM CPOH ALLYLCL  CAACET 5 monohalogenated and alpha substituted 123TCP CAOH beta substituted 13DCPENE cyclic 0 30 35 40 45 50 55 60 65 70 C-Cl Bond Distortion Energy (kcal/mol)

Figure 6-9: Govr versus C-X BDEs for Chlorinated Compounds

Compared to the brominated compounds, the chlorinated compounds have generally higher BDEs due to the larger stretching force constant for C-Cl bonds. There is a significant correlation for Govr with BDE among the group of monohalogenated compounds and compounds with  substituents (r2=0.82). The differences between the

BDE and Govr are much larger for chlorinated compounds – so large that the 1:1 line cannot be shown.

22DCP is an outlier as it has a very high BDE, but the Govr is lower than would be expected for that BDE. There also seems to be a pattern with the two compounds that

possess fluorine  substituents (11CFA and 1C11DFA) where the Govr falls below the

trend. 11CBA on the other hand has a larger Govr than would be expected. This points to the influence of other factors, perhaps related to the type of the halogen. A possible factor could be differences in the bond angle deformation energy (not accounted for in BDE), which is the energy required to bend the Y-C-Y angle at the TS (where C is the reactive carbon and Y is any possible substituent, including H).

As with the brominated compounds, Govr for the  substituted compounds are not significantly correlated with BDE. Compounds with halogen  substituents (12CFA,

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12CBA, 16DCH, 12DCA and 14DCB) share the same BDE (35.3 kcal/mol ±0.2), indicating that the BDE is not sensitive to the position of the chlorine substituent. This is with the exception of a  chlorine substituent in the 3° position in 13DCP and 13DCB, which did increase the BDE to ~ 38 kcal.mol. Govr, on the other hand does vary by 

substituent location. Govr also varied by the number of  chlorine substituents, as

evidenced by the trichlorinated 123TCP, which showed a drop in Govr relative to compounds with 2  substituents, even while there was actually a small increase in the C- Cl BDE relative to the same compounds. OH and OOH  substitution to CA (to form

CAOH and CAACET) result in an increase in BDE but a drop in Govr. The drop in

Govr despite the increase in BDE would be expected for these electronegative  substituents which would promote through space stabilization/intramolecular solvation effects. What is notable is that when an  chlorine is added to CAOH or CAOOH to form

DCAOH and DCAOOH, the increase in BDE and Govr parallels the trend between CA and 11DCA, indicating the effect of adding the  chlorine substituent is similar among these compounds, even though the absolute values are different.

In summary, Figure 6-8 and Figure 6-9 illustrate that: 1) There is a significant

correlation between Govr and BDE for monohalogenated and  substituted

compoundsGovr and BDE are not well correlated for  substituents, as Govr varies more with  substituent position and number; 3) There differences observed among

compounds with respect to the Govr/BDE relationship indicate varying degrees of stabilization at the TS that are attributable to a variety of potential factors, including through space stabilization/intramolecular solvation and differences in bond angle deformation energies.

The main implications are that Govr is determined by the extent of bond breaking that occurs in the TS (as measured by BDE) for monohalogenated and  substituted compounds, which in turn may be related to electron delocalization. BDE is also likely a

factor in determining Govr for the  substituted compounds. However the effect of BDE is obscured due to other stabilization effects related to the type, location and number of 

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substituents, which may be indicative of through space stabilization/intramolecular solvation effects.

6.6 Evidence for Electron Delocalization In Section 6.5.3, I demonstrated that the extent of bond distortion (as measured by BDE)

at the TS is the main determinant of differences in Govr for the series of monohalogenated and  substituted compounds. The relationship for  substituted compounds was not as clear due to possible stabilization effects from the  substituents, but the extent of BDE is still likely an influence. The underlying cause for variance in BDE has been attributed to bond coupling delay from electron delocalization by

substituents. To examine this hypothesis, I correlated changes in both BDE and Govr to measures of electron affinity.

6.6.1 Role of electron affinity in bond coupling delay As discussed in Section 6.2.2, higher electron affinity has been linked to increased delocalization and a delay in favorable bond coupling resulting in higher activation

energies. The calculated Govr values have been shown to correlate with looser transition states (higher BDEs) in the previous section. In this section, I demonstrate that higher

BDEs and Govr can be linked to higher electron affinity as measured both experimentally and computationally.

Electron affinity is the energy required to add a single electron to a neutral molecule and can be described in this manner:

X + e- X- E = -VEA

For purposes of evaluating the SN2 TS, the most appropriate measure of electron affinity is VEA (Shaik 1983). Traditionally, electron affinities have been determined adiabatically by comparing the anion with the neutral molecule, both in their energy minimized structures. In contrast to this, VEA is determined non-adiabatically by comparing the neutral molecule at equilibrium to the anion in the neutral molecule geometry before it has come to equilibrium. Non-adiabatic values are most appropriate for processes in which an electron is rapidly transferred to a neutral molecule (Aflatooni

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et al. 2000). Although the SN2 reaction is not an electron transfer reaction, it has the characteristics of a single electron movement. In practice, vertical attachment energy (VAE), which is the energy required for temporary attachment of an electron to a low lying orbital and is the opposite of VEA, is usually obtained and converted by the following:

VEA = -VAE

Another measure of electron affinity is the Lowest Unoccupied Molecular Orbital (LUMO) energy. LUMO energies have the advantage of being available from ab initio calculations. In order to see if the difference in BDEs were related to differences in the electron affinity of the substrate, I compared both experimental vertical electron affinity energies (VAEs) and the Lowest Unoccupied Molecular Orbital (LUMO) energies to

Govr and the distortion energies.

6.6.2 Correlation of BDE and Govr with VEA I obtained experimental data in the form of VAE from the literature, converted VAE values to VEA and then used these to determine the relationship between VEA and the calculated Govr.

VAE Experimental Background VAE measures the energy that it takes for a free electron to attach to an anion state with low lying C-X unoccupied * orbital and form a temporary short lived anion state (Aflatooni et al. 2000; Modelli and Jones 2004). VAEs have been measured experimentally for a number of compounds, and have proven useful in understanding reductive processes. Electron transmission spectroscopy (ETS) is the method used to generate temporary anion states from which vertical attachment energies can be determined (Modelli and Jones 2004). ETS makes use of the relatively sharp variation in the total electron-molecule cross section caused by resonance processes. In this case, there is a temporary capture of electrons in to empty molecular orbitals to form temporary anion states (Modelli 2003; Modelli and Jones 2004). An electron beam is produced and then is passed through the target gas consisting of the compound of interest at a sufficient pressure to attenuate the beam. As the beam passes through the gas, some electrons occupy a low lying unoccupied  orbital of the substrate to form a temporary anion

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state. Electrons that pass through the gas are collected on the other side. As the temporary state decomposes the electrons are collected and charted as the derivative of the transmitted current versus electron energy. The center of the distribution is where the value for VAE is designated (Aflatooni et al. 2000).

The ETS experimental output describes what occurs at the molecular orbital level with respect to substituent effects. When a substituent is added to a compound that has an existing C-X bond, there is the potential for a splitting of the anion state. The splitting of states results in a anion state that is even lower in energy because of the interaction between the C-X* wavefunctions (Aflatooni et al. 2000). For halogen substituents, this splitting can be substantial. When the substituents are located on the same carbon, there is a significant overlap and there is a large in-phase combination of the C-X* orbitals. As the distance increases, there is less overlap, and the two states become degenerate, resulting in small or no changes to the orbital energy.

For my analysis, VAE results were obtained from a variety of sources (Burrow et al. 1982; Guerra et al. 1991; Modelli et al. 1992; Pearl and Burrow 1994; Aflatooni et al. 2000; Modelli and Jones 2004; Pshenichnyuk et al. 2006). The values were converted to VEA by the relationship VEA = -VAE.

Comparison of BDE to VEA I first compared BDE to VEA to see if there were observable changes in the TS that correlated with changes in VEA. This comparison is shown in Figure 6-10 for the chlorinated compounds for which VEA (VAE) was available.

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65 22DCP 112TCA 60

55 111TCA 50 11DCA 1122TCA CYCPENTCL 45 2CP

BDE (kcal/mol) BDE 40 CA 12DCP CP 13DCP 35 CM 12DCA 14DCB 30 -4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 Experimental VEA (eV)

Figure 6-10: BDE versus VEA for chlorinated compounds

Figure 6-10 establishes the direct link between the physical change evident at the TS and electron affinity. There is a strong relationship between BDE and VEA. A couple of apparent outliers (22DCP and 112TCA) exist, which have very high BDE values, indicating a very loose transition state. These compounds warrant further investigation to determine the reason behind the high BDE. Excluding those compounds, the r2 for the monohalogenated and alpha substituted compounds is 0.96 and the r2 for the  substituted compounds is 0.99. What is remarkable is that such an agreement has been achieved between a measured value for VEA and the BDE, which is based entirely on computational results. Most importantly, this demonstrates that VEA does control the point at which the TS occurs, which lends support to the theory that increased electron delocalization (as occurs in compounds with higher VEA) is linked to bond coupling delay (as evidenced by looser TS and higher BDE).

Comparison of Govr to VEA

Figure 6-8 and Figure 6-9 showed that Govr was governed by BDE (for monohalogenated and  substituted compounds), and Figure 6-10 showed that BDE was

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governed by VEA. It follows that Govr should be thus also be governed by VEA, with

the exception that Govr will include variations in TS stabilization effects . To confirm this, I show the relationship directly in Figure 6-11.

Bromine 111TCA 20 Chlorine )

11DCA 15 2CP 11DBA CA 10 CP DCM

Govr (kcal/mol CM  5 BP BM 0 -4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 VEAVAE ( eV(eV))

Figure 6-11: Govr versus VEA for Chlorinated and Brominated substituted compounds

As Figure 6-11 shows, with the exception of DCM, there is a marked trend for the

relationship between Govr and VEA for  substituted compounds. As expected, the relationship for Govr essentially mirrors the changes in BDE with VEA shown in Figure 6-10. The results here are encouraging because I have now established a quantitative link between an experimental observable and a computational measure of reactivity. As VAE values are generated for other halogenated pollutants, it will now be possible to make informed estimates regarding their reactivity. Moreover, rather than merely developing a correlation, I have, through analysis of TS structures, established the probable mechanism by which substituent effects on haloalkane pollutants operate.

There are separate but parallel trends between Govr and VEA for the chlorinated and

brominated compounds. Clearly, with respect to the Govr/VEA relationship, the brominated and chlorinated leaving group compounds form two families. It is likely that

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the differences in bond strengths are the reason for the differences. These results demonstrate that Govr is related to the changes that occur in the VEA.

25 a substituted 111TCA 2 b substituted R = 0.8913 20 22DCP

Cyclopentyl 15 Chloride 11DCA 1122TetCA CA 2CP 10 R2 = 0.6556

Govr (kcal/mole) Govr CM

 CP 12DCA 12DCP 112TCA 13DCP 5 14DCB

0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0

Vertical Electron VEA (eV Affinity) (eV)

Figure 6-12: Govr versus VEA for chlorinated compounds ( and  substituents)

Figure 6-12 presents the relationship between Govr and VEA for chlorinated compounds with both  and  substituents. As a class, the Govr for  substituents is lower overall.  substituents generally have a mild effect on VEA. The compounds with only one  substituent tend to cluster somewhat together as a group. Compounds with both an  and

 substituent have higher VEAs with correspondingly higher Govr. The parallel but

lower trend of Govr for the  substituted compounds would be expected due to the through-space stabilization/intramolecular solvation effects of the electronegative halogen . Addition of a third  substituent, or the conversion to an unsaturated double

bond as with 1,3-dichloropropene seems to lower Govr, without a perceptible effect to VEA. Thus,  substituted compound reactivity is a function of number and location of  substituents and VEA (although VEA does not change significantly with  substituents).

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There may be other molecular properties such as polarizability or dipole moment that correlate with the  substitution effect.

6.6.3 Determination of VBCM values To allow assessment of my results from the standpoint of VBCM, I determined the

VBCM parameters using the calculated BDE and available ARX (VEA) values. The

ionization potential (Ix) used for acetate was 3.29 eV (Pearson 1986) These are shown in Table 6-3.

Table 6-3: VBCM parameter values

Govr A BDE I – A B f Compound RX x RX (kcal/mol) (kcal/mol) (kcal/mol) (kcal/mol) (kcal/mol) (-)

BM 2.4 -55.35 12.1 131.2 9.7 0.09 12BCA 2.6 -32.52 14.4 108.4 11.8 0.13 BB 5.1 -30.21 14.4 106.1 9.3 0.14 12DBA 2 -24.44 14.3 100.3 12.3 0.14 BA 5.7 -27.21 15.3 103.1 9.6 0.15 CYCHEXBR 12.7 -29.98 18.8 105.8 6.1 0.18 CM 6.4 -79.56 32.3 155.4 25.9 0.21 2BP 8 -27.90 18.7 103.8 10.7 0.18 2BB 7.2 -27.21 18.7 103.1 11.5 0.18 CP 9.3 -55.35 36.3 131.2 27 0.28 CA 9.8 -55.58 37.4 131.4 27.6 0.28 16DCH 5.9 -46.35 35.4 122.2 29.5 0.29 12DCA 5.8 -39.20 35.5 115.1 29.7 0.31 13DCP 5.6 -44.05 37.7 119.9 32.1 0.31 12DCP 8.5 -37.82 35.5 113.7 27 0.31 11DBA 10.9 -6.00 22.3 81.9 11.4 0.27 2CP 11.9 -45.43 42.7 121.3 30.8 0.35 CYCPENTCL 13.1 -44.51 44.5 120.4 31.4 0.37 123TCP 3.5 -27.67 35.9 103.5 32.4 0.35 11DCA 14 -31.36 46.3 107.2 32.3 0.43 22DCP 18.4 -32.52 61.8 108.4 43.4 0.57 1122TETCA 13.4 -13.84 49.5 89.7 36.1 0.55 111TCA 21.9 -14.76 50.4 90.6 28.5 0.56 112 TCA 7.2 -19.14 60.9 95.0 53.7 0.64 1 B = BDE - Govr 2 f = (Govr +B)/IX-ARX) = BDE/(IX-ARX)

The f value of 0.21 for CM is comparable to an experimental value found by Shaik et al (1992) for the identity reaction. The results in Table 6-3 are given in order of increasing f,

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the bond coupling delay index. Brominated compounds have lower f values than their chlorinated counterparts. The trend is apparent that f values tend to rise with increasing degrees of substitution, which agrees with the theory that substituents increase delocalization and thereby effect bond coupling delay which leads to looser transition states and increases in Govr.

6.6.4 Conclusions from VEA Analysis I have demonstrated that experiment based VEAs can be used to explain trends in BDE

and Govr. A linear Govr/VEA relationship is most valid for monohalogenated and  substituted compounds.  substituted compounds are influenced by factors related to  substitution (caused by through space stabilization/intramolecular solvation under my hypothesis). What is significant is that I have established the means to evaluate these effects separately. The f values provide a relative quantitative measure of the bond coupling delay potential for a number of halogenated pollutants. The trend of increasing f with increasing degrees of substitution provides support that increased bond coupling delay related to increased VEA is responsible for higher Govr. There is also evidence for

additional through space stabilization /intramolecular solvation effects as the Govr of 

substituted compounds deviated from the behavior of increasing Govr with increasing BDE, and appeared to be instead related to the type, number and position of  substituents.

6.7 Correlations of Govr with LUMO Energies An issue with the use of VAEs is that experimental data for VAEs is sparse, which limits my analysis to the compounds shown in the above section. It has been demonstrated that LUMO energies, which can be calculated, provide a reasonable approximation of VAEs (Aflatooni et al. 2000).

6.7.1 Relationship between VAE and LUMO LUMO energies are the energy of the low lying molecular orbital that the electron occupies in the ETS experiments. Thus, LUMO energies are directly related to the VAEs as they represent the energy required for electron attachment (Aflatooni et al. 2000). The

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use of LUMO energies allows me to perform an expanded comparison between electron affinity changes and Govr for the entire calculated data set.

0 CA CP 2CP -0.01 BB CM BA -0.02 22DCP 2BB -0.03 BM 11DCA 2BP DCM -0.04 112TCA -0.05 111TCA -0.06 11DBA -0.07 GS LUMO (eV) LUMO GS -0.08 Brominated -0.09 Chlorinated -0.1 -4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 VAE (eV)

Figure 6-13: LUMO versus VEA

Aflatooni et al. (2000) used a STO3G basis set to calculate their LUMO values. To validate the relationship between LUMO and VEA for my calculations, which used a 6- 311+g(d) basis set, I plotted the computational LUMO values against the available experimental VEA values. Figure 6-13 shows that there is a reasonable relationship between VEA and LUMO. In this comparison, CM is somewhat of an outlier. The LUMO energy is lower than what the VEA would indicate. Aflatooni et al (2000) found the same trend in their comparison of VAE versus calculated LUMO. There have been known problems with the calculation of the LUMO for CM due to mixing with nearby orbitals (Falcetta and Jordan 1990). I conclude that, for the compounds I have studied, LUMO can be used interchangeably with VEA with the exception of BM and CM. Now that I have validated the correlation between VEA and LUMO, I would like to test whether LUMO can be used to explain the broader range of compounds.

6.7.2 Comparison of Govr to LUMO The use of LUMO energies allows me to analyze the broad range of compounds in my data set. Figure 6-14 presents the results of the comparison between the Govr and LUMO. Lower (more negative) LUMO energies reflect higher electron affinities. Taken

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as a whole, there does not seem to be appreciable correlation between LUMO and the

Govr. One reason for this is that many of the compounds plotted are  substituted

compounds. As postulated,  substituents have an additional stabilization effect on Govr that is not accounted for by electron delocalization. In this sense, Figure 6-14 is useful in separating out  and  substituent effects.

The line drawn on Figure 6-14 represents the trend for the monochlorinated and chlorine  substituted compounds trend (CA, CB, CP, 11DCA and 111TCA). For the purpose of analysis, I will take this as the line best representing the relationship between Govr and LUMO for purely electron delocalization effects. In this manner, I can assess how and why other compounds may deviate from this line, as well as see if there are parallel trends to this line occurring.

CM CM deviates from the trendline in Figure 6-14 because of the previously noted problems with calculating LUMO for CM correctly. Based on the experimentally derived trendline in Figure 6-13 I find a LUMO value of 0.03 eV for CM by locating the point on where the CM VEA of -3.45 eV meets the extended trendline. Using this value for the CM

LUMO on Figure 6-14 with a CM Govr of 6.4 kcal/mole results in the CM point lying near the trendline. So, in this case, the CM deviation in Figure 6-14 can be ascribed to the computational error associated with the LUMO calculation for CM..

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25 - 111TCA Govr/LUMO trendline for e delocalization effects

1C11DFA 20 22DCP

mono and alpha substituted

beta substituted 11DCA 15 11CBA CYCPENTCL cyclic 2CP 1122TETCA 1C2MeP DCM 12DCB 10 12DCP CA DCAOOH 112TCA CB CP DCAOH 12DCA (kcal/mol) Govr EPIC  5 13DCP 12BCA 123TCP

CAOOH 13DCPENE 12CFA CPOH 0 ALLYLCL CM 132C2POH 13DCB 16DCH CAACET 14DCB -5 -0.070 -0.060 -0.050 -0.040 -0.030 -0.020 -0.010 0.000 Ground State LUMO (eV)

Figure 6-14: Govr versus LUMO

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CAOOH, DCAOOH This pair of compounds has the lowest energy LUMOs in the compound set. Thus, the electron affinity is very high and Govr would be expected to be high as well due to the extent of delocalization. However, the  OOH group apparently has a very high

stabilizing effect, which reduces the Govr significantly. The  substituent/delocalization effect is also apparent as going from CAOOH to DCAOOH decreases LUMO and raises

Govr.

16DCH, 13DCP, 123DCA, 12CBA This series of compounds presents an interesting study. All of the compounds have very

similar Govr (5.6 to 6.0 kcal/mol). Yet there is a large amount of variability in the LUMO. This implies that the magnitude of  stabilization effect would increase with decreasing LUMO (i.e. from right to left) in Figure 6-14 because the difference from the trendline increases. 16DCH has the highest LUMO, and thus the lowest electron affinity.

The chlorine in the terminal position still has a stabilizing effect because the Govr (5.9 kcal/mol) is lower than the trendline. Moving to 13DCP, the LUMO decreases which

means higher electron affinity. However, Govr actually drops to 5.6 kcal/mol, meaning that the  substituent effect must have been enhanced. This is plausible as the shorter chain may allow for the chlorine to interact more favorably. Craig and Braumann (1999)

showed that the effect of  substituents on SN2 activation energies was position dependent. Proceeding to 12DCA, the LUMO continues to decrease. This is because as the chlorine substituents become closer, the splitting of the anion states becomes more prominent, leading to lower, more stable orbitals. The Govr for 12DCA is 5.8 kcal/mol, which represents a slight increase over 13DCP, but again this indicates a probable small increase in the  stabilization effect to offset the Govr increase from electron delocalization. The final compound, 12CBA, is a bit puzzling. Compared to 12DCA, 12CBA has a lower LUMO because brominated substituents increase electron affinity

more than chlorinated substituents. However, why Govr should not rise in response to this doesn’t make sense, as it would not be expected that bromine would be significantly better at stabilizing the TS than chlorine.Other comparisons are possible.

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6.8 Evidence for Through Space Stabilization/ Intramolecular Solvation Based on the analysis thus far, there is ample evidence that supports the operation of through space stabilization/intramolecular solvation effects described in Section 6.2.4. Figure 6-8 and Figure 6-9 clearly show that the behavior of compounds with  substituents (Cl, Br, OH, OOH) is unique and shows evidence of an independent (separate from delocalization effects) effect dependent on the presence of  substituents. Bromine and chlorine substituents also seem to lower BDE independent of changes in

VEA as shown in Figure 6-10, which could also contribute to the lower Govr. It should not be overlooked that  substituents do also increase VEA, although to a lesser extent than  substituents, but this effect is outweighed by the magnitude of the  substituent stabilization effect.

6.9 Conclusions The overarching conclusion is that the reactivity of halogenated pollutants can be described in terms of two mechanisms, which are electron delocalization and through space stabilization/intramolecular solvation. Substituent effects can be described in terms of these mechanisms, which, in the case of electron delocalization are manifested in observable changes in the transition state structures. Additionally, there are independently determined descriptors available (VEA and LUMO) that capture the electronic

differences that are at the heart of each mechanism and that correlate with Govr. I was able to show that once one of the mechanisms was accounted for, the structure-reactivity relationships for the other mechanism became apparent.

It should be emphasized that the above analysis was conducted using values obtained from gas phase calculations. The insights gained are useful in understanding the implications for solvent behavior. The major difference in solvent will be that  substituent stabilizations will be dampened out. Thus, the reactivity will revert to the pattern that is based on the delocalization component. This means that the order of reactivity of  substituted compounds will often be reversed, as Gronert et al. (2001)

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found with 12DCA. Some compounds with very high electron affinities, such as haloalcohols will become non-reactive in solution.

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7 Docking Investigation of Haloalkane Binding in the Active Site of Haloalkane Dehalogenase

Haloalkane dehalogenase (HAD) is an enzyme that mediates the SN2 displacement of a chlorine atom from 12DCA. This is the first step in a biological pathway that converts 12DCA into growth and energy for the host microorganism (Janssen et al. 1985). Because experiments with HAD demonstrate that it has the ability to transform a wide range of haloalkanes (Janssen et al. 1988), this enzyme may have a broader role in determining the ultimate fate of halogenated pollutants in the environment. In this chapter, I describe a computational docking analysis that I conducted of the interaction of HAD with potential halogenated substrates to better define its substrate range and understand existing experimental data.

7.1 Introduction The mechanism of HAD has been elucidated (Janssen et al. 1985; Keuning et al. 1985; Janssen et al. 1988; Schanstra et al. 1996) and the structure of the enzyme has been obtained through crystallography (Franken et al. 1991). A key to haloalkane transformation by the enzyme is the ability of a substrate to interact with key amino acid functional groups within the enzyme active site. I used a computational ligand docking method (AUTODOCK) to examine how compound structure influences the enzyme- substrate interaction within the active site, and how this determines the catalytic range of HAD. I conducted docking simulations for a large number of haloalkanes with varying structures and classified compounds by their ability to form reactive complexes within the enzyme active site. I evaluated docking results using energy and distance based criteria and subsequently correlated docking success with experimental activity. The activity of outliers from the correlation could be explained with the aid of previously calculated activation energies. In recent years, computational investigations of chemical and biological phenomena have proven a critical adjunct to experiments (Kuhn and Kollman 2000; Kua et al. 2002). My computational approach for studying the substrate range of HAD leveraged existing experimental data to provide enhanced interpretation of the experimental trends.

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7.2 Haloalkane Dehalogenase

HAD utilizes an SN2 reaction to mediate the displacement of halides from haloalkanes. It is the first enzyme in a pathway mediated by Xanthobacter Autotrophicus (shown in Figure 7-1) that can transform halogenated alkanes into mineralized end products while providing growth and energy (Janssen et al. 1985).

Janssen et al., 1985 Figure 7-1: Xanthobacter Autotrophicus Pathway

Although its best known substrate is 12DCA, HAD has a broad substrate range and can transform a number of other haloalkanes (Janssen et al. 1985; Keuning et al. 1985). This broad substrate range implies that there is a tremendous potential for the enzyme to participate in the biodegradation of haloalkane pollutants, many of which are widespread in groundwater and soils (McConnell et al. 1975; Alexander 1981; Vogel et al. 1987). Experimental efforts have focused on better defining this substrate range and determining the transformation rates for different haloalkanes – which vary considerably (Janssen et al. 1985; Keuning et al. 1985; Schanstra et al. 1996). .

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7.2.1 Active Site and Mechanism Detailed crystallographic studies have been performed to understand the structure and mechanism of HAD (Franken et al. 1991). As shown in Figure 7-2 Error! Reference source not found., the active site is constructed to allow an aspartate residue (Asp 124) to perform a nucleophilic attack on the carbon of the substrate that is attached to the attached halogen (in this case, a chlorine of 12DCA).

Figure 7-2: Haloalkane Dehalogenase Active Site

The carboxylic functional group of aspartate is analogous to acetate, which is a nucleophile of moderate strength (Schwarzenbach et al. 1993). Two tryptophan residues (Trp125 and Trp175) bind the halogen of the haloalkane through hydrogen bonds (Verschueren et al. 1993b), which allows the reactive carbon to be in a position for nucleophilic attack by a negatively charged oxygen of the Asp124 aspartate carboxylate anion (Kennes et al. 1995; Devi-Kesavan and Gao 2003). There is evidence that the Trp125 and Trp175 may also help catalyze the displacement reaction by stabilizing the transition state (Schanstra et al. 1996). The mechanism behind this is that the electronegative nitrogens on the indole ring of the tryptophans act to polarize the N-H bond, which leads to a partial positive charge on the hydrogen which then stabilizes the incipient charge on the halogen leaving group (Kennes et al. 1995). Once the reaction occurs and the chloride is displaced, the substrate is covalently bound forming an intermediate. This intermediate undergoes a second nucleophilic substitution by a water

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molecule to form CAOH. In the last step, the displaced chloride and the alcohol are released from the active site.

7.2.2 Reaction Steps The enzymatic reaction can be broken down into a series of discrete steps (Schanstra et al. 1996). This is illustrated in Figure 7-3.

k1 k2 k3 k4 E+RX E.RX E-R.X E.X E+X k-1

ROH

Figure 7-3: Haloalkane Dehalogenase Reaction Steps

Any one of these steps can be rate limiting for the overall reaction. k1 and k-1 are the rate

constants for the initial reversible binding and dissociation of substrate to the enzyme. k2 is the rate constant of the SN2 halide displacement reaction, and is a function of the

activation energy barrier for the reaction. k3 is the rate constant for the hydrolysis

reaction, which is performed by a water molecule in the active site and k4 is the final release of halide and alcohol from the active site. The equation for the enzyme rate

constant kcat for steady state is given by:

k2k3k4 kcat  k2 k3  k2k4  k3k4

As shown in Figure 7-3, kcat is a function of all of the individual rate constants. However, if one rate constant is much smaller than the others (i.e. the step is much slower) than it will have the dominant effect on the overall rate constant and this step would be the rate nd limiting step. The 2 order rate constant kcat/Km is a function of the k1 k-1 and k2 rate constants.

k k k cat  1 2 Km k1  k2

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7.2.3 Substrate Range Table 7-1 shows the results of an experiment conducted by Keuning et al. (1985) that studied the ability of HAD to transform a variety of haloalkanes by measuring rates of halide liberation. The compounds in the left column showed varying levels of activity, with 12DCA having the highest rate. The compounds in the right hand column showed no activity. Inspection of the results shows that compounds with more than one terminal halogen, compounds longer than 3 carbons and haloalchols and haloacetates were non- active. In order to allow for estimates about what types of compounds can be transformed by HAD, an understanding of the underlying reasons for the observed pattern of reactivity is necessary.

Table 7-1: Experimental Activity for Haloalkane Dehalogenase (from Keuning et al. 1985) Active (activity = % halide displacement relative to Non-Active 12DCA) CM (28%) DCM CA (24%) 11DCA 12DCA (100%) 112TCA CP (51%) 2CP 3-chloropropene (45%) 12DCP 13DCP (80%) 1-chloropentane CB (31%) 16DCH BA (24%) 1,9 dichlorononane 12DBA (94%) CAOH BP (29%) CAOOH 1-iodopropane (14%) DCAOOH bromoacetate dibromoacetate 2-chloropropionate 3-chloropropionate

7.3 Factors Governing Enzyme Catalysis Two factors have been found do be important in enzyme catalysis. These are substrate orientation and substrate binding energy (Fersht 1999). Substrate positioning within the active site is a critical determinant in enzyme catalysis. Based on the steric constraints

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imposed by the enzyme and the shape of the compound, substrates may bind in a number of different possible configurations. The orientation of the compound relative to the active site catalytic residues of the enzyme can determine whether a given configuration can undergo catalysis in the enzyme (Kuhn and Kollman 2000; Kua et al. 2002). Substrate binding energy is also an important aspect of enzyme catalysis. The energy of substrate binding contributes to a lowering of kcat/Km (Fersht 1999). Thus, determining how strongly a substrate binds and whether it binds in a productive orientation are two critical aspects for enzyme catalysis. Computational docking methods have been employed to study this question (Kua et al. 2002). If the crystallographic structure for an enzyme is available, then ligand docking methods allow for an efficient search of configurational space. Docking allows a vast number of enzyme-substrate binding events to be tested efficiently. A scoring function based on van der Waals and electrostatic considerations allows rapid identification of the most likely orientations for binding. One docking method – AUTODOCK - uses simulated annealing and a scoring function based on van der Waals potentials and electrostatic potentials (Goodford 1985). The most extensive use of AUTODOCK has been in drug discovery to identify compounds that bind strongly to enzyme active sites and macromolecule binding sites (Schames et al. 2004). But AUTODOCK has been used successfully to study orientation and binding effects in enzyme catalysis (Soares et al. 1999; Kuhn et al. 2001; Kua et al. 2002).

7.4 Research Approach As described in previous chapters, I applied computational methods to develop structure- reactivity relationships for SN2 reactivity of haloalkane pollutants at the electronic level. Here, I describe my application of another computational method to identify the molecular basis and develop structure-activity relationships for the biodegradation potential of haloalkane pollutants. Molecular modeling is a very powerful tool to explore such issues. In order to determine the underlying cause for the observed HAD substrate range and to develop an understanding of the influence of structure on catalysis by HAD, I utilized the AUTODOCK suite of programs and the available crystallographic structure of HAD to account for enzyme-substrate interaction factors.

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7.4.1 HAD Docking Study with Haloalkane Pollutants The structure of the substrates will affect the binding orientations within the active because of steric and electrostatic interactions. Thus, the shape and electrostatic nature of the active site pocket will play a large role in determining which substrates will bind favorably for catalysis.

7.4.2 Hypotheses Based on evaluation of experimental data and proposed mechanisms, the following hypotheses will be tested.  Substrate steric interactions with the HAD active site affect enzyme catalysis.  Compounds must achieve a specific configuration within the active site to undergo catalysis.  Substrate-enzyme distance criteria can be used to evaluate docking results and separate active from non-active compounds.  Compounds with long (larger than 3) carbon chains and with multiple substituents may be excluded from the active site, or may not bind in the proper orientation for catalysis.

7.5 2HAD Crystallographic Structure The crystallographic structure of haloalkane dehalogenase from Xanthobacter Autotrophicus, PDB: 2HAD (Franken et al. 1991), with a resolution of 1.8Å was obtained from the RSCB Protein Data Bank (Berman et al. 2000). This is the macromolecular target for the small molecules in the AUTODOCK runs.

7.6 AUTODOCK Evaluation criteria For each individual docking result, AUTODOCK provides an orientation in three dimensional coordinates and binding energies. The energy of binding has been correlated

with kcat/Km values, so stronger (more negative) AUTODOCK binding energies indicate a higher potential for catalysis (Kua et al. 2002). Typically, the docking runs will produce similar orientations, so the results are clustered according to a specified root mean squared distance (RMSD). RMSD is calculated as follows:

n 1 2 2 2 RMSD  xi1  xi2  yi1  yi2  zi1  zi2 n i1

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Where: n= number of atoms i = atom # x,y,z = coordinates of atoms in –x, -y and –z coordinate system

To evaluate the docking results, I developed criteria based on enzyme-substrate distance considerations to distinguish between productive (those likely to lead to catalysis) and non-productive (those which prevent catalysis) dockings. Use of this criterium allowed each set of 100 dockings for each compound to be evaluated objectively.

To develop the distance criteria, I assumed that for nucleophilic substitution to occur, the Asp124 oxygen, as the nucleophile, should be able to approach the substrate carbon to within a certain distance and that the departing halide should be within a certain distance in order to interact with the tryptophan rings – specifically the polar hydrogen on the nitrogen and the departing halide. As a measure of this ability to make contact, I used the sum of the van der Waals radius for the two atoms involved. This criterium compares favorably to the distances found in a crystal structure with 12DCA bound in the active site, just prior to undergoing halide displacement (Verschueren et al. 1993a). This was one structure in a series of structures that followed the reaction as it occurred. As such, it provides a snapshot of the initial binding just before the 12DCA molecule underwent halide displacement. The distances between key atoms of the substrate and key enzyme atoms are shown below in Table 7-2.

Table 7-2: Van der Waals and Observed Distances for bound 12DCA Crystallographic Distance Van der Waals radii (2DHD) Enzyme-Substrate Interaction (Å) (Å)

OASP124-C12DCA 3.6 3.64

NHTRP125-Cl12DCA 2.98 2.74

NHTRP175-Cl12DCA 2.98 2.24

To define a productive docking, I assumed that the substrate carbon, which is the site of the nucleophilic attack would have to come within 3.5 Å of anAsp124 oxygen; that the substrate halogen attached to the same carbon would have to come within 3.5 Å of at

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least one of the Trp125 or Trp175 H atoms (attached to an N); and that the interaction energy should be favorable (have a negative value). The 3.5 Å was somewhat arbitrary based on an approximation to the actual observed distance. Additional research should be conducted to determine the sensitivity of the criteria. My criteria for a successful docking are summarized in Table 7-3Error! Reference source not found..

Table 7-3: Binding Distance Criteria

Criteria

OAsp124-Chaloalkane <3.5 AND

At least 1 NHTrp125/Trp175-Xhaloalkane < 3.5 AND Interaction Energy <0.0 kcal/mol

7.7 Results and Discussion I obtained docking results for a comprehensive set of 62 halogenated compounds. Overall, 24 compounds docked favorably while I identified 38 compounds that did not fit into the active site. These are shown in Table 7-4. A dash under both the HAD docking and Interaction columns indicate that the docking was not performed for that molecule, while a dash only under the Interaction column occurs when there was no successful dockings and so no interaction energy is reported.

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Table 7-4: Docking Summary for HAD HAD Interaction HAD Interaction Compound Docking Energy Compound Docking Energy Methanes 1-10DCD - 0 BM 0 -7.4 Trihalogenated Tribromomethane 0 - 1,1,1-tribromopropane 0 - Tetrabromomethane 0 - 123TBP 0 - CM 56 - 1,2,2-tribromopropane 0 - DCM 38 -8.9 111TCA 0 - Tetrachloromethane 0 - 1,1,1-trichloropropane - - Monohalogenated 112TCA 78 -7.4 BA 41 - 1,1,2-trichloropropane 0 - BP 25 -12.9 1,1,3-trichloropropane 32 -8.7 1-bromobutane 40 -13.4 1,1,4-trichlorobutane 0 - 1-bromohexane 0 - 1,2,2-trichloropropane 0 - 2BP 0 - 123TCP 21 -4.0 CA 52 -10.2 1,2,4-trichlorobutane 0 - CP 89 -13.2 1,1,3-trichlorobutane 0 - 2CP 5 -7.8 1,2,2-trichlorobutane 0 - Chloropropene 74 -13.4 2,2,3-trichlorobutane 0 - CB 52 -15.3 Perhalogenated 2CB 0 - 1,1,2,2-tetrabromoethane 0 - Chloropentane 29 -11.1 1,1,2,2-tetrabromopropane 0 - CH 0 - 1,1,2,3-tetrabromopropane 0 - Dihalogenated 1,1,1,3-tetrabromopropane 0 - 11DBA 65 -4.4 1,1,1,3-tetrabromopropane 0 - 12DBA 100 -11.3 1,1,1,3-tetrabromopropane 0 - 11DCA 53 -6.6 1,1,1,2-tetrabromopropane 0 - 12DCA 100 -11.9 1122TETCA 0 - 1,1-dichloropropane 0 - 1,1,1,2-tetrachloroethane 0 - 12DCP 80 -9.3 1,1,2,3-tetrachloropropane 0 - 13DCP 80 -14.0 1,2,2,3-tetrachlorobutane 0 - 12DCB 8 -0.3 1,2,2,4-tetrachlorobutane 0 - 13DCB 48 -10.9 1,1,1,3-tetrachloropropane 0 - 14DCB 26 -10.3 1,2,2,3-tetrachloropropane 0 - 16DCH 0 - 1,2,2,3-tetrachlorobutane 0 -

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HAD Interaction HAD Interaction Compound Docking Energy Compound Docking Energy 22DCP 0 - 1,1,1,2-tetrachloropropane 0 -

7.7.1 Validation for 12DCA docking To validate the use of my docking method I compared my docking result for 12DCA to an actual example of 12DCA binding within the active site obtained through crystallography (Verschueren et al. 1993a; Verschueren et al. 1993c). The PDB designation for this result was 2DHD. To avoid biasing the results, I used the native (substrate free) structure (2HAD). Figure 7-4 shows the highest ranked docking cluster. 84 docking runs out of 100 produced this result.

Trp125 12DCA

Asp124 Trp175

Figure 7-4: 12DCA bound in the active site from the 2.4 Å crystal structure (2DHD)

Table 7-5 presents the docking results versus the crystallographic result (2DHD) for 12DCA active site binding.

Table 7-5: Comparison of AUTODOCK versus crystallographic results for 12DCA

Crystallographic AUTODOCK (2DHD) Cluster #1 Enzyme-Substrate Distance Distance Difference Interaction (Å) (Å) (Å)

OASP124-C1,2-DCA 3.64 3.25 0.39

NHTRP125-Cl1,2-DCA 2.74 2.59 0.15

NHTRP175-Cl1,2-DCA 2.24 2.44 0.20

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The docking results matched the crystallographic results to within 0.4 Å RMSD. Based on this, I have confidence that AUTODOCK is able to accurately capture 12DCA binding. The AUTODOCK result for 12DCA also shows the molecule rotated into a gauche conformation (when the two halogen atoms are rotated by 60o looking down the axis of the two carbons) in agreement with the crystallographic result.

7.7.2 Modes of Binding Some examples of docking results are shown below for CA, 2CP and 111TCA. These graphically show of the types of configurations that are possible and provide a view of the implications of the docked configurations for the ability of HAD to catalyze the reaction catalysis.

CA Docking

42 out of 100 of the CA dockings are classified as productive. The remainder is considered unproductive. Representative dockings for both cases are shown in Figure 7-5 below. Note that the unproductive binding essentially turns the molecule completely around and places the sole halogen on the opposite side of the active site placing the carbon out of range of the Asp124 oxygen.

Trp125 CA Trp125 CA

Trp175 Trp175

Asp124 Asp124

Productive (42%) Non-Productive (48%)e Figure 7-5: Major Modes of CA Binding

This major alternative binding mode has an interaction energy similar to the productive mode. Thus, this may mean that a portion of the time that CA enters the active site, it may not be reactive. This “turned around” mode of binding is common for all mono-

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halogenated compounds. The enzyme is not specific enough to prevent this type of binding.

2CP Docking

2CP Trp125 2CP Trp125

Trp175 Trp175

Asp124 Asp124

Productive (5%) Non-Productive (5%)

Figure 7-6: Major Modes of 2CP Binding

For 2CP, only 5 out of 100 runs produced a productive binding. As shown in Figure 7-6, the majority of the AUTODOCK results placed the chlorine at some distance away from the halide binding position between the two tryptophans.

111TCA Docking

Trp175

Trp125 111TCA

Asp124

1,1,1 TRICHLOROETHANE

Figure 7-7: 111TCA Binding

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11TCA presents an example of how a large degree of steric bulk can affect the binding within 2HAD. As shown in Figure 7-7, the three chlorine substituents attached to the same carbon prevent 111TCA from fitting into the binding site productively.

7.7.3 Structure Effects on Docking Some patterns emerge from analysis of the docking results. The active site best accommodated smaller substrates - compounds that had four carbons or less. Compounds longer than four carbons were not able to dock successfully into the active site. When halogens existed on both the terminal (at the end of the carbon chain) and interior positions of a compound (e.g. 12DCP), a successful docking usually resulted in the terminal halogen in position for catalysis. Any compound that possessed any combination of four halogens was not able to dock successfully, which is likely due to the combined steric effect of the bulky halogens acting to prevent a proper fit within the constraints of the active site

7.7.4 Correlation between Docking and Experimental Activity To determine how docking success was related to enzyme activity, I compared the experimental results of Keuning et al. (1985) to results obtained from docking using the distance criteria as described above. Keuning measured relative rates of halide liberation for a variety of substrates with HAD. The reference substrate was 12DCA, which had a

Km of 1.1mM and a Vmax of mol/min per mg of enzyme. A comparison of how the rates of halide liberation compared to the docking results is shown in Figure 7-8.

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100 12DCA 13DCP 12DBA 80

60 CB CP

40 BP CM 16DCH, BA CA 11DCA 20 2-Chloroethanol, Chloroacetate % of Halide Liberation CPent 112TCA 12DCP DCM 2CP 0 0 102030405060708090100 Frequency of Succesful Docking %)

Figure 7-8: Experimental Halide Production Rate versus Productive Binding Frequency Determined by AUTODOCK

As shown in Figure 7-8, there is a general trend of increasing halide liberation rates with increasing docking frequency, with some obvious outliers apparent (112TCA, 11DCA, 12DCP, DCM). The group of compounds that had a docking success frequency of < 20% (16DCH, 2CAOH, CAACE, 2CP and CPENT) were non-reactive experimentally. The outlier compounds possessed a high (>70%) docking frequency but showed little or no experimental activity. With the exception of the outliers, there is a general relationship between the ability of a compound to dock successfully in a productive manner with a high frequency and its experimental activity.

Docking frequency provides information about the extent of alternative non productive binding. A low successful docking frequency indicates that there are conformational traps that the substrate may fall into which are non-productive. This would have the effect of lowering the observed rate of reaction because there are less frequent occurrences of the proper configuration. Kua et al.(2002) has noted a similar relationship between the frequency of successful dockings and the rates of reaction for a set of compound analogues in the active site of acetylcholinesterase. Although non-productive substrates could theoretically act as an enzyme inhibitor, this was not been observed in

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the experimental data with 2HAD for the non-active substrates of CAOOH, DCAOOH, 11DCA or CAOH (Keuning et al. 1985).

A comparison between CA and 12DCA is illustrative of this. CA docks about 50% of the time with the chlorine atom between Trp175 and Trp125 and next to the reactive oxygen in Asp124. However, the other 50% of the time, the chlorine atom is actually located on the other end of the active site. In essence the molecule is turned around. By contrast, 12DCA docks successfully 100% of the time because it has chlorine atoms on both ends of the molecule. Thus, every configuration represents a successful docking. Binding in one of these stable non-productive states could represent a pseudo-inhibitor complex. In the case of the outlier compounds (DCM, 11DCA, 112TCA and 12DCP), all of these docked productively into the active site with a high frequency, but experimentally showed little or no activity. These are examples of false positives that indicate there is some other factor, besides docking ability that influences the activity. This is likely related to the intrinsic reactivity of the compound, which is not accounted for in the docking analysis. This will be examined in the following section.

7.7.5 Relationship with Calculated Gcent Docking considers the interaction between the compound and enzyme prior to catalysis, but does not consider the favorability of the chemical reaction that follows binding. To account for this, I compared my previously calculated gas phase activation energies to the relative rates of halide liberation to examine whether activation energy could provide additional explanation for the observed enzyme activityError! Reference source not found.. The gas phase is a reasonable approximation of the environment within the enzyme environment (Maulitz et al. 1997). I used my results with the acetate nucleophilic as this has the same carboxylic functional group as Asp124, which serves as the nucleophile in the active site of HAD. In the case of the enzyme reaction with HAD, since the substrate and the nucleophilic residue (Asp124) come together in an initial binding step which resembles an ion-molecular complex (IMC), the appropriate

activation energy to use is Gcent (see Figure 2-5), which is the difference between the

IMC and the transition state (Maulitz et al. 1997). A comparison between Gcent and the relative rates of halide liberation is shown in Figure 7-9.

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100 High Docking Frequency 12DCA Compounds in green circles 12DBA 13DCP 80

CP Low docking frequency (<5%) compounds shown in red text 40 BP CA Chloroethanol

% Halide Liberation Halide % BA 20 DCM 11DCA 112TCA 12DCP CH 2CP ClAce 0 8 1012141618202224 Gcent (kcal/mol)

Figure 7-9: Calculated activation energies versus experimental rate of halide liberation

For reference, a previous estimate of the enzyme activation energy with 12DCA was found to be 15.3 kcal/mol (Devi-Kesavan and Gao 2003). This is in reasonable agreement with my calculated value There is not a significant correlation that is immediately apparent in Figure 7-9, which is expected due to the fact that the measured experimental rate is a function of many steps that includes the actual SN2 reaction step, but also includes binding, catalysis and halide release. To help visualize the influence of the binding step, the compounds that docked with low frequency are shown in red. Compounds that docked with high frequency are indicated by the green circle. For CH and 2CP, the fact that the docking frequency was low explains why these compounds are

non-reactive in spite of having Gcent equal to 12DBA and 13DCP. Recall that 11DCA, 112TCA and 12DCP were outliers in Figure 7-8 due to their high docking frequency but low halide liberation rates. Figure 7-9 provides an explanation for this, which is that successful binding (as represented in the docking results) is likely a necessary but not sufficient for reactivity. All of the outlier compounds from Figure 7-8 have relatively high Gcent values - greater than 18 kcal/mol. Other compounds that have Gcent values > 18 kcal (CAOH, 11DCA and CACE) are also non-reactive. These compounds also had low docking frequency, so they can be described as non-reactive due to both their

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inability to bind successfully and their inability to be transformed at an observable rate of reaction for the reaction of interest. The one remaining outlier – DCM – presents an interesting case. DCM had a 100% docking success frequency and Gcent of ~17 kcal/mol. DCM does have an observable, but low relative rate of halide liberation. Thus,

DCM seems to be near the threshold where Gcent begins to control the rate of catalysis. This suggests that activation energies influence rates in the range of 16-18 kcal/mol. The

relative observed rates for compounds with Gcent lower than 16 kcal/mol seemed to be

controlled by the docking success frequency. Compounds with high Gcent are non- reactive because the timescale in which catalysis takes place is longer than the timescale at which release of the substrate (reverse rate of binding) occurs.

7.8 Conclusions Predicting biological transformations of halogenated compounds is mechanistically similar to abiotic transformations but constrained by requirements of enzyme interactions. Using computational methods, I have modeled the first two steps – binding and halide displacement - and have developed quantitative criteria to evaluate the results of my calculations and relate them to experimental observations.

I found that reactant positioning and activation energy considerations are both necessary to explain the difference in observed experimental rates of halide liberation from different compounds with HAD. Docking frequency was related to the ability of a compound to

bind correctly, while Gcent accounted for the favorability of the reaction if successful binding was able to occur. Neither was sufficient on its own to fully explain the observed experimental behavior. However, when used in conjunction, the two metrics were able to explain the observed data. This supports the hypothesis that each was a necessary but not sufficient condition. The reaction must pass through both binding and catalysis steps successfully for there to be an observable rate of reaction. If a compound docked successfully, it could still be non-reactive – this would be a false positive from the docking standpoint. Likewise, if a compound had a favorable Gcent but could not fit into the active site, then this would be a false positive from the activation energy standpoint. I

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observed a Gcent threshold around 16-18 kcal/mol that signified non-reactivity, no matter how successful the compound was in docking.

As discussed in Section 7.2.2, the overall rate of reaction for HAD is dependent on a number of steps, each with an associated rate and each with the potential to be the rate limiting step for certain compounds. The significance of the docking characteristics and the activation energy of the compounds can be viewed in terms of the first two reaction

steps. The rate of the binding step is k1 and the associated rate of release (without

reaction) is k-1. The rate of the SN2 halide displacement reaction is k2. If k2 is very fast

(Gcent is small), then k1 will control the rate (assuming that all steps subsequent to k2 are fast). If the binding results in a non-reactive configuration, then the substrate will be released without reacting and another binding step will be necessary. In this case, the halide liberation rate was observed to be related to the docking success frequency. If k2 is

very slow, then it will control the rate of reaction. And if k2 is much slower than k-1, then the substrate will likely be released without undergoing reaction.

I conclude that the ability of HAD to transform halogenated pollutants is governed by compound shape and reactivity. Compound shape affects transformation mainly through steric effects by influencing the interaction with the enzyme active site. Reactivity is important, however, in the case of relative halide liberation rates, where I found that the activation energy acted more as a threshold barrier. Compounds with low Gcent did not seem to accelerate the observed halide release over a compound with a moderate Gcent. The frequency of successful docking actually was better correlated to halide liberation rates. This indicates the importance of productive docking. Accounting for both docking frequency and Gcent is important as different structural feature can have opposing effects on reactivity. For instance, the lengthening of the carbon chain does not affect gas phase reactivity, but can profoundly hinder docking – the compound simply becomes too long to fit in the active site.

Table 7-6 summarizes the effects of structural components on reactivity with HAD based

on my docking and Gcent analysis.

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Table 7-6: Effect of Structure on HAD activity Conclusions on Net Effect on observed HAD Structural Group Docking Gcent reactivity

Monohalogenation Compounds with Moderate Gcent BA, BP, CA, CP have terminal moderate level of reactivity haloagenation have compared to 12DCA about 50% productive docking Lengthening Over four carbons, Minor Effect CPent, CH, and 16DCH have Carbon Chain compound becomes no or low reactivity due to too large for active inability to bind frequently site

 methyl group The movement of Gcent increases  2CP is nonreactive as it substituent the halogen from a cannot dock productively terminal position to an interior position prevents successful docking.

 halide No noticeable effect Gcent increases 111TCA is nonreactive due substituent from a single  to inability to bind substituent, 2 or productively more  substituents DCM, 11DCA, 112TCA are prevent successful nonreactive with HAD as

docking. Gcent increases past threshold

 halide Docking frequency Gcent increases 12DBA, 12DCA, 13DCP substituent increases. have higher rates of reaction than BA and CA

-OH and -OOH Docking frequency Gcent increases CAOH and CAACE are functional groups is low or zero. nonreactive due their inability to bind

Using a combination of enzyme and catalysis considerations, I am able to explain much of the observed behavior of a set of halogenated compounds. My results explain why 12DBA and 12DCA are the best substrates for HAD. The fact that 12DCA is the optimal substrate is likely no coincidence as the first instances of this enzyme and the host microorganisms were obtained from isolations of cultures found near sites contaminated with 12DCA.

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In this chapter, I have established that both configurational considerations and binding energies determined by AUTODOCK correlate with experimental data and - in conjunction with gas phase ab initio calculations - can be used to explain observed experimental behavior of compounds with HAD. I established a set of distance criteria that can quickly and objectively evaluate a docking pose and classify it as a productive or non-productive binding. The frequency of productive dockings does seem to correlate with experimental rate constants when Gcent is used to screen out compounds where the chemical reaction is unfavorable. This agrees with the fact that the rate constant for binding at typical substrate concentrations is much less than the halide displacement rate. Thus, ability of a compound to bind productively and avoid non-productive dockings is an important component of reactivity.

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8 Quantitative Structure Activity Relationships In previous chapters, I explored the factors that affected reactivity at the electronic and molecular level. In this chapter, I establish Quantitative Structure Activity Relationships (QSARs) and develop models for reactivity based on experimental data. I accomplished this by correlating molecular data expressed as experimental rates or computed rate factors. I employed Principal Components Analysis (PCA) to determine major modes of variability among the compounds and a multivariate linear regression (MLR) analysis to correlate changes in descriptors with changes in activity.

Experimentally measured reaction rates relevant to environmental conditions are often obtained in the presence of buffered aqueous solutions, sediment or microorganisms. It is important to take into account the effects of multiple structural and environmental factors as well as interactions between these factors in deducing structure activity relationships. This can be aided by a statistical analysis that allows for correlations to be uncovered between these factors and activity. Work described in Chapter 6 has shown that the two major substituent-related mechanisms that can affect reaction rates of SN2 reactions are electron delocalization that inhibits bond coupling and through space stabilization/intramolecular solvation of the transition state (which can be dampened in the presence of solvent). In Chapter 7, I showed that compound structural features could determine whether interaction within enzyme actives was conducive to biotransformation rates. I used this knowledge to identify mechanistically relevant molecular descriptors that were reflective of the mechanisms and the response under different reaction environments. These can be generally categorized as being related to the activation energy of the gas phase reaction, the behavior of the compound in solute, and the ability to fit productively into an enzyme active site.

I developed three models of reactivity for hydrolysis (to test various combinations of descriptors) and one model for the enzymatic process. I compiled available experimental hydrolysis and biodegradation data from different sources and converted them to a common unit of activity. For the statistical analysis, I first used PCA to uncover the

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major modes of descriptor variability inherent in the compound set. I then applied MLR using the selected sets of descriptors. The best hydrolysis model was one that combined

Govr, experimental solvation energies and logarithm of the octanol-water coefficient (log Kow). The biodegradation model, which relied on docking results as one descriptor measuring enzyme-substrate interaction was less robust, implying that the relationship is non-linear and/or other descriptors may be needed.

8.1 Background QSAR analysis has proven useful for analyzing the relationships between experimental activity (e.g. in terms of transformation rates, toxicity, etc.) and properties of molecules (Tosato et al. 1990; Freidig et al. 1999). QSARs are developed statistically by correlating activity against descriptors that account for aspects of molecular structure or properties that vary among compounds. QSARs can involve a simple linear regression of activity versus one descriptor, or can involve sophisticated multi-variate non-linear models (Dunn et al. 1984; Manly 1994; Eriksson and Tysklind 1995) . QSARs are useful for their predictive power, but also have value in helping to understand observed experimental activity and test hypotheses regarding structural influences on activity. Activity can be defined in whatever form and units that are of interest. QSAR is particularly relevant in the environmental field, where reactions can take place in a variety of environments. Some early efforts focused on the development of structure-activity relationships for aquatic toxicity (Verhaar et al. 1992). Relationships have been developed for hydrolysis, reduction, and biodegradation (Wolfe et al. 1980; Dunn et al. 1984; Jonsson et al. 1989; Eriksson et al. 1990; Tosato et al. 1990; Verhaar et al. 1992; Eriksson and Tysklind 1995; Verhaar et al. 1996; Damborský et al. 1998; Damborský et al. 2001).

8.2 Approach The process I used for conducting my QSAR study was as follows: 1) Compiled experimental data 2) Determined appropriate descriptors 3) Performed a Principal Component Analysis (PCA) on the full data set 4) Performed MLR on the full data set

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In order to validate the results and provide greater insights into the applicability of the model for other compounds, I performed the following additional analysis using the best performing model: 5) Conducted cross validation using the Leave-One-Out method 6) Identified minimal training sets guided by the PCA results 7) Performed MLR on the training sets 8) Determined the predictive ability of the MLR equation developed from the training set.

8.3 Experimental Data Set As mentioned earlier, a major limitation in interpretation of reactivity for halogenated pollutants lies in the limitation of available data. I used a compilation of experimental data from a variety of sources corresponding to compounds that were included in my study. I converted all data to a first order rate constant (k) and half life. These quantities are related by the equation below:

t1/2 = 0.693/k

Available experimental data is summarized in Table 8-1.

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Table 8-1: Hydrolysis Experimental Data -1 Compound Source k (d ) logk t1/2 (d) log(t1/2)

BA MM 2.3E-02 -1.6 30 1.5

HM 3.3E-01 -0.5 2 0.3 BP MM 2.6E-02 -1.6 26 1.4

J96 7.3E-04 -3.1 944 3.0 CA MM 1.8E-02 -1.7 38 1.6

11DBA J96 3.5E-04 -3.5 2,005 3.3

1B1CA J96 6.9E-04 -3.2 1,003 3.0

11DCA J89 3.1E-05 -4.5 22,384 4.3

HM 1.7E-03 -2.8 416 2.6 111TCA J89 1.8E-03 -2.7 388 2.6

112TCA J89 1.4E-05 -4.9 50,808 4.7

1122TETCA J89 4.4E-03 -2.4 159 2.2

HM 4.7E-04 -3.3 1,486 3.2

12DBA J96 3.0E-04 -3.5 2,288 3.4

VR 7.7E-04 -3.1 901 3.0

12DBP VR 2.2E-03 -2.7 321 2.5

DBCP BLR 5.8E-01 -0.2 1 0.1

J89 2.6E-05 -4.6 26,714 4.4 12DCA J96 2.5E-05 -4.6 26,714 4.4

13DCP J89 8.5E-04 -3.1 820 2.9

2BP MM 3.3E-01 -0.5 2 0.3

2CP J96 1.7E-02 -1.8 40 1.6

22DCP J89 4.6E-01 -0.3 1.5 0.2

ALC MM 1.0E-02 -2.0 69 1.8

EPIC MM 8.5E-02 -1.1 8 0.9

MM = (Mabey and Mill 1978) HM = (Haag and Mill 1988a) J96 = (Jeffers and Wolfe 1996) J89 = (Jeffers et al. 1989) VR = (Vogel and Reinhard 1986) BLR = (Burlinson et al. 1982)

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Five compounds listed in Table 8-1 had data obtained from multiple sources. Most of the values are within an order of magnitude for the same compound. The relatively large discrepancies illustrate the difficulty inherent in attempting to establish a consistent model for reactivity from data obtained under variable experimental conditions.

8.4 Descriptors There are a wide variety of potential descriptors to choose from. An issue with MLR is the potential for descriptors to serve as fitting parameters without regards to actual correlative value. If a large number of descriptors are used, the coefficients may merely reflect the best fit, rather than being indicative of actual causation. For this reason, a sound approach involves limiting the number of descriptors, while at the same time making sure that important descriptors are not excluded. To accomplish this, I relied on the insights gained from the mechanistic analysis to rationally select my descriptor sets.

I chose descriptors that measure changes in properties that should correlate to the hypothesized mechanisms that drive changes in hydrolysis rates. The descriptors used in my analysis fall under four categories of effects related to activation energy, solvent, hydrophobicity and enzyme interaction and are shown in Table 8-2. The source of the descriptors was either from my ab initio calculations or experimental data.

Table 8-2: Descriptors Used in this Study Descriptor Source Relevance

Activation energy Activation energy of gas phase reaction. Provides a Govr Computed measure of intrinsic reactivity in the absence of external factors (e.g. solvent and enzyme) Energy of the most accessible vacant orbital. Provides a LUMO Computed measure of the orbital energy effects and the tendency for bond coupling delay through delocalization effects. Solvent effects

Measures the difference in Hovr between the gas phase Hgas->solv Computed and the solvent phase. Provides a measure of the differential effects of solvent on the SN2 reaction.

Gsolv Experimental Free energy of hydration. Provides a measure of the

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Descriptor Source Relevance additional energy required to desolvate the subtrate in solvent. Hydrophobic effects Octanol water coefficient. Provides a measure of the Log(Kow) Experimental tendency to move from water to a more hydrophobic environment. Enzyme Interaction Molecular docking frequency, energy and orientation. Provides a measure of how well a substrate structure fits Docking Computed into an enzyme active site given steric constraints. Also, determines ability to orient correctly for reactive groups to align.

8.4.1 Activation Energy Descriptors The first class of descriptors was related directly or indirectly to the activation energies and consisted of two descriptors obtained from my quantum chemistry calculations. I

employed Govr from my ab initio study as a primary descriptor of the intrinsic reactivity

of a compound. Quantum chemical descriptors such as Govr have been used to develop QSARs (Karelson et al. 1996). The use of calculated activation energies as descriptors for reactions has been established and has many advantages. Examples include a QSAR for reductive processes of anaerobic dehalogenation of halogenated aliphatic compounds (Rorije et al. 1997) and modeling of nucleophilic reactivity as measured through aquatic toxicity for a diverse set of chlorinated compounds (Verhaar et al. 1996). Karelson et al. (1996) provided a comprehensive list of quantum chemical descriptors. As an alternative

to Govr, calculated LUMOs of the substrate and also of the transition state were also selected as descriptors for their relationship to the electronic character of the compounds and their sensitivity relative to electron delocalization effects.

8.4.2 Solvent Effects Descriptors The second class of descriptor that I employed was selected to help account for differential solvent effects. Since the activation energy descriptors do not account for solvent effects explicitly, I needed to add a descriptor related to the behavior in solvent. I

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employed two descriptors under this category. The first measured the change in reaction enthalpy in going from gas to solvent, Hovr(gas->solv). This is defined as:

Hovr(gas->solv). = Hovr(solv). - Hovr(gas)

I used the Hovr(solv) and Hovr(gas) values from the Isodensity Surface Polarized

Continuum Model (IPCM) calculations listed in Chapter 5. Hovr(gas->solv) measures the change in activation energy between gas phase and solvent. As discussed in Chapter 5,

the change is always positive as Hovr(gas) is always smaller than Hovr(solv) because the reactants are solvated better than the transition state. Another important potential effect of

solvent on SN2 reactions is the energy cost to desolvate the substrate to allow it to react with the nuclophile (Chandrasekhar et al. 1985). A descriptor that can be used to account

for this is the solvation energy (Gsolv) of the substrate. Whereas Hovr(gas->solv) was

obtained from ab initio calculations of the reaction, Gsolv values are available through experiment, although they can also be determined through the use of molecular dynamics simulations.

8.4.3 Hydrophobic Effects Descriptors The third class of descriptor I used was related to the hydrophobicity of the compound. This might be expected to be important in identifying compound behaviors that are influenced by solvent, as it captures the tendency of substrates to partition from solvent to a hydrophobic environment. The measure of this tendency is the octanol water coefficient

(Kow) (Schwarzenbach et al. 1993), which will be employed in this analysis as a descriptor.

8.4.4 Enzyme Interaction Descriptors For biodegradation data, I used a descriptor that was obtained from the application of AUTODOCK (described in Chapter 7) to account for the ability of a substrate compound to interact favorably with an enzyme active site. In this analysis, I used the number of successful dockings as a descriptor.

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8.4.5 Structural Variability Covered by Descriptors Limitations on the availability of experimental and computational values for all the compounds led to variation in the composition of the compound set for each model. Nonetheless, the space of structural variability (as measured by the descriptors) was confirmed to be well spanned, which is an important criterium for a sound statistical analysis (Austel 1982; Tosato et al. 1990).

8.4.6 Experimental Data Relationship with Govr

The activation energy of the reaction, as measured by Govr, should be the descriptor that is most directly related to observed reaction rates. Before embarking on the QSAR analysis, I examined how much of the observed experimental activity might be explained

solely by changes in Govr. Figure 8-1 presents the experimental half lives for

compounds versus Govr. A first analysis reveals some scatter, however, when the data is

grouped by classes, clear relationships with Govr emerge.

The three classes that appear to capture consistent differences are monohalogenated and  substituted compounds,  substituted compounds, and compounds with halogens in the

2° position. This plot is significant in that an independently calculated gas phase Govr was able to order the observed reactivity compiled from five separate experiments within the established compound classes. There are clearly some outliers, such as 111TCA and 22DCP, which don’t belong to these groups and exhibit behavior that may be explained by the unique structural characteristics of the compounds, which possess a higher degree of halogenation in the terminal and secondary positions, respectively, relative to other compounds in the data set. It is apparent that there are some systematic differences that occur between the defined classes. The reasons that these classes capture the differences are likely due to the differences in mechanism that were discussed in Chapter 6. Compounds with  substituents will have lower reactivity (longer half lives) in solution compared to gas phase because the solvent dampens out the through space stabilization/intramolecular solvation effects. If these differences can be accounted for in a quantitative manner through a correlation with some property or set of properties, then an explanatory - as well as predictive – model of reactivity is possible. In the next section

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I describe how I applied MLR to the data with the selected descriptors to determine such a relationship with multiple factors could be established.

5.0  substituted 112TCA Monohalogenated 4.5 and  substituted 12DCA 11DCA 4.0

3.5 12DBA 11DBCA 11DBA 13DCP 3.0 Clacetamide CM CA 111TCA

(days) 2.5 12DBP CP 1122TCA 1/2 2.0 Allyl Cl log t 1.5 BA °substituted BM BP 2CP 1.0 Epic 0.5 2BP 22DCP 0.0 0 5 10 15 20 25

Calculated Govr (kcal/mole)

Figure 8-1: Half lives versus Calculated Govr

8.5 MLR I employed MLR using the regress function available in MATLAB. For a given set of observations, regress will return a least squares fit of to a set of descriptors.

MLR assumes a linear model of the form: y = X+ where: y = vector of observations (observed value of activity for each compound) X = matrix of descriptors (values of properties for each compound)  = regression coefficients (“fitting parameters” determined by model)  = residuals (differences between actual and fitted values)

For n number of observations, the solution is of the form:

yi   0  1 X i1   2 X i2   3 X i3

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for i=1,2…n

Additional details on the MLR method used in this analysis can be found in Chapter 4.

8.6 PCA Prior to MLR, a PCA was employed to gain insight into the major modes of variability in properties. The premise of PCA is that different properties of compounds can covary. PCA is a means to reduce the dimensionality of the data set to find combinations of the original data and to produce a set of indexes that are uncorrelated (Manly 1994). If descriptors co-vary, PCA breaks down the variation and assigns them to principal components (PCs). PCA provides an opportunity to reduce the data space into independent components. The goal of PCA in this analysis was to determine if there were similar ways that compounds varied structurally that could be linked to consistent changes in reactivity. Methods for PCA are well documented (Joliffe 1986; Wold et al. 1987) and have been described in Chapter 4.

8.7 Overview of Results I applied MLR to a variety of descriptor combinations in Table 8-2. I found that three descriptors, one selected from each of the categories worked best. There were some differences in the compounds used in each model due to some gaps in calculated or experimental values. In order to be included in the development of a particular model, a compound needed to possess values or data for all descriptors used in the study.

An effective QSAR model has both explanatory and predictive powers. It is important to establish the validity of any MLR model to ensure that it is not merely fitting the data through extraneous descriptor relationships. A summary table of the four models that produced the best results and their components is shown below in Table 8-3.

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Table 8-3: Descriptor Components of Models

Activation Solvent Effects Hydro- Enzyme Energy phobicity Inteactions

Hgas- Model Govr LUMO >solv Gsolv logKow # Dockings Hydrolysis-I X X X Hydrolysis-II X X X Hydrolysis-III X X X Biodeg X X X

I identified the best model as Hydrolysis Model II. Using this model, I conducted additional validation steps which included a fractional factorial design to select a minimal training set for testing against the other compounds. The advantage of this method was that it also provided further insight into the structural characteristics that spanned the compound set (Eriksson et al. 1990). I also performed a leave-one-out validation to provide additional support for the robustness of Model II terms of its predictive ability. For Model I, I was able to make some predictions for compounds that did not have experimental activity available. A description of each of the models follows.

8.8 Hydrolysis Model I

This model used computed values for Govr and the reaction solvent effect (Hovr(gas-

>solv)), and experimental Kow values. There were 7 compounds that had a complete set of values for these descriptors available. The descriptor properties and experimental activity are shown in Table 8-4. Three compounds had data from more than one experiment (CA, 12DBA and 111TCA). To preserve the variability inherent in experimental data, all data points from these compounds were included in the MLR. Govr is the value obtained from my ab initio calculations. The computed solvation reaction effect energy is the

difference in reaction enthalpies (Hovr-IPCMHovr-gas) from the gas phase to the solvent phase calculated with the Isodensity Surface Polarized Continuum Model (IPCM) to represent the effects of solvent. It essentially measures the difference between the

reaction taking place in solution versus the gas phase. Hovr(gas->solv) represents the

differential effect that solvent has on the SN2 reaction activation energies for different substrates. This is illustrated in Figure 8-2. The reason that enthalpies are used is that

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establishing entropies was not possible using IPCM and would have required explicit representation of water molecules in a molecular dynamics simulation.

Hovr(gas->solv). = Hovr(solv). - Hovr(gas)

Gas Hovr-gas Transition State

Energy Hovr-solv Solvent

Reactants Products

Reaction Coordinate

Figure 8-2: Solvent Effect on SN2 Reaction

As can be seen from Table 8-4, the solvent effect is generally higher for compounds with

 halide substitution. This is because  substituents lower Hovr in the gas phase due to the through space/intramolecular stabilization mechanism discussed in Chapter 6. This effect is lost in solvent, so there is a larger difference between the gas phase and solvent

phase Hovr for compounds that possess  substituents as compared to those that do not.

Table 8-4: Descriptors and Activity of Compounds Included in Hydrolysis Model I

Descriptors Activity

Govr Hovr(gas->solv) Compound (kcal/mol) (computed) logKow1 logK

BA 5.7 15 1.5 -1.64 -1.74 CA 9.8 13.6 1.21 -3.13 -3.11 12DBA 2.0 18.7 2.23 -3.33 -3.52 -4.58 12DCA 5.8 18.2 1.65 -4.59

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Descriptors Activity

Govr Hovr(gas->solv) Compound (kcal/mol) (computed) logKow1 logK

2BP 8.0 14.6 1.8 -0.48 2CP 11.9 14.3 1.51 -1.76 -2.75 111TCA 21.9 12.7 2.04 -2.78 1From Damborsky et al, 2001

8.8.1 Principal Components Analysis – Model I

The first step in the analysis was to conduct a PCA analysis on the compound descriptors. There were three PCs. PC1 explained 66% of the variance in descriptor properties, the PC2 explained 31% and the PC3 explained 3%. PC1 and PC2 are discussed below. PC3 is not discussed further since it only accounted for a small amount of the variance.

PC1 and PC2 were broken down as follows:

PC1: 0.62(Govr) - 0.69(Hovr(gas->solv)) -0.37(log Kow)

PC2: -0.47(Govr) - 0.05(Hovr(gas->solv)) -0.88(log Kow)

In PC1, which explains 66% of the variance in descriptor values, the two strongest

components are Govr and Hovr(gas->solv). PC1 accounted for the inverse relationships of

Govr with Hovr(gas->solv) and log Kow as illustrated below.



Govr Hovr(gas->solv) log Kow

PC1 captured the covariations among compound descriptors along two general axes: the presence of  versus  substituents on a compound, and whether the compound

possessed bromine versus chlorine substituents. The inverse relationship between Govr and the Hovr(gas->solv) accounts for the fact that  substituted compounds such as 12DBA

possess low Govr values because of through space stabilization/intramolecular solvation

effects in the gas phase, but also possesss high Hovr(gas->solv) values because these same effects are dampened out by the solvent, making the relative gas/solvent differences for  substituted compounds higher.

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The inverse relationship between Govr and logKow in PC1 accounts for the fact that

brominated compounds have lower Govr because bromine is a better leaving group, but

have higher log Kow because brominated compounds are more hydrophobic due to the bulkier size and lower electronegativity of the bromine substituent (Schwarzenbach et al.

1993). The same relationship exists in PC1 between Hovr(gas->solv) and log Kow, where brominated compounds have higher Hovr(gas->solv) and have a higher log Kow. The relationship between Hovr(gas->solv) and logKow simply reflects that hydrophobic compounds have less favorable interactions in solvent and hence want to “escape” to a more hydrophobic environment.

PC2 captures 31% of the variance in descriptor values. The interrelationships in PC2 are shown below:

Govr Hovr(gas->solv) logKow

PC2 captures a positive relationship between Govr and logKow and appears to distinguish among the presence and number of  substituents on a compound. In this case 

substituents lead to higher Govr (because of the previously discussed electron delocalilzation), while also increasing hydrophobicity.

8.8.2 Model I MLR I performed MLR and achieved an r2 of 0.80 versus the log of the first order experimental rate constant, k. Error! Reference source not found.This model is able to capture the differences in leaving groups, and  versus  classifications. The equation for this relationship is shown here:

logk = -0.27(Govr) - 1.04(Hovr(gas->solv)) + 1.93(logKow) + 12.5027

Qualitatively, the direction of the relationships makes sense. As Govr increases, log k

decreases. As Hovr(gas->solv) increases, log k also decreases. What this relationship

signifies is the importance of the much higher solvent reaction effect (Hovr(gas->solv)) for

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the -halide substituted compounds. As discussed in Chapter 6, there is a reversal of effect of -halide substituents between the gas phase and in aqueous phase because of the dampening of through space transition state stabilization effects. Since Govr is a gas phase value, the coefficient in the equation for Model I for reaction solvent effect takes the reversal of activity due to solvent into account. Finally, as the compound becomes more hydrophobic, log k increases. This trend makes sense as hydrophobicity increases, there is a higher tendency to shed solvent and thus a lower energy cost for desolvation, increasing the reaction rate. The reaction solvent effect descriptor is needed to account for the solvation dampening of  subsituents.

-5 -4 -3 -2 -1-0.5 0

-1 2BP CA BA -1.5 -2 CA 111TCA -2.5 12DBA 2CP -3 Predicted logk -3.5

-4

-4.5 12DCA -5 Experimental logk

Figure 8-3: Predicted versus Experimental logk for Hydrolysis Model I

The descriptors in Hydrolysis Model I were sufficient to discriminate between compound property differences that influenced their experimental behavior. This includes capturing the tendency for halide substituted compounds to reverse order of reactivity from the gas phase because of through-space dampening and capturing the fact that 111TCA is much more reactive than the Govr alone would indicate. Both deviations from the

intrinsic reactivities (as measured by Govr) are accounted for by the model in the

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variation of the solvent related descriptors. In the case of the  substituted compounds,

Hovr(gas->solv) is relatively higher than compounds without  substituents. This

counteracts the increase in log k from the lower Govr, leading effectively to a reversal of the intrinsic (gas phase) trend.

8.8.3 Model I Predictions There were a 10 compounds for which no experimental data was available but where all values for the Model I descriptors were available. This provided an opportunity to use the equation from Hydrolysis Model I in a predictive manner. Such use allows gaps to be filled in our knowledge of the behavior of certain classes of compounds such as haloalcohols and haloacids. The results are shown in Table 8-5. I applied the equation for Model I to the descriptors for each compound and derived a predicted logk, which I converted to a rate constant and half life.

Table 8-5: Predicted k and half lives using Hydrolysis Model I

Compound Govr Hovr(gas- Log (Kow) Predicted Predicted >solv) k t1/2 d-1 (days) ALLYLCL 6.2 12.3 1.43 7.04 2 hrs 2CB 9.1 15.4 1.92 6.32E-03 110 14DCB 5 18.7 2.46 3.35E-04 2,071 DCM 9.1 14.4 1.25 3.5E-03 198 Bromocyclohexane 12.7 17 2.17 6.32E-03 110 1B2MeP 6.8 16.7 2.2 4.9E-03 169 BAOH -0.3 16.4 0.55 4.43E-04 1,565 CAOH 3.4 16.4 0.26 1.24E-05 56,029 3CPOOH -1.3 22.7 0.62 3.31E-10 5.7M yrs DCAOOH 7.3 17.2 0.57 6.51E-07 1.1M

An examination of the properties of the compounds reveals that the haloalcohols and

haloacids share similar characteristics, with large Hovr(gas->solv) and small logKow The

large Hovr(gas->solv) leads to a small log k in solution, because haloalcohols and haloacids are well solvated which significantly increases the barrier in solution relative to

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the gas phase. Indeed, hydrolysis of these compounds is not readily observed. The half life for 3CPOOH seems very large due to the very low rate constant. The Hovr(gas->solv) for this compound is outside the range of values that was used to develop the model and this value is driving the low reactivity. QSARs usually work best in interpolating, and because of the extreme values of descriptors for 3CPOOH relative to the training set, this is entering the realm of extrapolation. However, the Model 1 equation does indicate the importance influence of the solvent effect, so the reactivity of 3CPOOH may indeed be very low.

8.9 Hydrolysis Model II Hydrolysis model II used descriptors similar to Hydrolysis Model I except it employed

experimental free energies of solvation energies (Gsolv) (Hine and Mookerjee 1975;

Cabani et al. 1981). Whereas Hovr(gas->solv) used in Model I measured the solvent effect

on the entire reaction, Gsolv reflects the solvation (and desolvation) energies of the individual compounds. It is expected to be important as the more solvated a compound,

the higher the energy cost to desolvate the substrate prior to the SN2 reaction will be,

leading to retardation in rates. As measured, a more negative Gsolv means that the compound is more well solvated. The compound set for Hydrolysis Model II is shown in Table 8-6.

Table 8-6: Compounds in Hydrolysis Model II with Descriptors and Activity

Descriptors Activity

Govr Compound (kcal/mol) Gsolv (kJ/mol) logKow logk BA 5.7 -3.43 1.5 -1.64 -0.48 BP 5.1 -2.34 1.9 -1.58 -1.74 CA 9.8 -2.5 1.21 -3.13 -3.11 12DBA 2.0 -8.79 2.23 -3.52 -3.33 12DBP 4.4 -8.1 2.54 -2.67

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Descriptors Activity

Govr Compound (kcal/mol) Gsolv (kJ/mol) logKow logk -4.58 12DCA 5.8 -7.25 1.65 -4.58 13DCP 5.6 -7.93 2.05 -3.07 112TCA 7.2 -8.4 2.04 -4.86 2BP 8.0 -2.0 1.8 -0.4769 2CP 11.9 -1.03 1.51 -1.76 11DCA 14 -3.5 1.56 -4.51 -2.75 111TCA 21.9 -1.1 2.04 -2.78

8.9.1 PCA Analysis for Model II I conducted a PCA for Hydrolysis Model II. PC1 explained 67% of the variance in descriptor properties, the PC2 explained 26%, while PC3 explained 7% of the variance.

PC1, as in Model I, aligned closely with changes in Govr. Clearly, the gas phase (intrinsic) order of reactivity is an important descriptor. The PCA components were as follows:

PC1: 0.57(Govr) + 0.66(Gsolv) -0.49(logKow)

PC2: -0.60(Govr) - 0.07(Gsolv) -0.75(logKow)

PC3: 0.56(Govr) - 0.75(Gsolv) -0.36(logKow)

The makeup of these PCs is very similar to those in the Hydrolysis Model I. The only change is that Gsolv is used instead of Hovr(gas->solv). The sign of these two are opposite, and once that is taken into account the PCA tells a similar story in terms of the important differences describing the compounds. PC1 captures the fact that the  substituted

compounds in the data set have low Govr and high Gsolv; and the fact that brominated

compounds have lower Govr relate to chlorinated compounds (because they are better

leaving groups), but have higher logKow (because they are more hydrophobic). PC2, as in

Model I, reflects that more highly substituted  compounds will have higher Govr

(because of electron delocalization) and higher logKow because of higher hydrophobicity.,

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8.9.2 MLR for Model II Hydrolysis Model II achieved an r2 of 0.86. The equation for the model was as follows:

log k = -0.21(Govr) + 0.64(Gsolv) + 1.85(logKow) – 1.43

The difference between Model II and Model I was the addition of 5 compounds (112TCA, 13DCP, 12DBP, BP, and 11DCA) and use of actual experimental solvation

free energy (Gsolv) data (Cabani et al. 1981) in place of computed solvation energies. This improved the overall fit of the model from 0.80 to 0.86. The improving r2 with increasing # of compounds for the same number of descriptors is particularly encouraging. The predicted versus observed values are shown in Figure 8-4.

0 -5 -4 -3 -2 -1BP 0 -0.5 BP

-1 2BP 2CP r2=0.86n=14 -1.5

-2 12DBP BA CA -2.5 CA 111TCA -3

Predicted logK Predicted 12DBA 11DCA -3.5 13DCP

-4 12DCA -4.5 112 TCA -5 Experimental logK

Figure 8-4: Hydrolysis Model II

8.9.3 Model II Cross Validation – Leave-One-Out Analysis To test the validity of Model II, I performed a leave-one-out cross validation. This is a standard diagnostic test for linear multivariate regression (Wold 1991). It is performed by leaving out one compound at a time, running an MLR on the reduced set, and then using the resulting equation to “predict” the activity of the compound that was left out. This is

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repeated for every compound. The square of the residuals (observed-expected) is summed and this generates the value for PRESS, which is the Predictive Residual Sum of Squares. PRESS is divided by the sum of squares of the observed response values (activities). If the model is a valid one, then the squared sum of errors should be well below the squared sum of observations. In the case of Model II, the value was 0.05. Any value below 0.10 indicates an excellent model. The results are shown in Table 8-7.

Table 8-7: Leave-One-Out Model Cross Validation Predictions for Hydrolysis Model II

Experimental Predicted Compound log k log k Difference BA -2.1 -1.6 0.5 BP 0.0 -1.6 -1.6 BP -0.5 -0.5 0.0 CA -2.7 -3.1 -0.4 CA -3.2 -1.7 1.4 11DCA -3.5 -4.5 -1.0 111TCA -2.9 -2.8 0.2 111TCA -3.0 -2.7 0.2 112TCA -4.5 -4.9 -0.4 12DBA -3.3 -3.5 -0.2 12DBA -3.4 -3.3 0.0 12DBA -3.4 -3.1 0.3 12DBP -2.9 -2.7 0.2 12DCA -4.2 -4.6 -0.4 12DCA -4.2 -4.6 -0.4 13DCP -4.0 -3.1 0.9 2BP -1.2 -0.5 0.7 2CP -1.7 -1.8 0.0

The predicted versus experimental log k are shown in Figure 8-5.

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2BP BP -1 BA CA BP 2CP -2 12DBP 111TCA -3 12DBA CA

logK Predicted 13DCP -4 12DCA 11DCA 112TCA -5 -5 -4 -3 -2 -1 0 Experimental logK

Figure 8-5: Leave-One-Out Cross Validation for Hydrolysis Model II

The validation results for Model II show that the model is fairly robust. Taking out one compound at a time and predicting the activity of the compound with an MLR of the remaining compounds allowed for reasonable predictions of experimental data.

8.9.4 Model II Validation – Development and Testing of a Training Set The model developed above was based on using the full available data set. It uses the maximum amount of information available. In order to provide a level of validation for the model developed and to gain knowledge of the bounds of the model equation when used in a predictive fashion, I developed a minimal set of compounds (training set) based on the PCA and performed a MLR on these compounds and then used the equation from this to predict the compounds that were left out of the training set. The PCA helped to guide the selection of the training set. I plotted a three dimensional space with PC1, PC2 and PC3 as the dimensions. This is shown in Figure 8-6.

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PC3

112TCA

12DBA

PC2

111TCA

PC1

Figure 8-6: Model II Descriptors 3-D Space Showing the 4 Training Set Compounds

This helped to visualize the space over which the compound descriptors varied. A cardinal rule of QSAR development is that the space must be spanned effectively. Based on this visual analysis, I selected CA, TCA, 112TCA and 12DBA. PCA1 explained the majority of variance between compounds, so the CA, TCA versus 12DBA compounds spanned this space, PC2 was spanned by CA and 111TCA. Finally, PC3, which had the least variation, was spanned by 111TCA and and 12DBA. Using these four compounds, I ran an MVR and obtained the following equation:

log k = -0.15(Govr) + 0.47(Gsolv) + 0.94(log Kow) – 0.958

The equation above was then used in a predictive manner by applying it to the test set. The test set is the remaining set of 13 compounds in the original data set. The predicted versus observed are shown in Figure 8-7. The training set compounds are indicated in red. Both 12DBA and CA have multiple experimental log k points plotted for the

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identical respective predicted log k due to the multiple data points available for these compounds.

BP BP -1

2CP 2BP

-2 BA CA 111TCA CA

-3 11DCA 12DBA 12DBP Predicted logk Predicted 12DCA 13DCP -4 112TCA

-5 -5 -4 -3 -2 -1 0 Experimental logk

Figure 8-7: Predicted Versus Actual Hydrolysis Rates for Testing Set (Compounds in Red indicate Training Set Compounds)

The results shown above validate the use of the model. The PCA results allowed me to develop an efficient training set. What is remarkable is that the combination of quantum chemistry descriptors and experimental solvation free energy results predict experimental activity results to within an order of magnitude. It is just as important to know the limitations of the developed model. In one case, the model can simply be used in an interpretive fashion. If the values stray outside the training set, then there is less likelihood of success – interpolation is always better than extrapolation. However, the results above are encouraging and show that for the domain of halogenated alkanes, we can develop robust QSARs models that can provide reasonable estimates of reactivity.

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8.10 Hydrolysis Model III The third model that I developed for hydrolysis substituted the Lowest Unoccupied

Molecular Orbital (LUMO) energies for the Govr descriptor used in Models I and II.

Govr and LUMO should be somewhat interchangeable based on the conclusions of my mechanistic analysis in Chapter 6. As illustrated in Chapter 6, the ground state LUMO of a compound correlated very well with Govr among compounds with  substituents, but not so well among compounds with  substituents.

Table 8-8: Compounds in Hydrolysis Model III

Descriptors Activity

Compound LUMOGS Gsolv (kJ/mol) log Kow log k BA -0.023 -3.43 1.5 -1.64 -0.48 BP -0.0291 -2.34 1.9 -1.58 -1.74 CA -0.003 -2.5 1.21 -3.13 -3.11 12DBA -0.041 -8.79 2.23 -3.52 -3.33 12DBP -0.039 -8.1 2.54 -2.67 -4.58 12DCA -0.018 -7.25 1.65 -4.58 13DCP -0.012 -7.93 2.05 -3.07 112TCA -0.041 -8.4 2.04 -4.86 2BP -0.022 -2.0 1.8 -0.4769 2CP 0.000 -1.03 1.51 -1.76 11DCA -0.025 -3.5 1.56 -4.51 -2.75 111TCA -0.053 -1.1 2.04 -2.78

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8.10.1 PCA Analysis for Model III The PCA analyses yielded the following results. Model III: PC1 (69% of variance), PC2 (25% of variance), PC3 (6% of variance)

PC1: -0.58(LUMOGS) - 0.48(Gsolv) +0.66(log Kow)

PC2: -0.55(LUMOGS) + 0.83(Gsolv) -0.30(log Kow)

PC3: 0.59(LUMOGS) - 0.30(Gsolv) -0.75(log Kow)

The meaning of the PC relationships were not as straightforward with the LUMOGS descriptor. Replacing Govr with LUMOGS as a descriptor notably changed the coefficients, which indicates that these two descriptors are not equivalent in terms of their influence on descriptor properties, nor in their interaction with other descriptors to influence descriptor properties

8.10.2 MLR for Model III MLR was applied to Model III. The results are discussed below. Model III (r2=0.63)

log k = 57.8(LUMOGS) + 0.39(Gsolv) +3.45(log Kow) -5.76

The r2 for this model was 0.63, so there was a loss of some statistical significance in

Model III that came from the use of LUMOGS (which replaced Govr used in Model II).

The trend is that a more stable (more negative) LUMOGS leads to higher delocalization

and lower transformation rate is apparent since the coefficient in front of the LUMOGS is positive. Figure 8-8 shows predicted versus experimental log k for Model III.

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0 2CP -1 BP BP 111TCA 2BP -2 13DCP CA CA -3 11DCA 12DBP

logk Predicted BA 12DCA -4 12DBA 112TCA -5 -5 -4 -3 -2 -1 0 Experimental logk

Figure 8-8: Hydrolysis Model III

8.11 Biodegradation Model Damborsky et al. (2001) used a biophore approach to develop structure-specificity

relationships for haloalkane dehalogenases. For the biodegradation model, I used Govr and docking results as described in Chapter 7. PCA analysis showed a strong PCA1 that accounted for 90% of the variance in descriptor properties.

Table 8-9: Biodegradation Model Descriptors and Activity

Descriptors Activity Docking Govr Frequency (# of % of Halide Compound (kcal.mol) dockings/100) Liberation CB 9.1 57 49 CA 9.8 46 25 CH 9.3 5 0 CM 6.4 56 29 CP 9.3 68 49 DCM 9.1 38 9 11DCA 14 0 0 112TCA 7.2 73 0

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Descriptors Activity Docking Govr Frequency (# of % of Halide Compound (kcal.mol) dockings/100) Liberation 12DCA 5.8 82 100 12DCP 8.5 80 0 13DCP 5.6 80 89 2CP 11.9 0 0

The model obtained using the above compounds achieved an r2 of 0.42, which is not considered statistically significant. The lack of success of this model may be due to the presence of a couple of outlier compounds (112TCA, and 12DCP) that were identified in Chapter 7. When these were removed the r2 improved to 0.82. This was not much improvement over a simple regression between the % docking descriptor and % halide liberation (once the outliers were removed), so the MLR does not contain any more explanatory power.

8.12 Conclusions I developed statistically significant models for hydrolysis through the use of PCA and MLR. I was not able to develop a similarly successful model for biodegradation, which may be due to the more complex nature of the enzyme reaction. Through rational selection of descriptors, I was able to account for many of the differences in hydrolysis between compound classes as illustrated in Figure 8-1 and obtain a single relationship based on the MLR equations. The models provide insight into the factors that govern observed experimental activity. Table 8-10 summarizes the model performance.

Table 8-10: Model Summary

Model Equation r2 CV* Hydrolysis I logk = -0.27(G ) - 1.04(H ) + 1.93(logK ) + 12.5027 0.8 - n=7 ovr ovr(gas->solv) ow Hydrolysis II logk = -0.21(G ) -0.64(G + 1.85(logK ) – 1.43 0.86 0.05 n=18 ovr solv ow Hydrolysis III logk = 57.8(LUMOGS) + 0.39(Gsolv) +3.45(logKow) -5.76 0.63 - n=18

Biodeg % halide liberation = -5.6(Govr) + 0.33 (docking frequency) + 62.43 0.42 ‘-

*Value of PRESS for Leave-One-Out Cross Validation. Only performed for Hydrolyis Model II.

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8.12.1 Model Performance and Interpretation I developed models using rationally determined descriptors that were selected to probe aspects of hypothesized mechanisms. The principal axes as determined through PCA

centered on descriptors that measured the potential for solvation and Govr the type of leaving group, and the number of  substituents. The best performing model for hydrolysis was selected for additional validation and testing and was demonstrated to be a robust model with reliable predictive power.

The hydrolysis models were able to capture individual trends fairly well. They were able to quantitatively account for the behavior of 111TCA, which had been an outlier when

plotted against Govr (Figure 8-1). The experimental data for 111TCA had been contradictory when SN2 transformation was assumed since it was more reactive than

compounds with lower degree of halogenations. This led to a proposal that an SN1 reaction was the likely route (Schwarzenbach, 1993). Queen and Robertson (1966) studied the similar phenomena where 2,2-dihalopropanes have lower than expected activation energies and noted that shifts in SN2 to SN1 reactions can occur with changes in the breakdown of the solvation shell. 111TCA had the smallest solvent reaction

(Hovr(gas->solv)) effect and degree of solvation (Gsolv) and the second highest Kow, both

of which reflect a higher tendency for desolvation and a shift to an SN1 mechanism. The QSAR model took the relative magnitudes of these descriptors into account and lowered

the logk. In terms of the descriptors, what the model tells us is that a high Govr (making the SN2 reaction unfavorable) coupled with a low desolvation energy (increasing potential

for SN1 reaction) may be enough to shift the favored reaction from SN2 to SN1.

8.12.2 Utility of Quantum Chemistry Descriptors I demonstrated the value of computationally derived quantum chemistry descriptors such

as Govr and LUMO in developing valid QSARs for hydrolysis and biodegradation. In the absence of these descriptors, there is no way to account for the “intrinsic” reactivity of compounds and thus no way to separate out environmental effects such as solvation or enzyme interactions. The quantum chemistry descriptors enabled a layer of order to be imposed on the experimental data. Even though gas phase calculations may seem at first glance irrevelant to understanding hydrolysis rates, I have demonstrated that they provide

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an essential role of accounting for intrinsic reactivity in the development of QSARs in non-idealized reaction environments such as hydrolysis. This is particularly important in when considering -halide substituted compounds, where through space stabilizing interactions can occur under gas phase conditions, but are lost in solvent. Since solvent effects can alter the reactivity order, it is important to have a descriptor that accounts for the differential solvent effects.

8.12.3 Common Modes of Compound Variance and Association with Mechanisms The PCA analysis was able to breakdown the descriptor properties into common modes that were related to the differential behavior of  and  substituted compounds between gas phase and solvent and the degree of substitution. By showing that the descriptors of compounds tended to co-vary according to similar substituent effects, this analysis confirmed the findings of Chapter 6 that the reactivity of the set of halogenated pollutants could be explained by the two mechanisms of electron delocalization and through space stabilization/intramolecular solvation, both of which were related to substituent effects.

8.12.4 Predictive Value I demonstrated the predictive value of the models through application to compounds that have no experimental data available. Using a four-compound training set in Hydrolysis Model II, I was able to successfully predict activity for other compounds within an order of magnitude. This will help to fill important experimental gaps, particularly among classes of compounds such as haloalcohols and haloacids. I found that the very large solvent effects on haloalcohols and haloacids would contribute to lower reaction rates. My models will perform best in the domain of halogenated alkanes as that is the compound class from which the relationships were derived.

8.12.5 Summary and Next Steps I have demonstrated that the observed hydrolysis rates of halogenated pollutants can be estimated using a small number of descriptors. In conjunction with the prior work discussed in earlier chapters which focused on building mechanistic understanding of reactivity, I am in a much better position to explain the causation behind the correlation at both a theoretical and field level. I have built a complete model of reactivity and

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developed predictive and explanatory models based on that understanding. And equally as important, this work offers a rational method to detect anomalies in relationships and a means to interpret deviations from expected. Next steps will include compiling descriptor data for a large number of compounds that have no established hydrolysis or biodegradation data and creating a list of predicted rates. Obtaining additional experimental data will also allow refinement of the models.

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9 Summary and Conclusions

Attempting to develop a broadly applicable model for structure-reactivity of SN2 transformation strictly from experimental data, which is limited by data gaps and inconsistent methods, would be a daunting if not impossible challenge. In addition, relevant transformations occur under different conditions, which result in varying patterns of reactivity, adding another dimension of complexity to consider. My application of computational methods was able to overcome these experimentally based obstacles by computing activation energy parameters relevant to SN2 transformations in a variety of environments for 76 compounds that encompassed a wide variety of chemical structures and structural variations.

This research effort has examined the underlying causes for observed variations in experimental SN2 reactivity for halogenated pollutants by evaluating the correlation between the measured and calculated activity, structure and reaction conditions. The goal of this work was twofold: the first is to synthesize the body of previous experimental work, to leverage what has been done and to gain the most amount of information possible. The ultimate goal is the ability to predict the reactivity of future compounds.

9.1 Suitability of Computational Methods for Studying Haloalkane Pollutant Behavior My overarching conclusion is that the ab initio calculations, in conjunction with existing

experimental knowledge, provided a powerful means to evaluate the SN2 reactivity of halogenated pollutants. At a first glance, when experimental data was plotted against calculated gas phase Govr, it became apparent that there were patterns that existed among compound classes. The fact that the values were able to establish patterns based on similar compound groupings indicates that the computational values are not far off

from reflecting reality. I conclude that gas phase Govr is a useful, semi-quantitative predictor for SN2 reactivity in the environment.

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There were a number of questions apparent from the experimental literature that could not be satisfactorily answered with the existing data that existed prior to this thesis research. 1) What are the major categories of structure-reactivity relationships and can these be applied consistently? 2) What are the underlying causes for observed structure-activity relationships? 3) Can a structure-activity relationship for one pair of experimental compounds be applied to another pair of compounds? 4) Can knowledge of experimental patterns for hydrolysis be applied to biodegradation and vice versa? Through the use of computational methods, I was able to provide answers to these questions. In addition, I was able to provide insight into the behavior of a few specific compounds whose behavior had been puzzling. I conclude that the increased reactivity of 111TCA is due to a change in mechanism to SN1 because the observed relative reaction rate was too high based on the gas phase Govr. The 1122TETCA E2 reaction is very

favorable as the SN2 reaction is competitive. 2CP must have a lower desolvation energy that allows it to be transformed in water faster than CA because the gas phase trend

indicates that 2CP has a higher Govr than CA.

9.2 Categories of Structural Effects I found that six categories of compound structure were sufficient to capture the structural variation of haloalkane pollutants. These were leaving group, chain length,  substituent,  substituent, vinyl group and cyclic group. The structural features that occurred within these categories produced qualitatively similar changes in Govr throughout the compound set. I found that substituent effects were additive, although not always in strict quantitative fashion. This implies that if there is data for a known compound, the reactivity of other compounds can be assessed by breaking down the structural differences and considering the combination of effects from stepwise changes in structure.

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The effect of leaving group differences in Govr was consistent among all compound types considered to within 0.5 Å. Thus, it should be possible to reliably estimate the reactivity of a compound with a bromine or chlorine leaving group, if data is available for the analogue compound with the other leaving group is available.

9.3 Major Modes of SN2 Reactivity

The findings of my research allowed me to condense SN2 reactivity of the 76 compounds to essentially three modes of variability, which corresponded to electron delocalization, through space stabilization/intramolecular stabilization, and enzyme interaction mechanisms (Table 9-1). The first two modes captured the electronic substituent effects and were applicable in both the aqueous phase and biologically, while the last mode captured enzyme-substrate steric interaction effects and was unique to considering reactivity in the enzyme environment. These modes captured the effects of major structural changes represented in the compound set and reduced them to variation along the three modes. I identified experimental and computational compound metrics that correlated with variation along the modes. The advantage of reducing the discussion to the modes of reactivity is that it allows a discussion to revolve around the underlying factors, rather than on the structural features themselves. It also allows for a quantitative approach. The retardation effect of  substituents had been commonly attributed to “steric” effects, but I have shown that electron delocalization is just as plausible a mechanism, and it has the advantage of being quantifiable and manifested in the transition state.

Table 9-1: Major Modes of Variability in SN2 Transformations of Halogenated Pollutants Electron Intramolecular Enzyme Steric Mode Delocalization Solvation Interactions Related compound VEA, LUMO Solvation Energy Steric Bulk/Shape metric Relevance in Dominant Dominant NA Intrinsic reactivity Relevance in Dominant Weak NA hydrolysis Relevance in Uncertain Uncertain Dominant Enzyme

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It should be possible, with a basic set of values for a given compound, to semi- quantitatively evaluate its potential SN2 reactivity relative to other compounds.

Enzyme docking frequency is an effective indicator of the substrate ability to fit. A common occurrence will be false positives, which I was able to screen using activation energy considerations.

9.4 Implications for Transformation Potential for Halogenated Pollutants There were a number of general insights gained from my research that have implications for the studying the potential of halogenated pollutants to degrade in the environment. The general lessons is that for the majority of halogenated pollutants considered, structural effects can be divided into two types related to electron delocalization and through space stabilization/intramolecular solvation. For hydrolysis, the  substituent effects are dampened out, but the delocalization effects remain.

9.4.1 Comparing Experiment to Intrinsic (Gas Phase) Order of Reactivity If compounds deviate from the expected gas phase order, this points to solvent effects, enzyme effects, or alternate reaction mechanism. In this case, the data point can be evaluated in terms of the environment and structural factors that have been identified, such as dampening of  substituent effects, steric hindrance, differential solvent effects or E2 competition. In addition, in evaluating experimental activation parameters, enthalpic variations may be manifested as entropic effects, especially when the Arrhenius equation is used to extrapolate activation parameters from rate constants obtained at high temperatures.

9.4.2 Measurable or Calculable Properties for Estimation of Reactivity In the course of my research, I identified compound properties that can be obtained that may be directly useful in characterizing the reactivity of halogenated compounds. The advantage of using these properties is that they can be employed in a quantitative manner.

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Also, since these values are directly correlated they do not rely on subjective estimates of the effects of structural features. Thus, the use of these properties allows for the consideration of structural features in SRRs to be subsumed by values that are directly relevant to the reaction of interest. VAEs are being obtained in the context of studying reductive processes, so the availability of these can be leveraged for the study of SN2 reactivity as well.

9.4.3 Use of Quantum Chemistry Values as Descriptors in QSAR analysis I have shown that values calculated using ab initio calculations can be useful descriptors in QSAR analysis. Govr was a particularly effective descriptors and served to initially order the substrates and allow for the isolation of other descriptors. I also found that LUMO descriptors could also be used as surrogate values for the VAEs discussed above.

9.4.4 Docking Frequency and Orientation I found that docking is an efficient and effective method that can be used to quickly screen compounds for their ability to interact with the enzyme active site. An important consideration is that judgment must be applied to develop appropriate biological criteria for evaluation of the docking results. Enzyme transformation is related to the frequency at which compounds dock successfully. This may be responsible for the accelerated rates of transformation for 12DCA and 12DBA in the active site. Compound shape is an important factor and was able to discriminate between compounds that could not fit or would not orient correctly. Long chain substrates and/or compounds with greater than 3 halogen substituents in any combination will not be good candidates for transformation by 2HAD. The outliers for the docking analysis were all false positives and could be attributed to favorable docking but unfavorable activation energy. I found that both productive binding ability and activation energy were necessary but not sufficient conditions for estimating enzyme reaction rates. By accounting for both, I was able to explain much of the observed experimental halide liberation rates and to develop an understanding of the influence of structural haloalkane features on HAD activity.

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9.4.5 Similarity of Substrate Range Among Nucleophiles

The order of reactivity with nucleophiles was OH->>HS->Cl->Acetate. Govr was most highly correlated for acetate and Cl-. Many of the general trends with respect to SRR do not change between nucleophiles, although further examination is warranted. The implications of this are that the SRRs I have developed may be generally applicable to evaluation of trends with other nucleophiles. This verifies that as a class, halogenated pollutants with similar structure behave in similar patterns.

9.5 Future Work Future work could involve the development of a more complete set of activation energies in solvent, perhaps with molecular dynamics (MD) simulations.

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