Thermodynamics in Large Hairpin Polyamide-DNA Interactions

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This Dissertation is brought to you for free and open access by the UMSL Graduate Works at IRL @ UMSL. It has been accepted for inclusion in Dissertations by an authorized administrator of IRL @ UMSL. For more information, please contact [email protected]. Thermodynamics in Large Hairpin Polyamide-DNA Interactions

A Dissertation

Yang Song

M.S., Chemistry, University of Missouri - St. Louis, 2016

B.S., Biochemistry, University at Buffalo, 2013

A Thesis Submitted to the Office of Graduate School at

The University of Missouri - St. Louis

In partial fulfillment of the requirements for the degree of

CHEMISTRY DOCTOR OF PHILOSOPHY

Feb 2018

Emphasis: Biochemistry

i Abstract Thermodynamics in Large Hairpin Polyamide-DNA Interactions

Yang Song, M.S., University of Missouri, St. Louis, MO, USA.

Chair of Committee: Dr. C. M. Dupureur

Human papillomavirus (HPV) is a common incurable sexually transmitted

virus responsible for most of cervical cancers. The naturally occurring linear

molecules distamycin (3 rings) and netropsin (2 rings) have previously been studied as inhibitors of HPV virus that act by binding into the minor groove of double helical DNA. Larger hairpin polyamides (PAs, 14-20 rings), which are inspired by these natural products, bind 10 base pairs of DNA and have been shown to be active against cancer-causing HPV strains in the test tube. Smaller hairpin PAs (8 rings) do not have such antiviral activity.

The interaction between PA and DNA is stabilized by two types of molecular forces: attractive and repulsive forces. Attractive forces include hydrogen bonds, van der Waals contacts and electrostatic forces between PA and DNA. Repulsive forces include the hydrophobic effect, which forces the PA out of the solvent into the DNA minor groove. These forces contribute to the enthalpy change (ΔH) and the entropy change (ΔS) differently. Thus, different thermodynamic signatures, that is, the contribution of ΔH and ΔS to the binding free energy (ΔG), are observed for different interactions. Analyzing how different

ii forces contribute to a PA-DNA interaction provides a way to characterize the

binding mode of that PA.

To understand this molecular interaction, van't Hoff analysis was

performed: binding affinities of different-sized PAs towards DNA were measured

using a fluorescence assay with different perturbations (temperatures, salt

concentration, etc.). We have observed that binding affinities of all PAs tested

(ranging from 6 to 20 rings) are not sensitive to salt, which indicates that electrostatic forces do not contribute significantly to the PA-DNA interactions.

Binding affinities at different temperatures were analyzed to determine which attractive or repulsive forces are dominating. The results indicate that the hydrophobic effect contributes more favorable ΔS to the total free binding energy of the large PA-DNA interactions. This contrasts with small PAs, the DNA binding of which, are dominated by hydrogen bonds.

Binding between small PAs and different DNA sequences have been observed to have different binding driving forces. This is validated by a PA of 14 rings. Different DNA sequence patterns have different minor groove widths and depths, which means they are varied in the degree of hydration in solution. This leads to differing contributions of hydrophobic effect in PA-DNA interactions.

These studies are conducive to understanding the mechanism of DNA minor groove binders. They could also provide valuable information for the optimization and development of new therapeutic PAs.

iii Dedication

To my parents: Nan Song and Jing Liu.

iv Acknowledgment After working on this dissertation for several months, now is the time to write this acknowledgment as the finishing touch. This five-year Ph.D. program has been an intensive learning period for me, not only in the science, but also on a personal level. I have learned so much during this period and I would like to thank people who have supported and helped me through this five years.

I would first like to thank my advisor Dr. Cynthia M. Dupureur, who has shown me, by her example, what a successful scientist should be. I learned a lot from her, especially on time management skills, which is very important in both scientific research and life in general. In addition, the way to think and carry out experiments logically and scientifically is another important thing she taught me.

Without her help, this dissertation would not have been possible. As my teacher and advisor, she has taught me more than I could ever give her credit for here.

I would also like to thank my committee members, Dr. James K. Bashkin,

Dr. Michael R. Nichols and Dr. Chung F. Wong, for their valuable guidance. They definitely provided me with the suggestions that I needed to choose the right direction and successfully complete my dissertation. My thankfulness also goes to Dr. Eike B. Bauer, who provided the materials and many valuable suggestions on the Fe catalyst project.

In addition, I would like to thank my lab mates and colleagues Jacquelyn

Niederschulte, Hyung Park, Howard Mayes, Kristin Bales and Helena Spikes.

Jacqui and I joined the lab together, and we fight together through this 5-year period. Thank you for all the help you have provided. Hyung, Howard, Kristin,

v and Helena have also contributed to this dissertation, and thank you for your

kindness. And I deep thankfulness goes to Dr. Elena Vasilieva who trained me in

many laboratory techniques that I used quite often in my research. My

gratefulness also goes to the lab mates of other labs for their support: Matthew

Queensen, Carlos Castaneda, Edith Fejer, Jose Scuderi, and Dr. Gaofei He.

My most gratitude to my parents for everything they did for me. Without

their suggestion and support, I would never choose to study abroad for my Ph.D. degree. And they are always patient to guide me when I am facing difficulties.

They are my heroes and I am always proud to be their son.

I want to give thanks to Department of Chemistry and Biochemistry of the

University of Missouri-St. Louis for the opportunity to complete my doctorate degree, ACS and NIH grants, and NanoVir for financial support. I want to give thanks to NanoVir for providing the material for my thesis.

vi Table of Contents Abstract ...... ii Dedication ...... iv Acknowledgment ...... v Table of Contents ...... vii List of Figures...... x List of Tables ...... xii List of Abbreviations ...... xiii Chapter 1 Introduction ...... 1 1.1 DNA Structure...... 1 1.2 DNA Binding Ligands...... 5 1.2.1 Intercalators...... 7 1.2.2 Groove Binders ...... 7 1.3 Development of Polyamides (PA) ...... 11 1.3.1 Introducing N-methylimidazole into PA...... 11 1.3.2 Hairpin PAs ...... 14 1.3.3 Other Building Blocks Introduced into PAs...... 17 1.3.4 Tail Modification ...... 20 1.4 Applications of PAs...... 20 1.4.1 Nucleosomal DNA Interacts with PAs...... 20 1.4.2 Inhibition or Upregulation of Gene Expression by PAs...... 22 1.5 Antiviral Applications of PAs ...... 25 1.5.1 Human Immunodeficiency Virus-1 (HIV-1) ...... 25 1.5.2 Herpes Simplex Virus (HSV) ...... 25 1.5.3 Human Papillomavirus (HPV)...... 26 1.6 Previous Biophysical Studies of Large PA-DNA Interactions...... 31 1.6.1 Larger PAs Do Not Always Follow Classic Binding Rules ...... 31 1.6.2 Improved Antiviral Activity of PAs with N-terminal Guanidinium Substitution...... 34 1.7 Thermodynamics of Binding Events ...... 34 1.7.1 Gibbs-Helmholtz Equation...... 34 1.7.2 ΔH and Related Binding Forces ...... 36 1.7.2.1 H Bonds ...... 38 1.7.2.2 The van der Waals (vdW) Contacts ...... 38

vii 1.7.2.3 How Forces Correlate with ΔH...... 38 1.7.3 How ΔS Contributes to ΔG...... 40 1.7.3.1 Configurational ΔS ...... 41 1.7.3.2 Desolvation ΔS and Hydrophobic Effect ...... 41 1.8 Entropy-enthalpy Compensation (EEC)...... 44 1.9 Overview of This Dissertation ...... 46 Chapter 2 Material Preparation and Methods...... 47 2.1 Materials ...... 47 2.1.1 DNA and Fluorescent Label ...... 47 2.1.2 PAs...... 47 2.2 Fluorescence Assay ...... 47 2.3 PA-DNA Binding Assay by Direct Titration...... 50 2.4 PA-DNA Binding Assay by Competition...... 52 2.5 Analyzing Thermodynamics of PA-DNA Interactions...... 52 2.5.1 Temperature Calibration on the Water Bath...... 52 2.5.2 Linear van’t Hoff (vH) Analysis ...... 55 2.5.3 Integrated Form of Gibbs Helmholtz equation (Non-linear Analysis).. 55 2.6 Salt Dependence of Binding Affinity...... 56 2.7 Calculation of Errors ...... 57 Chapter 3 PA-size-dependent Thermodynamics of Hairpin PA-DNA Interactions ...... 58 3.1 Introduction...... 58 3.2 Results...... 59 3.2.1 PAs and DNA Hairpins Used in This Study ...... 59 3.2.2 DNA Binding Affinities as a Function of PA Size ...... 61 3.2.3 Linear van’t Hoff Analysis: Different Sized Hairpin PAs have Distinct DNA Binding Thermodynamics ...... 63 3.2.4 Different DNA Sequences Result in Similar Energy Signatures for NV1028 Binding ...... 65 3.2.5 Potential Impacts of the Experimental Approach on Results...... 69

3.2.5.1 Competition Kds to Validate Direct Kd Measurements...... 69

3.2.5.2 Kd Comparison Between Competition and Direct Titration: KA1039, KA1002, and NV1028 ...... 69

3.2.6 Competition Kds of NV1042-DNA Interactions Could Not Be Obtained ...... 73 3.2.6.1 TAMRA-DNA Does Not Dissociate from NV1042 in Competition 73

viii 3.2.6.2 Exploring the Possibility of Dye Interference for NV1042-DNA Binding...... 74 3.3 Discussion ...... 74 3.3.1 Comparison of Linear and Non-Linear van’t Hoff Analysis of PA-DNA Interactions...... 74 3.3.2 Sequence Dependence of DNA Binding Thermodynamics of Small Groove Binders Netropsin, Dst. A, and Hoechst 33258 ...... 77 3.3.3 Sequence-Dependent Minor Groove Properties and Their Impact on Large PA-DNA Interactions ...... 82 3.3.3.1 Groove Widths Vary Depending on DNA Sequence Pattern...... 82 3.3.3.2 NV1028 Binds to DNA Depending on A/T Pattern ...... 84 3.3.4 Analyzing the PA Size Effect on ΔH/ΔS Compensation ...... 86 3.3.5 Comparing the Thermodynamics of DNA Intercalators and Groove Binders ...... 90

3.3.6 The Apparent Kds from Direct Titration are Macroscopic...... 93 3.3.7 Discrepancy Between ITC and van’t Hoff Analysis...... 95 Chapter 4 The Roles of Electrostatics in PA-DNA Interactions: Salt and pH- Dependent Binding Affinity of Hairpin PAs of Variable Sizes...... 103 4.1 Introduction...... 103 4.2 Models and Theories for General Electrostatic Interaction Analysis ...... 109 4.3 Results...... 111

4.3.1 SKobss in All Hairpin PA-DNA Interactions are Small...... 111 4.3.2 The PE Effect Contribution to the Total PA-DNA Binding Free Energy ...... 114

4.3.3 The Relationship between SKobs and the Number of Imidazoles in PAs ...... 117

4.3.4 Apparent pKa of DNA binding by KA1039...... 120 4.3.5 Charge of TAMRA at Assay pH...... 120 4.4 Discussion ...... 122

4.4.1 Small Salt dependence of Kd of Small Hairpin PA-DNA Binding ...... 122

4.4.2 Comparing PA-DNA ΔGpes to Other DNA Ligand Systems ...... 125 Chapter 5 Summary and Conclusions ...... 129 References ...... 130 Appendix A Reference of DNA Hairpins Used in This Study...... 145 Appendix B Spectroscopic Investigation and Direct Comparison of the Reactivities of Iron Pyridyl Oxidation Catalysts...... 146

ix List of Figures Figure 1.1 The two types of Watson-Crick base pairs...... 2 Figure 1.2 From left to right: A, B, and Z conformations of DNA...... 3 Figure 1.3 Crystal structure of CyHV-3 ORF112 Zα domain bound to Z-DNA...... 6 Figure 1.4 DNA binding of EtBr...... 8 Figure 1.5 Structure of drugs...... 10 Figure 1.6 Hydrogen network between a hairpin PA and DNA...... 12 Figure 1.7 Distamycin A antiviral activity...... 13 Figure 1.8 Benzimidazole-derivatives-containing PAs bind DNA...... 15 Figure 1.9 PA binding orientation...... 16 Figure 1.10 Different structures of PA units...... 18 Figure 1.11 Structure of nucleosome...... 21 Figure 1.12 PAs are used to modify transcription mechanisms...... 23 Figure 1.13 HPV genome map and life cycle...... 27 Figure 1.14 Properties of NV1028 and NV1042...... 29 Figure 1.15 Overlay of the binding sites of NV1028 (blue) and NV1042 (red) in LCR of HPV16 using AC data...... 33 Figure 1.16 Structure of TMG and Guan...... 35 Figure 1.17 Various sources of ΔH and ΔS. Arrow scale does not correlate with the degree of contribution of the sources to ΔG. The vdW contact picture is adapted from http://cdn.zmescience.com...... 37 Figure 1.18 Diagram of the vdW contacts...... 39 Figure 1.19 Translational (a) and rotational (b) freedom of an atom...... 42 Figure 1.20 The x-ray structure of the drug Hoechst 33258 with the dodecamer duplex DNA...... 43 Figure 1.21 Thermodynamic profile of benzamidines...... 45 Figure 2.1 Example of TAMRA-DNA hairpin design...... 49 Figure 2.2 An isotherm example of a direct titration...... 51 Figure 2.3 An isotherm example of the competition experiment...... 53 Figure 3.1 Thermodynamic data comparison of PAs in varied sizes...... 64 Figure 3.2 The Sequence-dependent Thermodynamics of NV1028...... 67 Figure 3.3 Comparison of competition Kds and direct Kds of KA1039 and KA1002...... 71 Figure 3.4 Comparison of competition and direct Kds of NV1028 with different DNA sequences...... 72 Figure 3.5 Competition kinetics of NV1042 with ODN-20-T3...... 75 Figure 3.6 Kinetics of NV1042 competition with 3 nM ODN-20-T16 showed no recovery...... 76 Figure 3.7 Comparison of linear and non-linear fits to the PA-DNA binding data...... 78 Figure 3.8 Comparison of linear and non-linear fits to the PA-DNA binding data...... 79 Figure 3.9 Structure of Hoechst 33258 and its DNA binding site...... 83 Figure 3.10 DNA minor groove width prediction of ODN-14-SFTT ...... 85 Figure 3.11 DNA minor groove width prediction of ODN-14-SFAT...... 85 Figure 3.12 Different degrees of hydration in different DNA sequence patterns. 87

x Figure 3.13 Comparison of the DNA binding energetics of Net., Dst. A, KA1039, KA1002, NV1028 and NV1042 at 25°C...... 88 Figure 3.14 Linear trends of energy terms of PAs in varied sizes...... 89 Figure 3.15 Comparison plot for thermodynamics of drug-DNA bindings...... 92 Figure 3.16 One example of the isostructural molecules that can be converted from intercalator to groove binder...... 94 Figure 4.1 Properties of charge-charge interactions...... 104 Figure 4.2 Structures of distamycin, netropsin, and Hoechst 32258 at pH 7.4. 106 Figure 4.3 One example of hairpin PA-DNA complex structure...... 107 Figure 4.4 Salt Dependence of equilibrium binding constants on NaCl concentration for KA1039 (black circle), KA1002 (blue triangle), NV1028 (green diamond) and NV1042 (red square) at 25°C...... 112 Figure 4.5 ΔG partition of PAs indifferent sizes...... 116 Figure 4.6 Correlation between SKobs and the number of imidazoles...... 118 Figure 4.7 pH dependence of KA1039 binding to its cognate DNA...... 121 Figure 4.8 The pKa prediction of TAMRA at pH 7.4 by MarvinSketch...... 123 Figure 4.9 Dependency of equilibrium binding constants on NaCl concentration for PAs...... 124 Figure 4.10 Structures of some DNA ligands other than PAs...... 127

xi List of Tables

Table 1.1 IC50 of NV1028, NV1042, and two NV1028 variants against 3 high-risk HPV episomes maintained in cells...... 30 Table 2.1 Extinction coefficients of PAs used in this study...... 48 Table 2.2 Sample Temperature Calibration on the Water Bath of the Fluorometer...... 54 Table 3.1 PAs and DNA hairpins used in this study...... 60 Table 3.2 Comparison of direct Kds of PA-DNA interactions using different methods at 25°C...... 62 Table 3.3 Comparison of the energies in different PA-DNA interactions at 25°C...... 66 Table 3.4 Comparison of sequence dependent ΔH/ΔS compensation of Netropsin (Net.), Dst. A and NV1028 at 25°C...... 68 Table 3.5 Comparison of competition and direct Kds of different PA-DNA interactions at 25°C by fluorescence assay...... 70 Table 3.6 Comparison of energies of PA-DNA direct binding by different fitting. 80 Table 3.7 Netropsin/Dst. A/Hoechst/NV1028 binding to DNA at 25°C...... 81 Table 3.8 Thermodynamic data for drug-DNA interactions at 25°C...... 91 Table 3.9 Thermodynamic parameters for small PA-DNA interactions by ITC at 25°C...... 96 Table 3.10 Comparison between calorimetry and van’t Hoff analysis at 25°C. .. 98 Table 3.11 Summary of ITC results on CAII-CBS complex formation from 14 labs...... 102 Table 4.1 SKobs of different sized PAs...... 113 Table 4.2 Summary of the Gibbs free energy dissection of different sized PA- DNA interactions...... 115 Table 4.3 ΔGpes of DNA ligands other than PAs...... 126

xii List of Abbreviations A Adenine AC Affinity cleavage ATM Ataxia telangiectasia mutated ATR Ataxia- and Rad-related bp Base pair C Cytosine CA II bovine carbonic anhydrase II CBS 4-carboxybenzenesulfonamide CD Circular dichroism CE Capillary electrophoresis DDR DNA damage response ΔG The Gibbs free energy ΔH Enthalpy change ΔS Entropy change Dp 3-(dimethylamino) propylamine DSB Double strand breaks Dst. A Distamycin A EEC Entropy-enthalpy Compensation EtBr Ethidium bromide Fr Furan G Guanine HCMV human cytomegalovirus HDAC histone deacetylase Hp N-methyl-3-hydroxypyrrole HPV Human Papillomavirus HSV Herpes Simplex Virus Ht 3-hydroxythiophene Hz Hydroxybenzimidazole

xiii IDT Integrated DNA Technologies Im N-methylimidazole Ip imidazopyridine ITC Isothermal titration calorimetry JGB Janus green blue LCR Long control region LFTT Long flank TT LTR Long terminal repeat MD Molecular dynamics MG Malachite green MIEP Major immediate-early promoter NCP Nucleosome core particles Net. Netropsin ORF Open reading frames PA Polyamide PB Poisson-Boltzmann PNA Peptide nucleic acids Pol II RNA polymerase II PKZ Z-DNA-dependent protein kinase Py N-methylpyrrole Pz N-methylpyrazole SAR Structure-activity relationship SFAT Short flank AT SFTT Short flank TT siRNA Small interfering RNA SSB Single-strand binding ssDNA Single-stranded DNA T Thymine Ta 3,3’ -diamino-N-methyldipropylamine

xiv TFs Transcription factors TFO Triplex-forming oligonucleotides Th 4-methylthiazole TMG Tetra-methyl guanidinium Tn 3-methylthiophene vdW Van der Waals vH van’t Hoff

xv Chapter 1 Introduction 1.1 DNA Structure DNA (deoxyribonucleic acid) is the molecule that stores genetic

information for growth, development, functioning, and reproduction. It is a long

nucleotide polymer. As shown in Figure 1.1, one nucleotide contains a

phosphate group, a 5-carbon sugar (deoxyribose) and one of the four types of

nitrogen-containing nucleobases: adenine (A), thymine (T), cytosine (C) and

guanine (G). In the 1950s, the 3D structure of DNA was analyzed by x-ray

diffraction for the first time (Franklin & Gosling, 1953). The results indicated the

structure to be a double-stranded helix.

It has been subsequently found that the double-stranded helix structure

has several conformations: A-DNA, B-DNA, and Z-DNA (Alberts, 2002; Franklin

& Gosling, 1953). They differ significantly in geometry and dimensions. These

different conformations depend on the environments and conditions such as pH,

DNA sequence, hydration, supercoiling etc. A-DNA (Figure 1.2 left) is a right-

handed double helix. It has deep, narrow major grooves and wide, shallow minor

grooves. The diameter is 23 Å. B-DNA (Figure 1.2 middle) form is most

common under the physiological conditions. It is a right-handed helix with wide,

shallow major grooves and narrow, deep minor grooves (Alberts, 2002), and has

a diameter of 20 Å. Z-DNA (Figure 1.2 right) is a left-handed double helix with a zig-zag pattern, which is different from other two. The diameter is 18 Å.

1 Figure 1.1 The two types of Watson-Crick base pairs. The red line indicates the minor groove, the blue line indicates the major groove. Edited from http://knowgenetics.org/nucleotides-and-bases/ (open source).

2 A-DNA B-DNA Z-DNA

Figure 1.2 From left to right: A, B, and Z conformations of DNA. From https://en.wikipedia.org/wiki/A-DNA (open source).

3 Within the DNA chain (strand), phosphodiester bonds link nucleotides

together, between the 3’ carbon atom of the deoxyribose of the previous

nucleotide and the 5’ phosphate group of the next nucleotide (Figure 1.1).

To hold two complementary DNA strands together in a double helical structure, one nucleic acid on a DNA strand is paired with another on the complementary strand through hydrogen bonds (Alberts, 2002). This binding is

structurally specific: A is always paired with T; C is always paired with G (Figure

1.1). One such unit is called a Watson-Crick base pair (bp).

DNA interacts with many other biological molecules such as proteins and

lipids. These interactions could be categorized into two families: shape readout

and base readout (Rohs et al., 2010). Shape readout can be subdivided into local

shape readout and global shape readout. Local shape readout refers to

recognition of DNA conformation, mainly the DNA groove and DNA kinks. As

shown in Figure 1.1, all types of nucleic acids have various hydrogen donors and acceptors on their nucleobases. These H bond donors and acceptors could be used as anchors for other molecules to bind (Rohs et al., 2010). Global shape

readout refers to distinction among different DNA forms (A-DNA, B-DNA, and Z-

DNA). Ligands distinguish their binding sites based on the groove size. For

example, A-DNA and B-DNA have distinct major and minor groove features. This

is due to the C3’-endo sugars of A-DNA being more accessible than the C2’-

endo sugars of B-DNA. Thus, nonpolar amino acid residues in a protein such as

alanine and leucine can interact with A-DNA. In Z-DNA, the zig-zag phosphate left-handed structure can be recognized by proteins such as Z-DNA-dependent 4 protein kinase (PKZ) (Figure 1.3). It has been shown that different non-coding

DNA sequences can have similar local structures (Parker et al., 2009). Thus,

both shape and base readout should be considered to design a DNA binding

ligand. And shape readout mode could be more important than base readout

mode.

Base readout can be further divided into major groove readout and minor

groove readout (Rohs et al., 2010). Hydrogen bonding and shape recognition is

the main mechanism of the binding site recognition by ligands. Since the major

groove has more hydrogen bond donors and acceptors, hydrogen bonding

contributes more to the binding specificity in major groove binding events. In

minor grooves, due to fewer hydrogen binding donors and acceptors, many

ligands cannot distinguish the T:A base pair from A:T, or the G:C base pair from

C:G.

Binding specificity does not only depend on the number of H-bond donors

and acceptors, but also on the hydrogen bonding geometry. Ligands bind their

target specifically as different DNA sequences have different hydrogen bond patterns. Water can also form hydrogen bonds within the grooves. Thus, it is possible to mediate the hydrogen bonding between ligands and DNA by changing the degree of hydration (Rohs et al., 2010).

1.2 DNA Binding Ligands As was mentioned before, there are several DNA binding mechanisms,

5 Figure 1.3 Crystal structure of CyHV-3 ORF112 Zα domain bound to Z-DNA Reprinted with permission from (Kus et al., 2015). Copyright (2015) The American Society for Biochemistry and Molecular Biology.

6 and based on these, ligands are categorized into different families, including intercalators and groove binders.

1.2.1 Intercalators Intercalators refer to the ligands that insert between planar bases of DNA.

Famous examples are ethidium bromide (EtBr), berberine, proflavine, and doxorubicin (Figure 1.4). They are applied in chemotherapeutic treatments to inhibit the DNA replication in cancer cells (Sambrook & Russell, 2001). Since intercalators insert between base pairs, the DNA molecule must unwind and extend to open the space for intercalators (Figure 1.4b). This conformational change in DNA is dependent on the size and shape of intercalators. Therefore, intercalators bind DNA and inhibit the replication, transcription, and DNA repair processes. This mechanism also increases the possibility of mutagenesis. It also makes most intercalators carcinogenic as functional changes are induced by the

DNA structural modification due to binding of intercalators.

1.2.2 Groove Binders Groove binders can be divided into two categories: major groove binders and minor groove binders. Due to the large dimensions of the major groove, large proteins are common major groove binders. But small DNA binders such as triplex-forming oligonucleotides (TFO) (Jain & Bhattacharya, 2010), peptide nucleic acids (PNA) (Nielsen, 1999), pluramycins, aflatoxins, and azinomycins

(Hamilton & Arya, 2012) are also reported to bind in the major groove. The groove binders interact with DNA non-covalently. Previous studies showed that

7 Figure 1.4 DNA binding of EtBr. (a) Structure of EtBr. (b) Intercalation of EtBr into a DNA helix. This is adapted with permission from Wikipedia (open source). Structures of (c) berberine, (d) proflavine, and (e) doxorubicin.

8 some can also act as intercalators (Hamilton & Arya, 2012). Groove binders

could also form covalent bonds with DNA, such as aminoglycosides. They are

developed as leads and major groove binding probes since they inhibit DNA

replication and transcription as alkylating agents. The electrophilic groups of

aminoglycosides react with the nucleophilic N7 positions on purine bases

(Hamilton & Arya, 2012).

Minor groove binders are generally smaller than those large protein major

groove binders, so they fit the narrow and deep minor groove on DNA. But their

structures and shapes are quite diverse, from a simple crescent shape such as

netropsin and distamycin A (Dst. A), to complicated structures such as

trabectedin (Figure 1.5a, b) (Barrett et al., 2013). The minor groove binders

interact with DNA through two types of forces: covalent or non-covalent. One

example that forms covalent bonds with DNA is CC1065 (Figure 1.5a), which belongs to duocarmycin family (Barrett et al., 2013). Anthramycin also forms

covalent bonds with DNA. It has a carbon-nitrogen double bond on the diazepine

ring, which reacts with the amino group of the base (Barrett et al., 2013).

Besides these crescent-shaped molecules, trabectedin (Figure 1.5) is an

example of a complicated minor groove binder. It is a natural product that has

been used to treat soft tissue sarcoma (D'Incalci & Galmarini, 2010). Its activity

depends on these specific bonds and substituents. Due to its synthetic

complexity, there are only a few systemic medicinal studies of it. Some other

9 a)

Distamycin A Netropsin

Trabectidin CC1065

DAPI Pentamidine

anthramycin

Figure 1.5 Structure of drugs. a) The structures of distamycin, and netropsin, trabectedin, CC1065, DAPI, pentamidine, and anthramycin (Barrett et al., 2013). b) The crystal structure of d(GGCCAATTGG) Complexed with Dst. A (PBD: 1K2Z) (Uytterhoeven et al., 2002).

10 synthetic compounds such as DAPI and pentamidine (Figure 1.5) are also well studied. But the cytotoxicity of this class of compounds (Barrett et al., 2013)

makes them difficult to apply in drug design.

1.3 Development of Polyamides (PA) 1.3.1 Introducing N-methylimidazole into PA PAs are inspired by the natural products netropsin and Dst. A both of

which are small A/T-sequence-preferring DNA minor groove binders. The simple

crescent shape of PAs, such as netropsin and Dst. A makes them very popular among scientists to synthesize and analyze. They consist of N-methylpyrroles

(Py) (Figure 1.5a). The amino group of Py forms a hydrogen bond with adenine

(A), thymine (T) or cytosine (C) (Figure 1.6). Both netropsin and Dst. A have relatively low binding affinity (~1 to 30 nM) and high cell toxicity (Figure 1.7)

(Bialer et al., 1979; Chaires, 1997; Zimmer et al., 1971).

N-methylimidazole (Im)-containing PAs were developed later (Kopka et al.,

1985). Im acts as an anchor of G by forming a hydrogen bond between N3 of Im

and exocyclic NH2 of G (Figure 1.6) (Swalley et al., 1996).

In early 1990s, an important study showed that a 3-ring PA (ImPyPy)

recognized the 5’-WGWCW-3’ (W=A or T) double-stranded sequence instead of

the 5’-WGWWW-3’ double-stranded sequence, which was expected to be 1:1

stoichiometry (Wade et al., 1992). Later on, this PA-DNA complex formation was

verified by NMR to be 2:1 stoichiometry (Mrksich et al., 1992). The NMR data

showed that two PAs are antiparallely stacked in the minor groove, and the Im-

11 Figure 1.6 Hydrogen network between a hairpin PA and DNA. Reprinted with permission from (Dervan & Edelson, 2003). Copyright (2003) Elsevier.

12 Figure 1.7 Distamycin A antiviral activity. Adapted with permission from (Edwards et al., 2011). Copyright (2011) Elsevier.

13 Py pair distinguished G-C base pair from a C-G base pair. The binding properties of this Im-Py pair in the dimeric form of this 3-ring PA (ImPyPy) were explored by a series of studies, such as looking at the binding with different sequence contexts and using crystallography to analyze the hydrogen bond network between PA and DNA sequences to show how Im recognize the exocyclic amine

of G (Kielkopf et al., 1998a).

1.3.2 Hairpin PAs Hairpin PAs were developed to further enhance the binding affinity

(Edwards et al., 2011; Mrksich et al., 1994). γ-aminobutyric acid is used to link

the head of a PA to the tail of another (Figure 1.6 and Figure 1.8). One hairpin

PA has been shown to bind DNA 100-fold more tightly than linear monomers

(Mrksich et al., 1994). An NMR structure of a hairpin PA-DNA complex also

confirmed that the γ-turn maintained the alignment of the paired rings. Thus, the

hairpin PA would not move along the minor groove (deClairac et al., 1997).

It has since been observed that some hairpin PA-DNA complexes have

two binding modes: forward and reverse (Figure 1.9) (Urbach & Dervan, 2001;

White et al., 1997). Forward orientation refers to N terminus of PA aligning with

the 5’ end of the DNA strand, and C terminus of PA aligning with the 3’ end of the

DNA strand. In the reverse orientation, the C terminus of PA aligns with the 5’

end of the DNA strand, and N terminus of PA aligns with the 3’ end of the DNA

strand. To make these small PAs (6 rings) prefer the forward orientation, an

amino group is added to the α carbon of the γ-turn (Herman et al., 1998). This

14 Figure 1.8 Benzimidazole-derivatives-containing PAs bind DNA. Py-Hz at left, Py-Ip at right. Adapted with permission from (Dervan & Edelson, 2003). Copyright (2003) Elsevier.

15 Figure 1.9 PA binding orientation. Open circle refers to imidazole, closed circle refers to pyrrole, diamond refers to β-alanine, positive sign refers to Dp tail, f refers to forward orientation, r refers to reverse orientation. Reprinted with permission from (Vasilieva et al., 2016). Copyright (2016) Elsevier.

16 amino group causes steric clash at the minor groove floor and only forward binding would be favored. Furthermore, the binding affinity is enhanced 13-fold. It

is thought due to the electrostatic interactions between amino groups and

phosphate groups on the DNA backbone.

1.3.3 Other Building Blocks Introduced into PAs To increase the binding specificity of PA, N-methyl-3-hydroxypyrrole (Hp)

was introduced to distinguish the T-A base pair from A-T, G-C, or C-G due to the unique positions of T-A base pair in the minor groove (Figure 1.6) (White et al.,

1998). This was verified by crystallography (Kielkopf et al., 2000; Kielkopf et al.,

1998b).

Since Hp was observed to degrade over time in acidic or free radical-rich environment, a more resistant heterocycle was needed to distinguish between T-

A and A-T base pair (Marques et al., 2002). But for a five-ring PA system,

molecular dynamics (MD) studies showed that even relatively small changes on

one of the rings would cause big changes in the crescent molecular shape and

the electronic properties. Heterocycles such as N-methylpyrazole (Pz), 3- methylthiophene (Tn), 4-methylthiazole (Th), furan (Fr) and 3-hydroxythiophene

(Ht) (Figure 1.10) have been shown to decrease the binding affinity between PA

and DNA, although some of them displayed selectivity for A, T bases (Marques

et al., 2002).

Benzimidazole received attention for its ability to bind the DNA minor

17 Dp Ta β-alanine N,N-dimethylamino, propylamine N-methyldipropylamine

Figure 1.10 Different structures of PA units. Reprinted with permission from (Dervan & Edelson, 2003). Copyright (2003) Elsevier.

18 groove (Seitz, 2003). This residue has a different structure from a 5-membered

ring and can increase the degree of curvature, which is complementary to DNA

minor groove, of PAs (Figure 1.8). Hoechst dyes (containing benzimidazoles)

and their derivatives have been incorporated into PAs.

Hydroxybenzimidazole (Hz) and imidazopyridine (Ip) were also introduced

to PAs (Renneberg & Dervan, 2003). DNase I footprinting shows that Py-Hz pair

distinguishes an A-T base pair from other Watson-Crick base pairs; Py-Ip pair

distinguishes C-G base pair from other W-C base pairs. In addition, Hz is

chemically resistant to free radicals and acids, which makes it a good

replacement of Hp in some synthetic PAs (Renneberg & Dervan, 2003).

Another modification was to replace Py or Im with β-alanine to improve

binding specificity (Figure 1.10). PAs are naturally crescent shaped. When there

are more than five consecutive rings, it bends too much and does not fit to the

minor groove anymore (Kielkopf et al., 1998a). Thus, the more flexible β-alanine

was used to reduce the curvature of PAs and better fit into the minor groove. It

has been shown that by replacing two Py/Py pairs in a 12-ring hairpin PA with

two Py/β-ala pairs, the binding affinity is enhanced by 10-fold, and the sequence

specificity is enhanced by ≥48 folds (perfect matched sequence vs. single

mismatched sequence) (Turner et al., 1998). Their data also indicates that a β/β

pairs should not be side-by-side, which reduces the binding affinity between PA

and DNA, make the binding thermodynamically unfavorable (Turner et al., 1998).

But there is also evidence showing that the position of a single β-alanine may

have different effects on the DNA binding affinity and sequence specificity (Wang 19 et al., 2012). It was shown that in some PAs, β-ala groups provide more flexibility

comparing to Py groups. This slows the association process between the PA and

the DNA, resulting in a weaker binding affinity (Wang et al., 2012).

1.3.4 Tail Modification The last modification in hairpin PAs discussed here is to add 3-

(dimethylamino) propylamine (Dp, +1 charge) or 3,3’ -diamino-N-

methyldipropylamine (Ta, +2 charge) as the tail of hairpin (Figure 1.10). This is

also considered to enhance the electrostatic interaction between PA and

negatively charged DNA backbone and to reduce the PA aggregation (Wang et

al., 2014)

1.4 Applications of PAs Since PAs bind DNA tightly and compete with many DNA-binding proteins,

they have a great potential in many application areas such as inhibition of gene

expression, and gene activation.

1.4.1 Nucleosomal DNA Interacts with PAs The nucleosome is the repeat unit of chromatin. The structure of the nucleosome is a segment of DNA (~1.7 turns of a left-handed superhelix) wound around eight histone proteins (Figure 1.11). Proteins are restricted when they bind to nucleosomal DNA due to their large sizes, but PAs are not. One study showed that six hairpin PAs bound tightly into the targeted nucleosomal DNA minor groove (up to 14 bp) facing away from the histone octamer (Gottesfeld et

al., 2001). This means that most of the minor groove in the nucleosome core

20 Figure 1.11 Structure of nucleosome Reprinted with permission from (Kim, 2014). Copyright (2014) Brain Tumor Research and Treatment.

21 particle is accessible for ligands. There are two exceptional sites with poor binding affinity: sites near N-terminal of histone H3 and H4. Removal of either of the terminal tails of nucleosome would allow PA to bind. This indicates the importance of the DNA structure and histone dynamic position in PA-nucleosome binding.

The X-ray crystallography was used to analyze the structure of PA- nucleosome complexes (Suto et al., 2003). No structural change has been seen

on the histone octamer, but strong distortions in DNA twist at the PA binding sites

and adjacent regions were observed. Since PA-nucleosome interaction is

sequence specific, PA can be a powerful tool to analyze the accessibility of DNA

in a nucleosome. This also can help with understanding the transcription

machinery of highly compacted nucleosomal DNA.

1.4.2 Inhibition or Upregulation of Gene Expression by PAs Since PAs can displace proteins (zinc fingers, transcription factors, and

activators etc.) in the DNA minor groove, many studies have applied PAs to

modify gene expression (Figure 1.12). PAs up-regulate gene expression mainly

by two approaches: one is to de-repress transcription factors; the other one is to

recruit transcription factors. For example, one study utilized hairpin PAs to

displace the repressor IE86 to DNA (Dickinson et al., 1999). Thus, transcription

of the human cytomegalovirus (HCMV) major immediate-early promoter (MIEP)

is upregulated.

22 Figure 1.12 PAs are used to modify transcription mechanisms. Upper: down-regulation; lower: up-regulation; TBP: TATA binding protein; TAFs: TBP-associated factors; RNA polymerase II (Pol II) and general transcription factors (TFs) are in yellow; AD: activation domain. Reprinted with permission from (Dervan & Edelson, 2003). Copyright (2003) Elsevier.

23 PAs can also up-regulate gene expression by enhancing recruitment of transcription factors (TFs). They are covalently linked to activation domains to enhance recruitment of transcription factors and mediators (lower pathway in

Figure 1.12) (Ansari & Mapp, 2002). These artificial TFs have been synthesized

and analyzed in in-vitro assays (Ansari et al., 2001; Arora et al., 2002; Mapp et

al., 2000). One example uses a hairpin PA linked to the short peptide activation

domain AH to activate an artificial transcription factor with a size of 4.2 kDa

(Mapp et al., 2000). The linker length has also been shown to affect activation

efficiency of the artificial transcription factor (Ansari et al., 2001; Arora et al.,

2002).

Down regulation of gene expression has also been observed for PAs. One

study utilized the transcription factor TFIIIA as a target to downregulate its target

genes since TFIIIA regulates relatively fewer genes and the interactions between

its zinc-finger domains and the DNA minor groove have been well studied. It has been shown that a hairpin PA (ImPyPyPy-γ-PyPyPyPy-β-Dp) suppressed transcription of 5S RNA genes by RNA polymerase III when it is bound to the binding site of TFIIIA, both in vitro and in cultured cells (Gottesfeld et al., 1997).

And another study took advantage of this model to elucidate the important minor

groove contacts between the target gene sequence and the binding domain of

TFIIIA with recombinant mutants of TFIIIA (Neely et al., 1997).

24 1.5 Antiviral Applications of PAs 1.5.1 Human Immunodeficiency Virus-1 (HIV-1) One example of an antiviral application of PAs is the case of HIV-1 long terminal repeat (LTR). A human protein named LSF binds to the promotor region of LTR and recruits transcriptional repressor protein YY1. Then YY1 recruits the histone deacetylases (HDACs). HDACs would allow histones to wrap the DNA more tightly and stop the transcription. LTR has been shown to relate to HIV latency as it stocks a certain amount of silent pathogen in human bodies. These

PAs have been shown to effectively block LSF-DNA binding and increase promoter expression (Coull et al., 2002). This could be combined with other HIV-

1 treatments to fully eliminate the HIV-1.

1.5.2 Herpes Simplex Virus (HSV) HSV infection causes genital herpes and cold sores. There are two types

of HSV (HSV-1 and HSV-2). HSV-1 normally is not associated with genital area

infection, while HSV-2 is commonly associated with sexually transmitted

diseases (Looker & Garnett, 2005). Currently, there is no licensed vaccine

against either HSV-1 or HSV-2 (Rajčáni et al., 2018). Thus, it is important to

develop anti-HSV drugs.

As mentioned above, Dst. A has a high cytotoxicity (Figure 1.7) (Bialer et

al., 1979; Chaires, 1997; Zimmer et al., 1971). But with aromatic addition

(convert into tetra- or pentapeptide), the activity against HSV is increased while

the cytotoxicity is reduced (Bialer et al., 1979). This indicates that cytotoxicity and

antiviral activity of Dst. A are independent of each other. Finally, it has been

25 shown that by adding a m- or o-aminobenzamidine, the anti-HSV activity is preserved without cytotoxicity (Bialer et al., 1979).

1.5.3 Human Papillomavirus (HPV) HPV is the most common sexually transmitted disease (Munoz et al., 2003;

zur Hausen, 2002). The HPV vaccines only protect against a small number of

HPV subtypes (Schiffman & Wacholder, 2009). Normally HPV infections cause

no symptoms and are cleared by the host immune system. However, sometimes

the virus becomes persistent and result in warts or precancerous lesions

(Ljubojevic & Skerlev, 2014). In the United States, about 27000 patients were

diagnosed with a cancer caused by HPV infection each year (CDC, 2015).

The most common high risk types are HPV 16 and 18. They are

responsible for about 70% of squamous cell carcinoma (Kurg, 2011). There have

also been some studies shown that there is a correlation between higher HPV

infection rate and patients with testicular cancer, even though it is not strong

enough to prove that HPV infection is the cause of testicular cancer (Bertazzoni

et al., 2013; Garolla et al., 2012).

The HPV viral genome is a double-stranded circular DNA that has six

early (E1, E2, E4, E5, E6 and E7) open reading frames (ORFs), two late (L1 and

L2) ORFs and an A/T rich non-coding long control region (LCR) (Figure 1.13)

(Longworth & Laimins, 2004). The E1 protein expressed from the E1 region

recruits the host’s replication factors and initiates viral DNA replication by binding

26 Figure 1.13 HPV genome map and life cycle. Reprinted with permission from (Lazarczyk et al., 2009). Copyright (2009) American Society for Microbiology.

27 to the LCR. The E1 proteins also form complexes with the E2 proteins to enhance its binding affinity to the binding sites on the LCR. (Melendy et al., 1995).

If we can stop the E1:E2 complex from binding to the LCR, viral DNA replication

can be prevented. Since the LCR is A/T rich, DNA binders (such as PAs) are

considered potential drug leads to prevent HPV replication.

Hairpin PAs NV1028 (14 rings) and NV1042 (20 rings) (Figure 1.14 and

Table 1.1) have been found to be antiviral active against three high-risk HPV

viruses (HPV 16, HPV 18, and HPV 31) in human keratinocytes with low

cytotoxicity (Edwards et al., 2011). All three strains of HPV have similar A/T-rich

E1 and/or E2 regions. NV1028 and NV1042 prefer A/T-rich binding sequences:

predicted binding site of NV1028 is W2GW7 (W refers to A or T); that of NV1042

is W2GW5GW4. The authors attributed the low cytotoxicity, high binding affinity,

and high sequence specificity to their larger size when compared to Dst. A and

netropsin. The authors have observed successful PA uptake by analyzing the

location of inherent fluorescence of PAs (Edwards et al., 2011). A previous study

also showed that a 24-ring PA could be taken up by MCF-7 and HEK293 cells,

and the PA size is not an important factor of cell uptake (Nishijima et al., 2010).

The two PAs mentioned above are smaller than this 24-ring PA. But their uptake

in vivo is still not well understood.

DNA damage response (DDR) is the defense mechanism in the host cells

against foreign DNA. The ataxia telangiectasia mutated (ATM) and Ataxia- and

28 b

Binding site: W2GW7

NV1042 Binding site: W2GW5GW4

Figure 1.14 Properties of NV1028 and NV1042. (a) Cytotoxicity of NV1028 (Edwards et al., 2011). Reprinted with permission from (Edwards et al., 2011). Copyright (2011) Elsevier. (b) Structure of two large PAs. W refers to A or T. Reprinted with permission from (Vasilieva et al., 2016). Copyright (2016) Elsevier.

29 Table 1.1 IC50 of NV1028, NV1042, and two NV1028 variants against 3 high- risk HPV episomes maintained in cells.a

HPV16 HPV18 HPV31 0.100 ± 0.02 0.657 ± 0.16 0.108 ± 0.02 NV1028 (n=4) (n=3) (n=4) 0.036 ± 0.0004 0.056 ± 0.01 0.030 ± 0.001 NV1042 (n=3) (n=6) (n=3) 0.30 0.20 0.13 NV1028-TMGb (n=4) (n=3) (n=4) 0.10 0.20 0.14 NV1028-Guanc (n=3) (n=3) (n=3) a. IC50s are in µM. Data of NV1028 and NV1042 is reproduced with permission from (Edwards et al., 2011). Copyright (2011) Elsevier.; Data of NV1028 variants is reproduced with permission from (Castaneda et al., 2016). Copyright (2016) The Royal Society of Chemistry. b. TMG: tetra-methyl guanidinium. c. Guan: N-terminal guanidinium.

30 Rad-related (ATR) kinases are two key sensors for DNA damage recognition.

ATM senses double strand breaks (DSBs), while ATR recognizes persistent single-stranded DNA (ssDNA) (Harper & Elledge, 2007). Either of the two large

protein kinases is required for all DNA damage checkpoint activation (Harper &

Elledge, 2007). As viral DNA is recognized as damaged DNA, it triggers DDR.

But it has been found that both HPV and HSV promote an active DDR in infected

cells during the amplification phase of the viral life cycle (Edwards et al., 2018;

Kadaja et al., 2007; Moody & Laimins, 2009). It is claimed that the DDR resolves

single-stranded DNA and DSBs to promote healthy viral DNA replication (Bristol

et al., 2017). The relationship between NV1042 and DDR in HPV 16 and HPV 31

have been examined before (Edwards et al., 2013b). It is shown that the

conformation of HPV episomes, including DNA strand twisting, unwinding dsDNA

to ssDNA, and DSBs, is altered by NV1042 binding. These altered conformations activate DDR, and finally lead to the elimination of HPV episomes (Edwards et al.,

2013b). There is also evidence shown that HPV episomal DNA is sensitive to

knockdown of ATR and ATM by small interfering RNA (siRNA) (Edwards et al.,

2013a).

1.6 Previous Biophysical Studies of Large PA-DNA Interactions 1.6.1 Larger PAs Do Not Always Follow Classic Binding Rules Dervan’s group has concluded that small synthetic PAs recognize DNA

binding sites in a specific way: the pair of Im/Py recognizes G·C, Py/Im recognizes C·G, and Py/Py does not distinguish between T·A and A·T (Figure

1.6) (Dervan, 2001). A series of perfect, single-mismatched, and double-

31 mismatched predict sites were analyzed in the 2150-2672 region of the HPV16

LCR sequences (He et al., 2014). The DNA binding affinities of NV1028 and

NV1042 (Figure 1.14) were analyzed by DNase I footprinting and affinity cleavage (AC). Capillary electrophoresis (CE) was used to analyze different binding fragments of HPV16 LCR sequences. Perfect and single-mismatched binding sites have similar binding affinities (Kds) in the low nanomolar range (~2

nM), which indicates that NV1028 does not distinguish the perfect-matched sites

from the single-mismatched sites. The double-mismatched sites have Kds

between 12 to 62 nM. It is rationalized that losing a few hydrogen bonds between

PA and DNA does not affect the binding affinity due to the large number of

hydrogen bonds between the large PA and DNA. Therefore, the binding rules

developed from small PAs are not completely suitable for large PAs NV1028.

The binding behavior of NV1042 toward the LCR of HPV16 genome

(7348-122 bp) was also analyzed and compared to that of NV1028 (Vasilieva et

al., 2016). It was shown that NV1028 tolerates up to quadruple mismatch sites

with good binding affinities (~10 nM). The NV1028 binding sequences cover

about 1/2 of the total LCR, which is much larger than what is predicted by

Dervan’s PA recognition rules. It was concluded that the W2GW5GW4 motif for which NV1042 was designed is not necessary for tight DNA binding. Both hairpin

PAs have a similar DNA binding specificity. However, distinct binding sites are

observed for both, which means they still bind DNA discriminately (Figure 1.15).

32 Figure 1.15 Overlay of the binding sites of NV1028 (blue) and NV1042 (red) in LCR of HPV16 using AC data. Black arrows indicate overlapping binding sequences of both PAs. Reprinted with permission from (Vasilieva et al., 2016). Copyright (2016) Elsevier.

33 Given these results, what else affects PA-DNA binding affinity and antiviral

activity and what are the differences in DNA binding mechanism between large

and small PAs? Thermodynamic studies might provide some insights.

1.6.2 Improved Antiviral Activity of PAs with N-terminal Guanidinium Substitution Bashkin group has reported recently that substituting the head group of

PAs with N-terminal guanidinium (Guan) or tetra-methyl guanidinium (TMG)

groups (Figure 1.16) leads to improved antiviral activities against HPV16, 18,

and 31 compared to NV1028, an unsubstituted PA, while not changing the binding affinities (Castaneda et al., 2016). IC50 of these two new PAs against

HPV16, 18, and 31 are around sub-micromolar level (Table 1.1). Both PAs with

Guan and TMG substitution gained some improved IC50 against HPV18

comparing to NV1028, and about the same IC50 against HPV31 comparing to

NV1028. The PA with Guan substitution lost some IC50 against HPV16 comparing to NV1028.

1.7 Thermodynamics of Binding Events 1.7.1 Gibbs-Helmholtz Equation The Gibbs-Helmholtz equation describes the temperature dependence of

the binding affinity of a molecular interaction:

1 = = ln ( . )

𝛥𝛥𝛥𝛥 𝛥𝛥𝛥𝛥 − 𝑇𝑇𝑇𝑇𝑇𝑇 −𝑅𝑅𝑅𝑅 𝟏𝟏 𝟏𝟏 𝐾𝐾𝑑𝑑 in which ΔG refers to the change in Gibbs free energy during the process, ΔH

34 Figure 1.16 Structure of TMG and Guan.

35 refers to the enthalpy change during the process, T refers to the temperature in

Kelvin and ΔS refers to the entropy change during the process. R refers to the ideal gas constant, Kd refers to the dissociation equilibrium constant, or the

binding affinity. When using Equation (1.1), ΔCp is assumed to be 0, which is

appropriate for most biological processes.

Various sources of ΔH and ΔS are summarized in Figure 1.17. These

sources can be either favorable or unfavorable. The sum of all these sources will

provide the thermodynamic profile of the binding process. And the details are

discussed in the next two subsections: 1.7.2 and 1.7.3.

1.7.2 ΔH and Related Binding Forces When ΔH dominates the interactions, according to Hess’s law (Record et

al., 1991):

= ( ) ( ) ( . )

∆𝐻𝐻𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 � ∆𝐻𝐻𝑓𝑓 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 − � ∆𝐻𝐻𝑓𝑓 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝟏𝟏 𝟐𝟐 ΔH is independent of the path of the transition, but dependent on the initial and final states (and always in equilibrium). It describes the energy changes in molecular interactions and desolvation during the transition.

In a chemistry reaction, forming/breaking covalent bonds is typically the largest contributor mostly to ΔH. However, no covalent bonds are formed in the

DNA binding events discussed in this thesis. Thus, non-covalent interactions such as the hydrogen bond (H bond) and van der Waals (vdW) contacts, are the main contributors to ΔH.

36 Figure 1.17 Various sources of ΔH and ΔS. Arrow scale does not correlate with the degree of contribution of the sources to ΔG. The vdW contact picture is adapted from http://cdn.zmescience.com.

37 1.7.2.1 H Bonds The H bond is one of the non-covalent interactions. It refers to the electrostatic interaction between a hydrogen atom which is covalently bound to a highly electronegative atom and another highly electronegative atom such as nitrogen, oxygen, and fluorine. H bonds have been shown to be one of the most important interactions in many small DNA binders, including PA, interacting with

DNA (Dervan, 2001; Neidle, 2001). Dervan et al. have shown the binding model

of PA-DNA interactions with putative H bonds (Dervan & Edelson, 2003) (Figure

1.6). The optimized topology of the hydrogen bond network also helps PA

recognize a specific DNA sequence. The hydrogen bond energy typically varies

between 0.2 and 40 kcal/mol (Steiner, 2002).

1.7.2.2 The van der Waals (vdW) Contacts The vdW contacts are electrostatic interactions involving permanent or induced dipoles (Figure 1.18). As a subset of the electrostatic interactions, it is distance and molecular weight dependent, but does not correlate to any chemical electronic bond. This is also one of the reasons that vdW is the weakest of the chemical forces. The energy of the vdW contact is about 1 kcal/mol, which can be weaker than a H bond (Pollard, 2017).

1.7.2.3 How Forces Correlate with ΔH Favorable ΔH of interactions is normally associated with forming H bonds

and van der Waals contacts. To optimize van der Waals contacts, the ligand

should geometrically fit well with the binding site. To optimize H bonding, the

38 Figure 1.18 Diagram of the vdW contacts. Adapted from http://cdn.zmescience.com.

39 angle and distance between H donors and H acceptors should be optimized. In a non-optimized situation, the strength of the H bonds between the ligand and the substrate is weaker than those between the ligand and the solvent. The strength of the bonds with the solvent are associated with the ligand desolvation.

Unfavorable ΔH of interaction is associated with the desolvation of the polar groups of the ligands. The enthalpic penalty of desolvation of polar groups commonly used in drug development is in the order of 8 kcal/mol at 25°C (Cabani

et al., 1981). Thus, if the strength of the bonds formed is strong enough to

compensate the energy penalty from the desolvation of the polar groups, the

interaction ΔH becomes favorable.

1.7.3 How ΔS Contributes to ΔG The change of freedom in a system, which is also called randomness, also

contributes to the Gibbs free energy change. This randomness includes the loss

of the rotational and translational degrees of freedom once a ligand binds a

receptor, and the change of other components in the surrounding environment,

such as water molecules, once the complex is formed. ΔS is heavily weighted by

the temperature. This means when the temperature is lower, the initial entropy

(which is a thermodynamic potential) would be lower as most of the molecules

are more ordered. When the temperature is higher, the initial entropy would be

higher as most of the molecules are more disordered (active). Entropy will

increase when the randomness increases, which leads to a positive (favorable)

ΔS. Positive ΔS leads to a more negative ΔG.

The integrated Gibbs-Helmholtz equation is: 40 ( ) = + ( . ) 𝑇𝑇 𝛥𝛥𝐺𝐺 𝑇𝑇 𝛥𝛥𝐻𝐻𝑟𝑟𝑟𝑟𝑟𝑟 − 𝑇𝑇𝛥𝛥𝑆𝑆𝑟𝑟𝑟𝑟𝑟𝑟 𝛥𝛥𝐶𝐶𝐶𝐶 �𝑇𝑇 − 𝑇𝑇𝑟𝑟𝑟𝑟𝑟𝑟 − 𝑇𝑇 𝑙𝑙𝑙𝑙 � �� 𝟏𝟏 𝟑𝟑 𝑇𝑇𝑟𝑟𝑟𝑟𝑟𝑟 ΔG here is related to binding affinity (Kd) and specific heat capacity change (ΔCp).

Subscripted “ref” refers to the reference temperature. This equation is used when the binding event has a non-zero ΔCp. The balance between the contribution of

enthalpy and that of entropy is the core component of a thermodynamic study.

1.7.3.1 Configurational ΔS Configurational ΔS refers to lengthening and stiffening of the bound DNA duplex and changes in both translational and rotational freedom of the ligand

(Figure 1.19), which of both are normally unfavorable in a binding process. In

PA-DNA binding, the change in DNA minor groove width upon PA binding and

the decrease in translational and rotational freedom of the bound PA contribute

to the unfavorable configurational ΔS. This can be minimized by optimizing the

conformation of the free ligands to be similar to that of the bound ligands.

1.7.3.2 Desolvation ΔS and Hydrophobic Effect When there is only DNA in solution, organized water molecules fill the

spaces of minor groove (Edwards et al., 1992; Patel & Anchordoquy, 2005). After

adding DNA binders, the hydrophobic surface will form H bonds with the groove

binders, release these organized water molecules to the bulk solvent and

generate favorable ΔS (Figure 1.20). This is called the hydrophobic effect (Spink

& Chaires, 1997). As both ligands and substrates are dissolved during the

binding process, the desolvation ΔS is associated with the hydrophobic effect. It

41 a

Figure 1.19 Translational (a) and rotational (b) freedom of an atom.

42 Figure 1.20 The x-ray structure of the drug Hoechst 33258 with the dodecamer duplex DNA. The structure was downloaded from the Protein Data Bank (ID code 264D) and processed with the program WEBLAB VIEWERLITE (Vega et al., 1994). In the absence of the drug there are 71 water molecules with the dodecamer (Edwards et al., 1992), and in the presence of the drug only 18 water molecules (green balls) are present. Reprinted with permission from Pal et al. (2003). Copyright (2003) National Academy of Sciences.

43 provides a big favorable energy to compensate for the entropic penalty from limiting the translational and rotational freedom of the bound ligand molecules.

According to the literature, burying a carbon atom from the solvent contributes in the order of 25 cal/mol/Å2 to the binding affinity (Sharp et al., 1991).

1.8 Entropy-enthalpy Compensation (EEC) EEC normally refers to a linear relationship between entropy change and

enthalpy change when there is a series of perturbations or exchanges in an

independent variable (temperature in most cases) (Sharp, 2001). This

phenomenon is also described by the definition of the change of heat capacity

(ΔCp):

= = ( . ) 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 𝛥𝛥𝛥𝛥𝑝𝑝 𝑇𝑇 𝟏𝟏 𝟒𝟒 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 where the derivatives are collected at constant pressure. And in many cases, it is

suggested that these opponents will at least partially cancel out (Dunitz, 1995).

But this is not an absolute law. Overcoming EEC is very important in drug

development and structure-activity relationship (SAR) studies. For example,

benzamidine was optimized to fill the S3 pocket of a serine protease with

different sized substituents (R group in Figure 1.21) (Klebe, 2015). As the R

group becomes larger and more hydrophobic, both ΔG and −TΔS become more

negative (favorable), but ΔH is less negative (unfavorable), and it is clearly

entropy-driven binding. The increasing lipophilicity of the R group from left to right

is correlated to the more negative −TΔS value which means a stronger entropic

contribution to the total binding free energy.

44 Figure 1.21 Thermodynamic profile of benzamidines. Reproduced with permission from (Biela et al., 2012). Copyright (2012) American Chemical Society.

45 Generally, a more entropic contribution is preferred. Freire et al. (Freire,

2008) have suggested that enthalpically optimized DNA-binding ligands are more

susceptible to viral resistance development by mutations in the viral gene; thus

they could not tolerate conformational modification of the binding sites caused by

gene mutations. Entropically optimized ligands, due to their high residual mobility

and flexibility can adopt different binding modes and are more tolerant to the structural changes at the binding site which developed from the viral resistance mechanism.

1.9 Overview of This Dissertation

The thermodynamics (temperature dependence of Kd) of hairpin PA-DNA interactions will be analyzed by a fluorescence assay to determine if Dervan’s binding rules for small PAs still apply to larger hairpin PAs (more than 14 rings).

In addition, different DNA sequence patterns in a PA binding site will be examined for a 14-ring hairpin PA to determine if they behave similarly to small

PAs such as netropsin and Dst. A. Contributions by the polyelectrolyte effect to

ΔG will also be examined by measuring the salt dependence of Kd of PA-DNA interactions.

46 Chapter 2 Material Preparation and Methods 2.1 Materials 2.1.1 DNA and Fluorescent Label Desalted DNAs were purchased from Integrated DNA Technologies (IDT,

Coralville, IA, USA), either attached with TAMRA or not. DNA was washed twice

with Milli-Q water using a Centricon filter and a Thermo Sorvall ST 16 centrifuge,

annealed by placing in boiled water cooled down to room temperature,

quantitated using the extinction coefficient ε260 provided from IDT, then lyophilized and stored at -20°C for future use.

2.1.2 PAs PAs were prepared by solid-phase synthesis on Boc-β-alanine-PAM resin as previously described and were obtained from Dr. Bashkin’s lab (Edwards et al.,

2011; Wurtz et al., 2001). PA stocks were prepared by dissolving in ACS

spectrophotometric grade DMSO (Sigma-Aldrich, St. Louis, MO). The quantitation was based on extinction coefficients shown in Table 2.1.

2.2 Fluorescence Assay This fluorescence assay was developed by Dr. Dupureur (Dupureur et al.,

2012). It is used to observe binding PAs to DNA. A specific fluorescent probe,

TAMRA, was attached to a thymidine on the target DNA hairpin through the NHS

linker (Figure 2.1). When PA binds DNA hairpin, the fluorescence intensity will change and can be used to monitor DNA binding.

47 Table 2.1 Extinction coefficients of PAs used in this study.a PA Extinction coefficient ε at 305 nm, M-1 cm-1 KA1039 80500 KA1002 103000 NV1028 92600 NV1042 91700 a. These extinction coefficients are measured via fluorescence assay (Dupureur et al., 2012).

48 Figure 2.1 Example of TAMRA-DNA hairpin design.

49 2.3 PA-DNA Binding Assay by Direct Titration This experiment was carried out with 2.1 mL 1X HNE buffer (10 mM

HEPES, 50 mM NaCl, 1 mM EDTA, pH 7.4) in a 3.5 mL acid-pre-treated quartz cuvette, using the T or S channel of a Fluorolog-3 (Spex) fluorospectrometer

(Horiba, Edison, NJ).

T channel has a cut-off filter (allowing all the light above a specific

wavelength to pass through) (Edmund Optics, Barrington, NJ), which provides a stronger signal when the fluorescence intensity is small. The S channel is equipped with a monochromator. The temperature of cell holder was maintained by a Lauda E100 circulating water bath. A magnetic stir bar in the bottom of the cuvette was used to mix the solution. TAMRA-DNA hairpin was excited at 559 nm and the emission was monitored either at 580 nm or through the cut-off filter.

At least three replicates (I1, I2, and I3) were used to provide one averaged intensity value (Iavg) by using Equation (2.1):

+ + = ( . ) 3 𝐼𝐼1 𝐼𝐼2 𝐼𝐼3 𝐼𝐼𝑎𝑎𝑎𝑎𝑎𝑎 𝟐𝟐 𝟏𝟏 When a PA binds DNA, the emission intensity changes. These intensity

values at different PA concentrations were averaged, normalized, and fitted to

Equation (2.2):

( [ ] ) = ( . ) ( [ ] 𝑛𝑛) 𝐾𝐾𝑎𝑎 𝐿𝐿 t 𝑛𝑛 𝛩𝛩 1+ 𝐾𝐾𝑎𝑎 𝐿𝐿 t 𝟐𝟐 𝟐𝟐 where [L]t refers to the total concentration of PA; Ka refers to the association

equilibrium constant; Θ refers to the fraction of DNA occupied by PA; n refers to

the hill number. An isotherm example of the direct titration is shown in Figure 2.2. 50 Figure 2.2 An isotherm example of a direct titration. Solid circles are normalized data collected via fluorescence assay. The solid line is the fitting line of the normalized data to Equation 2.2. Open squares are simulated using Scientist software based on the normalized data. Conditions: 0.1 nM ODN-14-SFAT-T3 titrated with NV1028 in 10 mM HEPES, 50 mM NaCl, 1 mM EDTA and 1 mM CHAPS in water, pH 7.4, at 20°C.

51 Scientist software (Micromath Research, St. Louis, MO) is also used to fit the data (open squares in Figure 2.2). Equation (2.2) is often applied when the

DNA concentration is much smaller than Kd so [L]t is close to [L]free. However, when fitting with Scientist, this condition is not necessary. Thus, Scientist may provide more accurate equilibrium constants under certain circumstances.

2.4 PA-DNA Binding Assay by Competition Competition experiments were used to validate the direct binding affinities obtained with TAMRA labeled DNA. Serial additions of unlabeled DNA were added to a pre-incubated 1:1 PA-DNA complex (at a concentration of ~10-fold above Kd, to assure complete binding) in HNE solution. The intensity was

monitored and averaged from at least three readings. The competition model of a

series of equations in Scientist software was used to fit these competition data

and provided the unlabeled DNA-PA Kd (Dupureur et al., 2012). Figure 2.3

provides an isotherm example of competition and the competition model.

2.5 Analyzing Thermodynamics of PA-DNA Interactions 2.5.1 Temperature Calibration on the Water Bath The temperatures in water bath and the sample holder are different as the

heat can escape between the two. Thus, it must be calibrated. The sample

holder temperature is measured by using a thermometer and a cuvette filled with

water, and the opening is covered by aluminum foil.

Table 2.2 shows the calibration on one day. This is only used as a

reference, since the varying room temperature may affect the actual sample

52 a b

Figure 2.3 An isotherm example of the competition experiment. (a) The competition model used in Scientist software. (b) Solid circles are data collected via fluorescence assay; open squares are data simulated based on the competition model in Scientist software. Conditions: 0.3 nM ODN-14- SFAT-T3 and 0.3 nM NV1028 were titrated with ODN-14-SFAT in 10 mM HEPES, 50 mM NaCl, 1 mM EDTA and 1 mM CHAPS in water, pH 7.4, at 40°C.

53 Table 2.2 Sample Temperature Calibration on the Water Bath of the Fluorometer.

Water bath temperature °C *Sample holder temperature °C

12.5 15.0

19.0 20.0

26.0 25.0

37.5 35.0

50.1 45.0

54 holder temperature. At the beginning of each experiment, the temperature is accurately calibrated again.

2.5.2 Linear van’t Hoff (vH) Analysis The binding affinities of PA-DNA interactions at different temperatures were

collected by the method above and used for van’t Hoff analysis using Equation

(2.3):

1 = = ln ( . )

∆𝐺𝐺 ∆𝐻𝐻 − 𝑇𝑇∆𝑆𝑆 −𝑅𝑅𝑅𝑅 𝟐𝟐 𝟑𝟑 𝐾𝐾𝑑𝑑 where ΔG refers to the Gibbs free energy; ΔH refers to the enthalpy change; ΔS

refers to the entropy change; T refers to the temperature; Kd refers to the

dissociation equilibrium constant, also known as binding affinity; R refers to the

ideal gas constant (1.987 cal K-1 mol-1). ΔGs were then plotted versus

temperature and fitted to the Equation (2.3) with the assumption of ΔCp being 0.

Equation (2.3) also provides ΔH and ΔS at specific temperatures.

2.5.3 Integrated Form of Gibbs Helmholtz equation (Non-linear Analysis)

The linear van’t Hoff analysis assumes ΔCp to be 0, and this is appropriate for general biological process (most previously reported PA has a relatively small

ΔCp for DNA binding) (Haq et al., 1997; Holbrook et al., 1999; Kozlov & Lohman,

2000). To estimate ΔCp, the Gibbs Helmholtz equation was used:

( ) = + ( . ) 𝑇𝑇 𝛥𝛥𝐺𝐺 𝑇𝑇 𝛥𝛥𝐻𝐻𝑟𝑟𝑟𝑟𝑟𝑟 − 𝑇𝑇𝛥𝛥𝑆𝑆𝑟𝑟𝑟𝑟𝑟𝑟 𝛥𝛥𝐶𝐶𝐶𝐶 �𝑇𝑇 − 𝑇𝑇𝑟𝑟𝑟𝑟𝑟𝑟 − 𝑇𝑇 𝑙𝑙𝑙𝑙 � �� 𝟐𝟐 𝟒𝟒 𝑇𝑇𝑟𝑟𝑟𝑟𝑟𝑟

55 in which ΔCp refers to the specific heat capacity change of the binding event.

Subscripted “ref” refers to values under the reference temperature, which were

chosen to be 25 °C.

2.6 Salt Dependence of Binding Affinity To examine the polyelectrolyte effect on PA-DNA interactions, the salt

concentration was varied in a direct titration binding experiment (described in

Section 2.3). Data were fitted with the Equation (2.5) and (2.6) (Friedman &

Manning, 1984):

° = [ ] ( . ) + ∆𝐺𝐺 𝑝𝑝𝑝𝑝 𝑧𝑧𝑧𝑧𝑧𝑧𝑧𝑧 𝑙𝑙𝑙𝑙 𝑁𝑁𝑎𝑎 𝟐𝟐 𝟓𝟓 log = = ( . ) log[ ] 𝜕𝜕 𝐾𝐾𝑎𝑎 , � + � −𝑧𝑧𝑧𝑧 𝑆𝑆𝐾𝐾𝑜𝑜𝑜𝑜𝑜𝑜 𝟐𝟐 𝟔𝟔 𝜕𝜕 𝑁𝑁𝑎𝑎 𝑇𝑇 𝑃𝑃 where Ψ is the fraction of sodium ions bound per phosphate group on DNA

backbone, z is the apparent charge on the ligand. -zΨ value (SKobs) provides the

number of cation released on the binding site. SKobs also refers to the salt

dependence of log Ka.

At a given salt concentration, the ΔG can be dissected by Equation (2.7)

(Friedman & Manning, 1984):

° = ° + ° ( . )

𝛥𝛥𝛥𝛥 𝛥𝛥𝐺𝐺 𝑝𝑝𝑝𝑝 𝛥𝛥𝐺𝐺 𝑛𝑛𝑛𝑛𝑛𝑛−𝑝𝑝𝑝𝑝 𝟐𝟐 𝟕𝟕 where ΔG°pe reflects the contribution of polyelectrolyte (PE) effect: the enthalpic

columbic force between counterions of solute molecules in the solution and

entropic disorder of the counterions upon ligand-DNA binding. ΔG°non-pe contains

56 all other contributions such as H bonding, conformational changes, and van der

Waals forces. Therefore, ΔG°non-pe is independent of the salt concentration.

2.7 Calculation of Errors Errors are calculated using (max-min)/2 method unless specified in this study. Mean value is calculated from at least three replicates. Then maximum

and minimum values are chosen to calculate the error by Equation (2.8):

= ( . ) 2 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 − 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 𝟐𝟐 𝟖𝟖 Then the entry is written as mean ± error.

57 Chapter 3 PA-size-dependent Thermodynamics of Hairpin PA-DNA Interactions 3.1 Introduction Characterization of thermodynamic parameters has been used to explore

the mechanisms of biological molecules broadly in the past few decades. It is

also the most direct way to systematically study the properties of a binding

system. For PA-DNA interactions, we think thermodynamic data could provide

insights into why PAs have different antiviral activities and binding affinities

toward DNA.

Thermodynamic studies have been conducted on the naturally occurring

PAs netropsin and distamycin. Marky and Breslauer measured the binding

affinities and specificities between netropsin and different DNA oligomers using

calorimetry, CD and UV spectroscopy to show its preference for AT-rich sequences (Marky & Breslauer, 1987). The results also indicate that netropsin

penetrates deeply into the minor groove of ATAT sequences. It was also shown

that the binding between netropsin and DNA is enthalpically driven. The same analysis was later performed on distamycin later, and similar conclusions were made (Rentzeperis et al., 1992).

Previous thermodynamic studies on a few small hairpin PAs have been

performed. The interaction between 6-ring hairpin PAs and their matched DNA

sequences were shown to be enthalpically driven (Pilch et al., 1999; Pilch et al.,

1996; Wang et al., 2013). Our previous studies of larger antiviral PAs have led us

to speculate about their DNA binding thermodynamic signatures: since they also

58 prefer long AT-rich sites, would their thermodynamic signatures be different to those of smaller PAs? In this study, we analyze DNA binding thermodynamics as a function of PA size.

3.2 Results 3.2.1 PAs and DNA Hairpins Used in This Study The materials used in this study are shown in Table 3.1. The PAs in Table

3.1 were selected based on their size: KA1039 (6 rings), KA1002 (8 rings),

NV1028 (14 rings) and NV1042 (20 rings). KA1039, which is HPV inactive, is used as a control to compare the results obtained from the fluorescence assay used in this study to those from calorimetry and SPR (Bashkin et al., 2013;

Dupureur et al., 2012; Wang et al., 2012). KA1002 (8 rings) is antivirally inactive

against HPV16 and HPV18 and has been studied previously (Wang et al., 2012).

NV1028 and NV1042 are active against HPV16 and HPV18 (Edwards et al.,

2011).

The duplex portion of DNA hairpin sequences in Table 3.1 is chosen from

the HPV 16 LCR except for that of KA1039 and KA1002. KA1039 and KA1002

have overlapping DNA recognition sequences. DNA hairpins from a previous

thermodynamic study of the PAs were used (Bashkin et al., 2013). In each case,

the TAMRA dye was attached on a modified thymidine through an NHS linker

(Dupureur et al., 2012).

59 Table 3.1 PAs and DNA hairpins used in this study. PA Antiviral activity d a, b size PA DNA Predicted PA-DNA binding scheme (IC50 against (rings) HPV16, µM)

ODN-6- 6 KA1039 Inactive T3

ODN-6- 8 KA1002 Inactive T3

ODN-14- LFTT-T6c

ODN-14- 14 NV1028 SFTT- 0.100 ± 0.02 T3c ODN-14- SFAT- T3c

ODN-20- T3 20 NV1042 0.036 ± 0.0004 ODN-20- T16

a. Closed circle refers to Im; open circle refers to Py; open diamond refers to β- ala. Dye position is in red; PA binding site is bold. b. Forward orientation is assumed for all these PA-DNA interactions (see Chapter 1 Section 1.3). c. LFTT refers to long flank TT sequence, SFTT refers to short flank TT sequence, SFAT refers to short flank AT sequence. d. See Appendix A for lab designations of DNA hairpins.

60 We assume the forward binding orientation (Table 3.1) is preferred in all cases, as mentioned in Chapter 1 Section 1.3. When designing the flanking sequences, we limited alternate binding sites to avoid overlapping sites (Chapter

2).

3.2.2 DNA Binding Affinities as a Function of PA Size

DNA binding affinities (Kds) as a function of PA size at 25°C by

fluorescence assay are shown in Table 3.2. They all bind with nanomolar affinities. We also compared our data to that of published literature via different methods. For KA1039, binding affinities obtained via fluorescence assay are slightly stronger than those via SPR (Wang et al., 2012) (Table 3.2). This could

be due to the different experimental conditions. It was also found that NV1028

binds differently with different sequences based on the Kds obtained via

fluorescence assay ranging from 0.01-0.12 nM (Table 3.2).

For NV1028 and NV1042, DNase I footprinting (Vasilieva et al., 2016)

provides slightly weaker binding affinities than those obtained via the

fluorescence assay. This could be due to the different conditions of both methods.

The fluorescence assay uses small TAMRA labeled DNA hairpins that contain

one PA target sequence. However, DNase I footprinting uses FAM or HEX

labeled DNA duplexes in hundreds of bps, containing more than one PA target

sequence. And the potential cooperativity between binding sites is unknown.

A macroscopic Kd refers to the total ligand concentration where half of the

61 Table 3.2 Comparison of direct Kds of PA-DNA interactions using different methods at 25°C.a DNA PA (size) Methods concentration DNA sequence Kd (nM) (nM) Fluorescence ODN-6-T3 0.5 1.6 ± 0.3 KA1039 assay (CCTTGGCTTC) (6) Biotin labelled SPR 25-50 7.1 ± 0.5b CCTTGGCTTC Fluorescence ODN-6-T3 0.2 0.2 ± 0.1 KA1002 assay (CCTTGGCTTC) (8) Biotin labelled SPR 25-50 0.3 ± 0.1b CCTTGGCTTC 0.25 ODN-14-LFTT-T6e 0.1 ± 0.1 Fluorescence 0.2 ODN-14-SFTT-T3e 0.1 ± 0.02 NV1028 assay 0.1-0.2 ODN-14-SFAT-T3e 0.01 ± 0.002 (14) DNase I HPV16 LCR region 0.2 0.7-20c, d footprinting 7782-7791 Fluorescence 0.1 ODN-20-T3 0.1 ± 0.02d NV1042 assay (20) DNase I 0.2-1 HPV16 LCR region 0.7-8c footprinting a. Conditions for fluorescence assay: 10 mM HEPES, 50 mM NaCl, 1 mM EDTA and 1 mM CHAPS in water, pH 7.4, at 25°C. Each Kd is the average of at least three individual measurements. The error is calculated by (maximum-minimum)/2 method. b. Conditions for SPR: 10 mM HEPES, 150 mM NaCl, 3 mM EDTA, and 0.05% P20 (pH 7.4) (Wang et al., 2012). c. Kd was obtained by measuring the protection of the DNA from NV1028 at different concentrations with a DNase I footprinting assay (Bashkin et al., 2013; Vasilieva et al., 2016). Conditions: TKMC buffer (10 mM Tris, 10 mM KCl, 5 mM MgCl2 and 5 mM CaCl2) with 10 mM CHAPS. d. Includes mismatch sequences. e. LFTT refers to long flank TT sequence, SFTT refers to short flank TT sequence, SFAT refers to short flank AT sequence.

62 DNA molecules in bound state, no matter how many binding modes/events exist

(Senear et al., 1986). By contrast, a microscopic Kd refers to the binding affinity

of a single binding mode/event in the context of multiple binding modes/events

existing in the system (Senear et al., 1986). The fluorescence assay can only

provide macroscopic Kds. Since one cannot tell how many binding modes/events

exist based on these macroscopic Kds, further kinetic studies are necessary to

look at different binding modes/events.

3.2.3 Linear van’t Hoff Analysis: Different Sized Hairpin PAs have Distinct DNA Binding Thermodynamics The van’t Hoff analysis has been widely utilized to explore the state

functions in a biological binding system (Tellinghuisen, 2006). For convenience,

the linear van’t Hoff equation (a first-order approximation) is usually employed in most biological processes within a temperature range near 298 K (25°C). This assumes that enthalpy and entropy do not change with temperature (ΔCp is 0),

which is generally true (Tellinghuisen, 2006).

The binding affinities of different sized PAs to their cognate DNA hairpin

oligomers at different temperatures were collected, and the temperature

dependence of these binding affinities was found to be essentially linear (Figure

3.1). They were therefore fit to the Equation 3.1:

= ( . )

𝛥𝛥𝛥𝛥 𝛥𝛥𝛥𝛥 − 𝑇𝑇𝑇𝑇𝑇𝑇 𝟑𝟑 𝟏𝟏 In Figure 3.1, the slope refers to −ΔS based on Equation (3.1). These

four PAs can be categorized into two groups by the signs of the slopes: smaller

63 Figure 3.1 Thermodynamic data comparison of PAs in varied sizes. Conditions: 10 mM HEPES, 50 mM NaCl, 1 mM EDTA and 1 mM CHAPS in water, pH 7.4; for KA1039: 0.2/0.5 nM ODN-6-T3; for KA1002: 0.1/0.2 nM ODN-6-T3; for NV1028: 0.25 nM ODN-14-LFTT-T6; for NV1042: 0.1 nM ODN- 20-T3 oligo. Data were fitted to Equation 3.1. Each point is the average of at least three individual measurements. Error is calculated by (maximum- minimum)/2 method.

64 PAs (KA1039 and KA1002) have positive slopes (unfavorable entropy). These

binding events are enthalpically driven due to the large negative ΔH values

(favorable) (Table 3.3). Larger, antiviral PAs (NV1028 and NV1042) have large

negative −TΔS (Figure 3.1) and small positive ΔH (unfavorable), indicating that

their interactions with DNA are entropically driven. This entropically driven

interaction is a unique observation for PA-DNA thermodynamics (Breslauer et al.,

1987).

3.2.4 Different DNA Sequences Result in Similar Energy Signatures for NV1028 Binding Different energy signatures have been observed for netropsin and Dst. A

when binding to different DNA sequences (for example, ATAT vs. T tract)

(Breslauer et al., 1987). Does the longer antiviral NV1028 exhibit similar behavior?

Three perfect matched DNA sequences (long flank TT, short flank TT and

short flank AT) (Table 3.1) were used in the fluorescence assay with NV1028 to

obtain direct Kd (binding affinity measured between labeled DNA and PA) as a

function of temperature. van’t Hoff analysis was applied to determine ΔH, ΔS,

and ΔG (Figure 3.2 and Table 3.4). Although all three linear fits indicate similar

favorable ΔS and relatively small ΔH, there is a trend that the TT sequence is more entropically driven than the AT sequence (Table 3.4). This means NV1028

can discriminate AT vs. TT sequences energetically as netropsin and Dst. A

(more details in Section 3.3.2).

65 Table 3.3 Comparison of the energies in different PA-DNA interactions at 25°C.a

ΔG° b ΔH° c −TΔS° c PA (size in rings) kcal/mol kcal/mol kcal/mol

KA1039 (6) -12.0 ± 0.1 -19 ± 1 7 ± 1

KA1002 (8) -13.4 ± 0.2 -20 ± 4 6 ± 4

NV1028 (14)d -13.4 ± 0.1 1 ± 1 -14 ± 1

NV1042 (20) -13.5 ± 0.1 12 ± 1 -26 ± 1

a. Energies are calculated based on the direct titration data. Conditions: 10 mM HEPES, 50 mM NaCl, 1 mM EDTA and 1 mM CHAPS in water, pH 7.4, at 25°C. b. Each ΔG° is the average of at least three individual measurements. The error is calculated by (maximum-minimum)/2 method. c. Errors of ΔH° and −TΔS° come from the van’t Hoff analysis in Kaleidagraph software. d. Using LF TT DNA hairpin (see Table 3.1).

66 Figure 3.2 The Sequence-dependent Thermodynamics of NV1028. SF TT refers to ODN-14-SFTT-T3, LF TT refers to ODN-14-LFTT-T6, SF AT refers to ODN-14-SFAT-T3. These sequences can be found in Table 3.1. Each point is the average of at least three individual measurements. The error is calculated by (maximum-minimum)/2 method.

67 Table 3.4 Comparison of sequence dependent ΔH/ΔS compensation of Netropsin (Net.), Dst. A and NV1028 at 25°C.

ΔG° a ΔH° b −TΔS° b PA DNA sequence kcal/mol kcal/mol kcal/mol

Poly(dA)·poly(dT) duplex -12.2 -2.2 -10.0 Net.c Poly[d(A-T)]·poly[d(A-T)] -12.7 -11.2 -1.5 duplex

Poly(dA)·poly(dT) duplex -11.4 -4.2 -7.2 Dst. Ac Poly[d(A-T)]·poly[d(A-T)] -12.6 -18.5 +5.9 duplex

ODN-14-LFTT-T6d -13.4 ± 0.1 1 ± 1 -14 ± 1

NV1028 ODN-14-SFTT-T3d -13.9 ± 0.2 8 ± 2 -22 ± 2

ODN-14-SFAT-T3d -14.9 ± 0.1 -5 ± 3 -10 ± 3

a. Each ΔG° is the average of at least three individual measurements. The error is calculated by (maximum-minimum)/2 method. b. Errors of ΔH° and −TΔS° come from the van’t Hoff analysis in Kaleidagraph software. c. Net. and Dst. A data is from Breslauer et al. (1987). No error was provided. d. LFTT refers to long flank TT sequence, SFTT refers to short flank TT sequence, SFAT refers to short flank AT sequence (see Table 3.1).

68 For the binding between NV1028 and short flank AT (SF AT) sequence:

ΔH is negative, which is different from those of the other sequences. The reason of those unfavorable positive ΔH is discussed in Section 3.3.6.

3.2.5 Potential Impacts of the Experimental Approach on Results

3.2.5.1 Competition Kds to Validate Direct Kd Measurements The binding affinity obtained from a direct titration experiment is an

apparent Kd that may not reflect the true binding affinity if there is dye interference (Dupureur et al., 2012). A competition experiment (detailed in

Chapter 2) is used to examine these possibilities. Briefly, unlabeled DNA hairpin

is titrated into a pre-formed PA-labeled DNA complex and signal recovery is

obtained.

3.2.5.2 Kd Comparison Between Competition and Direct Titration: KA1039, KA1002, and NV1028

Competition Kds at 25°C are summarized in Table 3.5. For KA1039 and

NV1028 (with SFAT and SFTT), the competition Kd is weaker than the direct Kd

(4.3 nM vs. 1.6 nM, 0.35 nM vs. 0.01 nM, and 0.45 nM vs. 0.1 nM respectively, more than four folds). For KA1002, the competition Kd and direct Kd are similar.

For NV1028 with LFTT, the competition Kd is stronger than the direct Kd (0.03 nM

vs. 0.1 nM respectively, about three folds). Competition Kd of NV1042 cannot be

determined (see Section 3.2.6).

Temperature dependences of competition and direct Kds are shown in

Figure 3.3 and Figure 3.4. All competition Kds of KA1039 are weaker than direct

69 Table 3.5 Comparison of competition and direct Kds of different PA-DNA interactions at 25°C by fluorescence assay.a

PA Direct Kd Competition Kd DNA (size in rings) nM nM KA1039 (6) ODN-6-T3 1.6 ± 0.3 4.3 ± 0.6 KA1002 (8) ODN-6-T3 0.2 ± 0.1 0.56 ± 0.10 ODN-14-LFTT-T6b 0.1 ± 0.1 0.03 ± 0.01 NV1028 (14) ODN-14-SFTT-T3b 0.1 ± 0.02 0.45c ODN-14-SFAT-T3b 0.01 ± 0.002 0.35 ± 0.08 NV1042 (20) ODN-20-T3 0.1 ± 0.02 n.a.d a. Conditions: 10 mM HEPES, 50 mM NaCl, 1 mM EDTA and 1 mM CHAPS in water, pH 7.4. Each Kd is the average of at least three individual measurements. The error is calculated by (maximum-minimum)/2 method. b. LFTT refers to long flank TT sequence, SFTT refers to short flank TT sequence, SFAT refers to short flank AT sequence. These sequences can be found in Table 3.1. c. No duplicates obtained. d. n.a.: not available.

70 Figure 3.3 Comparison of competition Kds and direct Kds of KA1039 and KA1002. Each point represents at least 3 replicates. The error is calculated by (maximum-minimum)/2 method. Fitted to linear equation (10 mM HEPES, 50 mM NaCl, 1 mM EDTA and 1 mM CHAPS in water, pH 7.4).

71 Figure 3.4 Comparison of competition and direct Kds of NV1028 with different DNA sequences. SF AT refers to short flank AT sequence, LF TT refers to long flank TT sequence, SF TT refers to short flank TT sequence. These sequences can be found in Table 3.1. Each point represents at least 3 replicates. The error is calculated by (maximum-minimum)/2 method.

72 Kds of which, this is due to the more complex nature of a competition experiment.

However, both direct and competition Kds of KA1039 have a similar temperature dependence. For KA1002 (Figure 3.3), the competition Kds are generally weaker than direct Kds, but have relatively smaller errors compared to those of KA1039.

NV1028 is quite different from the smaller PAs (Figure 3.4). Direct Kds of

NV1028 with SFAT have been shown in the top panel of Figure 3.4. This temperature dependence of its competition Kds have a positive slope, while that

of the direct Kds have a slightly negative slope. Direct Kds of NV1028 with LFTT

were determined at 5 different temperatures ranging from 278 K to 308 K. Due to technical issues, competition Kds of NV1028 with LFTT were determined at only

3 different temperatures. The temperature dependence of both types of Kds of

NV1028 with LFTT are similar to each other (Figure 3.4 middle panel). This

difference may be negligible since both slopes are relatively small and close to 0.

In NV1028 with SF TT (Figure 3.4 bottom panel), the direct Kds has almost no

temperature dependence. These competition Kds are noisy and the fitting slope

is slightly negative.

3.2.6 Competition Kds of NV1042-DNA Interactions Could Not Be Obtained 3.2.6.1 TAMRA-DNA Does Not Dissociate from NV1042 in Competition No recovery of intensity was observed after adding a competitor to the

NV1042-DNA complex. To examine this, a series of control experiments were

conducted. A 1:1 complex of NV1042 and TAMRA labeled DNA oligo were pre-

incubated for 10 minutes. Then competitor was added; the same amount of

73 DMSO was also added to a control sample and intensities recorded. The results indicate no changes in intensity were observed after more than 2 hours (Figure

3.5), which means that the competitor cannot compete with the TAMRA-DNA and bind to NV1042. It is probably due to the very slow dissociation kinetics of

NV1042-DNA binding (See Jacquelyn Niederschulte’s thesis).

3.2.6.2 Exploring the Possibility of Dye Interference for NV1042-DNA Binding Since the DNA competitor was unable to compete with TAMRA-DNA

(ODN-20-T3), it was speculated that dye placement may be an issue. We have observed that different dye positions on DNA hairpin can have different dynamic ranges in PA-DNA interactions (Dupureur et al., 2012). We tried another DNA

hairpin with a different TAMRA location (ODN-20-T16; shown in Table 3.1).

Kinetics of competition were followed as described above. The results showed

that there was no recovery with this DNA hairpin either (Figure 3.6). Thus,

NV1042 cannot be replaced from DNA via competition, consistent with very slow

dissociation kinetics.

3.3 Discussion 3.3.1 Comparison of Linear and Non-Linear van’t Hoff Analysis of PA- DNA Interactions Although the linear van’t Hoff equation has been widely utilized to examine

the state functions in a biological binding system, in some cases, enthalpy and

entropy do change a lot with temperature (ΔCp is not 0). For example, the intrinsic ΔCp for the association of single helical DNA strands to form the dsDNA found to be small, ~0.1 kcal mol-1 K-1 (Holbrook et al., 1999). Another small heat

74 Figure 3.5 Competition kinetics of NV1042 with ODN-20-T3. 3 nM DNA-PA complex, 10 mM HEPES, 50 mM NaCl, 1 mM EDTA and 1 mM CHAPS in water, pH 7.4, at 25°C. These two sets of data were individually obtained along time, not averaged.

75 Figure 3.6 Kinetics of NV1042 competition with 3 nM ODN-20-T16 showed no recovery. 60 nM competitor (ODN-20) was added. Condition: 10 mM HEPES, 50 mM NaCl, 1 mM EDTA and 1 mM CHAPS in water, pH 7.4, at 25°C. This set of data was individually obtained along time, not average.

76 capacity change (~ −0.2 kcal mol-1 K-1) was obtained for the E. coli single-strand binding (SSB) protein binding to dT DNA (Kozlov & Lohman, 2000). Hoechst

33258, which is a DNA minor groove binder, has a small negative heat capacity

-1 -1 change (0.33 kcal mol K ) when binding to a A3T3 duplex (Haq, 2002). For the

binding between TATA-box-binding protein Pho-TBP and its target DNA (TATA-

1), ΔCp is −1.19 kcal mol-1 K-1, and was considered to be large (Nagatoishi et al.,

2009). A large negative heat capacity change was also obtained for the

interaction of Na+ with thrombin, a principal enzyme of hemostasis, to be −1.1

kcal mol-1 K-1 (Bergqvist et al., 2004).

To assess possible nonlinear ΔCp values for PAs studied here, PA-DNA

Kds at variable temperatures were fit to a non-linear form of the Gibbs-Helmholtz

equation and compared to linear fits (Figure 3.7 and Figure 3.8). The curvatures are small and negligible except for KA1002, for which the data were exceptionally noisy. ΔCp of all four PAs ranges from –0.4 to 0.3 kcal mol-1 K-1 except for

KA1002 (ΔCp of which is 1.3 kcal mol-1 K-1) (Table 3.6). It is concluded that the

linear fit is sufficient for this study.

3.3.2 Sequence Dependence of DNA Binding Thermodynamics of Small Groove Binders Netropsin, Dst. A, and Hoechst 33258 The thermodynamics of netropsin and Dst. A-DNA interactions have been

reported in numerous publications (Breslauer et al., 1987; Majumder & Dasgupta,

2011; Martino et al., 2007; Rentzeperis et al., 1995). The results in Table 3.7

show that ΔH/ΔS compensation can vary with different DNA duplexes. Netropsin,

77 Figure 3.7 Comparison of linear and non-linear fits to the PA-DNA binding data. (A) Linear fit of ΔG of KA1039 with 0.2/0.5 nM ODN-6-T3 at varied temperatures. (B) Non-linear fit of ΔG of KA1039 in the same conditions in A. (C) Linear fit of ΔG of KA1002 with 0.1/0.2 nM ODN-6-T3 at varied temperatures. (D) Non-linear fit of ΔG of KA1002 in the same conditions in C. All reactions above are carried out in 10 mM HEPES, 50 mM NaCl, 1 mM EDTA and 1 mM CHAPS in water, pH 7.4. Each point represents at least 3 replicates. The error is calculated by (maximum-minimum)/2 method.

78 LFTT-T6 LFTT-T6

Figure 3.8 Comparison of linear and non-linear fits to the PA-DNA binding data. (A) Linear fit of ΔG of NV1028 with 0.25 nM ODN-14-LFTT-T6 in HNE buffer, 1mM CHAPS at varied temperatures. (B) Non-linear fit of ΔG of NV1028 at the same conditions in A. (C) Linear fit of ΔG of NV1042 with 0.1 nM ODN-20-T3 in HNE buffer, 1mM CHAPS at varied temperatures. (D) Non- linear fit of ΔG of NV1042 at the same conditions in C. All reactions above are carried out in 10 mM HEPES, 50 mM NaCl, 1 mM EDTA and 1 mM CHAPS in water, pH 7.4. Each point represents at least 3 replicates. The error is calculated by (maximum-minimum)/2 method.

79 Table 3.6 Comparison of energies of PA-DNA direct binding by different fitting.a

PA (size in ΔG° ΔH° −TΔS° ΔCp Fitting rings) kcal/mol kcal/mol kcal/mol kcal/K Linear -19.1 7.2 0 KA1039 (6) -11.8 Non-linear -19.5 7.5 -0.1 Linear -19.9 6.2 0 KA1002 (8) -13.3 Non-linear -27.1 13.6 1.3 Linear 1.0 -14.4 0 NV1028b (14) -13.4 Non-linear 0.2 -13.7 -0.4 Linear 12.2 -25.7 0 NV1042 (20) -13.5 Non-linear 13.4 -26.9 0.3 a. Conditions: 10 mM HEPES, 50 mM NaCl, 1 mM EDTA and 1 mM CHAPS in water, pH 7.4, at 25°C. Each ΔG° is the average of at least three individual measurements. The error is calculated by (maximum-minimum)/2 method. Errors of ΔH° and −TΔS° come from the van’t Hoff analysis in Kaleidagraph software. b. Data for NV1028 with ODN-14-LFTT-T6 presented here.

80 Table 3.7 Netropsin/Dst. A/Hoechst/NV1028 binding to DNA at 25°C. DNA ΔG°, ΔH°, −TΔS°, DNA sequence binder kcal/mol kcal/mol kcal/mol poly[d(AT)] -12.7 -11.2 -1.5 Netropsina poly(dA)·poly(dT) -12.2 -2.2 -10.0 poly[d(AT)] -12.6 -18.5 +5.9 Dst. Aa poly(dA)·poly(dT) -11.4 -4.2 -7.2 Hoechst poly[d(AT)] -10.3 -6.2 -4.1 b 33258 d(CGCAAATTTGCG)2 -7.7 +4.4 -12.1 ODN-14-SFAT-T3c -14.9 ± 0.1 -5 ± 3 -10 ± 3 NV1028d ODN-14-SFTT-T3c -13.9 ± 0.2 8 ± 2 -22 ± 2 a. Data of Net/Dst. A was collected from Breslauer et al. (1987). b. Data of Hoechst 33258 was collected from Han et al. (2005) and Haq (2002). c. SFTT refers to short flank TT sequence, SFAT refers to short flank AT sequence. These sequences can be found in Table 3.1. d. Each ΔG° is the average of at least three individual measurements. The error is calculated by (maximum-minimum)/2 method.

81 Dst. A, and Hoechst 33258 (Figure 3.9) have the same sequence dependent thermodynamic when binding to a DNA duplex: it is enthalpically driven with poly[d(AT)] duplexes; it becomes entropically driven with poly(dA)·poly(dT) duplexes.

As shown in Table 3.7, NV1028 binds DNA more strongly than other three minor-groove binders (ΔG° is more negative). One similarity among the DNA minor groove binders presented in this table is that the DNA binding thermodynamics is sequence dependent: the NV1028-SFAT interaction is less entropically driven than the NV1028-SFTT interaction, just like for netropsin and

Dst. A binding to different DNA sequences (Table 3.7).

NV1028 interacting with ODN-14-SFAT-T3 has a different binding thermodynamics in comparison to that of NV1028 interacting with ODN-14-SFTT-

T3: the later one has a more positive ΔH, and more negative −TΔS. This could be due to the different degrees of hydration of DNA sequences, and this will be further discussed in the next section.

3.3.3 Sequence-Dependent Minor Groove Properties and Their Impact on Large PA-DNA Interactions 3.3.3.1 Groove Widths Vary Depending on DNA Sequence Pattern The NV1028-DNA interaction was studied using two binding sequence patterns: TTGTTTTTTA (ODN-14-SFTT) and TAGATATATA (ODN-14-SFAT), which represent two opposite A/T patterns of the DNA sequence. It is well known

82 Figure 3.9 Structure of Hoechst 33258 and its DNA binding site. Reprinted with permission from (Haq et al., 1997). Copyright (1997) Elsevier.

83 that T tracts have wider minor grooves and can compress more water molecules

(Marky & Kupke, 1989).

To predict the differences in minor grooves for different DNA recognition

sequences of NV1028, a high-throughput DNA shape prediction online tool (Zhou

et al., 2013) was used. In Figure 3.10 and Figure 3.11, it is obvious that the

minor groove widths in the two sequences are quite different: ODN-14-SFTT has a wider minor groove than ODN-14-SFAT (the difference can be up to 3 Å at certain base-pairs). This difference in minor groove width directly relates to different degrees of desolvation of DNA minor grooves during PA binding.

3.3.3.2 NV1028 Binds to DNA Depending on A/T Pattern In Chapter 1, Dervan’s rule was discussed: imidazole recognizes G, and

pyrrole recognizes A and T with no preference. This no preference was due to

the similar hydrogen bond topology between pyrrole-A and pyrrole-T (Dervan &

Edelson, 2003). This is true when only focusing on each base pair on DNA.

Different A/T patterns in DNA sequences have different degrees of

hydration, which is related to the hydrophobic effect. It has been reported that the

poly(dA)·poly(dT) sequence is more hydrated than the poly[d(AT)] sequence,

which means they contain different amount of water in the grooves (Marky &

Kupke, 2000). The more hydrated sequence will provide a larger hydrophobic

effect upon PA binding as more water molecules being displaced by PA. An

84 Figure 3.10 DNA minor groove width prediction of ODN-14-SFTT (GCTAGTTTTTTAGCT). Underlined sequence refers to PA binding site. Data were computed using the tool from Zhou et al. (2013).

Figure 3.11 DNA minor groove width prediction of ODN-14-SFAT (GCTAGATATATAGCT). Underlined sequence refers to PA binding site. Data were computed using the tool from Zhou et al. (2013).

85 example of degrees of hydration in different DNA sequences is shown in Figure

3.12. It is obvious that the degree of hydration between the two DNA sequences is quite different.

Different sized minor grooves may also cause different hydrogen bond topology between PA and DNA, as the bond length and orientation can change.

If the distance between the H bond donor and acceptor increases, ΔH of the PA-

DNA interaction will decrease.

3.3.4 Analyzing the PA Size Effect on ΔH/ΔS Compensation ΔH/ΔS compensation is quite common in biological processes as described in Section 1.8. To examine how ΔH/ΔS compensation affects the DNA binding thermodynamics of PAs in this study, the thermodynamics of different sized PAs are summarized in Figure 3.13, and data of KA1039 from Wilson

(Wang et al., 2012) is also included for reference. While my data is consistent

with Wilson’s data in that KA1039-DNA binding is enthalpically driven, in this

study a significantly unfavorable −TΔS is reported. This could be due to the

different techniques.

Figure 3.14 summarizes the trends in a different form: PA size does affect the enthalpic and entropic contribution to total binding free energy. As PAs increase in size, ΔH becomes more positive (less favorable) and contributes less to ΔG, but −TΔS gets more negative (more favorable) and contributes more to

ΔG. For the larger PAs (NV1028 and NV1042), DNA binding is entropy driven.

86 a. b. B

Figure 3.12 Different degrees of hydration in different DNA sequence patterns. (a) The DNA sequence (CCTTTAAAGG) with less water molecules (DOI: 10.2210/pdb1IKK/pdb). (b) The DNA sequence (CGCATATATGCG) with more water molecules (DOI: 10.2210/pdb1DN9/pdb).

87 Figure 3.13 Comparison of the DNA binding energetics of Net., Dst. A, KA1039, KA1002, NV1028 and NV1042 at 25°C. Data for Net. and Dst. A were obtained from Marky and Kupke (2000). Data for KA1039 of Wilson were from Wang et al. (2012). Conditions of the data in this study: 10 mM HEPES, 50 mM NaCl, 1 mM EDTA and 1 mM CHAPS in water, pH 7.4, at 25°C.

88 Figure 3.14 Linear trends of energy terms of PAs in varied sizes. Conditions: 10 mM HEPES, 50 mM NaCl, 1 mM EDTA and 1 mM CHAPS in water, pH 7.4, at 25°C. Each ΔG is the average of at least three individual measurements. The error is calculated by (maximum-minimum)/2 method. Errors of ΔH and −TΔS come from the van’t Hoff analysis in Kaleidagraph software. Some error bars are too small to see.

89 Larger PAs could displace more water molecules in the minor groove and generate a larger hydrophobic effect, which leads to a more favorable −TΔS.

3.3.5 Comparing the Thermodynamics of DNA Intercalators and Groove Binders In Table 3.8, the DNA binding energetics of some groove binders and intercalators are summarized. When binding to DNA, the DNA groove binders can be either entropically or enthalpically driven; most of the DNA intercalators

(including bisintercalators) are enthalpically driven.

In Figure 3.15, ΔH was plotted versus −TΔS to compare the ΔH/ΔS compensation at 25°C as suggested by Jen-Jacobson (Jen-Jacobson et al., 2000)

and Chaires (Chaires, 2006). It is obvious that the cluster of groove binders is

ΔS-driven, and the cluster of intercalators is ΔH-driven. Since small PAs (6 or 8

rings) are more ΔH-driven, they are located in the cluster of intercalators (Figure

3.15). KA1039 from Wilson (6 rings #1) is right at the border between the two clusters, but it is still ΔH-driven (Wang et al., 2012).

Minor groove binders such as KA1039 (from this study), KA1002 and 6/8-

ring hairpin PAs are quite different compared to those ΔS-driven DNA groove

binders, but are instead similar to ΔH-driven DNA intercalators (Chaires, 2006).

The reason for this is unknown currently, but the following speculations are

offered:

1. This phenomenon relates to PA size. As size increases, the binding surface increases and more water molecules come off the DNA minor groove,

90 Table 3.8 Thermodynamic data for drug-DNA interactions at 25°C. Binding ΔG° ΔH° −TΔS° Drug Ref. mode kcal/mol kcal/mol kcal/mol Hoechst 33258 Ga -7.7 4.4 -12.1 Haq (2002) Hoechst 33258 G -11.8 10 -21.8 Han et al. (2005) Bernil G -8 0.6 -8.6 Haq (2002) DB226 G -8.5 -0.5 -8 DB244 G -9.9 -2.3 -7.6 Mazur et al. (2000) DB75 G -9 -2.2 -6.8 DB293 G -9.6 -3.6 -6 Propamidine G -7 -1.1 -5.9 Dst. A G -10.5 -5.8 -4.7 Haq (2002) Netropsin G -8.7 -5.8 -2.9 KA1039 G -11.1 -11.1 0 Wang et al. (2012) KA1039 G -11.8 -19.1 7.2 KA1002 G -13.3 -19.9 6.2 Presented in this study NV1028b G -13.4 0.9 -14.4 NV1042 G -13.5 12.2 -25.7 IIP-γ-PPPβDpc G -10 -6.9 -3.1 (Pilch et al., 1999) IPP-γ-PPPβDpc G -10.7 -6.7 -4.0 (Pilch et al., 1999) KA1033 G -11.7 -11.8 0.1 KJK6021 G -11.9 -14.1 2.2 Wang et al. (2014) KA2127 G -11.7 -13 1.3 Actinomycin D Id -7.5 -6.8 -0.7 Qu et al. (2003) Doxorubicin I -8.9 -7.4 -1.5 Ren et al. (2000) Propidium I -7.5 -6.8 -0.7 Chartreusin I -7.4 -7.1 0.3 Barcelo et al. (2002) Daunorubicin I -7.9 -9 1.1 (Ren et al., 2000) NB506 I -5.9 -7.2 1.3 (Carrasco et al., 2001) Ethidium I -6.7 -9 2.3 (Ren et al., 2000) WP762 BIS-Ie -16.3 -29.4 13.1 (Portugal et al., 2005) WP631 BIS-I -15.3 -30.2 15.1 (Leng et al., 1998) Echinomycin BIS-I -7.5 3.8 -11.3 (Leng et al., 2003) a. G: groove binder. b. LFTT sequence was used. c. Data were obtained at 20°C. d. I: intercalator. e. BIS-I: bisintercalators.

91 Figure 3.15 Comparison plot for thermodynamics of drug-DNA bindings. The green circles represent data for DNA groove binders; The red squares represent data for DNA intercalators (see Table 3.8). The solid vertical line indicates the boundary which separates the two distribution groups of groove binders and intercalators. Data for “NV1042”, “NV1028” and “KA1039” are from this study (Table 3.1 and Table 3.3). “6 rings” are: #1: KA1039 (ImImPy-γ-ImPyPy-β-Dp) (Wang et al., 2012); #2: ImImPy-γ-PyPyPy- β-Dp (Pilch et al., 1999); #3: ImPyPy-γ-PyPyPy-β-Dp (Pilch et al., 1999). “8 rings” are: #1: ImImPyIm-γ-PyPyPyPy-β-Dp; #2: ImImPyIm-γ-PyPyPyPy-β- Ta; #3: ImImPyIm-γ-PyβPyPy-β-Ta (Wang et al., 2014). Netropsin and Dst. A data are from Haq (2002). Adapted with permission from Chaires (2006). Copyright (2006) Elsevier.

92 which enhances the hydrophobic effect, makes the binding more ΔS-driven.

2. It has been reported that isostructural molecules can be converted from

intercalator to groove binder (Figure 3.16) (Ahmad et al., 2014). This is achieved

by carefully tuning the charge distribution of the system. For intercalation, there

may be an intermediate groove-bound state. The complex would prefer to

interact with the negative potential of DNA grooves if it forms a large dipole. This

intermediate state can be stabilized then. For small PAs, there might not be a

strong enough dipole to stabilize the groove-bound intermediate state, so it behaves more like an intercalator. Meanwhile, it should be kept in mind that many groove binders are also small, but do not energetically behave as

intercalators.

3.3.6 The Apparent Kds from Direct Titration are Macroscopic It is noticed that several ΔHs in Table 3.4 and Table 3.6 are positive

(NV1028 with ODN-14-LFTT-T6, NV1028 with ODN-14-SFTT-T6, and NV1042

with ODN-20-T3), which rarely happens in biological interactions. This could be

due to the apparent binding affinities obtained by fluorescence assay being

macroscopic (mentioned in Section 3.2.2). The fluorescence assay measures

the intensity changes in TAMRA fluorescence due to all binding events. What if

more than one PA molecule binds to one DNA molecule? It has been shown that

PA8:DNA binding ratio is more than 1 (Wang et al., 2012). Stoichiometry

experiments of various sized hairpin PAs have been done on circular dichroism

93 Figure 3.16 One example of the isostructural molecules that can be converted from intercalator to groove binder. Compound 4 and 5 are considered as intercalators; compound 9 and 10 are considered as minor- groove binders. Reprinted with permission from (Ahmad et al., 2014). Copyright (2014) John Wiley and Sons.

94 (CD) and fluorescence spectroscopy (by Jacquelyn Niederschulte and Kristin

Bales in our group). And the results indicate a PA:DNA binding ratio of more than

2.5:1 for both NV1028 and NV1042. If this is true, there is more than one binding

event for KA1002, NV1028 and NV1042 binding to DNA.

It is not necessary that these PAs bind to one DNA molecule in the same

way and with the same thermodynamics. It is possible that they bind differently

and have significantly different binding energies. This would explain why some

ΔHs of DNA binding for NV1028 and NV1042 are favorable, while others are

unfavorable. This has been observed before when studying small PA-DNA

interactions (Martino et al., 2007). In isothermal titration calorimetry (ITC) studies

of Dst. A binding to [d(TGGGT)]4 quadruplexes, it was shown that 2:1 and 4:1

drug/quadruplex stoichiometry have different binding affinities (about 10-fold) and

thermodynamics, though both are entropically driven (Table 3.9). Netropsin

binding with Poly[d(A-T)]·poly[d(A-T)] duplex was also determined to be

entropically driven (Table 3.9).

3.3.7 Discrepancy Between ITC and van’t Hoff Analysis The van’t Hoff analysis has been very popular to look at the

thermodynamic properties. Normally one would measure binding at several

specific temperatures. These are used to compute ΔH and ΔS at any

temperature indirectly. Isothermal calorimetry has been used to directly measure

the enthalpy change in a specific closed system.

95 Table 3.9 Thermodynamic parameters for small PA-DNA interactions by ITC at 25°C.

Ka ΔG° ΔH° −TΔS° PA DNA n (106×M-1) (kcal/mol) (kcal/mol) (kcal/mol)

a 1.8 ± 0.2 0.4 ± 0.3 -7.6 ± 0.5 -1.9 ± 0.2 -5.7 ± 0.5 Dst. A [d(TGGGGT)]4 4.2 ± 0.2 4.0 ± 3.0 -8.8 ± 0.5 -2.4 ± 0.2 -6.5 ± 0.5 Dst. Ab Poly[d(A- n.a. 0.6 -12.6 -18.5 6.0 T)]·poly[d(A-T)] b Net. duplex n.a. 0.5 -12.7 -11.2 5.0 a. This is adapted with permission from (Martino et al., 2007). Copyright (2007) American Chemical Society. b. Errors and n are not provided in the source. This is adapted with permission from (Breslauer et al., 1987). Copyright (1987) National Academy of Sciences.

96 Which one is more accurate? This debate has been existed for decades and still under discussion. ΔH from ITC (ΔHcal) and ΔH from van’t Hoff plot (ΔHvH)

have been compared in many different systems. The difference varies with the

system.

One system is 18-crown-6 with BaCl2 in 0.2 M sodium acetate buffer (pH

at 5). In Liu & Sturtevant’s paper (Liu & Sturtevant, 1995), thermodynamic data

was measured by ITC (Table 3.10). ΔHcal is −6.44 kcal/mol at 25 °C. ΔHvH is

−5.98 kcal/mol at 25 °C. The ΔCp (vH) of this system calculated by Equation 2.4

-1 -1 -1 -1 is 63 cal mol K , ΔCp (cal) is 9.4 cal mol K .

In another paper of Horn & Russell (Horn et al., 2001), the same system

was analyzed by ITC under the same conditions, but they got ΔHcal of −7.95 kcal/mol, which also agrees well with the value (−7.5 kcal/mol, by calorimetry) found by Briggner and Wadso (Table 3.10) (Briggner & Wadsö, 1991). This value

is somewhat different from Liu’s ΔHcal, but agrees with his ΔHvH. ΔCp (vH) is 38.2

-1 -1 -1 -1 cal mol K , and ΔCp (cal) is 30.3 cal mol K , which is quite different from what

Liu found. According to these data, the reason that Liu got a different ΔCp might

be the inadequate calibration of ITC. Comparison of data mentioned above is

shown in Table 3.10.

Another system examined in this way is the binding of 2’-CMP to RNase A

(Table 3.10). Horn & Russell got ΔHvH of −11.0 kcal/mol and ΔHcal of −11.7

kcal/mol. For this system, it is difficult to compare to data from other groups since

97 Table 3.10 Comparison between calorimetry and van’t Hoff analysis at 25°C.a

ΔH ΔCp Reference system Method (kcal/mol) (cal mol-1 K-1) Liu and van't Hoff -5.98 63 Sturtevant Calorimetry -6.44 9.4 (1995) 18-crown-6 with BaCl2 in Horn et al. van't Hoff -7.76 ± 0.21 38.2 ± 35 0.2 M (2001) Calorimetry -7.95 ± 0.05 30.3 ± 2 sodium Mizoue and b acetate van't Hoff n/a n/a Tellinghuisen buffer Calorimetry -7.65 n/a (2004) (pH=5) (Briggner & Calorimetry -7.50 ± 0.05 n/a Wadsö, 1991) Horn et al. 2’-CMP with van't Hoff -11.0 n/a (2001) RNase A Calorimetry -11.7 n/a a. No error available in the publications if not shown in this table. b. n/a: not available.

98 the binding constant is very sensitive to variables such as pH, protein and salt concentration. But for ΔHvH and ΔHcal from the same experiment, they are in

good agreement.

It is also noticed that the vH method tends to have larger errors in ΔCp

than that found by ITC. ΔCp (vH) is determined by the second derivative of the vH plot, while ΔCp (cal) is determined by the first derivative of the plot of ΔHcal vs. temperature.

A more recent paper from Mizoue and Tellinghuisen (Mizoue &

Tellinghuisen, 2004) used ITC to explain the discrepancy between vH method

and calorimetric method. They used 3 different limiting fit models in the least-

squares analysis: constant error (unweighted) model, proportional error model,

and correlated error model. The binding system is the same as above: Ba2+

binding with 18-crown-6, but in water. The results indicate that the difference

among run-to-run trials is always larger than the precision of the current ITC data.

They also compared their data with Liu’s under the same conditions. Among all

the data they have, the χ2 value (a parameter describing the goodness of fit of

data to hypothetical distributions) is worse than expected. When comparing the

three fit models, proportional error model is the best for these data sets. One

reason they proposed that may contribute to this difference is the ITC standard

procedure of subtracting a blank from the experimental thermogram. At the first

few injections of titrant, the reaction is complete and the free Ba2+ concentration

is almost 0, but the anion concentration is building up. After the equivalent point,

the free Ba2+ concentration begins to increase, while in blank sample, the 99 concentration of free Ba2+ is constantly increasing. This causes an issue of comparing “apples” to “oranges”. They noticed that for BaCl2, the blank error can

be up to 10% of the largest sample peak.

Another correction should be done for some systems is using activities,

but not the concentration of the substance (Pethica, 2015). “Concentration” is

used to describe the amount of substance actively involved in a closed system

ideally. But in real life, the concentration is not always equal to the activity of that

substance. It has been suggested that people should carry out ITC experiments

at different concentrations to estimate this effect on the measured properties

such as binding affinity and ΔH (Krimmer & Klebe, 2015). This is especially

important when comparing different series of substances globally since different

substances have different activities under different concentration.

To sum up data in Table 3.10, the difference of ΔH between van’t Hoff

method and calorimetric method within each groups’ data set is very small and

may fall within the experimental error range. But there are clearly some

differences in the results among what different groups observed with the same

system and conditions. This may be attributable to different correction methods

applied to the ITC data sets.

However, in another case that identical samples (bovine carbonic

anhydrase II (CA II) and 4-carboxybenzenesulfonamide (CBS)) were analyzed by

14 independent labs using the same procedure (Myszka et al., 2003).

100 Consistency among the different data sets indicates interlaboratory variation is very small and not statistically significant for this system (Table 3.11).

Thus, the differences between ITC and van’t Hoff evaluation are case (or system) dependent. In this study, we are analyzing a series of PAs using same conditions across all the experiments to minimize this difference, and always compare “apple” to “apple”. The contribution from the errors should be minimized.

101 Table 3.11 Summary of ITC results on CAII-CBS complex formation from 14 labs.a Molar Binding Ratio ΔHITC Participant (N) (kcal mol−1) 1 1.01 ± 0.01 −8.9 ± 0.1 2 0.95 ± 0.01 −7.2 ± 0.5 3 0.81 ± 0.01 −11.8 ± 0.02 4 0.929 ± 0.007 −10.8 ± 0.1 5 0.987 ± 0.003 −10.20 ± 0.04 6 0.921 ± 0.003 −10.95 ± 0.05 7 1.03 ± 0.01 −10.0 ± 0.2 8 0.97 ± 0.05 −4.3 ± 0.3 9 0.891 ± 0.002 −11.53 ± 0.04 10 0.96 ± 0.02 −12 ± 1 11 1.25 ± 0.01 −9.3 ± 0.1 12 1.000 ± 0.003 −10.51 ± 0.04 13 0.90 ± 0.02 −12.6 ± 0.3 14 0.55 ± 0.08 −15 ± 4 a. Standard errors were determined by nonlinear least squares analysis. b. These data were adapted with permission from (Myszka et al., 2003). Copyright (2003) The Association of Biomolecular Resource Facilities.

102 Chapter 4 The Roles of Electrostatics in PA-DNA Interactions: Salt and pH-Dependent Binding Affinity of Hairpin PAs of Variable Sizes 4.1 Introduction The electrostatic force, or Coulombic force, which is defined as the repulsive or attractive force between two or more charged particles (Figure 4.1a), is commonly involved in the interactions between biological molecules. For example, DNA molecules are negatively charged due to the phosphate group in each nucleotide. These negatively charged phosphate groups are evenly distributed on the DNA backbone and attract cations in solution, which leads to an increase in cation density surrounding the DNA molecules (Figure 4.1b) (Rau

et al., 1984). A positively charged ligand can bind DNA by forming electrostatic

interactions with phosphate groups and displacing these surrounding cations.

This phenomenon is also called the polyelectrolyte (PE) effect (Friedman &

Manning, 1984).

Furthermore, a non-uniform distribution of positively charged amino acids

(Arg, Lys, and His) in DNA binding proteins can be utilized to recognize specific

DNA sequences (Cherstvy, 2009). Some of the amino acids can be protonated

within the physiological pH range, which is 2-9 (Houghton et al., 1990) (e.g. pKa

of histidine is 6). For example, nucleosome core particles (NCP) in eukaryotic

cells wrap DNA with a sphere-cylinder like octamer of net positively charged

histone protein (Luger et al., 1997).

103 a. b.

Figure 4.1 Properties of charge-charge interactions. a) Scheme of electrostatic force. b) Cations around DNA molecule. Adapted from Williams (2017) with permission.

104 Smaller DNA binding ligands can also utilize positively charged groups to

enhance binding affinity and specificity (Bailly & Chaires, 1998). Examples

include netropsin, distamycin, and Hoechst 33258 (Figure 4.2). The amidine and

the imidazole rings in Hoechst 33258 provide positive charges to interact with the

DNA backbone. The aliphatic amidine has a pKa of ~12, which is always

protonated within the physiological pH range (Cozzi & Mongelli, 1998). It is

reported that only 15% of the total binding free energy between Hoechst 33258

and DNA comes from the polyelectrolyte (PE) effect, which is a small contribution

(Haq et al., 1997).

For netropsin and Dst. A, the amidines can participate in electrostatic

interactions. However, these amidines (pKa ~13) cannot be deprotonated within

the physiological pH range. One study has shown that netropsin can bind in the

minor groove, either completely or partially, while both positively charged

amidines always interact with the phosphate groups on the DNA backbone

(Freyer et al., 2007).

As mentioned earlier in Chapter 1, imidazoles, which can be protonated in

the physiological pH range (Walba & Isensee, 1961), have been introduced into

PAs to increase the binding specificity and affinity to DNA (Kopka et al., 1985;

Lown et al., 1986). The imidazole-containing synthetic PAs can therefore

potentially be more positively charged than netropsin and distamycin (Figure 4.3).

A free imidazole has a pKa of 6.96 (Walba & Isensee, 1961), but this may

105 Figure 4.2 Structures of distamycin, netropsin, and Hoechst 32258 at pH 7.4.

106 Figure 4.3 One example of hairpin PA-DNA complex structure. Lone pair electrons are indicated as circles with two black dots. Grey solid lines indicate putative hydrogen bonds. The hairpin PA shown is NV1028. W refers to A or T. For simplicity, only DNA binding sequence of NV1028 is shown here, but not the whole hairpin DNA sequence used in the experiments.

107 be different if it is incorporated into PAs. This means, at pH 7.4 (the condition

used in this study), an incorporated imidazole can be deprotonated depending on

its actual pKa. The charge on imidazole is important since it directly relates to the

number of the cations released/displaced after binding occurs at pH 7.4. In most

of the previous work on small PAs (Dervan & Edelson, 2003), it is assumed that

imidazoles are neutral (unprotonated) under experimental conditions (~pH 7). But

this may not be true (Bashkin et al., 2013).

To determine the electrostatic contribution to PA-DNA binding and how

many cations are being displaced by each bound PA, Equation 2.6 (Chaires,

1985, 1997; Record et al., 1978; Record et al., 1976) is used:

= = (2.6) [ ] 𝜕𝜕 log 𝐾𝐾𝑎𝑎 , + 𝑜𝑜𝑜𝑜𝑜𝑜 � 𝜕𝜕 𝑁𝑁𝑎𝑎 �𝑇𝑇 𝑃𝑃 −𝑧𝑧𝑧𝑧 𝑆𝑆𝐾𝐾 where Ψ is the fraction of sodium ions bound per phosphate group on DNA

backbone, z is the apparent charge on the ligand. zΨ value refers to the number of cations released from the binding site. -zΨ is also known as SKobs, which is

defined as the slope of the linear fit of logKd vs. log[salt]. More details are

discussed in Section 2.6.

The PE effect contribution of netropsin and Dst. A to the DNA binding free

energy has been analyzed. The results indicate that for both of them, PE effect

contributes little to the total free binding energy (SKobs is around -0.5, less than

10% PE contribution) (Marky & Kupke, 2000; Taquet et al., 1998). Details will be

discussed in Section 4.3.2.

108 Wilson’s group has analyzed the contribution of the PE effect to the free

binding energy between DNA and a 6-ring synthetic PA (containing 2 imidazoles)

(Wang et al., 2013). It has been shown that the PE effect contributes little to the

PA-DNA interactions (SKobs=−0.9, ~15% PE contribution). This small contribution

of PE effect to the PA-DNA interaction can be attributed to the large cation

concentration around DNA. The ionic atmosphere surrounding the DNA double

helix is difficult to be interfered by solvent or ligands (Mills et al., 1985).

For synthetic PAs, a 3-(dimethylamino) propylamine (Dp) or 3,3’ -diamino-

N-methyldipropylamine (Ta) tail is often attached to the C terminal of PAs (Wang

et al., 2014) (discussed in Chapter 1). A Dp tail has a 1+ charge, and a Ta tail

has a 2+ charge at pH 7.4. Dervan’s group has shown that the Dp tail effectively

improved the binding specificity of a 6-ring hairpin PA towards target DNA (44-

fold) by forming additional electrostatic interactions with a strong preference for

T·A base pairs (3-fold over G·C base pairs) (Swalley et al., 1999).

However, it is unknown that how much the PE effect contributes to the

total binding free energy for larger PAs (more than 8 rings) with Dp or Ta tails. In

this study, the binding affinities between hairpin PAs of varied sizes and their

cognate DNA sequences were determined as a function of salt concentrations or

pH using the fluorescent assay. How these parameters correlate with PA-DNA

binding energies was also examined.

4.2 Models and Theories for General Electrostatic Interaction Analysis To analyze the electrostatic contributions to a binding event, two models

are normally applied. The first is the Poisson-Boltzmann (PB) theory (Jacobson & 109 Saleh, 2016). Poisson-Boltzmann (PB) theory is used to map the 3D spatial

distribution of the ion atmosphere. It has been shown that PB theory prediction

matches experimental data quite well when describing the interactions of DNA

with monovalent cations, and there is no concentration limit for its application.

However, PB is not appropriate for higher valence ions (Jacobson & Saleh, 2016).

According to Jacobson and Saleh (2016), Oowasa-Manning counterion

condensation theory is another commonly used theoretical model for biological

systems. It is normally used under low-salt conditions (sub-molar level). Double-

stranded DNA is assumed to be a cylindrical polyelectrolyte and one charge

interacts with another charge. In the absence of the ligand, when its linear charge

density reaches a certain level (Manning, 1969), the monovalent counterions will

begin to condense onto its surface. According to Friedman and Manning (1984),

this higher local concentration of bound counterions and the screened Coulombic

interactions among the polyelectrolyte groups provide electrostatic stresses to

the system. These stresses will become greater if the average charge spacing on the substrate gets smaller. When ligands bind to the site, this charge spacing is increased by either neutralization, if the ligand is charged; or by substrate

lengthening, if the ligand is an intercalator. For DNA, groove binders may also

extend or bend the helix which leads to an increasing local charge spacing. Thus,

the electrostatic stresses are relieved. The release of counterion to the bulk solution also relieves the stress of the salt concentration gradient between the bulk solution and the substrate surface. These two theories are often combined to calculate the exact number of ions around the double-strand DNA. In our

110 approach, an accurate number of ions surrounding the DNA is not necessary to

analyze the contribution of the PE effect to the total binding free energy. Thus,

counterion condensation theory will be used.

4.3 Results

4.3.1 SKobss in All Hairpin PA-DNA Interactions are Small

SKobs (or -zΨ) represents the salt dependence of Kds of PA-DNA interactions (details see Section 2.6). Binding affinities of different sized hairpin

PAs towards their cognate DNA under different salt concentrations were

collected. Results are shown in Figure 4.4 and Table 4.1. Kds of NV1028 (14

rings) and NV1042 (20 rings) are lower than those of KA1039 (6 rings), which

means larger PAs bind more avidly across a wide salt concentration range (50 to

500 mM NaCl). In addition to this, all PA-DNA interactions have a small dependence on ionic strength (SKobs>−1). In other words, Kds of these PAs are

not significantly salt-independent.

Ψcalc is calculated based on zcalc (Table 4.1). Ψ stands for the fraction of phosphate in the DNA shielded by condensed counterions (cations here). In a previous study, it has shown Ψ to be 0.88 for double-strand B-DNA (Record et al.,

1978). Thus, z in theory (applying Ψ of 0.88) should be smaller than zcalc. This

can be explained that not every imidazole is protonated in PA-DNA interactions.

And for different PAs, this percentage of protonation is different. Since Ψcalc is

calculated by assuming completed protonation of imidazoles.

111 Figure 4.4 Salt Dependence of equilibrium binding constants on NaCl concentration for KA1039 (black circle), KA1002 (blue triangle), NV1028 (green diamond) and NV1042 (red square) at 25°C. LFTT DNA hairpin is used in NV1028 titrations. Conditions are pH 7.4, 1 mM CHAPS, 10 mM HEPES, 50 mM NaCl and 1 mM EDTA. Each point is the average of at least three individual measurements. The error is calculated by (maximum- minimum)/2 method. Some error bars are too small to be seen. Data are presented as a double logarithmic plot according to Revzin (1990). Least square linear fits are shown as solid lines. SKobss are obtained from the fitting slope.

112 a Table 4.1 SKobs of different sized PAs.

PA size b SKobs c PA PA sequence zcalc Ψcalc (rings) (-zΨ)

6 KA1039 4 -1.0±0.1 -0.25

8 KA1002 4 -0.9±0.2 -0.23

14 NV1028d 3 -0.2±0.1 -0.07

20 NV1042 4 -0.5±0.1 -0.13

a. Conditions: pH 7.4, 1 mM CHAPS, 10 mM HEPES, 50 mM NaCl, 1 mM EDTA. Data were fitted by KaleidaGraph. Closed circle refers to imidazole, open circle refers to pyrrole, open diamond refers to β-ala. Errors are obtained from the linear fitting in the Kaleidagraph software. b. zcalc is calculated apparent charge on the PA assuming imidazole is protonated (+1) at pH 7.4. a Dp tail has a 1+ charge, and a Ta tail has a 2+ charge. c. Ψcalc is calculated based on zcalc and SKobs. d. LFTT DNA hairpin is used in NV1028 titrations.

113 4.3.2 The PE Effect Contribution to the Total PA-DNA Binding Free Energy

Dissection of ΔG into ΔGpe (ΔG from the PE effect) and ΔGnon-pe (ΔG

excluding the PE effect) was computed by using SKobs (-zΨ, in Figure 4.4, details

in Section 2.6) in Table 4.1 with Equations 2.5, 2.6 and 2.7 (Chaires et al., 1996;

Record et al., 1991). These ΔG values are summarized in Table 4.2. Errors of

ΔG, ΔGpe, ΔGnon-pe are very small. Most of ΔGpe are relatively low compared to

ΔG.

The first observation obtained from Figure 4.5 and Table 4.2 is that the

contribution of ΔGpe to ΔGtotal is very small for all PAs of varied sizes, no matter

what the salt concentration is. This is expected as DNA double helix is coated

with condensed cations in solution (Record et al., 1978). These cations cannot

be easily displaced with the DNA binding ligands. Based on Equation 2.5 and

Table 4.2, % of ΔGpe is positively correlated with SKobs. To compare, a thermodynamic study of the F factor relaxase domain binding of single-stranded

DNA is used. This interaction has a SKobs of −4.3, and the ΔGpe proportion is

about 48% at 100 mM NaCl. This is considered a large contribution of ΔGpe to

ΔGtotal (Stern et al., 2004).

The percentage of ΔGpe contribution to ΔGtotal decreases as salt

concentration increases in all cases. These results are consistent with counterion condensation theory (Manning, 1969). Another observation in Figure 4.5 and

Table 4.2 is that ΔG°non-pe varies at different NaCl concentrations. For example,

114 Table 4.2 Summary of the Gibbs free energy dissection of different sized PA-DNA interactions.a

PA [NaCl] SKobs ΔG ΔGpe ΔGnon-pe PA % of ΔGpe size mM (-zΨ) kcal/mol kcal/mol kcal/mol 50 -12.0±0.0 -1.7±0.1 -10.3±0.1 14.1 100 -11.6±0.1 -1.3±0.1 -10.3±0.1 11.2 -11.4±0.0 -1.1±0.0 -10.3±0.1 9.4 KA1039 6 150 -1.0±0.1 200 -11.2±0.0 -0.9±0.0 -10.3±0.1 8.2 300 -11.0±0.0 -0.7±0.0 -10.3±0.1 6.2 50 -13.4±0.0 -1.5±0.4 -11.9±0.4 11.3 100 -13.3±0.0 -1.2±0.3 -12.1±0.3 8.8 KA1002 8 150 -0.9±0.2 -12.9±0.0 -1.0±0.2 -12.0±0.3 7.4 250 -12.9±0.0 -0.7±0.2 -12.2±0.2 5.4 280 -12.4±0.0 -0.6±0.2 -11.8±0.2 5.2 50 -13.9±0.1 -0.4±0.1 -13.4±0.2 3.1 100 -13.7±0.0 -0.3±0.1 -13.4±0.1 2.4 NV1028b 14 -0.2±0.1 200 -13.7±0.1 -0.2±0.1 -13.5±0.1 1.7 300 -13.6±0.1 -0.2±0.0 -13.4±0.1 1.3 50 -13.3±0.1 -0.9±0.2 -12.3±0.3 7.1 150 -13.1±0.0 -0.6±0.1 -12.5±0.1 4.6 NV1042 20 -0.5±0.1 300 -12.8±0.0 -0.4±0.1 -12.5±0.1 2.9 500 -12.5±0.0 -0.2±0.0 -12.3±0.0 1.7 a. Conditions: pH 7.4, 1 mM CHAPS, 10 mM HEPES, 25 °C, 1 mM EDTA. b. LFTT DNA hairpin is used in NV1028 titrations. Each ΔG is the average of at least three individual measurements. Errors of ΔGs are calculated by (maximum- minimum)/2 method. Errors of SKobs are obtained from the linear fitting in the Kaleidagraph software.

115 Figure 4.5 ΔG partition of PAs indifferent sizes. ΔG Partitioning was carried out using the data from Table 4.1 by applying equation 2 as described in the text. Conditions: 1mM CHAPS in 1x HNE buffer at 25 °C.

116 in Figure 4.5B, for KA1002 at 250 mM NaCl, the ΔG°non-pe is slightly larger than

those at 150 mM and 280 mM. This is due to experimental error in Kd

determinations at 100 and 250 mM NaCl (Figure 4.4).

4.3.3 The Relationship between SKobs and the Number of Imidazoles in PAs PAs used in this study also have different numbers of imidazoles (Table

4.1). Since imidazole recognizes guanine, more imidazoles were added to improve PA binding sequence specificity (Kopka et al., 1985; Lown et al., 1986).

As is discussed in Section 4.1, The charge of Imidazole could be a factor in the

electrostatic contribution to DNA binding.

To understand this, SKobs was plotted versus the number of imidazoles in

the PAs tested (Figure 4.6). Two relatively large PAs are in black, and the other

smaller PAs are in blue. Although the PE effect is very small, there is a positive

correlation between the number of imidazoles and SKobs. No correlation was

observed between PA size and SKobs (NV1042, the largest PA, has a medium

SKobs, while NV1028, a medium-sized PA, has the lowest SKobs). It could be

rationalized that a few imidazoles on PAs may be protonated (Scheme 4.1), so

they displace more cations in the DNA binding site at pH 7, which results in a

more negative SKobs. This would also explain why in Figure 4.5, NV1028, with

one imidazole, has the smallest ΔGpe (red column) of the PAs tested.

117 Figure 4.6 Correlation between SKobs and the number of imidazoles. Blue data points refer to smaller PAs, and black data points refer to larger PAs. Conditions: 1mM CHAPS in 1x HNE buffer at 25 °C. Each point is the average of at least three individual measurements. The error is calculated by (maximum-minimum)/2 method.

118 Scheme 4.1 Protonation of imidazole.

119 4.3.4 Apparent pKa of DNA binding by KA1039

The pH dependence of DNA binding provides an estimate of the pKa of the PA imidazoles. This experiment was performed with KA1039, which contains three imidazoles.

Kds of KA1039 binding to its cognate DNA at different pH were determined by Hyung Park using the fluorescence assay. Results are shown in Figure 4.7.

The Kd at pH 10 was about 3-fold larger than those at pH 5 and 7.4. Because the pH dependence of Kd is so small, Kds at only three pHs were obtained. These

data are enough to conclude that the pKa of imidazole is obviously larger than 7,

and therefore the imidazoles in KA1039 are protonated at assay pH (7.4). The

shift in pKa from that of free imidazole is probably due to the local environment in the PA. As imidazole becomes deprotonated at high pH, the charge-charge

interaction between PA and DNA is weakened slightly. This is expected since

ΔGpe is small. It also explains why SKobs is correlated with the numbers of

imidazoles in PAs.

4.3.5 Charge of TAMRA at Assay pH TAMRA is used as a fluorescent dye on DNA and it has been observed

that sometimes the dye can interfere with PA-DNA binding (Chapter 3 Section

3.2.5). It was necessary to analyze its overall charge at pH 7.4 to make sure the

interference does not come from an electrostatic effect. Since no relevant

literature could be found, MarvinSketch software (www.chemaxon.com) was used to predict the charge of free TAMRA at pH 7.4. To calibrate the program,

120 3x

Figure 4.7 pH dependence of KA1039 binding to its cognate DNA. Conditions: pH 5.0 (HMA): 1mM CHAPS, 3.3 mM HEPES, 3.3 mM MES, 3.3 mM acetate (pH 5.0), 50 mM NaCl, 1mM EDTA; pH 7.4 (HMA): 1mM CHAPS, 3.3 mM HEPES, 3.3 mM MES, 3.3 mM Acetate (pH 7.4), 50 mM NaCl, 1mM EDTA; pH 10 (CAPS): 1mM CHAPS, 10 mM CAPS (pH 10), 50 mM NaCl, 1mM EDTA. Hyung Park collected these data. The black solid line is not a fit, but a trend. Each point is the average of at least three individual measurements. Error is calculated by (maximum-minimum)/2 method.

121 MarvinSketch was applied to simple chemical compounds such as aniline,

phenol, and several amino acids. These predicted pKas are quite close to the experimental pKas. In Figure 4.8, the number next to the atoms of TAMRA represents its pKa in water at pH 7.4. The low pKa of the methylated amine group on the rhodamine core is probably due to the electron donating by the methyl groups. The overall charge of the TAMRA dye at pH 7.4 would be 0. As these pKas are quite low (smaller than 3), TAMRA should be neutral within our

experimental pH range (5-10).

However, the overall charge of zero does not mean that TAMRA does not

affect the electrostatic environment of the PA molecule and DNA. The local

charge due to deprotonation/protonation of a specific group (a methylated amine

group or a carbonyl group) of TAMRA may attract or repel the backbone of DNA

if these groups are positioned around the DNA backbone after binding.

Understanding this in more detail would require additional investigation.

4.4 Discussion

4.4.1 Small Salt dependence of Kd of Small Hairpin PA-DNA Binding

SKobs of some small PAs (6-8 rings, with 3 imidazoles in each) are

collected from the literature and shown in Figure 4.9. For all three small PAs, the

SKobs is close to -1, which means only one cation is displaced by each PA during

binding (Wang et al., 2014). This small SKobs indicates little contribution from the

PE effect to the total free energy. Thus, a smaller electrostatic effect is expected.

122 Figure 4.8 The pKa prediction of TAMRA at pH 7.4 by MarvinSketch (ChemAxon, Cambridge, MA) using default setting (considering tautomerization/resonance). The positive or negative sign refers to the charge on that atom at pH 7.4.

123 Figure 4.9 Dependency of equilibrium binding constants on NaCl concentration for PAs. KA1033 and KJK6021 (8 rings): SPR data with TGGAGA sequence, at 25 °C. (Wang et al., 2014); KA1039 (6 rings): SPR data with TGGCTT sequence, at 25 °C. (Wang et al., 2013). For inset PA schemes: closed circle refers to imidazole, open circle refers to pyrrole, single positive charge refers to Dp tail, two positive charges refer to Ta tail. Each point is the average of at least three individual measurements. The error is calculated by (maximum-minimum)/2 method. Some error bars are too small to see. SKobs values of all three PAs are around -1. Least square linear fits are shown as solid or dot lines. -ΔS is obtained from the fitting slope.

124 4.4.2 Comparing PA-DNA ΔGpes to Other DNA Ligand Systems

ΔGpes of some other DNA ligands are summarized in Table 4.3.

Doxorubicin and its variants are DNA intercalators (Chaires et al., 1993). Their

SKobs are ranged from -1.25 to -0.18. At pH 7, the amine group (R2 position in

Figure 4.10) is protonated, which results in a net charge of +1 on doxorubicin.

Substitution of a hydroxyl group for an amino group (hydroxyrubicin) will

neutralize the +1 net charge, which reduces the polyelectrolytic contribution to

the total binding free energy (Chaires et al., 1993).

Janus green blue (JGB) is a basic dye that binds DNA as an intercalator

(Saha & Suresh Kumar, 2016). At pH 6.2, it is positively charged. But the ΔGpe

contributes little to the total free energy (Table 4.3). It has a SKobs of -0.69, which

is similar to those of NV1042 and KA1002-DNA binding (Table 4.1). The authors

found that significant hydrophobic forces are the main sources of the non-

polyelectrolytic contribution.

Netropsin, distamycin, and the PAs in this study also show similar

behavior to DNA intercalators. The dissection of ΔG reveals a large non-

polyelectrolytic contribution to the total free energy (Table 4.2, Table 4.3, and the

blue histograms in Figure 4.5).

Some protein binding examples are also shown in Table 4.3. In the

system of two tyrosyl-phosphorylated peptides (CD3-ε and FcR-γ) binding to the

tandem SH2 domain of the Syk kinase, CD3-ε has a SKobs of -2.6 ± 0.1; FcR-γ

125 Table 4.3 ΔGpes of DNA ligands other than PAs.

DNA ligands ΔG°, kcal/mol ΔGpe°, kcal/mol Reference

Doxorubicina -8.8 -1.0

Daunorubicina -7.9 -1.0 Chaires et al. (1993) Hydroxyrubicina -7.2 -0.2

β anomer of doxorubicina -5.7 -1.0

Netropsinb -12.9 -2.6 Lah and Vesnaver Distamycin Ab -10.2 -0.8 (2004) Saha and Suresh Janus green bluec -6.5 -1.2 Kumar (2016) CD3-εd -11 -2.9 Grucza et al. (2000) FcR-γd -12 -3.4 deHaseth et Lac repressore -7.4 -10 al. (1977) Terry et al. EcoR If -16 -12 (1983) a. ΔG°s were collected with calf thymus DNA in a buffer of 6 mM Na2HPO4, 2mM NaH2PO4, 1mM Na2EDTA, 185 mM NaCl. pH 7.0, at 25 °C. b. These data were collected with binding to A tract duplex DNA at 25 °C by ITC. c. ΔG°s were collected with calf thymus DNA in 10 mM citrate-phosphate buffer containing 50 mM NaCl. pH 6.2, at 20 °C. d. These peptide ligands bind to Syk-SH2 domain at 150 mM NaCl. e. This data was collected at 4 °C, with buffer T (pH 8.0). ΔGpe° was collected at 50mM NaCl. f. The substrate of EcoR I is plasmid pBR322 (4362 bp). Data were collected at 50 mM NaCl, at 37 °C, with 0.02 M Tris·HCl buffer (pH 7.6).

126 Janus green blue (JGB)

Figure 4.10 Structures of some DNA ligands other than PAs. Reprinted with permission from (Chaires et al., 1993), copyright (1993) American Chemical Society; and from (Saha & Suresh Kumar, 2016), copyright (2016) Springer Nature.

127 has a SKobs of -3.1 ± 0.2 (Grucza et al., 2000). These SKobs are relatively larger

than those obtained from PA-DNA binding.

Lac repressor and EcoR I are two proteins that bind DNA electrostatically

(deHaseth et al., 1977; Terry et al., 1983). As shown in Table 4.3, both have

relatively large ΔGpe. It was concluded that the PE effect is an important contribution to the DNA binding. In these systems, it is also noticed that ΔGpe is

larger than ΔGtotal. This is due to the unfavorable contribution of ΔGnon-pe.

These data confirm that the SKobs presented in this study are relatively

small. Thus, the PE effect contributes little to the total free binding energy of the

PA-DNA interactions studied.

128 Chapter 5 Summary and Conclusions In summary, a complete thermodynamic analysis has been done on

hairpin PAs of varied sizes with different hairpin DNAs. Temperature perturbation

is introduced to carry out the van’t Hoff analysis, and the data is comparable to

those from ITC. Larger hairpin PAs bind DNA more entropically comparing to

those smaller enthalpic-driven PAs. And both large and small PAs bind different

DNA sequence patterns differently based on the degree of the DNA hydration. A

more hydrated DNA sequence will provide more favorable ΔS to the PA-DNA

interaction. Some of our data also suggest that there could be multiple binding

modes/events between large hairpin PAs and their cognate hairpin DNA

sequences. And further study is needed in the future.

The salt concentration dependence of Kd of PA-DNA interactions is also analyzed to figure out the contribution of PE effect to the total binding free energy.

Imidazoles were found to be partially protonated for different PAs. The salt concentration dependence of Kd is very small for each PAs, which means the

electrostatic force does not dominate the PA-DNA interactions in this study.

This is the first thermodynamic study for large hairpin PAs: NV1028 and

NV1042. These data could explain why these two PAs tolerates mismatches in

binding sites, and provide some clues for PA potency optimization.

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144 Appendix A Reference of DNA Hairpins Used in This Study.1 Hairpin name Lab designation Comments T3 labeled hairpin for KA1039 and ODN-6-T3 YS04109 KA10022 Unlabeled hairpin for KA1039 and ODN-6 JN04115 KA10022 ODN-14-LFTT- CD30004-13 T6 labeled hairpin for NV1028 (LF TT)3 T6 JN02076 ODN-14-LFTT JN03131-13C Unlabeled hairpin for NV1028 (LF TT)3 ODN-14-SFTT- YS08039 T3 labeled hairpin for NV1028 (SF TT)3 T3 ODN-14-SFTT YS07143 Unlabeled hairpin for NV1028 (SF TT)3 ODN-14-SFAT- JN02108 T3 labeled hairpin for NV1028 (SF AT)3, 4 T3 ODN-14-SFAT JN03077 Unlabeled hairpin for NV1028 (SF AT)3, 4 ODN-20-T3 CD26072 T3 labeled hairpin for NV1042 ODN-20-T16 JN04058 T16 labeled hairpin for NV1042 Unlabeled hairpin for NV1042 (for both ODN-20 JN04020 HP5 and HP6)4 1. TAMRA dye is used for all labeled DNA hairpins. 2. Hairpin sequences are obtained from a previous study (Wang et al., 2012). 3. LF TT refers to long flank TT sequence, SF TT refers to short flank TT sequence, SF AT refers to short flank AT sequence. 4. Hairpin sequences are obtained from a previous study (Dupureur et al., 2012).

145 Appendix B Spectroscopic Investigation and Direct Comparison of the Reactivities of Iron Pyridyl Oxidation Catalysts. This is a published work that I was working on during my first two years of

the Ph.D. program under the direction of Dr. Dupureur and Dr. Bauer (Song et al.,

2017).

Iron complexes are known to catalyze oxidation utilizing oxidants like

peroxides. And there is yet not an efficient and easy method to directly compare

these iron complexes’ reactivities. In this paper, our goal is to directly compare

the reactivities of a series of iron pyridyl complexes reacting with a dye substrate

called malachite green (MG), and peroxides or tert-Butyl hydroperoxide. This reaction provides colorless products. The UV-Vis spectroscopy is used: Spectra data and kinetic traces of absorbance at certain wavelengths were collected and fitted to the single exponential model.

The results indicate that tetracoordinate, nitrogen-based ligands that leave two coordination sites on iron open have the highest reactivities. And this spectroscopic method allows for the assessment and comparison of the reactivities/efficacy of different iron pyridyl complexes by analyzing the degradation of MG through changes in absorbance.

146 147 148 149 150 151 152 153 154