Developing a Bioscavenger Against Organophosphate Nerve Agents

Home , GH (nerve agent), GP (nerve agent), Omethoate

DEVELOPING A BIOSCAVENGER AGAINST ORGANOPHOSPHATE NERVE AGENTS

Reed B. Jacob

A Dissertation submitted to the faculty of the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Curriculum in Bioinformatics and Computational Biology

Chapel Hill 2017

Approved by:

Nikolay V. Dokholyan

Brian Kuhlman

Alexander Tropsha

Timothy C. Elston

Matthew R. Redinbo

ABSTRACT

Reed B. Jacob: Developing a bioscavenger against organophosphate nerve agents (Under the direction of Nikolay V. Dokholyan)

Organophosphate poisoning can occur from exposure to agricultural pesticides or chemical weapons. This exposure inhibits acetylcholinesterase resulting in increased acetylcholine levels within the synaptic cleft causing loss of muscle control, seizures, and death.

Mitigating the effects of organophosphates in our bodies is critical and yet an unsolved challenge. Weaponized organophosphates are a deadly threat to armed forces and civilians (e.g., exposure to one droplet of VX exceeds the LD 50 of ~1 5 μg/kg) . Here, we present two computational strategies to 1) identify a novel protein acting as a stoichiometric bioscavenger to covalently bind organophosphates, and 2) improve the stability and activity of organophosphate hydrolase as a catalytic bioscavenger through allosteric modulation.

We use our first computational strategy, integrating structure mining and modeling approaches, to identify novel candidates capable of interacting with a serine hydrolase probe

(either covalently or with equilibrium binding constants ranging from 20 to 120 µM). One candidate Smu. 1393c catalyzes the hydrolysis of the organophosphate omethoate (kcat /K m of

-1 -1 -1 3 -1 -1 (2.0±1.3)×10 M s ) and paraoxon (kcat /K m of (4.6±0.8)×10 M s ), V- and G-agent analogs respectively. In addition, Smu. 1393c protects acetylcholinesterase activity from being inhibited by two organophosphate simulants. We demonstrate that the utilized approach is an efficient and

iii highly-extendable framework for the development of prophylactic therapeutics against organophosphate poisoning.

Organophosphate hydrolase (OPH) degrades many classes of organophosphates and plays an important role in organophosphate remediation. Here, we demonstrate our second computational strategy of protein design to enhance OPH’s enzymatic hydrolysis of organophosphates and test on paraoxon, a G-agent analog, and omethoate, a V-agent analog. We identify five hotspot residues for allosteric regulation and assay these mutants for hydrolysis activity against paraoxon. A number of the predicted mutants exhibit enhanced paraoxon activity

-1 -1 over wild type OPH (k cat =907 s ), such as T54I/T199I (1260 s ) while the K m remains relatively unchanged. Computational protein dynamics study reveals four distinct distal regions that display significant changes in conformation dynamics. The enzymatic enhancement of OPH validates a computational design method that is both efficient and readily adapted as a general procedure for enzymatic enhancement.

This work is dedicated to my father Steve A. Jacob, who passed away before I began this journey. Education was very important to my father and he instilled the same in me. I also want

to dedicate this work to my wife Brooke Miel Jacob and my daughter Kara Mae Jacob. It was

your encouragement and support that made the many long hours worth it.

ACKNOWLEDGEMENTS

Over the last five years I have grown and developed as both a scientist and a person. To start here in Chapel Hill I moved my family from the northwest to an unknown state with no friends or family for support. Luckily, one of the greatest supports for our family has been our church and the friends we made there. We would not have survived these last years without these friends, especially the Conrad’s, who have become family.

Of utmost importance is my wife, Brooke Miel Jacob, who has tirelessly been a support.

She has been a sounding board listening to all of my ideas and frustrations. She has been a sturdy foundation that has been crucial to our family’s success. No matter what state of mind I presented to her during this time she was there to calm me down, or motivate me as needed.

During this five year journey we were blessed with the birth of our first child Kara Mae. It is thinking about the life I desire to create for both my wife and my daughter that has continued to motivate me in times of frustration.

Additionally, I would like to thank the Department of Biochemistry and Biophysics, and the rest of the support staff who made it possible for my project to maximize the funding provided for it, especially Marsha Lynn Ray and John Hutton. I would also thank the Program in

Molecular and Cellular Biophysics, and the Curriculum in Bioinformatics and Computational

Biology, for teaching me many skills, giving feedback for my work and assisting me in becoming a better scientist then when I started.

vi The members of my committee who always have expressed their unwavering support and crucial advice to advance my growth and development. My advisor, Nikolay Dokholyan, who encouraged me to think for myself and to think as a scientist. Lastly I want to thank all of those in my life, both educational and personal, who have influenced me in my path. I am excited for what the future will hold as I move forward.

vii

PREFACE

Parts of the work described in this dissertation has been previously published as:

Reed B. Jacob, James M. Fay, Cathy J. Anderson, Kenan C. Michaels, and Nikolay V.

Dokholyan. “Harnessing Nature’s Diversity: Discovering organophosphate bioscavenger characteristics among low molecular weight proteins.” Sci. Rep. 6, 37175; doi:

10.1038/srep37175 (2016)

Reed B. Jacob, Feng Ding, Dongmei Ye, Yazhong Tao, Eric Ackerman, and Nikolay V.

Dokholyan. “Computational redesign reveals allosteric mutation hotspots of organophosphate hydrolase that enhance organophosphate hydrolysis.” (In preparation)

viii

TABLE OF CONTENTS

LIST OF TABLES ...... xi

LIST OF FIGURES ...... xii

LIST OF ABBREVIATIONS AND SYMBOLS ...... xiii

CHAPTER 1: CHEMICAL WEAPONS OF MASS DESTRUCTION – A JOURNEY THROUGH HISTORY ...... 1

Acetylcholinesterase structure and mechanism ...... 2

Structure/History of organophosphate agents...... 5

Effects of OP exposure ...... 8

Current Protective Strategies ...... 9

Catalytic Bioscavengers ...... 10

Stoichiometric Bioscavengers ...... 11

CHAPTER 2: DISCOVERY, CHARACTERIZATION, AND INITIAL DESIGN OF LOW MOLECULAR WEIGHT PROTEINS WITH BIOSCAVENGER POTENTIAL ...... 14

Results & Methods ...... 16

Computational Workflow overview...... 16

Identify candidate scaffolds...... 19

Identification of top-ranked candidates...... 22

Protein expression and purification...... 25

OP covalent binding to top candidates...... 26

ix OP covalent binding to Smu. 1393c active site...... 29

Smu. 1393c catalytic activity and protection of AChE...... 29

Smu. 1393c activity against OPs...... 33

Discussion ...... 34

CHAPTER 3: COMPUTATIONAL REDESIGN OF ORGANOPHOSPHATE HYDROLASE TO ENHANCE ORGANOPHOSPHATE NERVE AGENT HYROLYSIS ...... 37

Results ...... 40

Prediction and experimental validation of single mutations...... 40

Prediction of double and triple mutations with experimental validations...... 43

Computational analysis of thermostability and conformational dynamics...... 45

Discussion ...... 49

Methods...... 51

CHAPTER 4: CONCLUSIONS AND FUTURE DIRECTIONS ...... 55

Stoichiometric bioscavenger ...... 56

Catalytic Bioscavenger ...... 58

APPENDIX A: SUPPLEMENTAL MATERIAL FOR CHAPTER 3 ...... 61

REFERENCES ...... 63

x LIST OF TABLES

Table 2.1 Characteristics of potential bioscavenger candidates...... 21

Table 2.2 Kinetic parameters for Smu. 1393c...... 31

Table 2.3 AChE protection by Smu. 1393c...... 33

Table 2.4 Kinetic parameters for Smu. 1393c against OPs...... 34

Table 3.1 Predicted stability scores and experimental data...... 42

Table 3.2 Specific heat calculations...... 47

Table 3.3 Analysis results of computational simulations...... 48

xi LIST OF FIGURES

Figure 1.1 AChE functional mechanism...... 3

Figure 1.2 AChE inhibition by OPs...... 4

Figure 1.3 Reactivation of AChE using oximes...... 5

Figure 1.4 Structures of OPNAs...... 6

Figure 1.5 AChE and BChE active site overlay...... 12

Figure 2.1 OP structures used in this study...... 16

Figure 2.2 Computational design workflow...... 18

Figure 2.3 Two-stage search workflow with identified search parameters...... 19

Figure 2.4 VX redocking to BChE...... 23

Figure 2.6 Experimental results of OP binding...... 27

Figure 2.7 Fluorescent polarization representations...... 28

Figure 2.8 Non-competitive Smu. 1393c inhibition...... 30

Figure 2.9 Smu. 1393c protects AChE function...... 32

Figure 2.10 Smu. 1393c catalytic activity...... 34

Figure 3.1 Structural representation of OPH...... 37

Figure 3.2 Residues selected for mutation consideration...... 40

Figure 3.3 Identified mutation positions...... 41

Figure 3.4 Illustrative example of kinetic data...... 44

Figure 3.5 Melting temperature calculated from computational simulations...... 46

Figure 3.6 Fluctuation analysis of computational simulations...... 48

Figure A.1 Specific Heat plots for all mutations...... 61

Figure A.2 Root Mean Square Fluctuations for all mutations...... 62

xii LIST OF ABBREVIATIONS AND SYMBOLS

AChE Acetylcholinesterase

ALS Amyotrophic lateral sclerosis

BChE Butyrylcholinesterase

CV Specific heat

CWC Convention on the Prohibition of the Development, Production, Stockpiling and Use of Chemical Weapons and on their Destruction

DFP Diisopropylfluorophosphate

DFP-probe ActiveX Desthiobiotin fluorophosphonate serine hydrolase probe

DMD Discrete molecular dynamics

DSM Demeton-S-methyl

FDA Food and Drug Administration

FP Fluorescence polarization huPON1 Human paraoxonase 1

IPTG Isopropyl β-D-1-thiogalactopyranoside kcat Enzymatic turnover number kDa kilo Dalton

Kd Equilibrium binding constant

Ki Inhibition constant

Km Substrate concentration at half maximum velocity

LD 50 Half lethal dose

OP Organophosphate

OPNA Organophosphate nerve agent

xiii OPH Organophosphate hydrolase

PDB Protein Data Bank

PNP-ac p-nitrophenyl acetate

PTE Phosphotriesterase

RMSD Root mean square deviation

RMSF Root mean square fluctuation

Rp Right handed plus chirality

Sp Left handed minus chirality

TFP-probe ActiveX Tamra fluorophosphonate serine hydrolase probe

Tm Calculated melting temperature

WHAM Weighted histogram analysis method

ΔG Free energy

ΔΔG Change in free energy; stability

ΔTm Change in calculated melting temperature

xiv

CHAPTER 1: CHEMICAL WEAPONS OF MASS DESTRUCTION – A JOURNEY THROUGH HISTORY

Organophosphate chemicals have been used as agricultural pesticides since their discovery in the early 20 th century. It was not until the culmination of World War I (WWI) that the governments of the world turned to chemical weapons and their development. 1 The first weaponized form of organophosphate chemicals was the organophosphate nerve agent (OPNA) tabun, first discovered in 1936 by Dr. Gerhard Schrader, an agricultural scientist. 1 OPNAs are small molecules comprised of both carbon atoms and at least one phosphate usually a chiral center, attached to different side groups. It is the identity of these groups that define the level of lethality associated with each organophosphate. OPNAs attack and inhibit acetylcholinesterase

(AChE) preventing acetylcholine clearage from the synaptic cleft indiscriminately of whether the host is an insect or a mammal leading to many muscular and neuronal symptoms such as bradycardia, bronchoconstriction, and peripheral nerve disease.2–4 Since their discovery many new OPNAs have been developed and categorized into the phosphotriester G-agents, and the phosphonothioate ester V-agents. When exposed to OPNAs the level of damage can be from mild to severe. The length and duration of exposure and the class of OPNAs determine the amount of time between exposure and symptom onset with initial symptoms starting mere minutes after exposure. 5–7 In many cases there is often insufficient time to administer treatment before death, such as exposure to soman (GD) leaving a window of treatment of two to six minutes.8,9 If treatment is administered in time the approved method of post exposure treatment

1 is a combination of oximes and atropines, which has the potential to reactivate AChE, though limited in effectiveness.

Due to the simplistic nature of these deadly chemical weapons, it is of great concern to develop means to protect against accidental exposure, in the case of pesticides, and purposeful exposure, in the case of terrorist attacks. This chapter will detail the mechanism of AChE inhibition, the nature of OPNAs with a brief history, the effects of AChE inhibition, and protection efforts currently under investigation.

Acetylcholinesterase structure and mechanism

Acetylcholinesterase is an enzyme located at synaptic junctions and is responsible for the degradation of the neurotransmitter acetylcholine, ending the current signal and preparing the junction to receive a new one. AChE falls in the family of enzymes known as cholinesterases.

The dominant feature of a cholinesterase is an esteric site consisting of a catalytic triad of three amino acids, serine, histidine, and glutamic or aspartic acid. In AChE the correct combination of these amino acids (Ser203, His447, and Glu334) leads to an activated serine that is capable of performing nucleophilic SN2 attack. (Figure 1.1a) The histidine is positioned to attract the hydrogen from serine thereby making serine negatively charged. This negative serine then attacks the carbonyl carbon of acetylcholine hydrolyzing the carbon-oxygen bond causing acylation of the serine. The deacylation of the serine proceeds when a water molecule attacks the now acylated serine and restores its functionality. (Figure 1.1b) This process is quick and

8 -1 -1 efficient occurring at a rate approximating the diffusion limit (k cat /K m of 1.5×10 M s ) making

AChE essential to brain-muscle communication. 10,11

Figure 1.1 AChE functional mechanism. A) The nucleophilic attack of SER203 releasing choline. B) A water molecule attacks the acylated serine restoring AChE functionality and releasing acetate.

Of key importance are three coordinating sites that help to direct acetylcholine into the proper orientation for hydrolysis. These sites consist of the oxyanion hole, an acyl pocket, and an anionic site. 12 The oxyanion hole is a necessary component for stabilization of the choline group in acetylcholine and operates by backbone amide groups (Gly121, Gly122, and Ala204) oriented to stabilize the negative charge from the deprotonated oxygen. The acyl pocket is comprised of aromatic amino acids, specifically two phenylalanine residues (Phe288 and Phe290) and is responsible for stabilizing the acetyl group in preparation for hydrolysis. The anionic site is responsible for stabilizing the partial positive from the choline group and contains a tryptophan as elucidated by an affinity labeling study (Trp84).13 The complexity of the AChE active site allows many different targets for drug therapies, including treatments for Alzheimer’s,

Parkinson, and other neurodegenerative diseases.14–18

When the function of AChE is interrupted there is an increase of the neurotransmitter acetylcholine within the synaptic cleft, effectively preventing signals from being cleared, making the brain unable to communicate causing loss of function that if left unchecked will lead to permanent damage even death for the exposed organism. This process is exactly what makes

3 organophosphates so dangerous. Organophosphates (OPs) are small molecules comprising carbon atoms and a phosphate chiral center and are detrimental to human health because of their ability to inhibit AChE (Figure 1.2). The difference in side groups defines class and lethality of

OPs. When AChE encounters an organophosphate it attempts to hydrolyze the molecule, cleaving the phosphate-oxygen bond in a similar manner to the carbon-oxygen bond in acetylcholine. The first half of the reaction proceeds as expected leaving a phosphorylated serine.

The problem arises that the water molecule does not present a large enough electronegative environment to overcome the favorability of the phosphate oxygen covalent bond, thus is unable to successfully reactivate the serine.

Figure 1.2 AChE inhibition by OPs. The mechanism by which OPs inhibit AChE, including the process of aging.

4 If caught in a timely manner it is possible to reactivate AChE after inhibition. This is accomplished through the use of oximes, such as pralidoxime. After the phosphorylation event occurs, if an oxime is presented to the inhibited AChE, it is possible to successfully reverse inhibition. This restoration of AChE activity occurs when the alcoholic oxygen of the oxime acts as a nucleophile to attack the phosphate, releasing the OP from the serine of AChE. (Figure 1.3)

Figure 1.3 Reactivation of AChE using oximes. The mechanism by which oximes, specifically pralidoxime, can restore AChE’s function prior to irreversible aging.

The ability to successfully reverse inhibition is only possible for a limited window post exposure as the combined AChE-OP complex undergoes a dealkylation event in which a water molecule acts as a nucleophile and separates the remaining ester group heightening the strength of the AChE-OP complex. (Figure 1.2). When AChE is exposed to OPNAs such as sarin and VX the time it takes for half of the AChE-OP complex to age is five hours and 48 hours respectively. 19 Theoretically, this would provide ample time for treatment, but the reality is the effects of AChE inhibition are felt at much shorter time intervals.

Structure/History of organophosphate agents.

As previously stated OPNAs are small molecules consisting of a chiral phosphate center with a terminal oxygen/sulfur connected to the phosphate by a double bond, two lipophilic

5 groups directly bonded to the phosphorus, and a leaving group (Figure 1.4a). The composition of the lipophilic groups and the leaving group differentiates OP pesticides from OPNAs and determines the toxicity level of the chemicals. The chirality around the phosphate is a key component for toxicity. The left handed minus chirality ( Sp ) is traditionally more toxic then the right handed plus chirality ( Rp ) as exemplified by the AChE inhibition rate for Sp -VX is 4×10 8

M-1min -1 compared to 2×10 6 M-1min -1 for Rp -VX. 20,21 The arrangement of the lipophilic groups in the Sp enantiomer is better suited to align to the AChE active site, as the AChE active site chirality is inverted from other serine hydrolases, thereby better stabilizing the transition state. 12,22,23 Though less toxic, the Rp enantiomer is still more than capable to deal a lethal blow, thus making OPNAs an effective chemical weapon and a real threat.

Figure 1.4 Structures of OPNAs. A) A generic structure of an organophosphate. B) Structures of the G-agent OPNAs. C) Structures of the V-agent OPNAs.

6 Chemical weapons came to the forefront during WWI, though organophosphates were not discovered until later. In fact, it was not until 1936 when Dr. Gerhard Schrader, a German agricultural scientist working for IG Farben, began experimenting with organophosphate molecules as insecticides and discovered that his newest creation, tabun, was extremely toxic to humans. Since this first OPNA was discovered by a German scientist, the nomenclature evolved to classify this type of OPNA as G-agents. The characteristics of tabun (GA) include a terminal

P=O, with lipophilic groups being a tertiary amine group and an ester bonded ethane group and the leaving cyanide group (Figure 1.4b). In addition, pure tabun is colorless, odorless, and volatile with a mouse LD 50 of 119 μg/kg for Sp -tabun and 837 μg/kg for Rp -tabun. Shortly after tabun toxicity against humans was revealed the German government started a OPNA development program headed by Dr. Schrader in which additional, more toxic variants were created, such as Sarin (GB), Soman (GD), and Cyclosarin (GF) (Figure 1.4b).

In response, Great Britain began their own OPNA program resulting in the discovery of an even more deadly class of OPNAs, the V-agents starting with VX in the 1950s. 24 A characteristic difference of this class of OPNAs lies in the ester bonded oxygen to be replaced with that of sulfur, defining the class as phosphonothioate esters (Figure 1.4c). In particular, it is in the leaving group location where this P-S bond occurs. In the G-agents the leaving group is small, often just one atom (fluorine), but in V-agents the addition of the Sulfur ester allows for an increased size of the leaving group, thereby increasing potential interactions with the AChE active site making V-agents more toxic then G-agents. Specifically, the leaving group of VX more directly mimics the choline group from acetylcholine. These differences allows VX to be

20 more toxic then any of the previous G-agents with an LD 50 of 12.6 μg/kg. Soon, additional V- agents were developed, Russian VX (VR) and Chinese VX (CVX) (Figure 1.4c). Since

7 inception, OPNAs have proven highly toxic and have prompted many attempts to formally ban their use in warfare and even in research and development, such as the Conference on

Disarmament in Geneva in the 1980’s. The United States was among the last nation to support the ban on OPNAs and did not take part until the CWC (Convention on the Prohibition of the

Development, Production, Stockpiling and Use of Chemical Weapons and on their Destruction) in 1993 where the United States formally recognized the dangerous potential of OPNAs and committed itself to their irradication. 25

Effects of OP exposure

Another factor involved in treating OP poisoning cases deals with the level of exposure.

Of great concern is the treatment and management of symptoms from low-level exposure that affects the muscarinic and nicotinic acetylcholine receptors located in both the central and peripheral nervous systems. The muscarinic receptor is a G-protein-coupled-receptor that activates a cascading pathway. The specific symptoms presenting from overstimulation of muscarinic receptors include loss of smooth muscle control leading to salivation, lacrimation, urination, defecation, gastrointestinal motility and emesis recalled by the common pneumonic of

SLUDGEM.6,7,26 Alternatively, the nicotinic acetylcholine receptors are gated ion channels that quickly perpetuate a signal and are more often found in the central nervous system. 27 As with the muscarinic receptors, when the nicotinic receptors get overstimulated the onset of symptoms such as anxiety, headache, convulsions, weakness and paralysis result. 28 In a study cataloging accidental exposure in farmers they found the most common reported symptoms include headaches, burning eyes, pain in muscles, rash or itchy skin, and blurred vision. 2

8 Additionally, it has been reported that sustained low-level OP exposure may affect the reproductive system. These symptoms in males often lead to poor semen, reduced seminal volume and motility, and decrease of sperm count per ejaculate. 29,30 The reproductive symptoms when a female is exposed involve menstrual cycle deviations, spontaneous abortions, stillbirths and potential birth defects. 29,30 Many studies have been conducted to ascertain the specifics of the onset of symptoms.

When OP exposure is acute reaching the level of lethal AChE inhibition occurs within minutes. After an acute exposure to OPNAs, if the treatment is not provided in time, the most common cause of death is asphyxiation from respiratory failure. 31,32 For example, the OPNA sarin reeked havoc when used in two terrorist attacks in Japan, leading to 19 dead with thousands more injured. 33–35 This fact illustrates the dangerous potential OPNAs have in the hands of terrorist and foreign nationals and why they pose a real threat to our armed forces and first responders.

Current Protective Strategies

In addition to destroying current stockpiles of OPNAs the United States in partnership with other nations is working toward preventative measures. Currently the only Food and Drug

Administration (FDA) approved pretreatment of organophosphate poisoning is pyridostigmine, an AChE reversible inhibitor that lessons the effect of OPNA when administered prior to exposure. 32,36 Pyridostigmine is only mildly effective and is classified as an AChE reversible inhibitor. When treated with pyridostigmine the symptoms from AChE inhibition present themselves though not as acutely as an irreversible inhibitor like OPNAs. An alternative method

9 for protection against OP exposure involves bioscavenger proteins. A bioscavenger is a small protein capable of identifying an OPNA and either hydrolyzing the OPNA (catalytic bioscavenger), or sequestering the OPNA through a covalent binding event that mimics AChE binding becoming permanently inhibited (stoichiometric bioscavenger).

Catalytic Bioscavengers

Organophosphate degrading enzymes, or catalytic bioscavengers, potentially provides the best solution for protection against OPNA exposure. Two main examples of catalytic bioscavengers include human paraoxonase 1 (huPON1) and organophosphate hydrolase (OPH).

HuPON1 is a glycoprotein expressed in the liver with a molecular weight of 43 kDa and is named after it was discovered to hydrolyze the degradation of the organophosphate paraoxon as well as other lactone and esters.37 HuPON1 has been studied in relation to many major neurodegenerative diseases such as Parkinson’s disease, Alzheimer’s disease, and amyotrophic lateral sclerosis (ALS). 38,39 A critical element in huPON1’s hydrolysis ability is the metal coordination of Ca 2+ ion. 40 There is a substrate-dependent residue at position 192 which whether it is a glutamine (Q192) or an arginine (R192) varies the catalytic efficiency from paraoxon

(R192) to soman and sarin (Q192). 40–43 The difficulty in using huPON1 as an effective catalytic bioscavenger is that it natively hydrolyses the less toxic OP enantiomer ( Rp ) preferentially to the more toxic one ( Sp ). 39,41 Additionally, the application of predictive protein design techniques to huPON1 is hampered because of the lack of a human crystal structure though there exists a crystal structure of a recombinant hybrid mammalian analog constructed from rabbit, mouse, rat and human. 41,44 Directed evolution has proven to be the most successful in improving the OPNA

10 4 6 -1 -1 6 hydrolysis activity of huPON1 from it’s initial k cat /K m of 10 to 10 M min to a k cat /K m of 10 to 10 8 M-1 min -1 for G agents.41,45,46 While successful in increasing the activity of huPON1 for G- agents, there has been little advancement in the activity of V-agents making it difficult to consider huPON1 as a broad spectrum catalytic bioscavenger.

The other major catalytic bioscavenger candidate is organophosphate hydrolase (OPH) also known as phosphotriesterase (PTE). OPH is a bimetallic protein dimer capable of hydrolyzing a multitude of OPNAs including the V-agents. The near native substrate for OPH is paraoxon with an enzymatic efficiency of ~10 8 M-1 s-1 which approaches the diffusion limit. 47

4 5 -1 -1 The efficiency of OPH for OPNAs is greatly diminished with k cat /K m between 10 and 10 M s

3 -1 -1 for G-agents, nearly half the efficiency of paraoxon, and a k cat /K m less than 10 M s for V- agents.48,49 By application of both directed evolution and computational design the OPH

5 6 -1 -1 efficiency for OPNAs has been increased to a k cat /K m between 10 and 10 M s for G-agents,

4 5 -1 -1 48,50,51 nearly a two magnitude increase, and a k cat /K m between 10 and 10 M s for V-agents.

This surge in activity for both classes of agents makes OPH the leading catalytic bioscavenger candidate.

Stoichiometric Bioscavengers

Another avenue approaching the pretreatment of organophosphate poisoning is a stoichiometric bioscavenger. Such a treatment would involve a protein capable of covalently binding and sequestering any OPNA. The primary protein under investigation is BChE. BChE and AChE share the same fold family, that of an α/β hydrolase fold, and ~50% sequence identity with key active site residues conserved. 52 In addition the active sites of both AChE and BChE

11 align to a 0.6 Å root mean square deviation (RMSD) showing the potential for BChE to effectively covalently bind OPNAs (Figure 1.5).

Figure 1.5 AChE and BChE active site overlay. The alignment of the active sites of AChE (gray) and BChE (red) with key residues shown in stick representation.

Human serum BChE is an effective prophylactic treatment under experimental conditions. Specifically, BChE purified from human serum prevented the toxicity induced by soman and VX in monkeys with a ~1.2 ratio of BChE:OPNA. 53 BChE in its native form is a tetrameric structure with a molecular weight of ~300 kDa raising concern that a large enough dose to provide sufficient protection against multiple OPNA LD 50 would initiate unwanted immunological responses. 54 In addition, the cost to get purified BChE in sufficient quantities is prohibitive. As it stands a large country like the United States can produce a maximum of 5,000 individual doses. 55 This then turns the focus from purified native BChE to that of a recombinant version. Work has shown that recombinant BChE is also effective, the problem lies in the lack of stability and effectiveness for extended periods of time. Native BChE experiments show a

12 protection level of greater than 80% for 32 hours, whereas the same level for recombinant BChE is maintained for just a few hours. 53,55 Due to the large molecular weight and difficulty in mass production there is need for a different alternative. We hypothesize the existence of a low molecular weight easily produced protein capable of mimicking covalent binding and detail our process for its discovery in chapter 2. In chapter 3 we detail our efforts to allosterically enhance the activity of a known catalytic bioscavenger, OPH. Finally, in chapter 4 we make final observations and comments on potential future directions.

CHAPTER 2: DISCOVERY, CHARACTERIZATION, AND INITIAL DESIGN OF LOW MOLECULAR WEIGHT PROTEINS WITH BIOSCAVENGER POTENTIAL

Since the discovery of OPs in the 1930’s there has been no defense formulated that proves effective. The closest candidate is a stoichiometric bioscavenger, a protein capable of sequestering the OP in the blood stream prior to acetylcholinesterase (AChE) inhibition.

Butyrylcholinesterase (BChE) is the leading stoichiometric bioscavenger candidate and has the benefit of being a human plasma derived protein so the likelihood of negative immunological response is minimal. BChE is a large tetrameric structure whose size limits its ability to be readily administered as a muscular injection, and reach effective full body protection. 56 In addition attempts to create a recombinant version of BChE to a monomer have destabilized the protein from multiple days to hours, severely diminishing the length of protective time available. 57 Probably the most limiting factor is the difficulty of procuring sufficient doses of

BChE. Currently, throughout the United States there is only enough capacity to produce roughly

5,000 doses a year. 58 These limitations highlight the necessity for the development of a new solution.

We hypothesize that there exists a low molecular weight protein with a known structure that can mimic AChE-OP binding, therefore becoming a novel alternative as a stoichiometric bioscavenger. To find such a candidate we employ a pattern matching utility created in our lab,

Erebus, which takes as input Cartesian coordinates of atoms extracted from the target protein. 59

14 In our case, we use the polar atoms in the catalytic triad of AChE, the position where OPs are known to bind. We further pipe the results from Erebus through our molecular docking suite,

MedusaDock, to estimate the binding energy of non-covalent forces, giving us a general idea of whether the OP of question, VX, will bind in the pocket. 60,61 We choose VX as target OP due to its extreme lethality and lack of protective or restorative measures (Figure 2.1). After considering the results from both the geometric pattern match, Erebus , and the molecular docking energy,

MedusaDock, we select the three most likely candidates for experimental characterization, namely: phosphoribosyl isomerase from Mycobacterium tuberculosis (PDB ID 2Y85), antigen

85-A from Mycobacterium tuberculosis (PDB ID 1SFR), and Smu. 1393c from Streptococcus mutans (PDB ID 4L9A). All three candidates exhibit some form of interaction with OPs, from non-covalent interactions, to full covalent binding (Smu. 1393c). We further show that Smu.

1393c protects the activity of AChE, making Smu. 1393c the leading candidate for an alternative stoichiometric OP bioscavenger. In addition we find that Smu. 1393c has measurable activity

-1 -1 -1 3 - against omethoate (kcat /K m of (2.0±1.3)×10 M s ) and paraoxon (kcat /K m of (4.6±0.8)×10 M

1s-1), V- and G-agent analogs respectively. This finding reveals the potential for Smu. 1393c to become a catalytic OP bioscavenger.

Figure 2.1 OP structures used in this study . In this figure we show the structure of all of the organophosphates used in this study.

Results & Methods

Computational Workflow overview.

We search the entire protein data bank (PDB) with over 100,000 structures to capitalize on the natural diversity found in Nature. This process is based on the assumption that there are known proteins existing with promiscuous activity similar to our target protein. The basic concept is to take an active site or other definable geometric piece of a protein of interest and search the PDB to discover any additional proteins that might serve in the same capacity regardless of their previous functional annotations. In our case, we seek a low molecular weight

16 protein with similar binding characteristics to AChE and BChE, which can serve as a replacement stoichiometric OP bioscavenger. To implement this search we use two main computational resources, namely Erebus , a rigid substructure search algorithm, and

MedusaDock , a small molecular docking algorithm. 59–62 The points of decision for the proposed workflow are outlined in Figure 2.2.

To begin, we realize that of over 100,000 structures in the PDB only a small percentage fit the required characteristics, namely a low molecular weight (less than 40 kDa) and a monomeric subunit biological assembly. Even before implementation of the workflow we significantly reduce the number of structures to be searched to ~ 40,000. With a refined PDB structure library we define the characteristics of a query scaffold and prepare it for input to

Erebus , which extracts a geometric distance relationship and rapidly searches the refined library to find a match. We access the returned matches visually for duplicates and surface accessibility.

The reduced candidate pool is then piped into a virtual docking screen using MedusaDock to access each candidate’s ability to bind with the selected small molecule. At each point in the workflow there is a place to access the current performance with options to return to scaffold selection, or filter criteria for the refined database, or even to alter the parameters for the rigid substructure search. This workflow is versatile and can easily be adapted to find promiscuous activity among proteins with a known structure. We design experiments in which we corroborate the three top predicted candidates, thus validating the computational workflow. Key features we test for are binding interactions, both non-covalent and covalent, location of binding, and ability for the candidates to protect AChE in an in vitro experiment.

Figure 2.2 Computational design workflow. In this figure we show the complete computational workflow, including all of the steps and decision points that occur in our computational design. The asterisks indicate where the two algorithms, Erebus and MedusaDock , are used .

18 Identify candidate scaffolds.

Before we perform an effective search we first curate the PDB structures creating a subset library of structures focusing on the characteristics of interest. Those characteristics include a low molecular weight (less than 40 kDa) and a monomeric biological unit, which we screen by requiring that a length less than 350 residues and less than four subunits. We use a rigid substructure search algorithm, Erebus , to identify proteins from the PDB that contain characteristics essential for OP binding (Figure 2.3a).

Figure 2.3 Two-stage search workflow with identified search parameters. A) In this illustration we depict the computational protocol as a funnel where the various levels represent the initial population of PDB structures (top of funnel), the substructure search algorithm, Erebus , the docking algorithm, MedusaDock , and the final predicted candidates (below funnel). Alongside the illustration are two columns where we list the filter conditions at each step (first column) and the number of resulting structures for that step (second column). The search database comprises a snapshot of the PDB database from December 2014. B) We show the geometric feature submitted to Erebus, which is comprised of four selected atoms from AChE (PDB ID 1F8U) and the connections between them to represent the geometric feature that Erebus searches for.

We derive these characteristics from the required amino acids in the binding sites of

BChE and AChE, the core structure of these proteins is that of an α/β hydrolase fold containing

19 the canonical key residues of the catalytic triad (serine, histidine, and glutamic acid).63 Both

BChE and AChE proteins have the ability to covalently bind and sequester OPs. The key atoms we use to construct our query scaffold are the hydroxyl oxygen from Ser203, the carboxylic oxygen from Glu334, the tele nitrogen from His447, all from the catalytic triad as oriented in the crystal structure of AChE (PDB ID 1F8U). We also include the indolic nitrogen from Trp86 to describe the binding pocket diameter (Figure 2.3b). The structure query is atomic coordinates in a PDB file format:

ATOM 1543 OG SER A 203 116.605 106.967-138.509 1.00 53.76 O

ATOM 3355 ND1 HIS A 447 116.432 102.538-135.187 1.00 56.88 N

ATOM 2485 OE1 GLU A 334 114.554 100.680-134.867 1.00 62.46 O

ATOM 634 NE1 TRP A 86 125.948 105.322-133.913 1.00 37.58 N

Erebus takes our query scaffold and extracts the geometric features, such as inter-atomic pairwise distances, and searches the refined PDB structure library for any matches. A match is defined when a structure aligns with the query scaffold with a root mean square deviation

(RMSD) below the desired cutoff set at four angstroms. The computational cost of Erebus is minimal with the time to search our ~40,000 refined structural library at ~1.5 hours.

As a positive control, we seed the refined PDB structural library with four AChE crystal structures (PDB IDs: 1B41, 1F8U, 3LII, 2X8B) and four BChE crystal structures (PDB IDs:

1P0I, 1P0M, 2XQF, 2WID). Four of these structures were returned as matches while the remaining crystal structures, 2X8B, 1P0I, 2XQF, and 2WID, revealed that they contained covalently modified active site serines such that Erebus did not recognize them as matches within the set search parameters. A potential solution is a decrease in the sensitivity parameters, allowing a greater variability to be considered a positive match. Unfortunately, this decrease increases the number of returned results from hundreds to thousands. We hold this solution in

20 reserve to be used when there is no identified result from the more stringent search parameters.

The returned candidate match list, containing ~230 matches is further reduced by visual inspection to remove duplicate proteins, and proteins where the match location is not solvent accessible leaving 21 viable candidates for molecular docking screening (Table 2.1).

Table 2.1 Characteristics of potential bioscavenger candidates. For each candidate we list the PDB ID, the scaffold RMSD to the control human BChE, the average ligand RMSD to the control, the top predicted energy returned by MedusaDock, the residue length, and the molecular weight.

Scaffold Average Ligand Top Docking Residue Molecular Weight Candidate RMSD (Å) RMSD (Å) Energy (kcal/mol) Length (kDa) 1SFR 3.7 6.52 -32.5 304 31.6 2Y85 2.04 5.22 -29.3 244 26.3 4L9A 2.37 5.65 -29.0 292 33.5 3BCN 4.03 8.93 -28.0 209 23.3 2OZ2 4.1 9.67 -27.5 215 23.7 2GHU 4.12 8.11 -26.7 241 27.2 1OCQ 2.03 14.42 -26.6 303 34.6 1H2J 2.77 8.71 -26.0 303 34.8 2V38 2.03 15.62 -25.9 305 34.9 1A3H 2.77 8.91 -25.9 300 33.7 3GI1 4.69 6.61 -25.5 286 32.0 3RJX 2.35 7.29 -25.5 320 37.6 1F29 4.07 9.38 -25.5 215 23.3 1QHZ 4.05 6.37 -25.2 305 34.4 1H5V 2.76 9.4 -24.7 304 35.6 1E5J 2.76 9.06 -24.5 305 35.1 1MEG 6.07 9.97 -23.1 216 23.7 2WHJ 2.04 8.61 -22.9 308 34.7 2OUL 4.15 8.44 -21.6 241 26.9 3BPF 4.03 8.9 -21.2 241 27.6 2BDZ 3.98 9.48 -20.9 214 24.2

21 Identification of top-ranked candidates.

Prior to covalent binding interactions it is necessary for non-covalent forces ( i.e. charge- charge interactions and Van der Waals interactions) to assist in proper orientation of the ligand for a serine nucleophilic attack. It is these non-covalent forces that we simulate with our docking suite MedusaDock . MedusaDock is a unique algorithm that considers the flexibilities of both receptor side chains and ligands. The receptor side chains near the binding site can sample all possible rotamers. The docking process consists of two steps, 1) a course docking with rigid- body minimization and 2) a fine-docking step where both side chain and ligand rotamers are considered in the final energy. This method of docking allows MedusaDock to successfully predict ~80% of blind pose prediction within 2.5 Å in the CSAR 2011 docking benchmark. 60,61

We first test whether MedusaDock is a viable option for our system by redocking a VX molecule to the active site of BChE and compare it to the x-ray crystal structure of BChE covalently inhibited by VX (PDB ID: 2XQF). We find that the redocking of VX closely aligns within the electron density map, thus confirming the successful application of MedusaDock to our system (Figure 2.4).

The input for MedusaDock consists of 1) a target protein in PDB format 64 , 2) a small molecule in a Mol2 file format 65 , and 3) the location of the search sphere marked by a small molecule or atom in a Mol2 file format. We select the top candidates for further characterization, both computational and experimental, by ranking the 21 candidates from the Erebus search by best-predicted binding energy (Table 2.1). This initial ranking consists of 100 independent docking simulations for each candidate with the small molecule VX. This method of ranking reveals a natural division between the candidates: 1) an Erebus RMSD less than 4 Å; 2) an

Figure 2.4 VX redocking to BChE. We show the validation of MedusaDock where the docked VX molecule lies within the electron density map of the phosphorylated serine of BChE (PDB ID 2XQF).

average ligand RMSD less than 8 Å; 3) a MedusaDock predicted binding energy less than -28 kcal/mol. There are three candidates that top this list, namely phosphoribosyl isomerase from

Mycobacterium tuberculosis (PDB ID 2Y85), antigen 85-A from Mycobacterium tuberculosis

(PDB ID 1SFR), and Smu. 1393c from Streptococcus mutans (PDB ID 4L9A).

Having selected the top three candidates we perform an in-depth docking study designed to create a predicted binding energy distribution. This process involves running over 1,000 independent docking simulations for each candidate as well as AChE and BChE controls. Each docking simulation takes between 4-6 minutes to complete. Thus, we distribute the 1,000 docking simulations over the killdevil computer cluster at UNC. When using molecular docking, it is important to determine the number of docking runs necessary to reach proper convergence to the lowest energy pose. This number is system specific and after a convergence study we

23 determine 1,000 docking simulations are required to compute the predicted binding energy distributions. The final distribution of predicted binding energies is a frequency histogram where the number of docked poses that fall within a set bin size is plotted. We use this distribution to compare the binding modes of VX from the candidate proteins to those obtained in the same manner from AChE and BChE (Figure 2.5).

Figure 2.5 Predicted Binding Energy distributions, structural overlays, and active site comparisons. A) (left) We show the predicted binding energy distributions of VX agent docked in the binding site of positive controls BChE (PDB ID 2XQF) and AChE (PDB ID 1F8U) (red and black curves respectively). (middle) We show that the control proteins show a similar size (~84 kDa) by the structural overlay of AChE (gray) and BChE (red). (right-top) We show the similarity of both AChE (gray) and BChE (red) active sites in the absence of VX. (right-bottom) We show that the lowest energy pose of VX docked with AChE (black) and BChE (gray) place the phosphate in the correct orientation to irreversibly bind the active serine. B) (left) We show that antigen 85-A (PDB ID 1SFR) (orange curve) is predicted to have binding interactions similar to control AChE and BChE (black and red curves respectively), through the overlap of predicted binding energy distributions of the organophosphate VX. (middle) We show antigen 85-A’s lower molecular weight by structurally aligning antigen 85-A (orange) with a molecular weight of ~35 kDa to BChE (red) with a molecular weight of ~84 kDa. (right-top) We show that the predicted residues of antigen 85-A (orange) align with the active site of BChE (red) in the absence of VX. (right-bottom) We show that the lowest energy pose of VX docked with antigen 85-A (orange) and BChE (gray) place the phosphate in the correct orientation to irreversibly bind the active serine . C) (left) We show that phosphoribosyl isomerase (PDB ID 2Y85) (green curve) is predicted to have binding interactions similar to control AChE and BChE (black and

24 red curves respectively), through the overlap of predicted binding energy distributions of the organophosphate VX. (middle) We show phosphoribosyl isomerase’s lower molecular weight by structurally aligning phosphoribosyl isomerase (green) with a molecular weight of ~25 kDa to BChE (red) with a molecular weight of ~84 kDa. (right-top) We show that the predicted residues of phosphoribosyl isomerase (green) align with the active site of BChE (red) in the absence of VX. (right-bottom) We show that the lowest energy pose of VX docked with phosphoribosyl isomerase (green) and BChE (gray) place the phosphate in the correct orientation to irreversibly bind the active serine. D) (left) We show that Smu. 1393c (PDB ID 4L9A) (blue curve) is predicted to have binding interactions similar to control AChE (PDB ID 1F8U) and BChE (PDB ID 2XQF) (black and red curves respectively), through the overlap of predicted binding energy distributions of the organophosphate VX. (middle) We show Smu. 1393c’s lower molecular weight by structurally aligning Smu. 1393c (blue) with a molecular weight of ~33 kDa to a monomer of BChE (red) with a molecular weight of ~84 kDa. (right-top) We show that the predicted residues of Smu. 1393c (blue) align with the active site of BChE (red) in the absence of VX. (right-bottom) We show that the lowest energy pose of VX docked with Smu. 1393c (blue) and BChE (gray) place the phosphate in the correct orientation to irreversibly bind the active serine.

When comparing the distributions, the greater the overlap suggests the greater likelihood of mimicking OP non-covalent binding patterned after AChE and BChE. In addition we examine the distance between the VX chiral phosphate and predicted serine and find that within the candidates examined in this manner the distance is less than 4 Å. This distance places the VX molecule in sufficient proximity to the predicted active serine to provide the correct conditions for covalent binding. Additionally when considering the best scoring pose we find that each candidate is within a 6 kcal/mol difference to the value of -35.3 kcal/mol as calculated for AChE.

With the overlapping distributions and the small range in energy differences, our three candidates show predicted characteristics similar to AChE.

Protein expression and purification.

Each candidate gene contains an added poly-His tag and TEV protease cut site at the N- termini and cloned into the pET14B vector for expression in the E. coli strain BL21 (DE3)

25 pLysS. The cells are grown in both standard LB for Smu. 1393c and phosphoribosyl isomerase, and super broth for antigen 85-A. In all cases we induce expression with 1 mM of Isopropyl β-D-

1-thiogalactopyranoside (IPTG) at 25°C overnight for phosphoribosyl isomerase and Smu.

1393c, and 72 hours at 18°C for antigen 85-A. The cells are then pelleted and stored at -80°C until use.

The cells are defrosted and resuspended in a buffer containing 50 mM phosphate pH 7.4,

500 mM NaCl, 40 mM imidazole, 5 mM βME, and 0.1 μM pepstatin A. We lyse the cells using sonication and centrifuge at 15,000 g for two hours. The supernatant is collected and filtered through a 0.22 μm syringe filter prior to loading onto a 5 mL HisTrap FF column.

The purified protein is eluted with a gradient to a buffer containing 50 mM phosphate pH

7.4, 150 mM NaCl, 1 M imidazole. We then cleave the poly-His tag using TEV protease by placing the protein containing fractions in dialysis with TEV in a 1:30 dilution in a buffer containing 50 mM phosphate pH 7.4, 500 mM NaCl. After 24 hours the solution is then again loaded onto the HisTrap column and the purified protein is collected from the flow through.

OP covalent binding to top candidates.

We investigate the potential of our candidates to bind OPs in a similar mechanism as

AChE. To do this we utilize serine hydrolase probes, specifically ActiveX Tamra fluorophosphonate serine hydrolase probe (TFP-probe) and ActiveX Desthiobiotin fluorophosphonate serine hydrolase probe (DFP-probe) (Figure 2.1). Each probe is designed to be visible in either a fluorescent gel (TFP-probe) or a western blot (DFP-probe). We adjust the

26 protocol and incubate the various hydrolase probes with the candidates at a ration of 1:30 concentrations. So for 5 μM protein we add 167 nM of the probe. The total reaction size is 20

μL. Each protein sample and probe is incubated at room temperature for 15-30 minutes. The reaction is quenched by adding 5 μL of SDS-PAGE loading dye (200 mM Tris pH 6.8, 10 mM

βME 10% SNS, 30% glycerol and bromophenol blue) and boiled for 5 minutes. The quenched reaction is run on a standard protein gel. This gel is processed following traditional western blot techniques, for the DFP-probe, or is simply imaged with a fluorescent gel scanner, for TFP- probes (Figure 2.6). Each gel contains a negative control with just the protein. A positive band from the gels indicates that the candidate successfully formed a covalent bond with the probe in a similar manner as OPs inhibit AChE, as anything other than a covalent bond would be washed away after denaturation of the protein.

Figure 2.6 Experimental results of OP binding. We show that candidate Smu. 1393c (PDB ID 4L9A) is covalently labeled by two serine hydrolase probes, whereas Smu. 1393c S99A, antigen 85-A (PDB ID 1SFR), and phosphoribosyl isomerase (PDB ID 2Y85) are not covalently labeled. For each serine hydrolase probe we include a parallel coomassie stained SDS-PAGE gel showing the presence of protein candidates.

27 It is clear from Figure 2.6 that both the TFP-probe and the DFP-probe show only one significant band, that for Smu. 1393c, indicating the covalent bonding potential with organophosphates. We realize the potential for non-covalent binding for antigen 85-A and phosphoribosyl isomerase.

We use fluorescence polarization to 1) quantify OP binding, and 2) identify any non- covalent binding forces acting between the probe and our candidates. These studies use the TFP- probe as substrate. We dilute the TFP-probe to a 200 nM concentration and titrate the protein, measuring the change in polarization until saturation is reached. We fit the data to a one site- specific binding model (Figure 2.7 and Equation 2.1).

∗ (2.1)

Figure 2.7 Fluorescent polarization representations. We show that candidates antigen 85-A (orange), and phosphoribosyl isomerase (green) interact with the serine hydrolase probe in a non- covalent manner as predicted and confirm the diminished binding of Smu. 1393c S99A (inverted blue triangle). Both the original fluorescence polarization and fraction bound plots are represented.

28 In quantitating the binding interactions we find that both candidates’ bind the TFP-probe with micromolar affinity, antigen 85-A with a Kd of 20.9 ± 1.6 μM and phosphoribosyl isomerase with a Kd of 120 ± 10 μM. The presence of any binding affinity, however low, suggests that both antigen 85-A and phosphoribosyl isomerase both interact with the TFP-probe in a non-covalent manner. All three candidates show binding interactions, confirming the prediction of non-covalent forces by our computational workflow.

OP covalent binding to Smu. 1393c active site.

To validate the predictive potential of our workflow we verify the location of the predicted site by mutating the predicted active serine (Ser99) to alanine and running the same experiments as previously outlined, specifically the active site labeling with both TFP-probe and

DFP-probe as well as fluorescent polarization experiments with the TFP-probe (Figures 2.6 and

2.7). The K d of this Ser99 mutant from fluorescent polarization is greater than 300 μM. The once visible band from the successfully labeled wild-type Smu. 1393c disappears when Ser99 is mutated to alanine. The lack of a covalent bond in the Ser99 mutant (Figure 2.6) suggests that we did in fact predict the correct location of the active site using our computational method.

Smu. 1393c catalytic activity and protection of AChE.

In Wang et el, Smu. 1393c was first crystallized and the activity characterized successfully showing by alanine mutation that Ser99, His249, and Glu123 are necessary for the observed activity against p-nitrophenyl acetate (PNP-ac) 66 . These three residues coincide with

29 those identified from our protocol. We duplicate the experimental results and determine the activity level of Smu. 1393c against PNP-ac by observing the absorbance of the leaving p- nitrophenyl group, which absorbs at 405 nm. We perform this assay over a PNP-ac concentration range of 0-12 mM. The initial velocities at each concentration are plotted and fit to the

Michaelis-Menton kinetic equation (Equation 2.3). We find the hydrolysis rate of PNP-ac to be

-1 -1 kcat /K m of 6.3±1.3 M s (Figure 2.8, Table 2.2).

∗ (2.3)

Figure 2.8 Non-competitive Smu. 1393c inhibition. A) We show the activity of Smu. 1393c against p-nitrophenyl acetate uninhibited (black triangles) and inhibited with demeton-S-methyl (DSM) (red circles). B) In this figure we show a Lineweaver-Burk plot of Smu. 1393c activity against PNP-ac with and without inhibitor DSM, illustrating the likelihood of non-competitive inhibition.

30 Table 2.2 Kinetic parameters for Smu. 1393c. We show the kinetic parameters of Smu. 1393c with substrate PNP-ac, and PNP-ac and inhibitor DSM.

-1 -3 -1 -1 kcat (s )(10 ) Km (mM) kcat /K m (M s ) PNP-ac 42±4 6.7±1.3 6.3±1.3 PNP-ac + DSM 22±3 6.0±1.9 3.6±1.3

However, when we expose Smu. 1393c to the V-agent analog demeton-S-methyl (DSM) we find that the activity level is reduced indicating an inhibitory effect. In fact, we perform the same kinetic assay with Smu. 1393c and a 10 μM concentration of DSM and calculate the inhibition constant using equation 2.4 to be a K i of 10.64±1.9 µM.

(2.4) [] 1

Further by examining a double reciprocal plot we find that DSM inhibits Smu. 1393c in a non-competitive manner (Figure 2.9b), suggesting a potential second site that when bound has direct ramifications on activity.

This non-competitive inhibition of Smu. 1393c by DSM indicates a protein/ligand interaction. The presence of this interaction suggests the potential for bioscavenger behavior. We explore the ability of Smu. 1393c to protect AChE from inhibition through a quantitative set of activity assays, modified from Cherny et al , designed to gauge the amount of AChE activity maintained after exposure to two OP simulates.50 We use two OPs, DSM as a V-agent analog and diisopropylfluorophosphate (DFP) as a G-agent analog. We purchase lyophilized

Electrophorus electricus AChE, DSM, and DFP from Sigma Aldrich. The general procedure is as follows: 1) we incubate equal amounts of Smu. 1393c and OP simulant (final concentration

31 500 nM) at 20 oC for 30 minutes; 2) we add AChE to each sample and incubate at 20 oC for 60 minutes; 3) we add 500 μM of the Ellman reagent 5,5-dithio-bis-(2-nitrobenzoic acid) (DTNB) to the samples as a detectable marker ; 4) we vary the amount of acetylthiolcholine (0-20 mM) and bring each sample to a volume of 50 μL using buffer (200 mM NaCl, 20 mM TRIS at pH

7.4).

We perform each assay in triplicate, plot the kinetic parameters, and fit them to the

Michaelis-Menton kinetic equation (equation 2.3). All measurements are expressed as relative activity to uninhibited AChE. We observe that Smu. 1393c protects AChE from full inhibition, preserving 84.4±4.1% of activity for DSM and 76±10% for DFP. Additionally, we find no significant change between the Km of active AChE (3.05±0.79 mM) and inhibited AChE

(4.43±0.63 mM for DSM and 3.2±1.3 mM for DFP) (Figure 2.9, Table 2.3). These results indicate that Smu. 1393c may act as an OP bioscavenger to protect AChE from inhibition.

Figure 2.9 Smu. 1393c protects AChE function. We show that Smu. 1393c protects AChE activity with (i) AChE uninhibited positive control (black), (ii) protected AChE activity from demeton-S-methyl (blue filled) and diisopropylfluorophospate (red filled), and (iii) AChE inhibited negative controls, demeton-S-methyl (blue outline) and diisopropylfluorophospate (red outline). All experiments were run in triplicate.

32 Table 2.3 AChE protection by Smu. 1393c. Kinectic parameters showing positive control (uninhibited AChE) and AChE that is protected by Smu. 1393c from both demeton-S-methyl (DSM) and diisopropylfluorophosphate (DFP).

-1 -1 -1 Km (mM) kcat (s ) kcat /K m (M s ) AChE 3.05 597.6 2.0 × 10 5 AChE/Smu. 1393c/DSM 4.43 504 1.1 × 10 5 AChE/Smu. 1393c/DFP 3.24 456 1.4 × 10 5

Smu. 1393c activity against OPs.

We investigate the catalytic potential of Smu. 1393c against OPs. Our first selection of

OP for V-agent analog is DSM and for a G-agent analog is DFP. Despite finding catalytic activity against DSM, we fall short of kinetic saturation due to limited supply. The G-agent analog selected, DFP, requires an experimental setup unsupported in our laboratory. For these reasons we change from DFP and DSM to paraoxon and omethoate as G- and V-agent analogs respectively (Figure 2.1). To monitor the rate of hydrolysis we detect the p-nitrophenyl group at an absorbance of 405 nm for G-agent analog paraoxon. For our V-agent analog omethoate we use an Ellman assay. 67 For each assay we use a range of 0-17 mM omethoate and 0-100 μM paraoxon. The final assay volume is brought to 1000 μL with 50 mM bis-tris-propane pH 7.4.

We initiate the catalytic reaction by adding Smu. 1393c for a final concentration of 72 nM for paraoxon, and 5 μM for omethoate. After a series of kinetic assays we find that Smu. 1393c

-1 -1 -1 catalyzes the hydrolysis of omethoate with a k cat /K m of (2.0±1.3)×10 M s (Figure 2.10, Table

2.4). When we test paraoxon hydrolysis the activity level is below the sensitivity of our instruments. Intriguingly, when we express Smu. 1393c with zinc and then test activity we see

3 -1 -1 hydrolysis of paraoxon with a k cat /K m of (4.6±0.8)×10 M s and a loss of detectable omethoate

33 activity. These findings suggest two different catalytic mechanisms. When we perform the same experiments with Smu. 1393c S99A mutant we find no activity for either omethoate or paraoxon, suggesting that Ser99 is crucial in both mechanisms.

Figure 2.10 Smu. 1393c catalytic activity. We show that Smu. 1393c catalyzes the hydrolysis of organophosphates omethoate (A) and paraoxon (B) with the initial velocity plot fitted to Michaelis-Menten equation.

Table 2.4 Kinetic parameters for Smu. 1393c against OPs. We show the kinetic parameters of Smu. 1393c with paraoxon and omethoate.

-1 -3 -1 -1 kcat (s )(10 ) Km (mM) kcat /K m (M s ) Paraoxon* 25±0.9 (5.5±1) ×10 -3 (4.6±0.8) ×10 3 Omethoate 0.52±0.09 2.6±1.6 0.2±0.13 * Smu. 1393c expressed with zinc

Discussion

The danger of OP poisoning is real with ~3,000,000 individuals exposed yearly recording up to 200,000 fatalities.68,69 To truly make an impact any therapeutics would need to be cheap

34 and effective, especially in developing countries where cheap OP pesticides provide profit in their agricultural industries. 26 While agricultural OP poisoning is a problem, the grave concern lies in the potential exposure from OP chemical nerve agents. These agents are cheap, deadly and can easily be used in warfare or a terrorist attack. We have no current defense other than protective equipment and decontamination procedures currently employed by the military. 70

Unfortunately, these measure are most effective if there is advanced knowledge of a potential exposure situation. A better alternative is an OP bioscavenger, whether stoichiometric or catalytic, that will protect AChE from inhibition by OP chemicals.

We present three candidates as producible bioscavenger candidates that also have a low molecular weight, antigen 85-A and phosphoribosyl isomerase from Mycobacterium tuberculosis and Smu. 1393c from Streptococcus mutans . Two of the structures, antigen 85-A and Smu.

1393c, share a common structure with AChE and BChE, that of an α/β hydrolase. What is interesting is that neither candidates has more than five percent identity with AChE and BChE. 66

Whereas phosphoribosyl isomerase has a core structure of a (β/α) 8-barrel scaffold, making it unique among the candidates. 71 We predict non-covalent interactions with OPs for each of these candidates and experimentally validate their prediction using serine hydrolase probes. We find that the candidates do indeed interact with OPs, such as covalent bond formed by Smu. 1393c and the equilibrium binding constants of 20 and 120 μM for antigen 85-A and phosphoribosyl isomerase respectively.

One candidate, Smu. 1393c shows potential for AChE protection as well as activity

-1 -1 -1 against omethoate (kcat /K m of (2.0±1.3)×10 M s ) in the absence of zinc but is active against

3 -1 -1 paraoxon (kcat /K m of (4.6±0.8)×10 M s ) when zinc is present, indicating that there may be a metal-coordination site present. Further it is possible that the Ser99 is a key element to both

35 modes of catalysis as when the tests are performed with the S99A mutation, no activity is found in either condition. Though Smu. 1393c shows promise as an OP bioscavenger, much work remains to be done. One key study is the investigation of immunological effects that introducing

Smu. 1393c into the immune system might cause.

We demonstrate the effectiveness of our computational workflow by identifying our three bioscavenger candidates. This workflow combines two successful algorithms; 1) Erebus 59 , a structural database search algorithm to identify molecules with substructures matching the query template; and 2) MedusaDock 62 , a small molecule docking algorithm to predict the probability of interaction. With further automation and development we believe this workflow can easily be extended into applications benefitting the pharmaceutical industry and the biotech community.

Specifically, by aiding in molecular therapeutics, drug repurposing, protein sensor design, epitope vaccine design, and drug adverse side-effect studies.

CHAPTER 3: COMPUTATIONAL REDESIGN OF ORGANOPHOSPHATE HYDROLASE TO ENHANCE ORGANOPHOSPHATE NERVE AGENT HYROLYSIS

The existing vulnerability to OPNAs due to limited protective measures is of great concern and has spurred investigations into the enzymatic hydrolysis of organophosphates for remedial and therapeutic use. The currently used enzyme for remediation is organophosphate hydrolase (OPH) 72 , which is a bi-metallo-coordinated enzyme existing naturally as a homodimer, where each subunit contains two zinc ions coordinated to form the binding site (Figure 3.1).

Figure 3.1 Structural representation of OPH. Native homodimer structure of organophosphate hydrolase. Zinc ions are shown as spheres with paraoxon ligand in stick figure (green).

37 An ideal enzyme engineered for remediation would be capable of degrading multiple organophosphate nerve agents. OPH has effect against most nerve agents with published

-1 -1 73,74 hydrolysis rates of a k cat of 0.30 s for V-agent VX and 56 s for G-agent sarin. The known

-1 75 simulant closest to native activity is paraoxon with a published k cat of 2400 s .

Recent successes in protein design detail the development of small molecule binding proteins 76,77 , an artificial bis[4Fe-4S] binding protein 78 , and a binding site redesign to favor a new substrate 79 . Additionally, enzyme design has progressed to engineer novel catalytic sites, such as for the retroaldolase reaction 80 , or serine hydrolases 81 . All of these examples rely on a combined protocol merging the power of computational simulation with experimental techniques, such as directed evolution. 82,83 Here, we present a successful application of purely computational predicted design to enhance paraoxon hydrolysis by OPH.

Our approach is novel in that the enhancement of catalytic activity occurs through the modulation of allosteric networks. Allosterically regulated proteins, such as G protein-coupled receptors and kinases, such as receptor tyrosine kinases, have been highly targeted for therapeutics. 84–93 Identification of key allosteric residues enhance the probability of identifying allosteric binding sites for directed drug development. 94–96 Currently, only one other reported technique probes allosteric mutations, and is computationally intensive requiring both molecular mechanics and molecular dynamics combined with docking efforts. 97 Our enzyme design workflow is computationally efficient and can readily scan potential mutations for a residue position in minutes.

Protein engineering protocols to improve OPH traditionally focus on modifying the substrate specificity for unique nerve agents such as VX. Di Sioudi et al. utilized rational design

98 to identify OPH mutations that increase k cat of VX by fourfold. Bigley et al. included directed

38 -1 99 evolution leading to a k cat increase to 137 s hydrolysis of VX. Whereas Cherny et al. used computationally directed evolution to increase the k cat /K m for OPH against VX from wild type

OPH 0.94×10 4 to 499×10 4 M-1 min -1.50 While promising, these efforts compromise the generalizability of OPH reducing the effectiveness for multiple nerve agents. Here, we hypothesize that stabilization of the active site increases the probability of the transition state formation upon ligand binding. To test our hypothesis, we redesign the protein with a focus on interior but not active site residues with potential allosteric interactions that enhance general enzymatic activity as assayed by paraoxon and omethoate. We show a rise in the hydrolysis rates of OPH with both single and multiple mutations. Since our approach is independent of the OPH active site, the combination of our mutations and those from other approaches are potentially additive.

Our design method relies on cycles of computational stability prediction of the enzyme in the active state combined with experimental validation. At the conclusion of each iteration we apply computational analysis through equilibrium discrete molecular dynamics (DMD), and replica exchange DMD to gain additional insight into the effect of each mutation.100–102 When applying this method to OPH, we identify five residue positions located between five and ten Å from the active site that increase activity against paraoxon by one order of magnitude experimentally. The successfully redesigned OPH demonstrates a potential application of this computational protocol to enhance native enzymatic activity.

Results

Prediction and experimental validation of single mutations.

We hypothesize that stabilizing the protein active site through protein design will stabilize functionally relevant states and increase the activity of the protein. Utilizing the known crystal structure (PDB ID: 1DPM), we estimate the changes in protein stability by calculating

ΔΔG, the free energy difference between the folded and unfolded states after mutation, using the protein stability prediction algorithm, Eris.103,104 We identify hotspot residues, residues where mutation significantly improves the estimated ΔΔG values, for all possible single- or multiple- mutations away from the active site (Methods and Figure 3.2).

Figure 3.2 Residues selected for mutation consideration. OPH monomer with the bound ligand (green), metal coordinated atoms (spheres), active site residues (lines), and the residues located between five and ten Ǻ beyond zinc ions considered by the protein stability scan (highlighted in magenta).

40 The results from scanning OPH for single-mutation indicated five mutations that significantly ( Z-score < -1.5) increase stability: T54M, D105T, T199I, C227M, and G251L with

ΔΔG scores of -5.0, -6.7, -8.1, -3.8, and -6.7 kcal/mol respectively (Table 3.1). Examination of the wild type crystal structure reveals that these residues are either near an internal void formed by residues T54, T199, C227 and G251, or corresponding to a buried charge, D105 (Figure 3.3a- b). The stabilization effect is either due to increases of the side chain volume to fill the cavity or to reduce the desolvation penalty of buried charges.

Figure 3.3 Identified mutation positions. A) Hotspot results of residue positions G251, C227, T199, and T54 (magenta) surrounding an internal void shown in mesh surface. B) Residue position D105 (magenta) showing a buried polar amino acid. All structure figures created using Pymol.105

Table 3.1 Predicted stability scores and experimental data. The predicted stability scores are a result of our scan of potential mutations using Eris for single, double, and triple mutations with associated Z-score. Also shown is the experimental data obtained for each mutation combination for paraoxon and omethoate.

Predicted Stability Paraoxon Omethoate Mutation ΔΔG k /K Z-score k (s -1) K (mM) k /K (s -1 M-1) k (s -1) K (mM) cat m (kcal/mol) cat m cat m cat m (s -1 M-1) OPH - - 907±18 0.898±0.059 (101±6.9)×10 4 (75.8±4.4)×10 -4 0.838±0.272 9.04±3.0

T54M -5 -2 1240±37 0.378±0.053 (32.9±4.7)×10 5 (37.3±2.6)×10 -4 0.208±0.154 18.0±13

D105T -6.7 -2.7 ------

T199I -8.1 -3.3 950±20 0.340±0.035 (27.9±2.9)×10 5 (53.7±7.1)×10 -4 1.21±0.55 4.43±2.1

C227M -3.8 -1.5 35.1±1.4 0.133±0.037 (26.3±7.5)×10 4 (173±8.8)×10 -4 0.485±0.172 35.6±13

42 G251L -6.7 -2.7 ------

1.37±0.8 T199I/T54I -12.5 -2.57 1260±150 0.703±0.31 (17.9±8.2)×10 5 (46.6±10.6)×10 -4 3.41±2.1 9 C227M/T199I -10.312 -2.05 112±1.6 0.158±0.015 (70.7±7.0)×10 4 (125±19)×10 -4 2.86±1.4 4.39±2.2

C227M/T54I -6.06 -2.4 193±8.3 0.139±0.043 (13.9±4.3)×10 5 (2510±340)×10 -4 17.4±3.6 14.5±3.6

C227M/T199I/T54I -14.85 -3 553±12 0.130±0.020 (42.5±6.7)×10 5 (4390±590)×10 -4 5.04±1.6 87.1±31

To assess the utility and accuracy of our computational methods we verify our predictions experimentally. Wild type and mutant OPH are expressed in traditional E. coli . The activity of OPH and mutants are measured and compared using paraoxon, a G-agent analog and omethoate, a V-agent analog. The activity of purified wild type OPH for paraoxon is kcat of 907 s-1. The OPH mutations T199I, C227M, and T54M each displays measurable catalytic kinetics

-1 -1 -1 against paraoxon with kcat values of 950 s , 35.1 s , 1240 s , respectively (Table 3.1). Mutants

G251L and D105T have no apparent activity. The relationship between the OPH mutants and wild type shows modulated activity with mutants T199I and T54M improve activity over wild type by 5% for T199I and 37% for T54M.

When we test wild type OPH and mutants against omethoate, a V-agent analog, we find

-1 -1 that the mutants T54M ( kcat = 37.3×10 -4 s ) and T199I ( kcat = 53.7×10 -4 s ), perform in the

-1 same order of magnitude as wild type OPH ( kcat = 75.8×10 -4 s ). Yet the mutant C227M

-1 increases the activity by one order of magnitude to a kcat of 173 ×10 -4 s , suggesting that each of the mutations, though located outside of the active site, affect the specificity of OPH for the different classes of agents as supported by the observations that T199I improves paraoxon activity, yet only C227M improves omethoate activity.

Prediction of double and triple mutations with experimental validations.

With the experimentally verified three hotspot residues, T54, T199 and C227, we extend our ΔΔG calculation for all possible double and triple mutations. Based on Eris calculations, we identify the following stabilizing double-mutants: T54I/T199I, T54I/C227M, and T199I/C227M; and triple-mutant: T54I/T199I/C227M (Table 3.1). We note that the stabilizing double- and

triple-mutations are not a simple combination of the stabilizing single-mutations, which is due to the coupling of these residues. For example, T54I was not as stabilizing as T54M in single mutation but becomes more stabilizing when combined with other mutations. The mutation combinations with the largest favorable change in calculated stability include those with T199I.

For example, T199I alone has a predicted ΔΔG of -8.1 kcal/mol, but when combined with

C227M the predicted score improved to -10.3 kcal/mol. Additionally, when T199I is combined with T54I the improved stability increased to a total predicted score for the double mutation

T54I/T199I of -12.5 kcal/mol (Table 3.1).

Figure 3.4 Illustrative example of kinetic data. We show an example of a standard Michaelis- Menton kinetic plot of the activity of the double mutation T199I/T54I performed at room temperature.

We also test these double and triple mutations experimentally and find that only the double mutant T199I/T54I increases paraoxon activity over wild type OPH (Table 3.1, Figure

3.4). When we test against omethoate we find that mutations containing C227M, specifically

-1 -1 C227M/T199I ( kcat = 125 ×10 -4 s ), C227M/T54I ( kcat = 2510 ×10 -4 s ), and C227M/T199I/T54I

-1 (kcat = 4390 ×10 -4 s ) increase the activity up to two orders of magnitude above the wild type kcat

of 75.8×10 -4 s-1 (Table 3.1). It is interesting to note that against paraoxon the mutations with

T199I increase activity, whereas against omethoate the mutations with C227M improve activity.

This finding suggests that allosteric modulation potentially acts to modify the specificity of the active site.

Computational analysis of thermostability and conformational dynamics.

Having completed the first few cycles of computational design and experimental verification. We seek to gain insight into the effects of mutations, specifically to identify any mechanisms that may explain the differing specificity upon allosteric mutation. Usually, a mutation that affects the stability of the entire protein is manifested by the change in the melting temperature, Tm. Additionally, the proposed mutations would impact the active site through allosteric coupling. 88,106,107 Hence, we computationally investigate our predicted and experimentally validated single, double and triple OPH mutations for both thermal stability and altered active site dynamics. Our first computational analysis examined the effect of OPH mutations on protein stability using replica exchange DMD combined with the weighted

100,101 histogram analysis method to calculate the specific heat (C v). The computed C v plot allows us to extrapolate an estimated Tm by identifying the temperature at which the specific heat reaches its maximum value. An increase in Tm indicates an increase in thermal stability. Single mutations T199I, C227M, and T54M have a similar Tm, varying from wild type OPH by ± 5 K

(Figure 3.5 and Appendix Figure A.1).

Figure 3.5 Melting temperature calculated from computational simulations. We show a comparison of the best single mutation, T199I (red), double mutation, T54I/T199I (blue), and triple mutation, T54I/T199I/C227M (green), with wild type OPH (black), specifically, the reaction coordinate obtained from replica exchange DMD processed by WHAM.

In addition, though G251L has a similar Tm, it, like D105T, disrupts the major transition peak creating multiple states that are non-functioning (Appendix Figure A.1). The double mutants are slightly destabilizing with a ΔTm ranging from -5.03 to -10.06 K. Triple mutant

T54I/T199I/C227M is destabilizing with the Tm of 329.4 K as compared to wild type OPH of

334.4 K (Table 3.2 and Appendix Figure A.1). Through comparing OPH mutants to wild type

OPH we establish that the predicted mutations have no significant impact on calculated thermal stability, with only slight variations in Tm, suggesting that no mutation with activity detrimentally destabilizes the protein on a global scale.

Table 3.2 Specific heat calculations. Calculated specific heat (C V) and melting temperature (T M) for each mutation combination using replica exchange DMD and weighted histogram analysis.

Mutation TM (K) ΔT M (K) CV (kcal /mol*K) WT 334.4 - 18.24 C227M 331.9 -2.50 15.12 T54M 335.6 1.25 14.08 T199I 334.4 0.00 14.26 C227M/T54I 328.1 -6.25 16.18 C227M/T199I 320.6 -13.75 13.89 T199I/T54I 326.9 -7.50 15.59 C227M/T199I/T54I 329.4 -5.00 16.67

Without significant changes in stability, we examine the conformational dynamics of wild type OPH and mutants to understand the affect on the active site. We perform ten independent DMD simulations with randomized initial velocities for each mutant at a temperature of ~311 K. This temperature was chosen as it is ~10 K lower than the Tm of the least stabilized mutation, T199I/C227M. We perform these simulations to identify changes in residue fluctuations between wild type and mutant OPH by calculating the RMSF for each of the 10 trajectories and averaging them (Figure 3.6). Our analysis reveals four segments — residues 80-

90, 207-224, 261-276, and 311-323 — that represents a significant ( p-value < 0.05) change in

RMSF (Table 3.3).

Comparing the behavior of these segments across the OPH mutations, the second segment is significantly ( p-values < 0.01) rigidified by all mutations with ∆RMSF ranging from -

2.93 Å for mutation T54I/T199I to -6.27 Å for mutation C227M.

Figure 3.6 Fluctuation analysis of computational simulations. We show the root mean square fluctuation per residue over 1,000,000 time steps of equilibrium DMD simulations. Each mutation is an average of ten equilibrium DMD runs with a setting of 0.62 reduced temperature units. We use a tube representation of protein structures, where the backbone trace radii are rescaled according to fluctuations and colored from stable (blue) to highly varying (red), to illustrate changes in fluctuations upon mutations. Highlighted segments represent those differences that have somewhat significant to significant values ( p-value < 0.05 and < 0.01 respectively). Corresponding segments and fluctuations are mapped to the crystal structure (PDB ID 1DPM). The asterisk on segment 2 indicates a highly significant change across all mutation combinations.

Table 3.3 Analysis results of computational simulations. Allosteric mutations change dynamics of four segments coupled to the active site. We show the change in fluctuation distance (ΔRMSF) as measured in Å with calculated p-values for each segment per mutation combination. The values we use in these calculations are obtained from an average of ten equilibrium DMD simulations per mutation. All values listed are at least somewhat significant (p-value < 0.05). Values highlighted are significant with a p-value < 0.01.

Segment 1 Segment 2 Segment 3 Segment 4 Mutation ∆RMSF ( p-value) ∆RMSF ( p-value) ∆RMSF ( p-value) ∆RMSF ( p-value) C227M - -6.27 Å (5.66×10 -5) 2.55 Å (2.70×10 -2) 1.75 Å (4.30×10 -3) T54M -2.70 Å (1.55×10 -2) -6.20 Å (2.35×10 -5) - 2.85 Å (9.85×10 -4) T199I - -4.79 Å (3.79×10 -3) 3.56 Å (3.35×10 -2) 1.81 Å (5.23×10 -3) C227M/T54I - -6.19 Å (7.09×10 -5) - 1.76 Å (4.72×10 -2) C227M/T199I - -6.24 Å (2.64×10 -6) 2.01 Å (3.90×10 -2) 1.53 Å (7.65×10 -3) T199I/T54I 2.58 Å (1.01×10 -2) -2.93 Å (2.57×10 -4) - 1.63 Å (9.15×10 -3) C227M/T199I/T54I 2.47 Å (1.11×10 -2) -5.54 Å (2.27×10 -4) 2.84 Å (2.22×10 -2) 2.25 Å (1.59×10 -2)

Whereas in the first segment the majority of mutations result in increased flexibility compared to the wild type; such as T54M/C227M (+2.00 Å), T54I/T199I (+2.58 Å), and T54I/T199I/C227M

(+2.47 Å) (Table 3.3). The one exception is T54M, which significantly rigidified the first segment, ∆RMSF = -2.70 Å. Note that single mutant T54M has an increased kcat for paraoxon activity but a reduced kcat against omethoate as compared to wild type OPH. Both of the remaining segments show somewhat increased flexibility from many of the mutations with segment three having a ∆RMSF range from 2.01 Å (T199I/C227M) to 3.56 Å (T199I) and segment four ranging from 1.53 Å (T199I/C227M) to 2.85 Å (T54M). Figure 3.6 shows the average RMSF of wild type OPH with the best performing single mutant T199I (red), double mutant T54I/T199I (blue), and triple mutant T54I/T199I/C227M (green) (Appendix Figure A.2).

Discussion

Organophosphates have presented a significant threat since their induction as weaponized nerve agents. The limited effectiveness of current countermeasures has spurred many investigative efforts with the majority of studies focusing on redesign of active-site residues of known organophosphate hydrolyzing enzymes, such as OPH. To further improve the activity of

OPH and its variants, we seek an orthogonal approach to enhance general enzymatic activity by investigating OPH mutations with an allosteric effect on the active site. For this purpose, we create and implement a general workflow with applications to any enzyme or protein utilizing a molecular design suite based on the Eris algorithm, for rapid estimation of changes in thermodynamic stability upon mutation. 103

Using this protein design protocol, we identify five single mutations, T199I, T54M,

C227M, G251L, and D105T. When we express them experimentally, three of the mutations,

T199I, T54M, and C227M show measurable kinetic rates. By examining them computationally by replica exchange DMD 100 , we find the derived specific heat profile of the mutants with no activity (G251L and D105T) shows no well-defined folding/unfolding transition peak (Appendix

Figure A.1).

When we examine the affects of the mutations on omethoate activity compared to paraoxon activity, we observe that the mutations with T199I provide the most enhancement of activity for paraoxon whereas a different mutation, that of C227M, provides the greatest increase in omethoate activity. This observation suggests that our allosteric mutations not only affect activity levels, but may also impact ligand specificity. Future efforts will concentrate on understanding the mechanism by which these mutations affect the ligand specificity.

The computational investigation revealed that the activity enhancement of the single, double, and triple mutations is due to allosteric effect on the active site. Our RMSF analysis of both wild type and mutant OPH variants indicated that residue segments 80-90, 207-224, 261-

276, and 311-323 are dynamically coupled to the mutation sites. Correlation with the catalytic activities of various mutations suggested that concerted dynamics of a whole protein are important for the catalytic function and that rigidification of segment 2 and increased flexibility of other segments are related to the enhancement of catalytic activity (Figure 3.6).

In summary, we have described a generalizable approach to enzyme enhancement that focuses on allosteric mutations away from the active site. Such an approach offers an additional handle to control protein active cites that are otherwise sensitive to direct modification. The central hypothesis is that increasing the stability of the catalytically-active state of an enzyme

may increase the population of this state and thus enhance the activity. Experimental characterizations combined with additional computational analysis using DMD simulation suggests that thermo-stability estimation may further improve the prediction accuracy. Further dynamics analysis confirmed the allosteric effect of our proposed mutations and revealed the coupled dynamics important for the catalytic activity of OPH. In total, this work adds a new dimension to the field of protein design and pushes the boundaries for allosteric engineering through our developed tool of protein stability prediction that we use to enhance the catalytic activity of organophosphate hydrolase increasing the k cat of wild type OPH by one order of magnitude for paraoxon and two orders of magnitude for omethoate. Since the allosteric mutations are away from the catalytic sites, we expect our approach can be combined with other active-site-oriented methods to maximally enhance the activity of OPH.

Methods

Protein stability prediction. Using our in-house developed protein stability algorithm, Eris , we identify mutations that stabilize OPH. The stability of the protein is quantified by the change in free energy (ΔΔG) between the wild type protein and its mutant. Using the high-resolution dimer structure of OPH (PDB ID: 1DPM), we exclude the residues that coordinate the zinc and are in contact with the substrate. We also exclude the surface residues to reduce possible interruption of protein-protein interactions of OPH. The mutated residues included 53-56, 58, 59, 61-67, 72, 73,

79, 82, 83, 86, 87, 90, 98-105, 107-110, 112, 113, 116, 124-135, 139, 150, 153, 167-172, 175-

181, 183, 184, 198-202, 204-212, 215, 216, 227-235, 239, 240, 243, 250-263, 265-270, 273-278,

280, 281, 297-300, 304-307, 309-316, 318-329, and 331-332. For each residue, the native amino

acid is mutated to the other 19 types. For each sequence (wild type and mutants), we perform

100 independent Monte-Carlo-based protein side-chain repacking using Dunbrack rotamer library 108 and all energy calculations utilize the MedusaScore scoring function 61 . With these 100 independent Medusa runs, we compute the average stability <ΔG> and its standard deviation

δ(ΔG). For any given mutation, we obtain the average ΔΔG by subtracting the wild type protein stability, <ΔG> mut -<ΔG> wt . The corresponding Z-score is obtained.

∆∆ (3.1) [∆ ∆ ]

Our scanning study suggests that the majority of the positions do not have strong stabilizing mutations and only a few hotspots can be mutated to increase the stability with significant Z- scores ( Z-score < -1.5).

Site-directed mutagenesis. Site-directed mutagenesis is performed using Quikchange II kit from

Agilent. Primers are designed according to manufacturer’s protocol and purchased from IDT. 50 m l of PCR reaction containing 5 m l of 10x reaction buffer, 1 m l dNTP mix, and 1.25 m l of each forward and reverse primers, 1 m l pfu ultra polymerase, and 10 ng DNA template is used for mutagenesis reaction. The PCR cycles are: 95 C, 2 min, 30 cycles of 95 C, 1 min, 55 C, 1 min and 72 C, 1.5 min, followed by 72 C, 10 min. The resulted PCR products are DpnI digested and then transformed into XL1-blue supercompetent cells.

Plasmid preparation. Plasmids are prepared using Invitrogen maxiprep kit with sequences confirmed by Eurofin operon. Plasmid concentrations are determined using nanodrop and adjusted to 1 m g/m l.

E. coli expression and purification. The wild type and mutant plasmids are cloned into our expression vector of pET14B and expressed in the E. coli strain BL21 (DE3) pLysS. A poly-His tag is added to the mutant T54M located on the C-termini to aid in purification. The cells are grown in standard LB and induced by 1 mM Isopropyl β-D-1-thiogalactopyranoside (IPTG) at

25°C overnight. We resuspend the collected cell pellets in 50 mM tris, 500 mM NaCl at a pH of

7.4 with 40mM imidazole, 5 mM βME, and 0.1 μM pepstatin. The cells are lysed using sonication and then centrifuged at 15,000 g for two hours. The collected supernatant is filtered through a 0.22 μm syringe filter and loaded on a 5 mL HisTrap FF column from GE. The proteins are eluted by a gradient to 50 mM tris, 150 mM NaCl at a pH of 7.4 with 1 M imidazole.

We determine protein concentration for E. coli expression system proteins using a nanodrop to measure A280/A260.

OPH activity assay . Using a SpectraMax i3 plate reader we perform kinetic assays measuring the absorbance of the paraoxon leaving group at 405 nm or the Ellman marker at 412 nm. Various levels of paraoxon or omethoate for final concentrations of 0, 0.5, 2, 4, 6, 8, 10, and 12 mM is added to a buffer containing 50 mM bis-tris-propane and 75 m M of ZnCl 2, pH = 7.4. For omethoate 2 m L of the 5 mM Ellman reagent is added.

Computational Analysis. All-atom DMD simulations are used to study both thermodynamic properties and the conformational dynamics of OPH. Specifically, we perform replica exchange

DMD simulations with 52 replicas ranging from 0.50 to 0.70 kcal/mol/k B incrementally by 0.004

6 kcal/mol/k B. Each simulation is run for 1x10 DMD time steps with a temperature exchange every 1,000 DMD time steps. Each DMD time step corresponds to ~50 fs, and the total simulation time of each replica is ~50 ns. We then apply the weighted histogram analysis method to calculate the specific heat as a function of temperature 109 , which is further used to determine the thermal stability of the mutants with respect to the wild type protein.

Constant temperature equilibrium simulations of OPH and mutants are performed at 0.62 kcal/mol/k B; this temperature is well below the folding transition temperature (Figure 3.5 and

Appendix Figures 1-2). To identify any major motion deviations we compute an average of the root mean square fluctuations (RMSF) per residue. We perform ten independent simulations for each mutation and wild type protein. From each independent trajectory we consider only the time points after the protein reaches equilibrium, ~300,000 time steps. The RMSF per residue is calculated using the analysis tool g_rmsf from Gromacs 110,111 then averaged to obtain the final

RMSF for comparisons.

CHAPTER 4: CONCLUSIONS AND FUTURE DIRECTIONS

In this work, we have investigated potential solutions to the organophosphate nerve agent

(OPNA) problem. OPNAs are small molecules with a chiral phosphate that upon exposure, quickly inhibit acetylcholinesterase (AChE) preventing signals across the synaptic junctions. The usual route of exposure is by inhalation or skin contact. Many of these OPNAs have no scent or color, thus making them difficult to detect. This difficulty potentially leads to many unplanned exposures of our armed forces, first responders, and civilians during warfare or terrorist attacks.

An effective method of protection is needed. Though OPNAs have been around for ~70 years there has been no significant progress made in protection efforts. There have been successes in both detecting the presence of OPNAs via an AChE biosensor and in decontamination by organophosphate hydrolase (OPH). 73,112–114

The ultimate goal of this project is a prophylactic protection method via small proteins that can sequester OPNAs in a stoichiometric manner (stoichiometric bioscavenger) or hydrolyze the OPNAs in a catalytic manner (catalytic bioscavengers). The benefits for such a prophylactic measure would be immense. To tackle this problem we chose a two-prong approach, that of designing a stoichiometric bioscavenger in concert with a catalytic bioscavenger.

Stoichiometric bioscavenger

For the stoichiometric bioscavenger we first identified an alternative to butyrylcholinesterase (BChE), the current strongest candidate. The limitations on BChE include large molecular weight and mass production issues. The benefits to BChE include the lack of an immunological response from injection as BChE is a human plasma derived protein. We introduced a computational workflow designed to search a library of known protein structures to discover promiscuous activity. Our source library was a subset extracted from the protein database (PDB), limited to those proteins below 40 kDa and with monomeric biological assembly. This refined structure library was searched to identify proteins capable of covalent binding similar to AChE and BChE, specifically those proteins containing residues oriented in a proper way to form a catalytic triad. Our search method included a rigid substructure search algorithm, Erebus 59 , in which we extracted the key atoms from the active site of AChE and submitted them as the search query. The over 200 results we obtained were visually inspected to remove duplicate proteins and ensure surface accessibility of the matched site. The remaining 21 proteins were excruciatingly examined using molecular docking. Specifically we utilized

MedusaDock 60–62 to simulate the non-covalent interactions between the small molecule VX and our candidates. This screen of the 21 candidates led to three candidates chosen for further analysis and experimental validation. These candidates are antigen 85-A and phosphoribosyl isomerase from Mycobacterium tuberculosis and Smu. 1393c from Streptococcus mutans .

We validated the interaction potential of each candidate through incubation with a serine hydrolase probe, designed to covalently bind and label in the same manner as an OPNA (Figure

1.2). The only positively labeled candidate was Smu. 1393c indicating a covalent bond

formation. To further investigate non-covalent interactions we used the same probe and performed a fluorescent polarization experiment to indicate that both antigen 85-A and phosphoribosyl isomerase interact with the serine hydrolase probe. We validated the location of the predicted covalent interaction of Smu. 1393c by creating a Ser99 to alanine mutation and we found the loss of covalent binding confirms the correct prediction of an active site. We implemented additional experiments to test the protective potential of Smu. 1393c to protect

AChE from inhibition by demeton-S-methyl and diisopropylfluorophosphate, V-agent and G- agent analog respectively. We found that Smu. 1393c helps AChE retain ~75% of it’s original activity after exposure to the analogs. Additionally, Smu. 1393c hydrolyzes the V-agent analog omethoate and when expressed with zinc hydrolyzes the G-agent analog paraoxon.

Future investigations will increase our understanding of the mechanism behind Smu.

1393c hydrolysis of both agent classes. Our ultimate goal is for Smu. 1393c to become a broad- spectrum bioscavenger therapeutic. To accomplish this goal we must increase the catalytic properties for the G-agents and the V-agents. Our best chance will be to engineer two different bioscavengers from the same Smu. 1393c platform, one specializing in G-agents, the other in V- agents. Prior to engineering efforts it is important to obtain a crystal structure co-crystallized with zinc and OPNA analogs, both paraoxon and omethoate. Once obtained, this crystal structure will become the basis for improved redesign. The process we will utilize is similar to that applied to OPH in chapter 3. We will first examine the residues in the active site using Eris 103 to estimate the ΔΔG, or the stabilization cost of mutating each residue in the active site. One feature of Eris that will be useful is the ability to estimate the ΔΔG with a rigid ligand bound. Once we have discovered mutations that strengthen the stability of the protein with the rigid ligand bound, we will then proceed to simulate the molecular docking potential of this new recombinant, using

MedusaDock 60,62 . If the result of the docking study analysis, similar to what was described in chapter 2, is favorable then these candidates will proceed to experimental verification. Based on the experimental outcome this process is repeated and guided in an iterative fashion. The end result is expected to be either 1) a bioscavenger candidate that can hydrolyze both agents or 2) multiple bioscavenger candidates built on the same framework so that when administered in concert provide the broadest protection against the most OPNAs as possible.

Catalytic Bioscavenger

Organophosphate hydrolase (OPH) is a known catalytic bioscavenger for various OPNAs with applications in decontamination. We examined OPH to see if there are stabilizing mutation hotspots occurring outside the active site but close enough to affect the performance and stability of the active site. We used our mutation stabilization software, Eris 103 , to screen each residue position that falls between five and ten angstroms from the active site. After scanning this second shell set of residues we found five positions with mutations that are potentially stabilizing,

T54M, D105T, T199I, C227M, and G251L. Four of these residue locations line an internal void and the mutations help to fill that void to stabilize the protein and position D105 represents an internal polar residue (Figure 3.3). When we examined the effect that each mutation has on OPH we find that mutations G251L and D105T do not express or show no activity with computational simulations supporting this conclusion by showing that neither G251L nor D105T increase calculated stability. Therefore, mutations G251L and D105T were not included when considering multiple mutations. The multiple mutation combinations that included T199I have activity levels increased for G-agent analog, paraoxon. Whereas when considering the level of

activity against omethoate, the V-agent analog, we found that mutations containing C227M show a greater increase in activity. This finding interestingly indicates that despite the distance from the primary active site, that allosteric mutations not only positively affect activity levels but also can assist modulating the specificity of the active site for different OPNAs. Therefore the combination of T199I/T54I provides a 40% increase in activity against paraoxon, and mutation

C227M/T54I provides a 3,300% increase in omethoate activity, and mutation

C227M/T199I/T54I provides a 5,700% increase also in omethoate activity.

We have shown that by using purely computational means we have successfully increased the activity of OPH for both G- and V-agent analogs, paraoxon and omethoate respectively. Because the location of these mutations lie outside the realm of the active site it is hoped that they can be successfully combined with active site mutations to increase both specificity and activity. To make OPH a therapeutic bioscavenger it will be necessary to engineer two specific recombinants, one designed for G-agent protection and another for V-agent protection. The process involves first testing our current mutations against live OPNAs. This is possible through an established collaboration with Dr. Steve Harvey and Dr. Douglas Cerasoli.

Once we have OPNA hydrolysis results we will continue designing an improved OPH protein through iteration of the procedure outlined in chapter 3. In concert with experimental efforts we will seek to simulate changes to the active site by these mutations in an endeavor to understand how allosteric mutations affect ligand specificity. We plan on using discrete molecular dynamics

(DMD) in conjunction with MedusaDock to better understand the change in binding conditions upon mutation.

In the next iteration we will extend the residues considered for mutation from the second shell to the third shell. This iterative process, including testing our designs with OPNAs will help

us develop the best set of mutations for both agent classes. Once we have exhausted all design possibilities in this iterative manner it would be interesting to study the effects of combining our mutations with mutations derived from specific active site mutations. Overall, in the process of designing OPH we have made progress in understanding the effects of allosteric modulation and the direct impacts of allostery on enzyme performance.

APPENDIX A: SUPPLEMENTAL MATERIAL FOR CHAPTER 3

Figure A.1 Specific Heat plots for all mutations. Replica exchange DMD simulations with WHAM applied displaying calculated specific heat reaction coordinates for single (A), double (B) and triple (C) mutations.

Figure A.2 Root Mean Square Fluctuations for all mutations. We show the RMSF observed from an average of ten equilibrium DMD runs per mutation at a reduced temperature of 0.62. Panel A) single mutations, B) double mutations and C) triple mutations.

REFERENCES

1. Hickey, L. J. L. & Bryant, S. M. in Surgery During Natural Disasters, Combat, Terrorist Attacks, and Crisis Situations 191–201 (Springer International Publishing, 2016). doi:10.1007/978-3-319-23718-3_18

2. Strong, L. L. et al. Health symptoms and exposure to organophosphate pesticides in farmworkers. Am. J. Ind. Med. 46, 599–606 (2004).

3. Carey, J. L., Dunn, C. & Gaspari, R. J. Central Respiratory Failure during Acute Organophosphate Poisoning. Respir. Physiol. Neurobiol. (2013). doi:10.1016/j.resp.2013.07.022

4. Page, W. F. Long-term health effects of exposure to sarin and other anticholinesterase chemical warfare agents. Mil. Med. 168, 239–45 (2003).

5. Medical Management of Chemical Casualties Handbook . (U.S. Department of Defense, Army Medical Research Institute of Chemical Defense, 1995).

6. Leikin, J. B., Thomas, R. G., Walter, F. G., Klein, R. & Meislin, H. W. A review of nerve agent exposure for the critical care physician. Crit. Care Med. 30, 2346–2354 (2002).

7. Peter, J. V., Sudarsan, T. I. & Moran, J. L. Clinical features of organophosphate poisoning: A review of different classification systems and approaches. Indian J. Crit. Care Med. 18, 735–45 (2014).

8. Cannard, K. The acute treatment of nerve agent exposure. J. Neurol. Sci. 249, 86–94 (2006).

9. Sidell, F. R. & Groff, W. A. The reactivatibility of cholinesterase inhibited by VX and sarin in man. Toxicol. Appl. Pharmacol. 27, 241–252 (1974).

10. Pohanka, M., Hrabinova, M., Kuca, K. & Simonato, J.-P. Assessment of Acetylcholinesterase Activity Using Indoxylacetate and Comparison with the Standard Ellman’s Method. Int. J. Mol. Sci. 12, 2631–40 (2011).

11. Quinn, D. M. Acetylcholinesterase: enzyme structure, reaction dynamics, and virtual transition states. Chem. Rev. 87, 955–979 (1987).

12. Dvir, H., Silman, I., Harel, M., Rosenberry, T. L. & Sussman, J. L. Acetylcholinesterase: from 3D structure to function. Chem. Biol. Interact. 187, 10–22 (2010).

13. Weise, C. et al. Anionic subsites of the acetylcholinesterase from Torpedo californica: affinity labelling with the cationic reagent N,N-dimethyl-2-phenyl-aziridinium. EMBO J. 9, 3885–8 (1990).

14. S Anchez-Santed, F., Colomina, M. T. & Herrero Hern Andez, E. Organophosphate

pesticide exposure and neurodegeneration. (2016). doi:10.1016/j.cortex.2015.10.003

15. Haley, R. W. et al. Cholinergic autonomic dysfunction in veterans with Gulf War illness: confirmation in a population-based sample. JAMA Neurol. 70, 191–200 (2013).

16. Sogorb, M. a & Vilanova, E. Enzymes involved in the detoxification of organophosphorus, carbamate and pyrethroid insecticides through hydrolysis. Toxicol. Lett. 128, 215–28 (2002).

17. Colovic, M. B., Krstic, D. Z., Lazarevic-Pasti, T. D., Bondzic, A. M. & Vasic, V. M. Acetylcholinesterase Inhibitors: Pharmacology and Toxicology. Curr. Neuropharmacol. 11, 315–335 (2013).

18. Kamal, M. a, Greig, N. H., Alhomida, a S. & Al-Jafari, a a. Kinetics of human acetylcholinesterase inhibition by the novel experimental Alzheimer therapeutic agent, tolserine. Biochem. Pharmacol. 60, 561–70 (2000).

19. Dunn, M. A., Hackley, B. E. & Sidell, F. R. in Medical Aspects of Chemical and Biological Warfare (eds. Sidell, F. R., Takafuji, E. T. & Franz, D. R.) 181–196 (Aberdeen Proving Ground, 1997).

20. Benschop, H. P. & De Jong, L. P. A. Nerve agent stereoisomers: analysis, isolation and toxicology. Acc. Chem. Res. 21, 368–374 (1988).

21. van der Schans, M. J., Lander, B. J., Wiel, H. van der, Langenberg, J. P. & Benschop, H. P. Toxicokinetics of the nerve agent (±)-VX in anesthetized and atropinized hairless guinea pigs and marmosets after intravenous and percutaneous administration. Toxicol. Appl. Pharmacol. 191, 48–62 (2003).

22. Sussman, J. L., Harel, M. & Silman, I. Three-dimensional structure of acetylcholinesterase and of its complexes with anticholinesterase drugs. Chem. Biol. Interact. 87, 187–97 (1993).

23. Sussman, J. L. & Silman, I. Acetylcholinesterase: structure and use as a model for specific cation—protein interactions. Curr. Opin. Struct. Biol. 2, 721–729 (1992).

24. Delfino, R. T., Ribeiro, T. S. & Figueroa-Villar, J. D. Organophosphorus compounds as chemical warfare agents: a review. J. Braz. Chem. Soc. 20, 407–428 (2009).

25. Bajgar, J., Fusek, J., Kassa, J., Kuca, K. & Jun, D. in Handbook of Toxicology of Chemical Warfare Agents 17–24 (2009). doi:10.1016/B978-0-12-374484-5.00003-1

26. Kwong, T. C. Organophosphate pesticides: biochemistry and clinical toxicology. Ther. Drug Monit. 24, 144–149 (2002).

27. Colquhoun, D. et al. in Burger’s Medicinal Chemistry and Drug Discovery (John Wiley & Sons, Inc., 2003). doi:10.1002/0471266949.bmc030

28. Mileson, B. E. et al. Common Mechanism of Toxicity : A Case Study of Organophosphorus Pesticides. 0, 8–20 (1998).

29. Mendola, P., Messer, L. C. & Rappazzo, K. Science linking environmental contaminant exposures with fertility and reproductive health impacts in the adult female. Fertil. Steril. 89, e81–e94 (2008).

30. Peiris-John, R. J. & Wickremasinghe, R. Impact of low-level exposure to organophosphates on human reproduction and survival. Trans. R. Soc. Trop. Med. Hyg. 102, 239–245 (2008).

31. Eddleston, M., Buckley, N. A., Eyer, P. & Dawson, A. H. Management of acute organophosphorus pesticide poisoning. Lancet 371, 597–607 (2008).

32. Thiermann, H., Worek, F. & Kehe, K. Limitations and challenges in treatment of acute chemical warfare agent poisoning. Chem. Biol. Interact. 206, 435–443 (2013).

33. Yanagisawa, N., Morita, H. & Nakajima, T. Sarin experiences in Japan: acute toxicity and long-term effects. J. Neurol. Sci. 249, 76–85 (2006).

34. Tokuda, Y., Kikuchi, M., Takahashi, O. & Stein, G. H. Prehospital management of sarin nerve gas terrorism in urban settings: 10 years of progress after the Tokyo subway sarin attack. Resuscitation 68, 193–202 (2006).

35. Okumura, T. et al. Report on 640 Victims of the Tokyo Subway Sarin Attack. Ann. Emerg. Med. 28, 129–135 (1996).

36. Lorke, D. E. et al. Pretreatment for acute exposure to diisopropylfluorophosphate: in vivo efficacy of various acetylcholinesterase inhibitors. J. Appl. Toxicol. 31, 515–23 (2011).

37. Aldridge, W. N. Serum esterases. II. An enzyme hydrolysing diethyl p-nitrophenyl phosphate (E600) and its identity with the A-esterase of mammalian sera. Biochem. J. 53, 117–24 (1953).

38. Androutsopoulos, V. P., Kanavouras, K. & Tsatsakis, A. M. Role of paraoxonase 1 (PON1) in organophosphate metabolism: implications in neurodegenerative diseases. Toxicol. Appl. Pharmacol. 256, 418–24 (2011).

39. Furlong, C. E., Marsillach, J., Jarvik, G. P. & Costa, L. G. Paraoxonases-1, -2 and -3: What are their functions? Chemico-Biological Interactions (2016). doi:10.1016/j.cbi.2016.05.036

40. Billecke, S. et al. Human Serum Paraoxonase (pon1) Isozymes Q and R Hydrolyze Lactones and Cyclic Carbonate Esters. Drug Metab. Dispos. 28, 1335–1342 (2000).

41. Rochu, D., Chabrière, E. & Masson, P. Human paraoxonase: A promising approach for pre-treatment and therapy of organophosphorus poisoning. Toxicology 233, 47–59 (2007).

42. Durrington, P. N., Mackness, B. & Mackness, M. I. Paraoxonase and Atherosclerosis. Arterioscler. Thromb. Vasc. Biol. 21, (2001).

43. Khersonsky, O. & Tawfik, D. S. Chromogenic and Fluorogenic Assays for the Lactonase Activity of Serum Paraoxonases. ChemBioChem 7, 49–53 (2006).

44. Harel, M. et al. Structure and evolution of the serum paraoxonase family of detoxifying and anti-atherosclerotic enzymes. Nat. Struct. Mol. Biol. 11, 412–419 (2004).

45. Masson, P. & Lushchekina, S. V. Emergence of catalytic bioscavengers against organophosphorus agents. Chemico-Biological Interactions (2015). doi:10.1016/j.cbi.2016.02.010

46. Goldsmith, M. et al. Evolved Stereoselective Hydrolases for Broad-Spectrum G-Type Nerve Agent Detoxification. Chem. Biol. 19, 456–466 (2012).

47. Caldwell, S. R., Newcomb, J. R., Schlecht, K. A. & Raushel, F. M. Limits of diffusion in the hydrolysis of substrates by the phosphotriesterase from Pseudomonas diminuta. Biochemistry 30, 7438–44 (1991).

48. Tsai, P.-C. et al. Enzymes for the Homeland Defense: Optimizing Phosphotriesterase for the Hydrolysis of Organophosphate Nerve Agents. Biochemistry 51, 6463–6475 (2012).

49. Rastogi, V. K., Defrank, J. J., Cheng, T. & Wild, J. R. Enzymatic Hydrolysis of Russian- VX by Organophosphorus Hydrolase. Biochem. Biophys. Res. Commun. 241, 294–296 (1997).

50. Cherny, I. et al. Engineering V-Type Nerve Agents Detoxifying Enzymes Using Computationally Focused Libraries. ACS Chem. Biol. 8, 2394–2403 (2013).

51. Goldsmith, M. et al. Catalytic efficiencies of directly evolved phosphotriesterase variants with structurally different organophosphorus compounds in vitro. Arch. Toxicol. (2015). doi:10.1007/s00204-015-1626-2

52. Pezzementi, L., Nachon, F. & Chatonnet, A. Evolution of acetylcholinesterase and butyrylcholinesterase in the vertebrates: an atypical butyrylcholinesterase from the Medaka Oryzias latipes. PLoS One 6, e17396 (2011).

53. Raveh, L., Grauer, E., Grunwald, J., Cohen, E. & Ashani, Y. The stoichiometry of protection against soman and VX toxicity in monkeys pretreated with human butyrylcholinesterase. Toxicol. Appl. Pharmacol. 145, 43–53 (1997).

54. Ashani, Y. et al. Butyrylcholinesterase and acetylcholinesterase prophylaxis against soman poisoning in mice. Biochem. Pharmacol. 41, 37–41 (1991).

55. Nachon, F., Brazzolotto, X., Trovaslet, M. & Masson, P. Progress in the development of enzyme-based nerve agent bioscavengers. Chem. Biol. Interact. 206, 536–44 (2013).

56. Doctor, B. P. & Saxena, A. Bioscavengers for the protection of humans against organophosphate toxicity. Chem. Biol. Interact. 157–158, 167–71 (2005).

57. Rosenberg, Y. J. et al. Pharmacokinetics and immunogenicity of a recombinant human butyrylcholinesterase bioscavenger in macaques following intravenous and pulmonary delivery. Chem. Biol. Interact. 242, 219–226 (2015).

58. Huang, Y.-J. et al. Recombinant human butyrylcholinesterase from milk of transgenic animals to protect against organophosphate poisoning. Proc. Natl. Acad. Sci. U. S. A. 104, 13603–8 (2007).

59. Shirvanyants, D., Alexandrova, A. N. & Dokholyan, N. V. Rigid substructure search. Bioinformatics 27, 1327–9 (2011).

60. Ding, F. & Dokholyan, N. V. Incorporating backbone flexibility in MedusaDock improves ligand-binding pose prediction in the CSAR2011 docking benchmark. J. Chem. Inf. Model. 53, 1871–1879 (2013).

61. Yin, S., Biedermannova, L., Vondrasek, J. & Dokholyan, N. V. MedusaScore: an accurate force field-based scoring function for virtual drug screening. J. Chem. Inf. Model. 48, 1656–62 (2008).

62. Ding, F., Yin, S. & Dokholyan, N. V. Rapid flexible docking using a stochastic rotamer library of ligands. J. Chem. Inf. Model. 50, 1623–32 (2010).

63. Silman, I. & Sussman, J. L. Acetylcholinesterase: how is structure related to function? Chem. Biol. Interact. 175, 3–10 (2008).

64. Berman, H. M. The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000).

65. Clark, M., Cramer, R. D. & Van Opdenbosch, N. Validation of the general purpose tripos 5.2 force field. J. Comput. Chem. 10, 982–1012 (1989).

66. Wang, Z., Li, L. & Su, X.-D. Structural and functional characterization of a novel α/β hydrolase from cariogenic pathogen Streptococcus mutans. Proteins 82, 695–700 (2014).

67. Ellman, G. L. Tissue sulfhydryl groups. Arch. Biochem. Biophys. 82, 70–77 (1959).

68. Eddleston, M. & Phillips, M. R. Self poisoning with pesticides. BMJ 328, 42–4 (2004).

69. Eyer, P. The Role of Oximes in the Management of Organophosphorus Pesticide Poisoning. Toxicol. Rev. 22, 165–190 (2003).

70. Ganesan, K., Raza, S. K. & Vijayaraghavan, R. Chemical warfare agents. J. Pharm. Bioallied Sci. 2, 166–78 (2010).

71. Due, A. V, Kuper, J., Geerlof, A., von Kries, J. P. & Wilmanns, M. Bisubstrate specificity in histidine/tryptophan biosynthesis isomerase from Mycobacterium tuberculosis by active

site metamorphosis. Proc. Natl. Acad. Sci. U. S. A. 108, 3554–9 (2011).

72. Cho, T.-H., Wild, J. R. & Donnelly, K. C. Utility of organophosphorus hydrolase for the remediation of mutagenicity of methyl parathion. Environ. Toxicol. Chem. 19, 2022–2028 (2000).

73. Kanugula, A. K. et al. Immobilization of organophosphate hydrolase on biocompatible gelatin pads and its use in removal of organophosphate compounds and nerve agents. Indian J. Biochem. Biophys. 48, 29–34 (2011).

74. Grimsley, J. K., Calamini, B., Wild, J. R. & Mesecar, A. D. Structural and mutational studies of organophosphorus hydrolase reveal a cryptic and functional allosteric-binding site. Arch. Biochem. Biophys. 442, 169–79 (2005).

75. Wong, K.-Y. & Gao, J. The reaction mechanism of paraoxon hydrolysis by phosphotriesterase from combined QM/MM simulations. Biochemistry 46, 13352–69 (2007).

76. Tinberg, C. E. et al. Computational design of ligand-binding proteins with high affinity and selectivity. Nature 501, 212–6 (2013).

77. Jacob, R. B., Michaels, K. C., Anderson, C. J., Fay, J. M. & Dokholyan, N. V. Harnessing Nature’s Diversity: Discovering organophosphate bioscavenger characteristics among low molecular weight proteins. Sci. Rep. 6, (2016).

78. Roy, A., Sarrou, I., Vaughn, M. D., Astashkin, A. V & Ghirlanda, G. De Novo Design of an Artificial Bis[4Fe-4S] Binding Protein. Biochemistry 52, 7586–7594 (2013).

79. Grisewood, M. J. et al. OptZyme: Computational Enzyme Redesign Using Transition State Analogues. PLoS One 8, e75358 (2013).

80. Bjelic, S. et al. Exploration of alternate catalytic mechanisms and optimization strategies for retroaldolase design. J. Mol. Biol. 426, 256–271 (2014).

81. Rajagopalan, S. et al. Design of activated serine-containing catalytic triads with atomic- level accuracy. Nat. Chem. Biol. 10, 386–91 (2014).

82. Wijma, H. J. & Janssen, D. B. Computational design gains momentum in enzyme catalysis engineering. FEBS J. (2013). doi:10.1111/febs.12324

83. Bornscheuer, U. T. & Pohl, M. Improved biocatalysts by directed evolution and rational protein design. Curr. Opin. Chem. Biol. 5, 137–143 (2001).

84. Wild, C. & Cunningham, K. A. Allosteric Modulation of G Protein-Coupled Receptors : An Emerging Approach of Drug Discovery. Austin J Pharmacol Ther. 2, 3–5 (2014).

85. De Smet, F., Christopoulos, A. & Carmeliet, P. Allosteric targeting of receptor tyrosine kinases. Nat. Biotechnol. 32, 1113–1120 (2014).

86. Nickols, H. H. & Conn, J. P. Development of allosteric modulators of GPCRs for treatment of CNS disorders. Neurobiol. Dis. 61, 55–71 (2014).

87. Wang, L., Martin, B., Brenneman, R., Luttrell, L. M. & Maudsley, S. Allosteric modulators of g protein-coupled receptors: future therapeutics for complex physiological disorders. J. Pharmacol. Exp. Ther. 331, 340–8 (2009).

88. Karginov, A. V, Ding, F., Kota, P., Dokholyan, N. V & Hahn, K. M. Engineered allosteric activation of kinases in living cells. Nat. Biotechnol. 28, 743–7 (2010).

89. Wenthur, C. J., Gentry, P. R., Mathews, T. P. & Lindsley, C. W. Drugs for allosteric sites on receptors. Annu. Rev. Pharmacol. Toxicol. 54, 165–84 (2014).

90. Wootten, D., Christopoulos, A. & Sexton, P. M. Emerging paradigms in GPCR allostery: implications for drug discovery. Nat. Rev. Drug Discov. 12, 630–44 (2013).

91. Dokholyan, N. V. Controlling Allosteric Networks in Proteins. Chem. Rev. 116, 6463– 6487 (2016).

92. Dagliyan, O. et al. Engineering extrinsic disorder to control protein activity in living cells. Science (80-. ). 354, 1441–1444 (2016).

93. Dagliyan, O. et al. Rational design of a ligand-controlled protein conformational switch. Proc. Natl. Acad. Sci. U. S. A. 110, 6800–4 (2013).

94. Hardy, J. A. & Wells, J. A. Searching for new allosteric sites in enzymes. Curr. Opin. Struct. Biol. 14, 706–15 (2004).

95. Goodey, N. M. & Benkovic, S. J. Allosteric regulation and catalysis emerge via a common route. Nat. Chem. Biol. 4, 474–82 (2008).

96. Süel, G. M., Lockless, S. W., Wall, M. A. & Ranganathan, R. Evolutionarily conserved networks of residues mediate allosteric communication in proteins. Nat. Struct. Biol. 10, 59–69 (2003).

97. Sirin, S., Pearlman, D. a & Sherman, W. Physics-based enzyme design: Predicting binding affinity and catalytic activity. Proteins 82, 3397–409 (2014).

98. Di Sioudi, B. D., Miller, C. E., Lai, K., Grimsley, J. K. & Wild, J. R. Rational design of organophosphorus hydrolase for altered substrate specificities. Chem. Biol. Interact. 119– 120, 211–23 (1999).

99. Bigley, A. N., Xu, C., Henderson, T. J., Harvey, S. P. & Raushel, F. M. Enzymatic neutralization of the chemical warfare agent VX: evolution of phosphotriesterase for phosphorothiolate hydrolysis. J. Am. Chem. Soc. 135, 10426–32 (2013).

100. Shirvanyants, D., Ding, F., Tsao, D., Ramachandran, S. & Dokholyan, N. V. Discrete Molecular Dynamics: An Efficient And Versatile Simulation Method For Fine Protein

Characterization. J. Phys. Chem. B 116, 8375–8382 (2012).

101. Ding, F., Tsao, D., Nie, H. & Dokholyan, N. V. Ab Initio Folding of Proteins with All- Atom Discrete Molecular Dynamics. Structure 16, 1010–1018 (2008).

102. Proctor, E. A., Ding, F. & Dokholyan, N. V. Discrete molecular dynamics. Wiley Interdiscip. Rev. Comput. Mol. Sci. 1, 80–92 (2011).

103. Yin, S., Ding, F. & Dokholyan, N. V. Eris: an automated estimator of protein stability. Nat. Methods 4, 466–7 (2007).

104. Yin, S., Ding, F. & Dokholyan, N. V. Modeling backbone flexibility improves protein stability estimation. Structure 15, 1567–76 (2007).

105. Schrödinger, LLC. The {PyMOL} Molecular Graphics System, Version~1.3r1. (2010).

106. Kota, P. et al. The N-terminal domain allosterically regulates cleavage and activation of the epithelial sodium channel. J. Biol. Chem. 289, 23029–42 (2014).

107. Proctor, E. a. et al. Rational coupled dynamics network manipulation rescues disease- relevant mutant cystic fibrosis transmembrane conductance regulator. Chem. Sci. 6, 1237– 1246 (2014).

108. Dunbrack, R. L. & Karplus, M. Backbone-dependent rotamer library for proteins. Application to side-chain prediction. J. Mol. Biol. 230, 543–74 (1993).

109. Chodera, J. D., Swope, W. C., Pitera, J. W., Seok, C. & Dill, K. a. Use of the Weighted Histogram Analysis Method for the Analysis of Simulated and Parallel Tempering Simulations. J. Chem. Theory Comput. 3, 26–41 (2007).

110. Berendsen, H. J. C., van der Spoel, D. & van Drunen, R. GROMACS: A message-passing parallel molecular dynamics implementation. Comput. Phys. Commun. 91, 43–56 (1995).

111. Pronk, S. et al. GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29, 845–54 (2013).

112. Shamagsumova, R. V., Shurpik, D. N., Padnya, P. L., Stoikov, I. I. & Evtugyn, G. A. Acetylcholinesterase biosensor for inhibitor measurements based on glassy carbon electrode modified with carbon black and pillar[5]arene. Talanta 144, 559–568 (2015).

113. Songa, E. A. & Okonkwo, J. O. Recent approaches to improving selectivity and sensitivity of enzyme-based biosensors for organophosphorus pesticides: A review. Talanta 155, 289–304 (2016).

114. Salerno, A. et al. In vitro skin decontamination of the organophosphorus pesticide Paraoxon with nanometric cerium oxide CeO2. Chem. Biol. Interact. (2016). doi:10.1016/j.cbi.2016.04.035