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SMALL MOLECULE ACTIVATORS OF ANGIOTENSIN-CONVERTING ENZYME 2

By

LIDIA VLADIMIROVNA KULEMINA

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL

OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2011

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© 2011 Lidia Vladimirovna Kulemina

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To my grandmother, Galina Sedova,

my parents, Elena and Vladimir

and my sister, Olga

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ACKNOWLEDGMENTS

I would like to thank my research advisor Dr. Sukwon Hong for accepting me into

his group, his mentoring, knowledge, guidance and support that made this work possi-

ble. I would also like to thank my co-chair Dr. David Ostrov for the exciting project, op-

portunity to learn and explore the field of science I’ve always been intrigued about –

drug discovery. I would like to thank my committee: Dr. Nigel Richards, Dr. Adrian Roit-

berg and Dr. Aaron Aponick for many helpful discussions, suggestions and critical

comments that encouraged me to think about my work. A special thanks to all Hong and

Aponick group members past and present, especially David, Sebastien, Nick and Kai,

for their endless patience while teaching a biochemist how to do synthesis and their

great sense of humor.

I thank Ran Zheng, Carolyn Diaz and all the staff of University of Florida ICBR

Proteomics Core for their help with the experiments and setting up the instruments. I

also would like to thank Kit, Daniel and Anya for their friendship and invaluable help

throughout the process. I’m thankful for Bus’ki for being such a good sport and helping

me keep my sanity through the times when I was discouraged and tired. I would like to

thank Dr. Carmen Allegra for the opportunity to work together and learn from a great

doctor and a friend. I also thank all the good people that I met here in Gainesville, you

made this experience truly unforgettable. I’m thankful for American Heart Association

and UF/Moffitt grants for providing support for my studies while pursuing this degree.

I’m forever grateful to my family for believing in my abilities, providing me with the

tools to succeed and for their love and support they have given me while pursuing this

degree and throughout all my life.

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TABLE OF CONTENTS

page

ACKNOWLEDGMENTS .................................................................................................. 4

LIST OF TABLES ............................................................................................................ 8

LIST OF FIGURES .......................................................................................................... 9

LIST OF ABBREVIATIONS ........................................................................................... 11

ABSTRACT ................................................................................................................... 13

CHAPTER

1 INTRODUCTION .................................................................................................... 16

Hypertension as a Special Target in Drug Discovery .............................................. 16 Angiotensin-Converting Enzyme 2 .......................................................................... 18

ACE2 Expression, Localization and Protein Family .......................................... 19

ACE2 Crystal Structure and Mechanism .......................................................... 20 Research Objectives ............................................................................................... 23

2 STRUCTURAL ANALYSIS AND TARGET SITE EVALUATION ............................. 26

Protein Dynamics and Internal Motions in Enzymatic Catalysis .............................. 26

Proteins as Conformational Ensembles ........................................................... 27 Structural Analysis and Future Goals ............................................................... 29

Results and Discussion........................................................................................... 30 Analysis of Molecular Surface and Solvent-Accessible Surface Areas ............ 30 Searching for Regions of Highest Flexibility: Analysis of Temperature

Factors .......................................................................................................... 32 Analysis of Hinge Regions in ACE2 ................................................................. 33

Effects of Small Molecules Binding Site 1 on ACE2 Ability to Bind the Substrate ....................................................................................................... 34

Experimental Section .............................................................................................. 36

3 MOLECULAR DOCKING AND EFFECTS OF TOP-SCORING COMPOUNDS ON ACE2 ................................................................................................................ 38

Structure-Based Drug Discovery of Allosteric Modulators ...................................... 38 Protein-Ligand Binding ..................................................................................... 39 Molecular Docking, Scoring Functions and Grid-Based Scoring ...................... 40 Compound Libraries ......................................................................................... 42

Results and Discussion........................................................................................... 44 Molecular Docking of FDA-Approved Drugs ..................................................... 44 Effects of FDA-Approved Drugs on ACE2 Catalytic Activity. ............................ 45

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EC50 and Lineweaver-Burk Analysis. ................................................................ 51

HPLC Analysis of Ang II Cleavage ................................................................... 54 Experimental Section .............................................................................................. 56

Structure Preparation and Docking .................................................................. 56 ACE2 Enzyme Kinetic Assays .......................................................................... 57 Inner Filter Effect .............................................................................................. 58 Statistical Significance ...................................................................................... 59

4 DESIGN, SYNTHESIS AND FUNCTIONAL TESTING OF DIMINAZENE DERIVATIVES ........................................................................................................ 60

Ligand-Based Approach ......................................................................................... 60 Results and Discussion........................................................................................... 61

Derivatization Strategy ..................................................................................... 61

Modifications to the Core .................................................................................. 63 Aromatic Triazoles as New Derivatives of Diminazene .................................... 65

Modifications to the Amidine Ends ................................................................... 66 Selection of Derivative Compounds for Synthesis. ........................................... 68

General Strategy for the Synthesis of Diminazene Derivatives ........................ 69 Derivative Compounds Prepared in the Study and Their Effects on ACE2

Activity ........................................................................................................... 70

Validation of the Structural Model ..................................................................... 72 Experimental Section .............................................................................................. 75

Structure Preparation and Docking .................................................................. 75 General Information on Synthesis .................................................................... 76 Safety Precautions ........................................................................................... 77

Procedure for Preparation of 4-Azidobenzonitrile. ............................................ 77 General Procedure for Preparation of 1,4-Disubstituted 1,2,3-Triazoles. ......... 77

General Procedure for Transformation of Nitriles to Hydroxyamidines. ............ 80

5 CONCLUSIONS AND FUTURE WORK ................................................................. 83

Conclusions ............................................................................................................ 83 Small Molecule Activators of ACE2 Identified in the Study ............................... 83 Utility of Using Small Molecules to Probe Enzyme Specificity .......................... 83

Future Work ............................................................................................................ 84 Structural Studies in ACE2 and Establishment of Structure-Activity

Relationship .................................................................................................. 84 Expanding the DMZ Derivative Library ............................................................. 85

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APPENDIX

A ACE2 SEQUENCE ANALYSIS ............................................................................... 86

ACE2, Collectrin and ACE Sequence Alignment .................................................... 86

ACE2 Conserved Residue Analysis ........................................................................ 88 Finding Evolutionarily Conserved Regions in ACE2 ............................................... 90

B STRUCTURAL ANALYSIS ..................................................................................... 91

ACE2 Protein Surface Analysis .............................................................................. 91 Analysis of Hinge Regions in ACE2 ........................................................................ 92

Defining the ―Druggable‖ Surface in ACE2: Open Conformation Sphere Clusters .. 94

C MOLECULAR DOCKING AND FUNCTIONAL TESTING ..................................... 102

Docking Algorithm Parameters ............................................................................. 102

Sphere Set Coordinates Defining the Target Site ................................................. 103 Clinical Uses of Selected FDA-approved Compounds .......................................... 104 Effect of DMZ on Catalytic Activity of ACE ............................................................ 106

Analysis of Ang II Cleavage by MALDI-TOF ......................................................... 107

D DESIGNING DERIVATIVE COMPOUNDS ........................................................... 108

Chemical Structures of Derivative Compounds (Library 1) ................................... 108 Docking Scores of Top-scoring Derivative Compounds ........................................ 109 1H NMR Spectra of New Triazolic Derivatives of Diminazene .............................. 112

REFERENCES ............................................................................................................ 125

BIOGRAPHICAL SKETCH .......................................................................................... 132

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LIST OF TABLES

Table page

3-1 Kinetic parameters of ACE2 in presence of select compounds. ......................... 48

3-2 Chemical structures and names of selected compounds. .................................. 49

4-1 Kinetic parameters of top 5 DMZ derivatives ...................................................... 71

B-1 ACE2 protein surface parameters ...................................................................... 91

C-1 Clinical uses of selected FDA-approved compounds ....................................... 104

D-1 Docking scores of 35 top-scoring derivative compounds (Library 1) ................ 109

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LIST OF FIGURES

Figure page 1-2 Sequence alignment scheme of ACE2, ACE and collectrin. ............................... 20

1-3 Proposed mechanism of ACE2-catalysed peptide cleavage .............................. 21

1-4 (A) ACE2 open conformation; subdomains I and II are shown in grey and blue. (B) Close-up view of ACE2 active site: Zn2+ cofactor, catalytic His 345 and E375 in the closed conformation. ................................................................ 22

2-1 Cross-section of enzyme free energy landscape, adapted from (61) ................. 28

2-2 Identifying ―druggable‖ solvent-accessible surface pockets in ACE2. ................. 31

2-3 Molecular surface of ACE2 in the open conformation colored by B-factor. ......... 33

2-4 Analysis of residue B-factors in crystal structures of ACE2. ............................... 34

2-5 Structural analysis of hinge regions in ACE2. ..................................................... 35

2-6 Structural model of Mca-APK-(Dnp)-OH fluorogenic peptide substrate binding the active site of ACE2. ...................................................................................... 36

3-1 DOCK6 Energy-scoring grid calculated for ACE2 Site 1. ................................... 42

3-2 ACE2 hinge region site 1 selected for molecular docking of FDA-approved compound library. ............................................................................................... 44

3-3 ACE2 initial velocity in presence of 50 µM top-scoring compounds ................... 45

3-4 ACE2 kinetic parameters in presence of selected FDA approved compounds. .. 46

3-5 Structural comparison of FDA-approved compounds that had the strongest effect on ACE2 activity: DMZ, LAB, XNT and CTX. ............................................ 51

3-6 Lineweaver-Burk double-reciprocal plot for ACE2 in presence of select five compounds ......................................................................................................... 52

3-7 Lineweaver-Burk double-reciprocal plot for ACE2 in presence of 0-100µM diminazene. ........................................................................................................ 53

3-8 ACE2 Dose-response curve for activation with diminazene. .............................. 54

3-9 DMZ enhances cleavage of natural substrate Angiotensin II. ............................. 55

4-1 Diminazene hydrolysis under acidic conditions .................................................. 62

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4-2 (A) Structure of Surfen C. (B) Diminazene derivatization strategy. (C) Three-dimensional structure of diminazene .................................................................. 63

4-3 Partial double-bond character due to resonance in triazenes ............................. 64

4-4 Chemical structures of the core that were considered as a replacement to triazene linkage in diminazene ........................................................................... 64

4-5 General scheme for ―click-chemistry‖ synthesis of triazoles. .............................. 65

4-6 Chemical structures and three-dimensional models of DMZ and 4,4'-(1H-1,2,3-triazole-1,4-diyl)dibenzimidamide. ............................................................. 66

4-7 Possible end-cap modifications to amidine ends in diminazene. ........................ 67

4-8 Schematic view of predicted binding positions of DMZ (orange) and XNT (yellow) modeled into the target site of ACE2. .................................................... 67

4-9 Predicted binding orientations of the three highest-scoring derivative compounds ......................................................................................................... 69

4-10 Chemical structures of compounds selected for synthesis listed in their ranking order. ..................................................................................................... 69

4-11 Original approach to making triazolic derivatives of DMZ. .................................. 70

4-12 Alternative synthetic plan for making amidine derivatives. ................................. 70

4-13 Chemical structures and three-dimensional models for the three most efficient derivative compounds comparison to diminazene. ................................ 74

C-1 Effect of DMZ on catalytic activity of ACE ........................................................ 106

C-2 MALDI mass-spectrum of Angiotensin 1-7 generated by cleavage of Ang II .... 107

D-1 Chemical structures of Library 1 derivative compounds. .................................. 108

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LIST OF ABBREVIATIONS

ACE Angiotensin-Converting Enzyme

ACE2 Angiotensin-Converting Enzyme 2

ACE-i Angiotensin-Converting Enzyme inhibitors

Ang 1-7 Angiotensin 1-7

Ang I Angiotensin I

Ang II Angiotensin II

APR Aprindine

ARBs Angiotensin Receptor type 1 blockers

ASA Solvent-accessible surface area

Asn Asparagine

AT1 Angiotensin Receptor type 1

CCP4 Comprehensive Computing Suite for Protein Crystallography

CHD Collectrin Homology domain

CTX Minithixen

DMSO Dimethyl Sulfoxide

DMZ Diminazene, (Benzenecarboximidamide,4,4'-(1-triazene-1,3-

diyl)bis,dihydrochloride)

E Glutamic acid

FDA Food and Drug Administration

FMB Fominoben

Gln Glutamine

Glu Glutamic acid

HCl Hydrochloric acid

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His Histidine

HPLC High Performance Liquid Chromatography

HXZ Hydroxyzine

IFE Inner filter effect

LAB Labetalol

MALDI Matrix Assisted Laser Desorption/Ionization

N Asparagine

NCI National Cancer Institute

NMR Nuclear Magnetic Resonance

NO Nitric oxide

PDB Protein Data Bank

Q Glutamine

RAS Renin-Angiotensin System

RMSD Root mean square deviation

SBDD Structure-Based Drug design

SHR Spontaneous Hypertensive Rats

TFAA Trifluoroacetic acid anhydride

TIA Tiaramide

TLC Thin layer chromatography

Tyr Tyrosine

vdW van der Waals

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Abstract of Dissertation Presented to the Graduate School

of the University of Florida in Partial Fulfillment of the

Requirements for the Degree of Doctor of Philosophy

SMALL MOLECULE ACTIVATORS OF ANGIOTENSIN-CONVERTING ENZYME 2

By

Lidia Vladimirovna Kulemina

August 2011

Chair: Sukwon Hong Major: Chemistry

Structure-based drug design efforts traditionally focused on development of en-

zyme inhibitors and receptor blockers. This strategy has been successfully applied to

inhibit a number of enzymes relevant to hypertension including Angiotensin Converting

enzyme (ACE) and Angiotensin receptor (AT). Despite the usefulness of frequently pre-

scribed ACE inhibitors and AT blockers in reducing high blood pressure, these com-

pounds are much less effective in preventing and reverting hypertension-induced end-

organ damage while side effects and development of resistance remain serious limita-

tions to their use.

Discovery of Angiotensin Converting Enzyme 2 (ACE2), a new regulator of Renin-

Angiotensin System, provided a valuable alternative target for treatment of hyperten-

sion. ACE2 is a zinc-dependent metallopeptidase that plays a protective role in the early

stages of heart failure. ACE2 can cleave a number of peptides implicated in high blood

pressure, thus pharmacological enhancement of ACE2 activity could be beneficial in

therapy. Structural information about ACE2 mechanism was used in the design of its

inhibitors; however there were only a few reports on rational enzyme enhancement for

therapeutic purposes to date. Recently, we identified a novel structural pocket in the

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hinge-bending region of ACE2 and demonstrated that targeting of small molecules to

this site could selectively enhance ACE2 activity and reduce hypertension in vivo.

In this study we aimed to get further insight on the mechanism of ACE2 enhance-

ment with the ultimate goal of finding better, more potent activators of ACE2. Since sta-

bilization of the open conformation may facilitate faster product release and accelerate

the overall rate, we used structural analysis to select the regions of highest intrinsic flex-

ibility. Comparison of the crystal structures revealed three major structural hotspots con-

trolling the conformational equilibrium in ACE2. Site 1 located in the largest hinge be-

tween the two subdomains was predicted to contribute the most to the conformational

shuffling and was selected to be probed with a chemical library of FDA-approved com-

pounds. We used molecular docking of small molecule library into Site 1 followed by

functional testing of the top-scoring compounds and identified four FDA-approved drugs

capable of enhancing ACE2 activity. One of these compounds, aromatic diamidine dim-

inazene, acts as a potent activator of ACE2 in vitro and reduces high blood pressure in

vivo. We aimed to further probe stereochemical preferences of ACE2 and test our strat-

egy by using additional compound libraries. We took the ligand-based approach com-

bined with principles of combinatorial chemistry to design and synthesize a small library

of novel, more efficient drug-like derivatives of diminazene.

This dissertation describes the work that has been done to achieve these goals as

well as three novel chemical entities with improved biological properties and stronger

effects on ACE2 than the lead compound diminazene that were created in our laborato-

ry. This study offers a new conformation-based selection method that utilizes existing

structural and dynamic information on enzyme behavior to design and develop novel

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small molecule activators for highly flexible enzymes like ACE2 as a basis for alternative

pharmacotherapy.

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CHAPTER 1 INTRODUCTION

Human genome project and structural genomics efforts revolutionized the field of

medicine and brought a lot of hope for finding new cures for the disease. Despite enor-

mous amount of information that was obtained in the process, one of the main ques-

tions was remaining open: how can genetic information about the disease be translated

into useful medicines? Current structure-based drug design methods that take ad-

vantage of recent developments in structural biology, biophysics, computational and

pharmaceutical chemistry are starting to unravel this problem. Clinical success of small

molecule inhibitors like captopril (1) and Gleevec (2) established a ―hit-to-lead‖ drug

discovery paradigm used up to this day in the development of new treatment options

for the disease (3). In this strategy, potential drug candidates are identified by screening

compound libraries against a particular target, usually receptor or enzyme that controls

a specific biochemical pathway associated with the pathological condition (4). While a

large number of compounds can be identified by this approach as appropriate modula-

tors in vitro and in vivo, majority of them fail at the stage of investigational clinical trials.

This is well illustrated by a relatively low number of new drug approvals compared to the

number of investigational new drug (IND) applications by the Food and Drug Admin-

istration over the last decade (5).

Hypertension as a Special Target in Drug Discovery

Chronic diseases like heart disease, cancer and diabetes are nowadays the lead-

ing cause of disability and death in the United States (6). Chronic diseases are especial-

ly challenging targets for small molecule drug discovery due the higher risk of adverse

drug reactions, toxicity and development of resistance associated with prolonged drug

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use. Therefore, there is a strong need in novel, more potent yet safe treatment and pre-

vention options that can be used in chronic conditions.

Hypertension is a serious health problem and a major risk factor for heart failure,

kidney damage, blindness and stroke. Hypertension affects nearly one in three Ameri-

cans and its prevalence has risen significantly over the past decade (7). Although cur-

rently available pharmacotherapy is capable of reducing high blood pressure, it is much

less effective in preventing and reverting hypertension-induced end-organ damage (8).

Side effects and development of resistance remain serious limitations to the use of

some of the most commonly prescribed anti-hypertensives - thiazide diuretics, sympa-

tholytics, calcium-channel blockers, alpha-adrenoreceptor antagonists, angiotensin re-

ceptor blockers and Angiotensin-Converting enzyme inhibitors (9-11).

Traditional therapeutic approach to controlling hypertension generally relies on in-

hibition of several key enzymes that regulate production of a powerful vasoconstrictor

peptide Angiotensin II (Ang II). For many years Ang II has been recognized as the cen-

tral molecule of the Renin-Angiotensin System (RAS), the main long-term regulator of

blood pressure and fluid homeostasis in the body. RAS is activated in response to de-

creased blood pressure by the release of proteolytic enzyme renin into the bloodstream.

In the bloodstream, renin converts the protein angiotensinogen to Angiotensin I (Ang I).

Circulating Ang I gets picked up by Angiotensin-Converting enzyme (ACE) that cleaves

two terminal aminoacid residues off the peptide to form Ang II (Figure 1-1). Ang II bio-

logical actions are determined by selective binding to Angiotensin receptors type 1

(AT1) and type 2 (AT2) (12). Ang II binding to the AT1 receptor activates a downstream

cascade of events that lead to vasoconstriction and elevation of blood pressure as well

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as smooth muscle cell proliferation, formation of reactive oxygen species and fibrosis

(13). Angiotensin-Converting enzyme ACE and AT1 receptor are the major pharmaco-

logical targets for treatment of hypertension associated with overreactive RAS.

Angiotensin-Converting Enzyme 2

Discovery of Angiotensin-Converting Enzyme 2 (ACE2) (14), a novel metallopepti-

dase with 42% homology to ACE, introduced an additional level of complexity to our un-

derstanding of RAS. ACE2 catalyzes cleavage of Ang II to Angiotensin 1-7 (15). Ang1-7

is a biologically active peptide that attenuates cardiovascular effects of Ang II by binding

to the G-protein coupled receptor Mas that stimulates release of nitric oxide NO and

causes vasodilation (16), (17). Several studies have shown that another angiotensi-

nase, a neutral endopeptidase (EC 3.4.24.11), can produce Ang1-7 by an alternative

route in vivo. Subsequent experiments, however, demonstrated that Ang II is the main

source for Ang 1-7 in the human heart and ACE2 is the principal enzyme of this path-

way (14) (18).

Figure 1-1. Role of ACE2 in Renin-Angiotensin System

Taken together these findings indicate that activation of ACE2 can produce cardi-

ovascular effects similar to ACE-inhibitors (ACE-i) and AT1-receptor-blockers (ARBs).

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Significantly, ACE2 gene transfer experiments in Spontaneous Hypertensive Rats

(SHR) demonstrated a long-term reduction in high blood pressure and beneficial effects

on cardiac remodeling (19), (20), (21), (22). Thus, ACE2 is an important regulator of

RAS and a promising new drug target for treatment of cardiovascular disease(23).

ACE2 Expression, Localization and Protein Family

ACE2 (EC 3.4.24.B1) is a type I integral membrane protein of 805 aminoacids that

is expressed predominantly on the apical surface of endothelial cells of heart and kidney

(24). ACE2 gene maps to the Xp22 chromosome locus associated with hypertension

(25) (26). Altered expression of this enzyme has been observed in cardiovascular and

renal pathology (27) (28) (29). ACE2 protein belongs to the M2 gluzincin metallopepti-

dase family characterized by a HEXXH+E zinc-binding consensus sequence in the ac-

tive site, where X is any aminoacid. Like most other members of the M2 family, ACE2 is

a secreted ectoenzyme, found in both membrane-bound and soluble forms (24). C-

terminal portion of ACE2 shares 48% identity with a transmembrane protein collectrin

(30), and thus is called collectrin homology domain (CHD). The catalytic (metallopepti-

dase) domain of ACE2 is 61% similar to that of its closest homolog, somatic ACE (EC

3.4.15.1) (25). ACE2 alignment scheme to ACE and collectrin is shown in Figure 1-2,

regions of similarity between the proteins are shown shaded; additional details and se-

quence alignments are provided in Appendix A. Despite the similarities, the two en-

zymes function in distinct ways: ACE is a dimeric peptidyl dipeptidase and ACE2 is a

strict carboxypeptidase (31). ACE2 is a multifunctional enzyme that hydrolyzes a num-

ber of biological substrates including Ang II, apelin and dynorphin by removing the C-

terminal amino acid residue of the peptide. Investigation of ACE2 substrates (31) identi-

fied a consensus sequence of Pro-X(XX)-Pro-Hydrophobic/Basic, where hydrolysis

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occurs between the proline (P1 residue) (32-34) and the hydrophobic amino acid resi-

due (P1’).

Figure 1-2. Sequence alignment scheme of ACE2, ACE and collectrin.

Based on the catalytic efficiency of ACE2 measured for a variety of peptides, proline

and leucine are the preferred P1 residues (kcat/Km > 105 M-1 s-1) (31) with penultimate

proline being the most conserved residue in ACE2 substrates, while hydrophobic amino

acids are generally favored over the basic ones at P1’ position.

ACE2 Crystal Structure and Mechanism

The first crystal structures for ACE2 in its native and inhibitor-bound states

(PDBID: 1R42 and 1R4L) appeared in 2004 (32). The structures revealed existence of

two conformations: open and closed. Electron density for largely disordered collectrin

homology domain was very weak and only extracellular metallopeptidase domain has

been solved. Metallopeptidase domain of ACE2 can be further divided into two subdo-

mains (I and II) which form the sides of a large cleft of ∼40 Å long by ∼15 Å wide by

∼25 Å deep (Figure 1-4A). The deeply recessed active site is buried between the two

subdomains and contains a single zinc ion cofactor coordinated by the two histidine

side-chains (His 374 and His 378), and C-terminal glutamate Glu 402 (35) (Figure 1-

4B).

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Figure 1-3. Proposed mechanism of ACE2-catalysed peptide cleavage

Comparison of ACE2 crystal structures revealed a large (~16°) inhibitor-

dependent hinge-bending movement of one subdomain relative to the other. Similar

subdomain motions were seen in other metallopeptidases as a common mechanism for

arranging critical groups around substrates and reaction intermediates (36), (37). In the

proposed mechanism for ACE2, binding of the substrate is thought to induce a confor-

mational change that leads to initial enzyme-substrate (E•S) complex formation and

closing of the cleft. In the next step, zinc-bound water initiates a nucleophilic attack on

the carbonyl of the scissile amide bond that leads to a formation of the tetrahedral in-

termediate (Figure 1-3, Figure 1-4B). Tetrahedral intermediate stabilized by active site

residues (E375, E402, H374, H378) gets collapsed by a transfer of the proton onto de-

parting nitrogen of the P1’ residue which is followed by bond cleavage and product re-

lease (38).

Structural information about ACE2 mechanism and active site specificity has

been successfully applied to rational design of inhibitors for this enzyme (39). There

have been no reports, however, of using this strategy to develop ACE2 activators, be-

sides the previous study done in our laboratory (40).

E E* + S ES ES’ EP E + P

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Figure 1-4. (A) ACE2 open conformation; subdomains I and II are shown in grey and blue. (B) Close-up view of ACE2 active site: Zn2+ cofactor, catalytic His 345 and E375 in the closed conformation.

23

In the earlier study, we proposed a hypothesis that stabilization of a certain confor-

mation can increase its proportion in the total protein population that could make it more

accessible for catalysis and potentially increase the overall rate. We decided to target

the open conformation of ACE2 and screened a small molecule library of 140,000 com-

pounds within the structural constraints of the hinge between the two subdomains.

Small molecule compounds predicted to bind in this region, are expected to preferential-

ly target the open form however part of the target site is accessible in both open and

closed conformations. Functional testing of the compounds with the highest energy

scores from docking revealed several compounds that enhance ACE2 activity in vitro

and reduce high blood pressure in vivo. Given such promising preliminary results, we

aimed to further confirm and improve this strategy by looking into more potent activators

of ACE2.

Research Objectives

The ultimate long-term goal of this project is to develop novel potent non-essential

activators of Angiotensin Converting Enzyme 2 that could provide an alternative treat-

ment strategy for hypertension. Enhancement of ACE2 activity through gene transfer

experiments has shown not only to lower high blood pressure but also to reduce fibrosis

in vivo. For this reason, new strategies of regulating ACE2 activity could be particularly

beneficial for treatment of hypertension-associated cardiovascular disease and in drug-

resistant hypertension. Unfortunately traditional approaches used for the design of the

inhibitors do not apply to making new activators. In view of a limited number of studies

on non-essential enhancement of enzyme activity, new rational strategies need to be

developed to address this problem. This study offers a new method that utilizes existing

24

structural and dynamic information on enzyme behavior to design novel small molecule

activators for highly flexible enzymes like ACE2.

One of the main objectives of the work described in this dissertation was to test

the hypothesis that enzyme activity can be specifically enhanced by the small mole-

cules, with a long term goal of designing therapeutics that can ―activate‖ enzymes that

are deficient in the disease state. In order to achieve this goal, we started with re-

evaluating the original strategy described in (40). We performed structural analysis on

currently available crystal structures of ACE2 to define catalytically and dynamically im-

portant regions of ACE2, select the best possible site for molecular docking as well the

stage for further exploration of this strategy with other enzymes. Since we were looking

to discover new series of molecules that enhance ACE2 activity, we probed the target

site with a smaller, easily obtainable library of FDA-approved compounds that would al-

low quick examination of docking predictions. In vitro testing identified several potential

activators of ACE2 that could be used as leads. One of those compounds, FDA-

approved veterinary drug diminazene, was tested in vivo and was shown to produce a

significant reduction in high blood pressure in hypertensive rats. We further explored

this strategy and took the ligand-based design approach to design, synthetize and test a

small combinatorial library of derivative compounds. Combinatorial library that was de-

signed based on the results from two screenings, structural information and known

physicochemical characteristics of the top hits, yielded three compounds with improved

in vitro efficiency and physicochemical characteristics compared to the lead, in vivo ex-

periments with the best derivatives are underway.

25

The interdisciplinary approach described in this study is commonly used in phar-

maceutical industry where computational medicinal chemistry methods are used to es-

timate binding and predict structures for ligand-receptor complexes, while organic and

pharmaceutical chemists draft the design the new compounds that can be screened

computationally or synthesized and tested in vitro and in vivo. This dissertation de-

scribes how structural analysis, combinatorial and computational chemistry can be

combined to study novel enzyme activators for highly dynamic proteins that are relevant

to human disease.

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CHAPTER 2 STRUCTURAL ANALYSIS AND TARGET SITE EVALUATION

Protein Dynamics and Internal Motions in Enzymatic Catalysis

Conformational motions play an essential role in biological function of enzymes.

Over the course of evolution, many enzymes developed into extremely efficient cata-

lysts, in some cases approaching diffusion-controlled rates of substrate conversion.

From this perspective, evolutionarily-―successful‖ enzymes with second-order rate con-

stants of 108-1010 M-1 s-1 present nearly perfect catalytic machines. These high micro-

scopic rate constants, however, do not always translate into turnover numbers of equiv-

alent magnitude. One such example that has been extensively studied is Ribonuclease

A (RNase A) that catalyzes breakdown of RNA into nucleotide 3’-phosphates. RNAse A

has a second-order rate constant indicative of a diffusion-controlled reaction, while its

steady-state turnover numbers rarely exceed 3000 s-1 (41).Therefore, the overall rate

must be determined by the steps other than chemistry, like conformational changes as-

sociated with substrate binding or product release. Further studies have shown this was

the case with RNase A as well as many other well-characterized enzymes (42) (43) (44)

(45) (46). Characterization of those rate-limiting transitions is crucial to understanding of

catalytic function, regulation of enzyme activity and optimization of protein-ligand inter-

actions for drug discovery.

The main principle of catalysis is based on the ability of an enzyme to lower the

activation energy barrier for the transition state which results in faster conversion of

substrate into products. In enzymatic catalysis, rate enhancement is achieved by provid-

ing an alternative, more thermodynamically-efficient pathway of several intermediate

steps characterized by relatively low activation energy barriers. Detailed analysis of the

27

catalytic mechanism and kinetics of dihydrofolate reductase, cytochrome P450 and

several other enzymes showed existence of multiple closely-related reaction paths for

conversion of substrates into products (47) (48). This would only be possible if the pro-

tein was capable of sampling a variety of conformations in response to changing envi-

ronment. Indeed, experimental evidence from rapid kinetics measurements, flash pho-

tolysis experiments and molecular dynamics simulations strongly suggest existence of

internal conformational equilibria in proteins (49) (50) (51) (52).

Proteins as Conformational Ensembles

Over the years, our understanding of conformational transitions evolved from clas-

sical folding and allostery theories (53) (54) to recognition of folding funnels and energy

landscapes (55). High-resolution X-ray crystallography, NMR relaxation experiments

and QM/MM calculations have all contributed to what we know about dynamic behavior

of proteins today. In modern biophysical science proteins are treated as ensembles of

different isoforms or conformational substates characterized by their energy minima (56)

(57). Isoform interconversions are constrained by activation energy barriers that ulti-

mately determine the most predominant conformations that can exist under given condi-

tions (Figure 2-1).

Binding of a ligand can help to stabilize a particular conformation and increase its

proportion in a total protein population. As a result, one can drive such transitions faster

or make them slower by changing the energy of ground state in presence of a ligand or

through mutation. This property can be taken advantage of in pharmacological modifica-

tion of catalytic activity in proteins where large conformational changes are an integral

part of the catalytic cycle and significantly contribute to the overall rate (58) (59) (60).

Figure 2-1 illustrates cross-section of protein energy landscape in an enzyme undergo-

28

ing conformational changes where conformational sampling is plotted as a function of

free energy. Red line represents protein in its native state, while blue line shows the free

energy landscape when external force is being applied, for example in ligand binding or

upon change in the protein buffer composition.

Figure 2-1. Cross-section of enzyme free energy landscape, adapted from (61)

The timescales of conformational motions span the range of 10-12 to 10-1 s, where

transitions of ~10-9 s correspond to small bond vibrations, 10-9 - 10-6 s to loop move-

ments and 10-6 s and greater usually describe large domain motions. Because most en-

zymes characterized to date turnover their substrates at the rates under 106 s-1, con-

formational dynamics on the ―slower‖ timescale is particularly interesting for drug dis-

covery.

ns

ps

µs to ms

Free

en

erg

y, G

conformational coordinate

Transitions:

faster than 10-9

s - small bond vibrations

10-9

-10-6

s - loop movements

slower than 10-6

s - large domain motions

29

Structural Analysis and Future Goals

Due to the nature of the packing forces in the process of crystallization, protein X-

ray crystal structures typically capture most populated conformational substates charac-

terized by low energy that can exist under given conditions (62). Thus, when the protein

is captured in two distinctly different conformations in crystal structures, the energy gap

between such substates is likely to be high. Analysis of the differences between static

X-ray structures for apoenzyme and inhibitor-bound forms of ACE2 showed large con-

formational fluctuations with RMSD over 10Å in the most flexible region. Conformational

motions of such large interdomain displacement most likely occur on the slower time-

scale and thus can significantly contribute to the rate. From this perspective, the rate of

ACE2 reaction can be modulated by the changing the conformational equilibrium and

availability of ―active‖ conformation for catalysis. Results of our previous study (40) sug-

gest that small molecule approach to targeting specific enzyme conformations can be

beneficial for enhancing ACE2 activity. Despite certain limitations of using the crystal

structures for molecular docking (63), Protein Data Bank ACE2 structures 1R42 and

1R4L allowed us to identify molecules that enhance ACE2 activity and we continued to

use them throughout this work. In this study, we aimed to further test our hypothesis by

screening a different compound library within the previously selected Site 1 with the ul-

timate goal to find new compounds that enhance ACE2 activity.

Human ACE2 presents a challenging crystallography target that is difficult to ex-

press in large quantities; therefore, it is especially important to critically evaluate the

structure and focus site-directed mutagenesis and crystallography efforts on pre-

determined sites of structural importance. For this reason, we started with defining the

residues that control conformational shuffling between the two isoforms of the enzyme.

30

This chapter describes the methods used in structural analysis of ACE2 crystal struc-

tures, identification of flexible regions and validation of Site 1. The results of structural

analysis described here will be ultimately used to guide site-directed mutagenesis and

X-ray crystallography experiments and infer structure-activity relationships associated

with modification of this region.

Results and Discussion

Analysis of Molecular Surface and Solvent-Accessible Surface Areas

Since fluctuations in the hydration shell and bulk-solvent surrounding the enzyme

frequently correlate with the internal protein motions (64), our initial goal was to identify

the regions of highest solvent accessibility. The hypothesis was that targeting small

molecules to the sites characterized by large changes in solvent accessibility between

the two conformations can be used to stabilize a particular conformation.

Previously, molecular surface regions with largest changes in solvent accessible

surface areas (ASA) were analyzed by DSSP software (65). In this study we performed

more detailed structural analysis to identify other potential docking sites that have not

been covered earlier. For this purpose, Sphgen and DockPrep developed by Irwin Kuntz

(66) were used to calculate and analyze molecular surface and identify other potentially

―druggable‖ regions (Figure 2-2). ACE2 molecular surface was calculated based on the

crystal structures for the open and closed conformations (PDBID 1R42 and 1R4L) after

all ligands, non-complexed ions and solvent water molecules have been removed. Once

molecular surface has been generated, Sphgen was used to probe the surface and sat-

urate the crevasses with small spheres in order to pinpoint the sites that can be later

used for docking. Figure 2-2 shows ACE2 secondary structure (ribbon) colored by sol-

31

vent-accessibility; five largest sphere clusters specific to open conformation of ACE2

are shown in yellow.

Figure 2-2. Identifying ―druggable‖ solvent-accessible surface pockets in ACE2.

Spheres generated by this method were joined into clusters (sets of overlapping

spheres) and analyzed individually. Sphgen analysis identified more than ten clusters of

14 spheres or more for both open and closed conformations (additional details on

sphere cluster coordinates defined for the open conformation of ACE2 can be found in

Appendix B).

Cluster size and location have a strong impact on the success of docking simula-

tion, since they ultimately determine the structural constraints and screening selection

stringency for the small molecules. Generally, clusters that are too small (less than 10

Sphere

cluster 1

Sphere

cluster 2

Sphere

cluster 4

Sphere

cluster 3

Sphere

cluster 5

32

spheres) can result in a lot of variability and many positive hits, but few true lead com-

pounds. Using large clusters of more than 50 spheres, however, is computationally ex-

pensive and not very practical for initial screening when searching for the primary lead.

Only 4 out of 135 clusters identified were marked as suitable for small molecule docking

based on the cluster size, concavity of the respective surface, size of the potential bind-

ing site and proximity to the active site. Structural coordinates of the sphere set that was

selected for docking can be found in Appendix C.

Searching for Regions of Highest Flexibility: Analysis of Temperature Factors

Stabilization of a specific conformation can be achieved by ―locking‖ the sites of

highest intrinsic flexibility in the protein that are often marked by residues with high tem-

perature factors. Temperature factors (B-factors or Debye-Waller factors) are used in X-

ray crystallography to describe the degree of electron density scattering for individual

atoms and aminoacid residues with respect to the three-dimensional structural model

and can be calculated by where Ui

2 is the mean square displacement of

atom. Since B-factor values are a function of mean square displacement for a particular

atom, they can indicate the regions of high dynamic mobility I, that can be locked by the

small molecules. We analyzed average residue temperature factors of ACE2 by using

CCP4 structural analysis module Areaimol (58) and UCSF Chimera (67) to identify the

sites of highest mobility. Structural regions were considered ―flexible‖ when more than

five out of ten consecutive aminoacid residues presented high B-factor values. Figure 2-

3 shows ACE2 molecular surface colored by residue B-factors: red for the highest, blue

– lowest, yellow spheres represent the site and the spheres selected for in silico screen-

ing.

33

Figure 2-3. Molecular surface of ACE2 in the open conformation colored by B-factor.

Figure 2-4 shows the plot of average B-factor values calculated for aminoacid res-

idues of ACE2 in the open (red line) and closed (blue) conformations. Based on the

graph, there are several distinct regions of high flexibility: within aminoacid residues 19-

23, 87-105, 421-450 and 600-610. High B-factor values for residue numbers 19-23 and

600-610 are somewhat expected since they correspond to N- and C-terminal ends.

Aminoacid residues 87-105 and 421-450 fall within the flexible loop regions in the open

and closed conformations of ACE2.

Analysis of Hinge Regions in ACE2

Dyndom and ArealMol structural analysis programs from CCP4 suite were used to

identify the hinge regions in ACE2. Dyndom (68) searches for dynamic domains and in

terdomain screw axis by analyzing and clustering rotation vectors from two crystal struc-

tures. Based on the analysis, there are three potential axes in ACE2, but only one

34

Figure 2-4. Analysis of residue B-factors in crystal structures of ACE2.

major hinge located between the two subdomains that is responsible for the dynamic

behavior of this enzyme. Figure 2-5 shows hinge axes determined by Dyndom (red dot-

ted lines); ACE2 crystal structures for the open and closed conformations are shown in

grey and orange, respectively. According to hinge analysis results, previously selected

site 1 is located within the ―main‖ hinge region (aminoacid residues 87-105 and 202-

210). Additional details on dynamic hinges are provided in Appendix B.

Effects of Small Molecules Binding Site 1 on ACE2 Ability to Bind the Substrate

Since Site 1 is connected to the active site, it was important to consider the effect of

small molecule binding Site 1 on the ability of an enzyme to bind Angiotensin II. In order

to estimate binding position of fluorogenic substrate and Ang II within the active site, we

created three-dimensional models for Angiotensin II and fluorogenic peptide substrate

Mca-APK-Dnp-OH that was used throughout this study for in vitro evaluation of kinetic

Residue number

Ave

rag

e B

-fa

cto

r

Site 3 Site 2 Site 1

main hinge

ACE2

active site

35

Figure 2-5. Structural analysis of hinge regions in ACE2.

parameters of ACE2. We used structural coordinates for substrate-based inhibitor of

ACE2 MLN-4760 (32) to identify critical interactions that lead to formation of tetrahedral

intermediate and modeled our substrates based on known stereochemical preferences

of ACE2. Model structures were created in ChemDraw, energy minimized in Chimera

and superimposed over MLN-4760 with PyMOL software (DeLano Scientific, (69)) by

matching the geometry of tetrahedral intermediate. Based on our model, neither of the

substrates is expected to bind close to the Site 1 and ACE2 substrate specificity should

not be affected by binding of the small molecules. Figure 2-6 shows predicted binding

position of synthetic peptide substrate in the active site of ACE2. ACE2 crystal structure

for the open conformation is shown in grey, closed – in orange, peptide substrate is rep-

resented as sticks and colored by element: yellow for carbon, blue for nitrogen, red for

oxygen, white for hydrogen.

hinge

axis 1

hinge

axis 2

hinge

axis 3 (main)

B

hinge

motion

A

36

Figure 2-6. Structural model of Mca-APK-(Dnp)-OH fluorogenic peptide substrate bind-ing the active site of ACE2.

Site 1 selected for docking is located in the hinge region separating the two sub-

domains of the enzyme and is characterized by high solvent accessibility and flexibility.

Site 1 presents a good docking target due to its role in conformational shuffling as it is

likely to be the major contributor to ACE2 flexibility. Further experiments would be nec-

essary to experimentally confirm its significance, e.g. site-directed mutagenesis and mo-

lecular dynamics simulations of the mutant enzyme might be able to answer this ques-

tion.

Experimental Section

Analysis of solvent-accessible areas, b-factors and hinges was performed using

ACE2 crystal structures for the open and closed conformations (PDB ID: 1R42 and

1R4L) and structural analysis module of CCP4. Analysis of ―druggable‖ regions and sur-

face concavity was performed in Sphgen and Surface Racer 5.0, by using a spherical

probe of defined radius to ―roll over‖ the van der Waals surface of the protein and find

the sites of surface curvature that could be used for docking (70). Energy minimizations

His345

(open)

His345

(closed)

ACE2 active site

Mca-APK-(Dnp)-OH

model

37

were done using ChemDraw3D MM2 algorithm for small molecules and DockPrep (Mo-

lecular modeling tool kit and AMBER) in UCSF Chimera for protein structures. Figures

were generated in Chimera and Pymol.

38

CHAPTER 3 MOLECULAR DOCKING AND EFFECTS OF TOP-SCORING COMPOUNDS ON ACE2

Structure-Based Drug Discovery of Allosteric Modulators

Drug discovery process typically involves 7-12 years of research, participation of

large pharmaceutical teams and millions of dollars invested along the way from target to

therapy. Application of computational methods to exploration of chemical space and re-

ceptor-ligand interactions allowed scientists to significantly accelerate this process. Alt-

hough currently we do not have complete understanding of the forces that drive molecu-

lar recognition, modern structure-based methods already provide the means for deter-

mination of local electrostatic, van der Waals and hydrophobic interactions that define

most protein-ligand complexes. As a result, the likelihood of small molecule binding a

particular receptor can be predicted rather accurately, provided their three dimensional

structures are available. From this perspective, identification of active site inhibitors for

proteins that have been co-crystallized with their substrate analogs may seem like a triv-

ial task. The number of new drug approvals by FDA, however, shows that this is not true

(71).

Design of allosteric modulators presents even more challenging goal since it re-

quires understanding of protein dynamics associated with catalysis (72). This chapter

describes some of the theory behind computational methods in drug discovery and how

they can be applied to identification of inhibitors or activators of therapeutically relevant

enzymes. It also describes how these methods were used in this study to search for the

new activators of Angiotensin-Converting enzyme 2 and discovery of the new molecules

that enhance ACE2 activity in vitro and reduce high blood pressure in vivo.

39

Protein-Ligand Binding

Structure-based drug design methodology uses three-dimensional structures of

the target as the basis for lead compound selection. The likelihood of small molecule

binding to a target is determined by free energy of the interaction (Eq.3-1).

(Eq. 3-1)

In the equilibrium reaction of protein-ligand binding, Ka (or Kd) represents equilibrium

constant for E∙L complex formation, that can be determined experimentally.

[ ][ ]

[ ]

(Eq. 3-2)

When a system reaches an equilibrium, that is the rates of E∙L complex formation and

dissociation are the same and ΔG=0, the standard change in Gibbs free energy be-

comes directly proportional to Ka.

(Eq. 3-3)

Hess’s law states that change in energy is pathway independent, provided that ini-

tial and final reaction conditions are the same. Since Gibbs free energy is the state func-

tion, Hess’s law extension can be used to calculate the change in free energy from

the individual components associated with binding (Eq. 3-4).

(Eq. 3-4)

This principle is used in virtual screening and molecular docking programs that can

estimate for a large number of molecules and rank them according to energy

score. If the total energy for the complex is lower than the energies for the receptor and

ligand, complex formation is going to be favorable. The lower the value for , the more

spontaneously E∙L association will proceed. Small molecules with better energy scores

40

are predicted to be more specific and likely to bind the target and thus have a greater

potential to become a new lead.

Molecular docking in its current form, however, cannot give an answer to the ques-

tion: what kind of molecule can bind a given site? Rather, it can be used to narrow down

the pool of potential candidates and prioritize scientist’s efforts when searching for the

new lead. Success of this process depends on having a structurally diverse compound

library and an effective way to ―filter‖ the compounds. Since the main filtering constraints

are defined by the structural stringencies of the target site, it is important to select the

site that is relevant to enzymatic activity. Additional details can be fine-tuned by adjust-

ing the docking parameters that will be discussed below.

Since structural analysis (Chapter 2) confirmed Site 1 as a reasonable docking

target, our next goal was to validate additional parameters that were used in identifica-

tion of XNT. We aimed to achieve this by in silico screening of a different compound li-

brary within the pre-defined structural space of Site 1 followed by in vitro testing of the

top hits. Our hypothesis here was that if the structural model was able to capture some

of the most important protein-ligand interactions, it can be used to identify additional

modulators of ACE2 activity in other libraries of considerable diversity.

Molecular Docking, Scoring Functions and Grid-Based Scoring

In DOCK, the shape of the binding cavity is estimated by saturating the target site

with small spheres of a given radius. Selected subset of spheres then serves as a de-

sign template for geometric matching of the ligand atoms with the centers of the

spheres (73). Since there is more than one binding orientation possible for each ligand,

resulting maybe quite different. This issue is resolved by using scoring functions

that help prioritize conformations for a particular ligand based on statistically weighed

41

parameters. These terms may include empirical scoring functions, molecular mechanics

force fields or knowledge-based functions that take advantage of statistical distribution

of previously derived information on protein-ligand interaction.

DOCK6 takes advantage of molecular mechanic force field function (MMFF) to eval-

uate non-covalent protein-ligand interactions as a fast and computationally inexpensive

way to score candidate molecules. Typical MMFF functions used in docking estimate

E∙L affinity by summing the energies of electrostatic and van der Waals interactions be-

tween the ligand and the receptor. Additional scoring parameters like bond strain and

desolvation energies can also be included in the equation.

(Eq. 3-5)

Electrostatic component is usually calculated using Coulomb’s law, while Lennard-

Jones potential(74) is used to describe van der Waals interactions. As a result, free en-

ergy scoring function for protein-ligand interactions in DOCK6 can assume the form:

∑ (

) ∑ (

) (Eq. 3-6)

In order to minimize overall computational time, free energy calculations are performed

using only a fraction of total protein surface, typically within 10 Å of the target site. Since

receptor contributions to the score ( ) are potentially repetitive, they can be pre-

calculated. Those values can be stored on a three-dimensional energy grid and quickly

retrieved when needed (73). Figure 3-1 shows energy scoring grid (grey mesh) and

―bump‖ filters employed to avoid for steric clashes (represented as purple surfaces).

Molecular docking spheres are colored in yellow, small molecule docked into Site 1 is

shown as sticks and colored pink

42

Figure 3-1. DOCK6 Energy-scoring grid calculated for ACE2 Site 1.

Compound Libraries

Virtual screening methods can be applied to any chemical database that contains struc-

tural coordinates for the small molecule. According to some estimates, there are more

than 1060 drug-like compounds in the chemical space; manipulation of this giant com-

pound library is currently outside the scope of computational methods used in drug dis-

covery (75). Even if such task was attainable, it is more practical to focus on the data-

bases of commercially available compounds so that the docking hypotheses can be rap-

idly tested in vitro.

Many of the structural databases that contain such information are either too ex-

pensive to be purchased for routine use in academic labs (e.g. ACD (76), ChemNaviga-

43

tor (77)) or need to be further parameterized before they can be used in docking (for

example ChemBank (78), Ligand.info (79)). Compilation of ZINC free database for vir-

tual screening provided new research opportunities for a broad community of scientists

worldwide (80) (81). ZINC contains 3D coordinates for over 700,000 compounds com-

mercially available from more than 80 different vendors including open chemicals repos-

itory maintained by NCI Developmental Therapeutics program (82). In our earlier study,

virtual screening of NCI library of ~140,000 structures yielded several compounds ca-

pable of enhancing ACE2 activity (40). In this study, we chose to screen a library of

1,217 FDA-approved drugs for the molecules that can increase ACE2 activity.

FDA-approved drug library was selected for a number of reasons. First, FDA-

approved compounds generally have highly favorable drug-like characteristics: like sol-

ubility and bioavailability and low toxicity, while most of these compounds follow orally-

active drug criteria described in the fundamental work of Lipinski and others (83) (84).

Analysis of 68 FDA-approved drugs performed in 2003 has shown that it takes an aver-

age of 12 years and over $280 million dollars to get from the initial development to final

drug approval (85). Thus, there is a lot of interest in re-profiling the compounds that

have been already approved for other purposes as they can provide accelerated route

to in vivo experiments and into the clinic (86). Third, substantial amount of information

has been accumulated on the toxicity, adverse side effects and mechanism for many of

the FDA-approved drugs which allows scientist to focus on the compounds with desira-

ble properties. And finally, wide availability of the FDA-approved compounds from both

commercial sources and government initiatives like NCI Developmental Therapeutics

program makes it easy to obtain the compounds for functional testing of docking predic-

44

tions. The main limitation of using FDA-approved compound library is in its size that

does not allow for much structural diversity. However considering abovementioned ben-

efits and ability to quickly obtain the compounds of interest, 1,217 compound library

provides a good tool for quick testing of docking hypotheses and evaluating primary tar-

get constraints. This chapter describes molecular docking of FDA-approved compound

library into the Site 1 followed by functional testing of the top scoring compounds effects

on ACE2 activity.

Results and Discussion

Molecular Docking of FDA-Approved Drugs

We used structural stringencies of Site 1 to dock a library of 1,217 FDA-approved

compounds and rank them by energy scores (van der Waals + electrostatic). Figure 3-2

illustrates the binding pocket selected for high-throughput molecular docking and the

positions of high scoring small molecule compounds. Functional effects of the top-

scoring compounds were evaluated by measuring their effects on ACE2 maximal veloci-

ty and Km.

Figure 3-2. ACE2 hinge region site 1 selected for molecular docking of FDA-approved compound library.

ACE2

Subdomain I

Subdomain II

Site1

45

Effects of FDA-Approved Drugs on ACE2 Catalytic Activity.

The highest scoring 38 compounds were tested under saturating substrate condi-

tions for their ability to modulate ACE2 enzyme activity in vitro. ACE2 experimental ini-

tial velocities were determined in presence of 38 top-scoring FDA-approved compounds

(50µM) and compared to those in the absence of the drugs. Figure 3-3 shows how

ACE2 initial velocities in presence of the drug (grey bars) compare to their predicted

energy score from docking. Dark blue bars represent change in initial velocity in pres-

ence of a particular compound, where

and 100% corresponds to

no change. Nine compounds with the highest initial velocities identified in this screening

were selected for further testing. Their docking scores and kinetic parameters are re-

ported in Table 3-1; chemical structures are shown in Table 3-2.

Figure 3-3. ACE2 initial velocity in presence of 50 µM top-scoring compounds

46

Figure 3-4 demonstrates ACE2 kinetic curves determined for ten compounds that

produced the largest effect on initial velocitiy. Kinetic constants for ACE2 in presence of

the 50µM compounds and 25-125µM substrate were determined by non-linear regres-

sion fit of experimental data into Michaelis-Menten equation [ ]

[ ] using Sig-

maPlot 11.0 Systat Software, San Jose, California). Three of the compounds were

shown to increase maximal velocity at least 2-fold compared to ACE2 alone (for exam-

ple, Vo’LAB 21±1.92, Vo’XNT 35±3.08 and Vo DMZ 41±2.69 RFU∙sec-1 vs. Vo ACE2 10±1.30

RFU∙sec-1). Incubation with FDA-approved aprindine, minithixen and hydroxyzine (APR,

CTX and HXZ respectively) resulted in 2.1 to 3.5-fold reduction in Km (Table 3-1). De-

spite the fact that many of the compounds demonstrated beneficial effects on either

Vmax or Km, only 4 out of selected 10 compounds had a statistically significant effect

on overall enzyme efficiency (Vmax/Km) – HXZ, XNT, CTX and DMZ.

Figure 3-4. ACE2 kinetic

parameters in presence of selected FDA approved compounds.

47

Hydroxyzine and minithixen act by increasing specificity for the synthetic substrate

which is reflected by reduction in Km (Km’HXZ =11±1.04µM, Km’CTX=13±0.81 µM vs

KmACE2 =39±1.46 µM). In contrast, diminazene, labetalol and previously identified XNT

produce an increase in Vmax that comes at a cost of loss in substrate specificity

(Km’DMZ=82±10.34 µM, Km’LAB=60±12 µM and Km’XNT=92±14.55 µM, Table 3-1). Inter-

estingly, some of the known anti-hypertensive agents were identified in this screen. For

example, 2-hydroxy-5-(1-hydroxy-2-((1-methyl-3-phenylpropyl) amino) ethyl) ben-

zamide) - labetalol is an antihypertensive drug marketed as Normodyne. However most

of the compounds that had a strong effect on ACE2 activity have not been previously

used for treatment of hypertension or related cardiovascular diseases: HXZ is an anti-

histamine used in treatment of allergies and hyperalgesia, CTX is a dopamine receptor

antagonist used as anti-psychotic and DMZ is an anti-protozoan chemotherapeutic used

in veterinary medicine. Compound structures and their full names are shown in Table 3-

2, additional details on the clinical applications of the compounds are provided in Ap-

pendix C.

Three out of ten compounds that produced a significant effect on ACE2 activity

had xanthene core topology similar to XNT (ESP, CTX and HCT). FDA-approved veter-

inary drug diminazene (benzenecarboximidamide,4,4'-(1-triazene-1,3-

diyl)bis,dihydrochloride) that had the strongest effect on ACE2 activity, is structurally

similar to a known antihypertensive labetalol, that is thought to act as a mixed alpha-

/beta-adrenergic receptor antagonist. Figure 3-5 shows structural alignment of the most

efficient compounds identified in this study: minithixen, labetalol (colored in orange),

DMZ and XNT (shown as grey sticks). Since DMZ was the most effective compound in

48

vitro and because it was similar to a known antihypertensive labetalol, we selected it for

further evaluation.

Table 3-1. Kinetic parameters of ACE2 in presence of select compounds.

Name Rank

(library)

Score

ΔG○(kcal/

mol)

Km (µM) Vmax,

(RFU s-1)

Vmax/ Km

(RFU M-1

s-1 •104)

ACE2 at 0.1M NaCl

pH 7.4

- - 40±1.5 10±1.3 25±1.3

NSC 354677 (XNT) #3 (NCI) -55.38 92±14.5 36±3.1 39±3.2

NSC 290956 (ESP) #4 (FDA) -47.10 36±2.2 6±1.2 16±1.2

NSC 357775 (DMZ) #7 (FDA) -46.53 82±10.3 41±2.7 50±2.8

NSC 169188 (HXZ) #10 (FDA) -45.43 11±1.0 4±0.6 36±0.6

NSC 169899 (CTX) #18 (FDA) -43.51 13±0.8 6±0.7 46±0.7

NSC 134434 (HCT) #20 (FDA) -43.20 20±1.1 6±0.8 30±0.8

NSC 293901 (FMB) #21 (FDA) -43.10 33±1.3 9±1.1 27±1.1

NSC 289337 (TIA) #30 (FDA) -42.04 23±1.1 7±0.9 30±0.9

NSC 284614 (APR) #32 (FDA) -41.96 19±1.5 4±0.8 21±0.8

NSC 290312 (LAB) #35 (FDA) -40.52 60±12 21±1.9 35±2.0

49

Table 3-2. Chemical structures and names of selected compounds.

NSC number Chemical name Structure

NSC 354677 (XNT) 1-[[2-

dimethylamino)ethyl]amino]-4-

(hydroxymethyl)-7-[[(4-

methylphenyl) sulfonyl]oxy]-9H-

xanthen-9-one

3-1

NSC 290956 (ESP) 8-[3-(2-chlorophenothiazin-

10-yl)propyl]-4-thia-1,8-

diazaspiro[4.5]decan-2-one

hydrochloride

3-2

NSC 357775 (DMZ) 4-[2-(4-carbamimidoylphenyl)

iminohydrazinyl]benzene-

carboximidamide dihydro-

chloride

3-3

NSC 169188 (HXZ) 2-[2-[4-[(4-chlorophenyl)-

phenylmethyl] piperazin-1-

yl]ethoxy]ethanol

3-4

NSC 169899 (CTX) (3Z)-3-(2-chlorothioxanthen-

9-ylidene)-N,N-

dimethylpropan-1-amine hy-

drochloride

3-5

50

Table 3-2. Continued.

NSC number Chemical name Structure

NSC 134434 (HCT) 1-(2-

diethylaminoethylamino)-4-

(hydroxymethyl)thioxanthen-

9-one

3-6

NSC 293901 (FMB) N-[4-chloro-2-[[methyl-(2-

morpholin-4-yl-2-

ox-

oethyl)amino]methyl]phenyl]

benzamide hydrochloride

3-7

NSC 289337 (TIA) 5-chloro-3-[2-[4-(2-

hydroxyethyl)piperazin-1-yl]-

2-oxoethyl]-1, 3-benzothiazol-

2-one hydrochloride

3-8

NSC 284614 (APR) N'-(2,3-dihydro-1H-inden-2-

yl)-N,N-diethyl-N'-

phenylpropane-1,3-diamine

hydrochloride

3-9

NSC 290312 (LAB) 2-hydroxy-5-[1-hydroxy-2-(4-

phenylbutan-2-

ylamino)ethyl]benzamide hy-

drochloride

3-10

51

Figure 3-5. Structural comparison of FDA-approved compounds that had the strongest effect on ACE2 activity: DMZ, LAB, XNT and CTX.

EC50 and Lineweaver-Burk Analysis.

ACE2 Site 1 constraints that were used for docking in this study, are specific to the

open conformation, however the site is accessible for small molecule binding in both

open and closed conformations. As a result, small molecules can potentially bind to

both the free enzyme (open conformation) and enzyme-substrate complex (closed con-

formation). This becomes important when considering the mechanism of enhancement

and developing more potent activators of ACE2.

In order to estimate effects of small molecule binding on the specificity of ACE2 for

the substrate, we analyzed kinetic data by generating Lineweaver-Burk plots for the five

compounds that had the strongest effect on ACE2 (Figure 3-6). The plot suggests that

both DMZ and XNT bind outside the active site and increase Vmax ~4-fold and Km ~2-

fold (KmACE2 = 40±1.46 µM, Km’XNT = 92±14.55 µM and Km’DMZ = 82±10.34 µM), but the

exact mechanism of enhancement by these compounds might be different while more

detailed consideration of the mechanism was outside the scope of this study. LB plot

demonstrating effects of DMZ on initial velocity of ACE2 is shown in Figure 3-7.

Minithixen/XNT Labetalol/DMZ

52

Figure 3-6. Lineweaver-Burk double-reciprocal plot for ACE2 in presence of select five compounds

-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

1 /

V

1 / [S]

ace2

50 uM XNT

50 uM DMZ

50 uM 169188

50 uM 169899

50 uM 290312

53

Figure 3-7. Lineweaver-Burk double-reciprocal plot for ACE2 in presence of 0(○), 10µM(■), 50µM (▲) and 100µM(◊) diminazene.

Despite the fact that even low concentrations of diminazene are sufficient to pro-

duce a boost in maximal velocity, overall enzyme efficiency responds more slowly to in-

creasing concentrations of the compound and starts to approach 2.5-fold of the control

levels only in presence of 100µM drug. The plot shows a significant change in slope as

the drug concentration increases while overall substrate specificity of ACE2 stays rela-

tively constant (change in x-intercept is within standard deviation). Since initial com-

pound binding has an effect on Km, DMZ has affinity for the free enzyme. However

once the target site has been saturated, additional binding does not further compete

with the substrate, hence change in y-intercept at increasing compound concentration,

but not x-intercept (-1/Km). We were interested in testing diminazene in animals and

needed to find the most effective drug concentration that produces a substantial in-

crease in ACE2 activity. Titration of ACE2 with diminazene (0.01-1000µM) results in bi-

phasic dose-response curve illustrated in Figure 3-8

54

Figure 3-8. ACE2 Dose-response curve for activation with diminazene.

At low concentrations, the enzyme is activated with EC50 of 8.04µM, whereas at

high concentrations it is partially inhibited with IC50 of 200.01 µM. Remarkably, even af-

ter the partial inhibition ACE2 initial velocity remains significantly higher than in the ab-

sence of the drug. Previously discovered XNT and RZT (EC50 = 20±0.8 and 19±0.4 µM)

produced only about 70% of maximal increase in initial velocity of the diminazene (40).

DMZ was most efficient at a concentration of 100 µM although slight (~ 15%) increase

in activity can be detected even at nanomolar concentrations of the compound. The ob-

served effect was independent of the substrate concentration in range of 50-100µM

substrate.

HPLC Analysis of Ang II Cleavage

It has been determined that vasoconstrictor peptide Angiotensin II is a preferred

natural substrate for ACE2. Ang1-7, generated by enzymatic cleavage of Ang II, is one

of the main effectors responsible for beneficial effects of ACE2 on cardiovascular sys-

tem. Classical photo- and fluororimetric methods could not provide accurate detection

and quantification of Ang 1-7 formation, therefore we used LC-MS-based assay system

to directly analyze ACE2-catalyzed hydrolysis of Ang II and validate our kinetic assay

data. The effectiveness of DMZ as an ACE2 activator was confirmed by analysis of

cleavage of the octapeptide Ang II, which is considered the main effector of the RAS

and the most physiologically significant natural substrate for ACE2. Two-hour incubation

of 85 µM Ang II with 10nM ACE2 in presence of 50 µM DMZ resulted in 1.5-fold in-

crease in Ang 1-7 formation compared to that in the absence of the drug. MALDI-TOF

spectrometry was used to determine peak identities and confirmed generation of Ang 1-

7 (899 m/z), which provides direct evidence for Ang II cleavage by ACE2. Figure 3-9

55

shows HPLC chromatograms for ACE2 in presence (solid line) and in the absence of

DMZ (dashed line). Additional details on MALDI parameters and results are provided in

the experimental section and in Appendix C. HPLC and spectroscopy experiments were

performed at University of Florida ICBR Proteomics Core facilities.

Figure 3-9. DMZ enhances cleavage of natural substrate Angiotensin II.

In Vivo Experiments

DMZ and three other most promising compounds (HXZ, CTX and FMB) were test-

ed in Spontaneous hypertensive rats by giving them a bolus injection of the drug dis-

solved in saline; experiments were done in the lab of our collaborator Dr. Mohan Raiza-

da. Based on the results of their laboratory, aromatic diamidine diminazene decreases

high blood pressure more than the previously dscovered XNT and administration of

899 m/z Ang 1-7 characteristic peak

( MALDI, Appendix 3)

56

DMZ at a dose of under 3 mg/kg of weight produced a reduction in high blood pressure

of more than 80Hg/mm.

These data are consistent with the hypothesis that ACE2 conformational changes

associated with substrate binding and/or product release may be rate limiting. This be-

comes particularly important in proteolytic cleavage of longer substrates, where fast

turnover might be more difficult to achieve. These findings suggest that Site 1 specifici-

ties can be used for identification of novel small molecule activators of ACE2. In this

screening, we identified four compounds that enhance ACE2 activity; however after ex-

amination of their solubility, stability, synthetic feasibility and toxicity profiles, we have

chosen diminazene as the most promising lead and decided to focus our derivatization

efforts on this compound. Overall, these results provide a good starting point, but further

extension of this model, requires establishment of a detailed structure-activity relation-

ship, which remains an important long-term goal for this project.

Experimental Section

Structure Preparation and Docking

We used the crystal structure of the apo form of human ACE2 in the open confor-

mation (PDB ID: 1R42) as the template for molecular docking. To prepare the site for

docking, all water molecules have been removed, protonation of ACE2 residues was

done in SYBYL (87) (Tripos). Atomic coordinates for each of 1,217 FDA-approved small

molecules from ZINC database were docked in 1000 different orientation into the se-

lected structural pocket (Appendix C) and scored; contact and electrostatic scores were

calculated for the best compound orientations. UF High-Performance Computing Center

Linux cluster facilities were used to run the docking jobs. Chimera (67) software pack-

57

age was used for initial preparation of the structures and creating molecular graphic im-

ages.

ACE2 Enzyme Kinetic Assays

Forty of highest-scoring compounds were obtained from the Developmental Ther-

apeutics Program of NCI for in vitro testing. Effects of top-40 compounds selected in vir-

tual screening were tested in fluorescence-based kinetic assays using 10nM recombi-

nant human ACE2 (Enzo Life Sciences, PA) and fluorogenic peptide substrate Mca-

APK(Dnp)-OH (Anaspec, CA). ACE2 assays for catalytic activity were carried out in a

total volume of 100 μL, containing 75 mM Tris-HCl, pH 7.4, 0.1M NaCl, 0.5 μM ZnCl2

and 0.01% Triton-X. Small molecule stocks were prepared by dissolving the compounds

in DMSO to a concentration of 50-100mM, final compound concentration of 50 µM was

used in all screening experiments. Kinetic parameters for ACE2 in presence of selected

compounds were determined under steady-state conditions in presence of saturating

amounts of the substrate. Enzyme concentration was adjusted to ensure that <15% of

the substrate was consumed at the lowest substrate concentration; product formation

was linear with time. Compounds were pre-incubated in 96-well black plates with 10 nM

enzyme for 15 minutes at 37o C. Reactions were initiated by addition of 25-250 µM fluo-

rogenic substrate and monitored continuously for 30 minutes with Spectra Max Gemini

M5 Fluorescence Reader from Molecular Devices at λexcitation=325 nm, λemission=395 nm.

Initial velocities of ACE2 in absence and in the presence of 50 µM compounds were de-

termined by measuring increase in fluorescence upon hydrolysis of the substrate. Non-

linear regression analysis and fitting of the data into Michaelis−Menten equation was

done with Sigmaplot 11.0 software. Turnover numbers (kcat) were calculated from the

58

equation

[ ] . EC50 values for DMZ were determined by fitting initial velocity

data for ACE2 in presence of 50 µM Mca-APK(Dnp)-OH and varying concentrations of

DMZ into non-linear regression model for 4-parameter logistic Hill equation

. Here E corresponds to effect (initial velocity) at a given substrate concentra-

tion), Emax – maximal velocity measured, C - drug concentration and α - Hill coefficient

of sigmoidicity (88).

Inner Filter Effect

Inherent small molecule fluorescence may affect observed initial velocity, kcat and

Km as a result of inner filter effect (IFE). IFE is a reduction in fluorescence signal due to

absorption of the exciting light and/or emitted radiation in presence of a second fluoro-

phore. As a result, only a fraction of the photons reach the fluorophores and get picked

up by the detection system. For this reason, we corrected observed fluorescence (Fobs)

for inner filter effect using the equation:

described in (89). Where

⁄ and A is absorbance at given wavelength.

HPLC Analysis of Ang II Cleavage

ACE2-catalyzed peptide cleavage was carried out in a total volume of 100 μL in

presence or in the absence of DMZ. Reaction mixture contained 10nM ACE2, 100 mM

Tris-HCl, pH 7.4, 0.1M NaCl, 0.5 μM ZnCl2. ACE2 was preincubated for 5 min with 50

μM DMZ and reaction was initiated by addition of Ang II to a final concentration of 85

μM. In the control reaction, Ang II was added to the reaction mixture immediately after

the enzyme. Reactions were run for 2 hr at 37 °C and monitored during the first 15 min-

utes at λex=328nm and λem=392 nm to ensure that enzyme was catalytically active. Re-

59

actions were quenched by adding 10 µL of 0.5M EDTA, followed by heating to 100 °C

for 2 min. Samples were centrifuged at 11600 x g for 10 min, supernatants were collect-

ed and concentrated under vacuum. Resulting residue was resuspended in 50 μL of the

mobile phase B and applied to C18 reverse-phase HPLC column with a UV detector set

at 214 nm (Column: Bondclone 10 μm, 150 × 2.1 mm, Phenomenex; HPLC system: Ag-

ilent 1100 series). Peptides were separated by linear solvent gradient of 11−100% B

running for 15 min, followed by 10 min at final conditions and 10 min re-equilibration.

Mobile phase A consisted of 0.08% (v/v) phosphoric acid, mobile phase B consisted of

40% (v/v) acetonitrile in 0.08% (v/v) phosphoric acid. The peaks corresponding to full

length peptides and peptide hydrolysis products were compared with the standards and

peak areas were integrated to calculate the extent of hydrolysis. Peak identities were

confirmed by MALDI-TOF spectrometry using Applied Biosystems Voyager System

6031. Ang (1–7) resolved with a characteristic peak at m/z = 899.65, Ang II at m/z =

1046.52. All solvents used in the experiment were HPLC-grade.

Statistical Significance

All kinetic data presented are expressed as mean ± standard deviation. Unpaired

Student’s t-test and 1-way ANOVA were performed for statistical analysis. Differences

were considered significant at a p<0.01 or p<0.001. All statistical analysis and curve fit-

ting was performed with SigmaPlot 11.0 software.

60

CHAPTER 4 DESIGN, SYNTHESIS AND FUNCTIONAL TESTING OF DIMINAZENE DERIVATIVES

Ligand-Based Approach

Development of a reliable pharmacophore model starts with a high resolution

three-dimensional structure of the ligand-receptor complex, as it allows accurate exami-

nation of candidate molecules within the local protein environment (90). Derived data is

then used to define important protein-ligand interactions and build a model that will

serve as a basis for drug design strategy (91) (92). This standard algorithm has been

successfully applied to design enzyme inhibitors for many well defined targets like dihy-

drofolate reductase, HIV protease, ACE, thymidylate synthase and many others

(63,75,93). Despite practicality of this approach, further optimization of a potential lead

becomes restricted by scientist’s ability to quickly generate such model. From this per-

spective, building a pharmacophore becomes a particularly challenging goal when work-

ing with novel therapeutic targets and enzymes for which high-resolution crystal struc-

tures cannot be easily obtained.

Many highly dynamic proteins and enzymes with multiple post-translational modifi-

cations fall under this category, including our target ACE2. While site-directed mutagen-

esis and structural studies of ACE2 with activator molecules remain important long-term

goals for this project, alternative strategies for obtaining structure-activity relationship

information must be considered. In this study, we chose to probe the specificities and

stereochemical preferences of ACE2 by using structural analogs of our lead compound

diminazene with the ultimate goal of generating better, more potent activators.

Although molecular docking calculations that are frequently used for such purpos-

es are becoming increasingly accurate (94), structural models that rely on docking as a

61

sole source of binding predictions should always be treated with a grain of salt. Such

models can be particularly misleading when experimental structural information for lig-

and-receptor complex is unavailable and cannot be used for generation of a training set.

Since molecules capable of binding a specific target tend to be topologically similar (95),

we can speculate that a group of closely-related structural analogs with shared physico-

chemical characteristics would also have the preference towards a particular site. If this

is true, then close structural analogs of diminazene should bind ACE2 in a similar fash-

ion and could also enhance enzyme activity. This chapter describes a ligand-based ap-

proach (96) that was used to test this hypothesis and design, synthesize and evaluate

novel derivatives of DMZ. In order to achieve this goal, ligand-based drug discovery

methods combined with principles of combinatorial chemistry and in silico screening

were used to create and test a small library of compounds that mimic local electrostatic

potentials and overall geometry of the lead.

Results and Discussion

Derivatization Strategy

Our lead compound DMZ was shown to be an effective activator for human ACE2

in vitro and decrease high blood pressure in vivo, but the chronic study in Spontaneous

Hypertensive rats done in Raizada laboratory demonstrated, that hypotensive effect of

diminazene significantly diminishes over time. This could be explained by partial com-

pound hydrolysis into its counterparts: p-aminobenzamidine and benzyldiazonium ion

(Figure 4-1). Analysis of the literature confirmed that diminazene hydrolyzes under

acidic conditions (pH 3.0) within 30 minutes or less (97). Some additional details on tri-

azene metabolism can be found in the references (98,99).

62

Figure 4-1. Diminazene hydrolysis under acidic conditions

This raised a concern that observed effect of diminazene should in fact be attribut-

ed to one of its counterparts. Therefore, our first goal was to identify the exact species

that contributed to the effect on ACE2. For this purpose, we evaluated DMZ stability un-

der in vitro assay conditions (pH 7.4, average running time <30 minutes) and deter-

mined that extent of compound hydrolysis in our assay was negligible. Therefore, DMZ

is the principal species responsible for enhancing ACE2 activity. This, however, created

another challenge. Compounds that undergo fast dissociation under physiological con-

ditions have limited functionality for treatment of chronic conditions like hypertension

since they require more prolonged effects in vivo. For instance, some of the commonly

prescribed ACE-inhibitors have half-lives ranging from 11 to over 24 hours (lisinopril and

ramipril, respectively) (100). Considering this information, we set the following specific

aims: 1) to mimic diminazene structure, 2) to increase compound stability by introducing

a non-hydrolysable moiety instead of triazene linkage and 3) to introduce new functional

groups to probe ligand requirements for ACE2.

63

C

A

Modifications to the Core

Diminazene is an aromatic diamidine used predominantly in veterinary medicine

that was originally derived from Surfen C (small molecule antagonist of heparin sulfate,

aminoquinoline) (101) two amidinophenyl moieties are linked by a triazene bridge and

overall structure is fairly rigid. We split the structure in three parts: triazene ―core‖ and

polar amidine ―end-caps‖ to target the portions of the molecule individually (Figure 4-2).

Figure 4-2. (A) Structure of Surfen C. (B) Diminazene derivatization strategy. (C) Three-dimensional structure of diminazene

Since diminazene is susceptible to hydrolysis, modification of the ―core‖ was the primary

goal in the development of novel derivatives of this compound. Some of the main crite-

ria that have been considered were topological and electrostatic similarity to triazene,

water solubility and stability of the resulting derivative compounds. For this reason we

64

had to eliminate some of the simplest solutions. For instance, replacement of the tri-

azene moiety by a saturated hydrocarbon linkage could have solved the stability prob-

lem, but it lacks the polarity of triazene and rotation about the carbon-carbon bond leads

to several possible conformations. Since triazene core has a partial double-bond

character, it is defined by restricted rotation about N2—N3 bond that should be ac-

counted for in designing derivative compounds.

Figure 4-3. Partial double-bond character due to resonance in triazenes

We investigated several different possibilities including secondary amines, ami-

dines and ureas by creating three-dimensional and electrostatic models for the respec-

tive derivative series and concluded that a cyclic core containing a five-membered ring

would be the best choice for retention of geometry. Figure 4-4 demonstrates structures

of the ―core‖ that have been considered.

Figure 4-4. Chemical structures of the core that were considered as a replacement to triazene linkage in diminazene

Synthetic feasibility of potential derivatives was another important criterion that

was taken into account when designing the molecules that mimic diminazene. Because

65

our goal was to generate a small library for screening, overly complex structures that

required long synthetic routes or expensive starting materials were not considered. We

circumvented this problem by introducing a triazolic core (4-b) that provides rigidity, re

tains polarity of the lead compound and can be rapidly synthesized.

Aromatic Triazoles as New Derivatives of Diminazene

Triazole is a thermally and hydrolytically stable synthetic auxiliary that has been

used in a variety of antifungal and antibacterial drugs (e.g. antibiotic tazobactam, anti-

fungal drug fluconazole). Substituted 1,2,3-triazoles are commonly used as building

blocks for more complex chemical compounds and pharmaceuticals. Many triazole

drugs are considered a safe treatment option for life-threatening invasive fungal infec-

tions that are frequently seen in immuno-compromised cancer patients (102). Triazoles

can be made from the respective alkynes by azide-alkyne Huisgen cycloaddition. Huis-

gen reaction was later extended by K. Barry Sharpless and converted it into fast, inex-

pensive and non-toxic method for making stable triazoles at room temperature condi-

tions in presence of copper salts (Figure 4-5).

Figure 4-5. General scheme for ―click-chemistry‖ synthesis of triazoles.

We compared diminazene with its the closest triazolic analog – 4,4’-(1H-1,2,3-

triazole-1,4-diyl)dibenzimidamide with respect to three-dimensional structure, relative

polarity, solubility and size. Based on our results, the two compounds are similar in

terms of overall shape, topology and polarity. Figure 4-6 demonstrates MM2 energy

minimized three-dimensional structures of diminazene (top) and 4,4'-(1H-1,2,3-triazole-

66

1,4-diyl)dibenzimidamide (bottom). Van der Waals surface and partial charges were

calculated using UCSF Chimera software, molecular surface is colored by charge where

blue is negative, red – positive

Figure 4-6. Chemical structures and three-dimensional models of DMZ and 4,4'-(1H-1,2,3-triazole-1,4-diyl)dibenzimidamide.

Modifications to the Amidine Ends

Although triazolic derivative of diminazene (Figure 4-6) was similar to the lead in polari-

ty, this compound as well as several other related symmetrical molecules with amidine

moieties on both ends scored significantly worse than diminazene when the compounds

were docked in silico. For this reason, we considered alternative end-cap modifications

as well as non-symmetrical variants of 4,4'-(1H-1,2,3-triazole-1,4-diyl)dibenzimidamide

and DMZ.

67

Figure 4-7. Possible end-cap modifications to amidine ends in diminazene.

Molecular docking predictions cannot serve as a replacement of a crystal struc-

ture, but their examination can provide useful insights on local interactions that control

binding. We used Ligandscout and Pymol modeling software to identify possible sites of

interaction by selecting aminoacid residues within 3.5Å of predicted binding position of

diminazene (Figure 4-8).

Figure 4-8. Schematic view of predicted binding positions of DMZ (orange) and XNT (yellow) modeled into the target site of ACE2.

Potential hydrogen bonds to the ligand are shown as dotted lines. ACE2 amino ac-

id residues are colored cyan. Residues within 4Å of the small molecules are labeled by

68

letter codes. Our results suggest at least five H-bonding interactions between DMZ and

amino acid residues in the hinge region of ACE2. Most of the interactions were predict-

ed to be between the charged amidine ―ends‖ and aminoacid residues in the loop Q101-

N103. Given strong basic properties of aromatic amidine groups in diminazene (pKa

11); it is likely to participate in hydrogen bonding and target polar residues in protein.

We were curious to see how a replacement of one of the amidine groups would

translate into effects on ACE2. For this purpose, we designed a series of molecules with

different substituents at the ―end-cap‖; their structures are shown in Appendix D.

Selection of Derivative Compounds for Synthesis.

We used DOCK6.0 to screen the library of 100 derivative compounds (derivative library

1), that was created according to structural requirements of Site 1 and similarity to dimi-

nazene (Figure 3-2). Compounds were first docked as rigid bodies and later re-docked

with flexible parameters employed; additional details on the docking algorithm can be

found in Appendix C. Compounds were ranked by the energy scores and their predicted

binding positions were visually inspected in Pymol. Derivative compounds were re-

ranked according to both geometric fit and total energy score. Twenty of the highest-

ranked compounds were further evaluated and prioritized based on synthetic feasibility,

number of synthesis steps and availability of the starting materials. Based on these pa-

rameters, we selected eight compounds that had the highest energy score, best overall

geometry fit and that could be made in less than 6 steps. Figure 4-9 shows the three

highest-scoring derivatives (shown as yellow sticks). ACE2 molecular surface is colored

by the element: grey for carbon, blue for nitrogen, red for oxygen.

69

Figure 4-9. Predicted binding orientations of the three highest-scoring derivative com-pounds

Figure 4-10. Chemical structures of compounds selected for synthesis listed in their ranking order.

General Strategy for the Synthesis of Diminazene Derivatives

There is a variety of synthetic routes for preparation of 5-substituted 1H-triazoles from

organic azides available in literature. Based on retrosynthetic analysis, we originally

planned to use ―click‖-chemistry approach discovered independently by M. Meldal (103)

and K. B. Sharpless (104) (105), followed by Pinner conversion of nitriles into amidines.

70

Feringa group reported modification to the general procedure shown below using phos-

phoramidite ligands that allows significant rate acceleration and produces high yields

(106) (Figure 4-11). Nitrile to iminoether conversion using HCl gas by Pinner method

however required substantial amounts of starting materials. Since our goal was to make

a small combinatorial library for rapid screening, alternatively, we can convert nitrile first

to hydroxyamidine and then to respective amidines (Figure 4-12) (107) (108) (109).

Figure 4-11. Original approach to making triazolic derivatives of DMZ.

Figure 4-12. Alternative synthetic plan for making amidine derivatives.

Derivative Compounds Prepared in the Study and Their Effects on ACE2 Activity

We were able to make nitrile and amidoxime precursors to six amidines; however we

were not able to get substantial amounts of pure amidine final products. We tested ac-

tivity of all compounds made in the study and determined that three of the derivatives

were at least as effective as DMZ in vitro yet stable under hydrolytic conditions. Those

71

were 4-(4-(4-(dimethylamino)phenyl)-1H-1,2,3-triazol-1-yl)-N'-hydroxybenzimidamide 4-

m-8, 4-(4-(3,4-difluorophenyl)-1H-1,2,3-triazol-1-yl)-N'-hydroxybenzimidamide 4-m-4

and N'-hydroxy-4-(4-(3-hydroxyphenyl)-1H-1,2,3-triazol-1-yl)benzimidamide 4-m-2. De-

rivative compounds 4-m-8, 4-m-4 and 4-m-2, were shown to increase enzyme efficiency

1.9, 2.2 and 3-fold respectively while their effects on enzyme specificity were similar to

that of diminazene (Table 4-1).

Table 4-1. Kinetic parameters of top 5 DMZ derivatives.

Name Chemical structure Km

(µM)

Vmax,

(RFU/s)

Vmax/Km

(RFU•M-1

s-1 •104)

4-m-8

4-(4-(4-(dimethyla

mino)phenyl)-1H-

1,2,3-triazol-1-yl)-

N'-hydroxybenzi

midamide

89±2.8 42±7.9 47±0.3

4-m-4

4-(4-(3,4-difluoro

phenyl)-1H-1,2,3-

triazol-1-yl)-N'-

hydroxybenzimid-

amide

84±8.5 46±8.2 54±0.8

4-m-2

N'-hydroxy-4-(4-(3-

hydroxyphenyl)-1H-

1,2,3-triazol-1-

yl)benzimidamide

91±6.7 68±4.5 75±0.7

72

DMZ

(E)-4,4'-(triaz-1-

ene-1,3-diyl) diben-

zimidamide

82±10.3 41±2.7 50±2.8

Validation of the Structural Model

73

Since none of the compounds that worked were part of the original derivative library (li-

brary 1), we decided to use the information obtained in the functional assay and re-

evaluate effectiveness of our structural model for predicting the molecules that can bind

ACE2. For this purpose, three-dimensional structures of the compounds were generat-

ed and appended to the small molecule library that was created earlier to yield deriva-

tive library 2. Library 2 was docked into Site 1, using the same docking parameters as

before (Chapter 3, Appendix C). Interestingly, most of the amidoxime derivatives scored

higher than the respective amidines. Two (4-m-8 and 4-m-4) out of three derivative

compounds that showed effects on ACE2 activity were among top-10 highest scoring

74

molecules, the third one (4-m-2) ranked #11 out of about 150 structures docked. Our

lead compound DMZ ranked #18 compared to derivatives made and #27 overall. We

analyzed the similarity of those compounds to DMZ. Figure 4-13 shows the struc-

Figure 4-13. Chemical structures and three-dimensional models for the three most effi-cient derivative compounds comparison to diminazene.

tures of the three compounds (orange) superimposed with DMZ (grey) with RMSD in

the aromatic region of 0.011Å or less. Structures are colored by element - blue for nitro-

gen, red for oxygen, green for fluorine and white for hydrogen.

75

Based on these results, the model needs to be adjusted since some of the com-

pounds that ranked higher showed no effect on ACE2 activity and also because the

magnitude of the compounds effects on ACE2 did not match their rank. In order to re-

solve the possibility of compounds binding in a different orientation than the one sug-

gested by molecular docking and improve the model, we aim to obtain more structural

information in the future experiments on ACE2 as well as a larger pool of derivative

compounds to be tested that we’re planning to obtain by expanding our derivative library

In conclusion, we were able to design, synthesize and test a small compound li-

brary of diminazene derivatives as a part of our strategy for rational enhancement of

ACE2 activity. We could not obtain target amidine derivatives in high purity necessary

for in vitro functional testing, but we were able to generate a series of hydroxyamidine

derivatives (4-m). of DMZ that were useful in testing stereochemical preferences of

ACE2 and were more stable than the lead under the hydrolytic conditions. Our result

shows that molecular docking using existing model increases the likelihood of finding

novel effectors for ACE2. Three derivative compounds obtained in this study - 4-m-2, 4-

m-8 and 4-m-4 (scoring #3, #7 and #11 respectively) were more potent than the lead

compound DMZ.

Experimental Section

Structure Preparation and Docking

Two-dimensional structures for the small molecules were generated in ChemDraw

Ultra 12.0 and converted into three-dimensional coordinates using ChemDraw3D suite.

Structures were first minimized using MM2 force field parameters that are commonly

used for assigning correct geometry to hydrocarbons in small organic molecules (110).

Derivative compounds were assigned local charges based on their protonation state at

76

pH 7.4 and optimized in UCSF Chimera using Antechamber that can further parameter-

ize small molecules using generalized AMBER force fields (111) (112). Structures pre-

pared this way were docked (DOCK6.4) into the Site 1 with the flexible ligand parame-

ters enabled.

General Information on Synthesis

All reactions and NMR measurements were carried out in clean, oven dried glass-

ware. Copper sulfate pentahydrate, sodium ascorbate, dimethyl sulfoxide and aromatic

alkynes were purchased from Sigma-Aldrich, aminobenzonitrile was purchased from

Fisher, MonoPhos phosphoramidite catalyst was purchased from Strem. NMR spectra

were used to confirm identity of the compounds synthesized and were recorded on a

Mercury300 (300 MHz) or VXR300 (300 MHz) instruments using DMSO-d6 as a solvent

unless otherwise indicated. Flash chromatography was done on silica gel. Reaction

completeness was monitored by thin layer chromotography (TLC) using Merck F-254

silica plates using staining with KMnO4 or ninhydrin reagent and by visualizing the spots

under 254 nm UV lamp. Retention factors are indicated as Rf.

All NMR data are reported as follows: chemical shift, multiplicity (s=singlet,

d=doublet, t=triplet, q=quartet, dd=doublet of doublets, dt=doublet of triplets, td=triplet of

doublets, m=multiplet, br=broad), coupling constant (Hz), and integration. Mass spectra

were obtained by electrospray ionization method on Agilent 6210 TOF-MS mass spec-

trometer. NMR spectra provided for known compounds were in accordance with pub-

lished experimental data, high-resolution mass data is provided for all new compounds.

77

Safety Precautions

Organic azides are potentially explosive and toxic, therefore should be handled with

care. Heating and shaking should be avoided whenever possible and blast shield

should be used when heating azides in presence of copper.

Procedure for Preparation of 4-Azidobenzonitrile.

Sodium azide NaN3 (1.5 eq) was added at room temperature to a

stirred solution of 4- bromobenzonitrile (1.0 eq) in 1:4 water/acetone

mixture. The resulting mixture was stirred for 36 hours and water

layer was extracted with dichloromethane, dried over MgSO4 and

remaining solvent was removed under reduced pressure. Azide was essentially pure

and did not require further work up.

4-Azidobenzonitrile. Yield 35%. Orange-brown solid. 1H NMR (300.00 MHz, CDCl3):

7.52 (td, J= 9.57, 1.8 Hz, 2H), 7.05 (td, J= 9.57 Hz, 0.6 Hz, 2H); 13C NMR (75.43 MHz,

CDCl3): δ: 145.1, 134.5, 120.0, 118.7, 108.5.; MS (ESI) m/z calculated for C7H4N4:

144.04 found 144.04

General Procedure for Preparation of 1,4-Disubstituted 1,2,3-Triazoles (4-n).

In a sample vial 1.4 µmol of CuSO4·5H2O and 7.0 µmol

sodium ascorbate were dissolved in 0.5 mL of distilled

water. 1.6 µmol of MonoPhos was resuspended in 0.12

mL DMSO and combined with the mixture. The result-

ing solution was vigorously stirred for 15 min. The solution was then transferred to a 25

mL roundbottom flask containing 0.14 mmol of azide and 0.40 mmol of alkyne in 0.75

mL of DMSO/H2O 1:3 mixture. Sealed reaction mixture was vigorously stirred for 10-24

hours. Upon completion, reaction was quenched with 5 mL of dH2O and moved to an

N N

N

R4-n

N

78

ice bath. Yellow (or off-yellow) solid product precipitated out and was filtered and

washed with cold water (106). Excess solvent was removed under vacuum.

4-(4-(3,4-difluorophenyl)-1H-1,2,3-triazol-1-yl)benzonitrile.

Yield 84%. Orange-yellow solid 1H NMR (300.00 MHz,

CDCl3): 8.61 (s, 1H), 8.20 (d, J=8.1 Hz, 1H), 8.08 (d,

1H), 8.01 (m, 2H), 7.90 (d, 1H), 7.48 (s, 1H), 7.21 (m,

2H). 13C NMR (75.43 MHz, CDCl3): δ=149.9, 148.0,

141.0, 133.1, 133.1, 131.7, 130.2, 126.4, 123.1, 118.8, 117.9, 115.4, 112.0. MS (ESI)

m/z calculated for C15H8 F2N4: 282.07, found: 282.07

4-(4-(2,5-dimethylphenyl)-1H-1,2,3-triazol-1-yl)benzonitrile

Yield 90%, bright yellow solid, 1H NMR (300MHz,

CDCl3, δ): 8.12 (s, 1H), 7.97 (d, 2H), 7.87 (d, 2H), 7.64

(s, 1H), 7.19 (m, 2H), 2.48 (s, 3H), 2.38 (s, 3H). 13C

NMR (75.43 MHz, CDCl3): δ=148.1, 140.0, 136.0,

134.1, 134.0, 132.7, 131.2, 129.7, 129.7, 128.8, 121.1, 120.9, 119.4, 118.0, 112.4, 21.1,

21.1. MS (EI+): m/z calculated for C17H14N4: 274.12, found: 274.12

4-(4-(2,4,5-trimethylphenyl)-1H-1,2,3-triazol-1-yl)benzonitrile

Yield 76%. Bright yellow-orange solid, 1H NMR

(300MHz, CDCl3, δ): 8.11 (s, 1H), 7.96 (m, J=8.47 Hx,

2H), 7.82 (m, J=8.47, 2H), 7.62 (s, 1H), 7.11 (d,

J=7.30Hz, 1H), 2.47 (s, 6H), 2.36 (s, 3H). 13C NMR

(75.43 MHz, CDCl3): δ: 148.1, 140.0, 137.6, 134.6, 134.2, 134.1, 133.0, 132.6, 130.3,

126.4, 121.0, 120.6, 119.0, 118.0, 112.4, 21.0, 19.6, 19.4. MS (EI+) m/z calculated

N

N N

N

N

N N

N

F

F

N

N N

N

79

for C18H16N4: 288.14, found: 288.15

4-(4-(4-(dimethylamino)phenyl)-1H-1,2,3-triazol-1-yl)benzonitrile

Yield 74%, orange-yellow solid, 1H NMR (300MHz,

CDCl3, δ): 8.15 (s, 1H), 7.97 (d, J=8.21 Hz, 2H), 7.82

(d, J=8.01 Hz, 2H), 7.78 (d, 2H), 6.81 (d, J=7.49 Hz,

2H), 3.05 (s, 6H) 13C NMR (75.43 MHz, CDCl3):

δ=150.0, 148.1, 140.9, 136.1, 133.4, 131.6, 128.7, 123.0, 120.0, 118.6, 112.4, 41.5. MS

(ESI+): m/z calculated for C17H15N4: 289.13 found 289.14

4-(4-(3-hydroxyphenyl)-1H-1,2,3-triazol-1-yl)benzonitrile

Yield 19%, pale yellow solid, 1H NMR (300MHz, DMSO-

d6, δ): 9,62 (s, 1H), 9.39 (s, 1H), 8.16 (d, J=8.1 Hz, 4H),

7.39 (m, 2H), 7.29 (m, 1H), 6.79 (d, 1H). 13C NMR

(75.43 MHz, DMSO-d6): δ=147.8, 142.7, 139.0, 135.0,

133.2, 128.4, 126.8, 122.0, 120.9, 119.4, 112.5. MS (EI+): m/z calculated for

C15H10N4O 262.09, found 262.08.

4-(4-(3-methoxyphenyl)-1H-1,2,3-triazol-1-yl)benzonitrile

Yield 83%, pale yellow solid, 1H NMR (300MHz, DMSO-

d6, δ): 9.39 (s, 1H), 8.05 (d, 2H), 7.97 (d, 2H), 7.42 (m,

3H), 7.31 (t, 1H), 3.78 (s, 3H). 13C NMR (75.43 MHz,

DMSO-d6): δ=148.0, 144.7, 136.1, 134.1, 134.0, 132.7,

131.2, 129.7, 129.7, 129.6, 123.1, 120.9, 119.4, 118.0, 110.5, 55.6. MS (EI+): m/z cal-

culated for C16H12N4O: 276.10, found 276.10

N

N N

OH

N

N

N N

N

N

80

General Procedure for Transformation of Nitriles to Hydroxyamidines.

0.30 mmol of triazolylbenzonitrile were combined with

excess (3 eq.) hydroxylamine and potassium car-

bonate (1.5 eq.) in a round-bottom flask and resus-

pended in 3 mL of ethanol. Reaction mixture was

heated to reflux for 5-12 hours to yield respective hydroxyamidines. Solid white or off-

white product was filtered off and washed with acetone. The crude hydroxyamidine

products were purified by flash column chromatography using 5:1 mixture of hex-

anes:ethyl acetate. Reaction completeness was monitored by TLC using ninhydrin test;

hydroxyamidine formation was confirmed by appearance of characteristic peak at about

156 ppm on 13C NMR and disappearance of the nitrile peak at about 119.4 ppm. 1H-

NMR spectra for the new compounds are provided in Appendix D.

(E)-4-(4-(3,4-difluorophenyl)-1H-1,2,3-triazol-1-yl)-N'-hydroxybenzimidamide.

Yield 79%. White solid, 1H NMR (300MHz, DMSO-d6,

δ): 9.40 (br.s., 1H), 8.14 (m, 2H), 7.65 (br.s., 1H),

7.47 (m, J=8.08, 1H), 6.96 (d, 2H), 13C NMR (75.43

MHz, DMSO-d6): δ=157.5, 146.7, 141.1, 133.0,

133.0, 130.2, 126.8, 121.9, 117.9, 115.0. MS (ESI+) m/z calculated for C15H11F2N5O:

315.09, found 315.09.

(E)-4-(4-(2,5-dimethylphenyl)-1H-1,2,3-triazol-1-yl)-N'-hydroxybenzimidamide.

Yield 85%. Off-white solid. 1H NMR (300MHz, DMSO-

d6, δ): 9.27 (s, 1H), 9.19 (s, 1H), 8.14-8.18 (m, 2H),

8.06-8.14 (m, 2H), 7.77 (m, 2H), 7.52 (br. s.,1H), 6.84

N

N N

F

F

N

H2N

OH

N

N N

N

H2N

OH

81

(d, J=8.78, 2H), 2.96 (s, 6H), 2.52 (m, 3H). 13C NMR (75.43 MHz, DMSO-d6): δ=156.6,

148.0, 139.8, 136.1, 134.2, 132.4, 131.0, 129.8, 120.9, 117.7, 115.5, 19.6, 19.5. MS

(ESI+) m/z calculated for C17H17N5O: 307.14, found (m+1):308.15

(E)-N'-hydroxy-4-(4-(2,4,5-trimethylphenyl)-1H-1,2,3-triazol-1-yl)benzimidamide

Yield 85%. Off-white solid, 1H NMR (300MHz, CDCl3,

δ): 8.11 (s, 1H), 7.97 (m, J=8.47 Hz, 2H), 7.83 (m,

J=8.47, 2H), 7.62 (s, 1H), 7.18 (d, J=7.30Hz, 1H),

7.11 (m, 1H), 2.48 (s, 6H), 2.36 (s, 3H). 13C NMR

(75.43 MHz, DMSO-d6): δ=156.6, 148.0, 140.0, 137.1, 134.2, 134.1, 133.0, 131.8,

130.0, 129.8, 120.9, 117.7, 115.5, 21.2 19.6, 19.5. MS (EI) m/z calculated for

C18H19N5O: 321.16 found (m+1): 322.16

(E)-4-(4-(4-(dimethylamino)phenyl)-1H-1,2,3-triazol-1-yl)-N'-hydroxybenzimidamide

Yield 77%. White solid, 1H NMR (300MHz, DMSO-

d6, δ): 9.22 (s, 1H), 8.14 (s, 2H), 7.73 (d, J=8.49 Hz,

2H), 6.81 (d, J=8.49, 1H), 2.93 (s, 6H). 13C NMR

(75.43 MHz, DMSO-d6): δ=157.8, 149.9, 148.1,

140.8, 137.1, 134.2, 133.4, 131.6, 128.8, 123.9, 120.0, 117.7, 112.4, 41.5. MS (EI+):

m/z calculated for C17H18N6O: 322.15, found 322.15

(E)-N'-hydroxy-4-(4-(3-hydroxyphenyl)-1H-1,2,3-triazol-1-yl)benzimidamide.

Yield 45%. Off-white solid. 1H NMR (300MHz, DMSO-

d6, δ): 9.35 (s, 1H), 9.22 (s, 1H), 8.15 (m, J=8.0 Hz,

2H), 7.95 (m, J=7.8, 2H), 7.5 (s, 1H), 7.37 (m, 2H),

7.29 (m, 1H), 6.79 (d, 1H), 5.92 (s, 1H). 13C NMR

N

N N

OH

N

H2N

OH

N

N N

N

H2N

OH

N

N NN

N

H2N

OH

82

(75.43 MHz, DMSO-d6): δ=156.6., 148.0, 142.8, 139.0, 135.9, 134.0, 133.2, 129.8,

123.3, 120.9, 115.5

(Z)-N'-hydroxy-4-(4-(3-methoxyphenyl)-1H-1,2,3-triazol-1-yl)benzimidamide

Yield 34%. Off-white solid, 1H NMR (300MHz, CDCl3,

δ): 8.26 (br.s., 1H), 7.99 (m, J=8.21 Hz, 2H), 7.87 (m,

J=8.21 Hz, 2H), 7.52 (br.s., 1H), 7.47 (m, 3H), 6.96 (d,

J=6.23 Hz, 1H), 3.91 (s, 3H). 13C NMR (75.43 MHz,

CDCl3): δ=155.0, 148.0, 144.2, 136.1, 134.0, 133.0, 132.7, 129.7, 123.1, 120.9, 118.0,

112.2, 52.2.

83

CHAPTER 5 CONCLUSIONS AND FUTURE WORK

Conclusions

Small Molecule Activators of ACE2 Identified in the Study

Identification of ACE2 activator XNT inspired us to further pursue the small mole-

cule strategy for rational enhancement of enzyme activity. By using structure-based ap-

proach, we were able to identify critical residues and molecular surface regions that play

an important role in conformational transitions in ACE2. Molecular docking of small mol-

ecule libraries (National Cancer Institute repository, FDA-approved compounds library

and DMZ derivatives libraries 1 and 2) into ACE2 target Site 1 yielded several genera-

tions of activators of different strength and potency. Interestingly, some of the com-

pounds discovered were previously used in treatment of cardiovascular disorders and

related conditions. One such FDA-approved compound labetalol is thought to act as a

mixed alpha/beta adrenergic antagonist and here we have shown that it might also have

effect on ACE2. FDA-approved veterinary drug diminazene was effective in both en-

hancing enzyme activity in vitro and reducing high blood pressure in animals in vivo.

This first round of screening provided us with the basis for further exploration of this

method and we designed a series of derivatives based on the structure of diminazene

and some of the compounds that had the strongest effect on ACE2. Functional testing

of the small combinatorial library of derivatives that we created identified three other

compounds that were better enhancers of ACE2 activity and more stable than the lead

compound DMZ.

Utility of Using Small Molecules to Probe Enzyme Specificity

One of the important goals in this study was to test the structural stringencies of

84

the previously discovered Site 1. We took the ligand-based approach to probe the

site specificity and evaluate stereochemical and electrostatic preferences of ACE2 for

activators with a small combinatorial library of DMZ derivatives. Three hydroxyamidine

compounds from a series of non-hydrolysable mimics of DMZ were more potent in vitro

than the lead. Even though there hydroxyamidine moiety is not identical to amidine

group in terms of basicity, amidoxime compounds are more stable under physiological

onditions which makes them better as potential drug candidate and we are planning to

continue pursuing them. We are also going to continue working on our ligand-based

strategy. Specifically, it would be interesting to explore additional amidoxime and ami-

dine derivatives of DMZ as well as C2-symmetrical analogs of this compound to further

test our hypothesis.

Future Work

Structural Studies in ACE2 and Establishment of Structure-Activity Relationship

Since the establishment of structure-activity relationship is very important for fur-

ther exploration of this strategy, one of the main future goals for this project would be

obtaining a high-resolution crystal structure for ACE2 bound to DMZ or any other activa-

tor molecule. Site-directed mutagenesis will also be performed to confirm the signifi-

cance of the site that was selected computationally and reveal structural elements that

define pharmacological specificities. In order to evaluate the main hypothesis that prod-

uct release might be the limiting step in ACE2-catalysed peptide cleavage, we are plan-

ning to collaborate with other groups to use rapid kinetics and hydrogen-deuterium ex-

change methods to derive the kinetic mechanism and contribution of conformational

changes to the overall rate.

85

Expanding the DMZ Derivative Library

Since the method described in this study allows for quick generation of hydroxy-

amidine derivatives in good purity and has already yielded several derivative activators

for ACE2, we would like to continue expanding derivative compounds library by intro-

ducing additional functional groups to the compounds that showed the most effect on

ACE2 activity. Some of the substituents at the aromatic ring that will be explored are go-

ing to include both polar derivatives like imines, tetrazoles or amidoximes, as well as

non-polar moieties. Structural information in site-directed mutagenesis experiments and

crystallography will be used to direct the efforts in exploring DMZ alternatives.

Based on our results, the strategy described in this dissertation can be used to

identify new drug candidates by targeting pre-defined structural features in clinically rel-

evant proteins. Information gained in these studies will be used to adjust overall ap-

proach and design better, more potent activators of ACE2 as a basis of a new anti-

hypertensive therapy. Enhancing enzyme activity with small molecules may open the

door to novel therapeutic approaches targeting other hinge-bending enzymes or en-

zymes whose conformational equilibrium is critical to their activity and designing new

compounds with improved therapeutic values.

86

APPENDIX A ACE2 SEQUENCE ANALYSIS

ACE2, Collectrin and ACE Sequence Alignment

ACE2 1 mssssWLLLS LVA------- ---------- ---------- -VTAAQSTIE

ACE 1 ML---NLCFM LTrhavhsld sflrklvfcs lpsslpsptg hITAASLTAQ

Collectrin 1 ML---WLLFF LVT------- ---------- ---------- -AIHAElc--

ACE2 23 EQaktfldkf nheaedlfyq ssLASWNYNT NITEENvqnm nnagdkwsaf

ACE 48 RHlslrikp- ---------- --IATKNYNT NITTETskil lqknmqianh

Collectrin 18 ---------- ---------- ---------- ---------- ----------

ACE2 73 lkEQSTLAQM YPLQEIQNLT VKLQLQALQq ngsSVLSEDK SKRLNTILNT

ACE 85 tlKYGTQARK FDVNQLQNTT IKRIIKKVQd lerAALPAQE LEEYNKILLD

Collectrin 18 ---------- ---------- ---------- ---------- ----------

ACE2 123 MSTIYSTGKV CNPDnpqECL LLEPGLNEIM ANSLDYNERL WAWESWRSEV

ACE 135 METTYSVATV CHPNg--SCL QLEPDLTNVM ATSRKYEDLL WAWEGWRDKA

Collectrin 18 ---------- ---------- ---------- ---------- ----------

ACE2 173 GKQLRPLYEE YVVLKNEMAR ANHYEDYGDY WRGDYEvngv dgydysrgQL

ACE 183 GRAILQFYPK YVELINQAAR LNGYVDAGDS WRSMYEtp-- --------SL

Collectrin 18 ---------- ---------- ---------- ---------- ----------

ACE2 223 IEDVEHTFEE IKPLYEHLHA YVRAKLMNAY p-SYISPIGC LPAHLLGDMW

ACE 223 EQDLERLFQE LQPLYLNLHA YVRRALHRHY gaQHINLEGP IPAHLLGNMW

Collectrin 18 ---------- ---------- ---------- ---------- ----------

ACE2 272 GRFWTNLYSL TVPFGQKPNI DVTDAMVDQA WDAQRIFKEA EKFFVSVGLP

ACE 273 AQTWSNIYDL VVPFP SM DTTEAMLKQG WTPRRMFKEA DDFFTSLGLL

Collectrin 18 ---------- ---------- ---------- ---------- ----------

ACE2 322 NMTQGFWENS MLTDPGNVQK AVCHPTAWDL GKG-DFRILM CTKVTMDDFL

ACE 323 PVPPEFWNKS MLEKPTDGRE VVCHASAWDF YNGkDFRIKQ CTTVNLEDLV

Collectrin 18 ---------- ---------- ---------- ---------- ----------

ACE2 371 TAHHEMGHIQ YDMAYAAQPF LLRNGANEGF HEAVGEIMSL SAATPKHLKS

ACE 373 VAHHEMGHIQ YFMQYKDLPV ALREGANPGF HEAIGDVLAL SVSTPKHLHS

Collectrin 18 ---------- ---------- ---------- ---------- ----------

ACE2 421 IGLLSpdfqe DNETEINFLL KQALTIVGTL PFTYMLEKWR WMVFKGEIPK

ACE 423 LNLLSsegg- SDEHDINFLM KMALDKIAFI PFSYLVDQWR WRVFDGSITK

Collectrin 18 ---------- ---------- ---------- ---------- ----------

ACE2 471 DQWMKKWWEM KREIVGVVEP VPhdetycDP ASLFHVSNDY SFIRYYTRTL

ACE 472 ENYNQEWWSL RLKYQGLCPP VPrtqgdfDP GAKFHIPSSV PYIRYFVSFI

Collectrin 18 ---------- ---------- ---------- ---------- ----------

ACE2 521 YQFQFQEALC QAAKHEGPLH KCDISNSTEA GQKLFNMLRL GKSEPWTLAL

ACE 522 IQFQFHEALC QAAGHTGPLH KCDIYQSKEA GQRLATAMKL GFSRPWPEAM

Collectrin 18 ---------- ---------- ---------- ---------- ----------

ACE2 571 ENVVGAKNMN VRPLLNYFEP LFTWLKDQnk nsfvgwstdw SPYADQSIKV

ACE 572 QLITGQPNMS ASAMLSYFKP LLDWLRTEne lhgek----- ----------

Collectrin 18 ---------- ---------- ---------- ---------- QPGAENAFKV

87

ACE2 621 RISLKSALGD RAYEWNDNEM YLFRSSVAYA MRQyflkvkn qmilfgeedV

ACE 607 -------LGW PQYNWTPNsa r--------- ---------- ----------

Collectrin 28 RLSIRTALGD KAYAWDTNEE YLFKAMVAFS MRKvpnreat eish-----V

ACE2 671 RVANLKPRIS FNFFVTAPKn vsdiIPRTEV EKAIRMSRSR INDAFRLNDN

ACE 621 ---------- ---------- ---------- ------SEGP LPDSGRVSFL

Collectrin 73 LLCNVTQRVS FWFVVTDPSk nht-LPAVEV QSAIRMNKNR INNAFFLNDQ

ACE2 721 SLEFLGIQPT LGPPNQPPVS IWLIVFGVVM GVIVVGIVIL IFTGIRDRKK

ACE 635 GLDLDAQQAR VGqwl----- --LLFLGIAL LVATLGLSQR LFSirhrslh

Collectrin 122 TLEFLKIPST LAPPMDPSVP IWIIIFGVIF CIIIVAIALL ILSGIWQRRR

ACE2 771 KNKarsgen- ---------- -----PYASI DISKGENNPG FQNTDDVQTs

ACE 678 rhshgpqfgs evelrhs--- ---------- ---------- ----------

Collectrin 172 KNKepsevdd aedkcenmit iengiPSDPL DMKGGHINDA FMTEDERLTp

ACE2 805 f

ACE 695 -

Collectrin 222 l

88

ACE2 Conserved Residue Analysis

ACE2 multiple sequence alignment for the ACE2 protein expressed in the six different species: Homo sapiens, Danio rerio, Rattus norwegicus, Felis catus, Mus musculus and Macaca mulatta. ACE2 CONSERVED RESIDUES MULTIPLE SEQUENCE ALIGNMENT:

Ace_2_Homo_s 1 MSSSSWLLLS LVAVTAAQST IEEQAKTFLD KFNHEAEDLF YQSSLASWNY

Ace_2_Danio_ 1 mc-ARWLLLL ALASVACCQT VEDRAREFLN KFDEEASDIM YQYTLASWAY

Ace_2_Rattus 1 MSSSCWLLLS LVAVATAQSL IEEKAESFLN KFNQEAEDLS YQSSLASWNY

Ace_2_Felis_ 1 MSGSFWLLLS FAALTAAQST TEELAKTFLE KFNHEAEELS YQSSLASWNY

Ace_2_Mus_mu 1 MSSSSWLLLS LVAVTTAQSL TEENAKTFLN NFNQEAEDLS YQSSLASWNY

Ace_2_Macaca 1 MSGSSWLLLS LVAVTAAQST IEEQAKTFLD KFNHEAEDLF YQSSLASWNY

Site1 Ace_2_Homo_s 51 NTNITEENVQ NMNNAGDKWS AFLKEQSTLA QMYPLQEIQN LTVKLQLQAL

Ace_2_Danio_ 50 NTDISQENAD KEAEAYAIWS EYYNKMSEES NAYPIDQISD PIIKMQLQKL

Ace_2_Rattus 51 NTNITEENAQ KMNEAAAKWS AFYEEQSKIA QNFSLQEIQN ATIKRQLKAL

Ace_2_Felis_ 51 NTNITDENVQ KMNEAGAKWS AFYEEQSKLA KTYPLAEIHN TTVKRQLQAL

Ace_2_Mus_mu 51 NTNITEENAQ KMSEAAAKWS AFYEEQSKTA QSFSLQEIQT PIIKRQLQAL

Ace_2_Macaca 51 NTNITEENVQ NMNNAGEKWS AFLKEQSTLA QMYPLQEIQN LTVKLQLQAL

Site1 Ace_2_Homo_s 101 QQNGSSVLSE DKSKRLNTIL NTMSTIYSTG KVCNPDNPQE CLLLEPGLNE

Ace_2_Danio_ 100 QDKGSGALSP DKASELRNIM SEMSTIYNTA TVCKIDDPTD CQTLEPGLES

Ace_2_Rattus 101 QQSGSSALSP DKNKQLNTIL NTMSTIYSTG KVCNSMNPQE CFLLEPGLDE

Ace_2_Felis_ 101 QQSGSSVLSA DKSQRLNTIL NAMSTIYSTG KACNPNNPQE CLLLEPGLDD

Ace_2_Mus_mu 101 QQSGSSALSA DKNKQLNTIL NTMSTIYSTG KVCNPKNPQE CLLLEPGLDE

Ace_2_Macaca 101 QQNGSSVLSE DKSKRLNTIL NTMSTIYSTG KVCNPNNPQE CLLLDPGLNE

Ace_2_Homo_s 151 IMANSLDYNE RLWAWESWRS EVGKQLRPLY EEYVVLKNEM ARANHYEDYG

Ace_2_Danio_ 150 IMAESRDYDE RLHVWEGWRV ATGMKMRPLY EKYVDLKNEA AKLNNYEDHG

Ace_2_Rattus 151 IMATSTDYNR RLWAWEGWRA EVGKQLRPLY EEYVVLKNEM ARANNYEDYG

Ace_2_Felis_ 151 IMENSKDYNE RLWAWEGWRA EVGKQLRPLY EEYVALKNEM ARANNYEDYG

Ace_2_Mus_mu 151 IMATSTDYNS RLWAWEGWRA EVGKQLRPLY EEYVVLKNEM ARANNYNDYG

Ace_2_Macaca 151 IMEKSLDYNE RLWAWEGWRS EVGKQLRPLY EEYVVLKNEM AGANHYKDYG

Ace_2_Homo_s 201 DYWRGDYEVN GVDGYDYSRG QLIEDVEHTF EEIKPLYEHL HAYVRAKLMN

Ace_2_Danio_ 200 DYWRGDYEti ddpkYSYSRD QVIEDARRIY KEILPLYKEL HAYVRAKLQD

Ace_2_Rattus 201 DYWRGDYEAE GVEGYNYNRN QLIEDVENTF KEIKPLYEQL HAYVRTKLME

Ace_2_Felis_ 201 DYWRGDYEEE WTDGYNYSRS QLIKDVEHTF TQIKPLYQHL HAYVRAKLMD

Ace_2_Mus_mu 201 DYWRGDYEAE GADGYNYNRN QLIEDVERTF AEIKPLYEHL HAYVRRKLMD

Ace_2_Macaca 201 DYWRGDYEVN GVDGYDNNRD QLIEDVERTF EEIKPLYEHL HAYVRAKLMN

Ace_2_Homo_s 251 AYPSYISPIG CLPAHLLGDM WGRFWTNLYS LTVPFGQKPN IDVTDAMVDQ

Ace_2_Danio_ 250 VYPGHIGSDA CLPAHLLGDM WGRFWTNLYP LMIPYPDRPD IDVSSAMVEQ

Ace_2_Rattus 251 VYPSYISPTG CLPAHLLGDM WGRFWTNLYP LTTPFLQKPN IDVTDAMVNQ

Ace_2_Felis_ 251 TYPSRISPTG CLPAHLLGDM WGRFWTNLYP LTVPFGQKPN IDVTDAMVNQ

Ace_2_Mus_mu 251 TYPSYISPTG CLPAHLLGDM WGRFWTNLYP LTVPFAQKPN IDVTDAMMNQ

Ace_2_Macaca 251 AYPSYISPTG CLPAHLLGDM WGRFWTNLYS LTVPFGQKPN IDVTDAMVNQ

Ace_2_Homo_s 301 AWDAQRIFKE AEKFFVSVGL PNMTQGFWEN SMLTDPGNVQ KAVCHPTAWD

Ace_2_Danio_ 300 GWDEIRLFKE AEKFFMSVNM PAMFDNFWNN SMFIKP-EER DVVCHPTAWD

Ace_2_Rattus 301 SWDAERIFKE AEKFFVSVGL PQMTPGFWTN SMLTEPGDDR KVVCHPTAWD

Ace_2_Felis_ 301 SWDARRIFKE AEKFFVSVGL PNMTQGFWEN SMLTEPGDSR KVVCHPTAWD

Ace_2_Mus_mu 301 GWDAERIFQE AEKFFVSVGL PHMTQGFWAN SMLTEPADGR KVVCHPTAWD

Ace_2_Macaca 301 AWNAQRIFKE AEKFFVSVGL PNMTQGFWEN SMLTDPGNVQ KVVCHPTAWD

89

Active site Ace_2_Homo_s 351 LG-KGDFRIL MCTKVTMDDF LTAHHEMGHI QYDMAYAAQP FLLRNGANEG

Ace_2_Danio_ 349 MGnRKDFRIK MCTKVNMDDF LTVHHEMGHN QYQMAYRNHP YLLRDGANEG

Ace_2_Rattus 351 LG-HGDFRIK MCTKVTMDNF LTAHHEMGHI QYDMAYAKQP FLLRNGANEG

Ace_2_Felis_ 351 LG-KGDFRIK MCTKVTMDDF LTAHHEMGHI QYDMAYAVQP FLLRNGANEG

Ace_2_Mus_mu 351 lG-HGDFRIK MCTKVTMDNF LTAHHEMGHI QYDMAYARQP FLLRNGANEG

Ace_2_Macaca 351 LG-KGDFRII MCTKVTMDDF LTAHHEMGHI QYDMAYAAQP FLLRNGANEG

Ace_2_Homo_s 400 FHEAVGEIMS LSAATPKHLK SIGLLSPDFQ EDNETEINFL LKQALTIVGT

Ace_2_Danio_ 399 FHEAVGEIMS LSAATPSHLQ SLGLLPSDFK QDYETDINFL LKQALTIVGT

Ace_2_Rattus 400 FHEAVGEIMS LSAATPKHLK SIGLLPSNFQ EDNETEINFL LKQALTIVGT

Ace_2_Felis_ 400 FHEAVGEIMS LSAATPNHLK TIGLLSPGFS EDSETEINFL LKQALTIVGT

Ace_2_Mus_mu 400 FHEAVGEIMS LSAATPKHLK SIGLLPSDFQ EDSETEINFL LKQALTIVGT

Ace_2_Macaca 400 FHEAVGEIMS LSAATPKHLK SIGLLSPDFQ EDNETEINFL LKQALTIVGT

Ace_2_Homo_s 450 LPFTYMLEKW RWMVFKGEIP KDQWMKKWWE MKREIVGVVE PVPHDETYCD

Ace_2_Danio_ 449 LPFTYMLEEW RWQVFKAKIP KDEWMQQWWQ MKRELVGVAE AVPRDETYCD

Ace_2_Rattus 450 LPFTYMLEKW RWMVFQDKIP REQWTKKWWE MKREIVGVVE PLPHDETYCD

Ace_2_Felis_ 450 LPFTYMLEKW RWMVFKGEIP KEQWMQKWWE MKREIVGVVE PVPHDETYCD

Ace_2_Mus_mu 450 LPFTYMLEKW RWMVFRGEIP KEQWMKKWWE MKREIVGVVE PLPHDETYCD

Ace_2_Macaca 450 LPFTYMLEKW RWMVFKGEIP KDQWMKKWWE MKREIVGVVE PVPHDETYCD

Ace_2_Homo_s 500 PASLFHVSND YSFIRYYTRT LYQFQFQEAL CQAAKHEGPL HKCDISNSTE

Ace_2_Danio_ 499 PPALFHVSGD YSFIRYFTRT IYQFQFQEAL CKAAGHTGPL YKCDITNSTK

Ace_2_Rattus 500 PASLFHVSND YSFIRYYTRT IYQFQFQEAL CQAAKHDGPL HKCDISNSTE

Ace_2_Felis_ 500 PASLFHVAND YSFIRYYTRT IYQFQFQEAL CRIAKHEGPL HKCDISNSSE

Ace_2_Mus_mu 500 PASLFHVSND YSFIRYYTRT IYQFQFQEAL CQAAKYNGSL HKCDISNSTE

Ace_2_Macaca 500 PASLFHVSND YSFIRYYTRT LYQFQFQEAL CQAAKHEGPL HKCDISNSTE

Ace_2_Homo_s 550 AGQKLFNMLR LGKSEPWTLA LENVVGAKNM NVRPLLNYFE PLFTWLKDQN

Ace_2_Danio_ 549 AGDKLRHMLE LGRSMSWTRA LEEVAGTTKM DSQPLLHYFS TLMEWLKEEN

Ace_2_Rattus 550 AGQKLLNMLS LGNSGPWTLA LENVVGSRNM DVKPLLNYFQ PLFVWLKEQN

Ace_2_Felis_ 550 AGKKLLQMLT LGKSKPWTLA LEHVVGEKKM NVTPLLKYFE PLFTWLKEQN

Ace_2_Mus_mu 550 AGQKLLKMLS LGNSEPWTKA LENVVGARNM DVKPLLNYFQ PLFDWLKEQN

Ace_2_Macaca 550 AGQKLLNMLK LGESEPWTLA LENVVGAKNM NVRPLLNYFE PLFTWLKDQN

Ace_2_Homo_s 600 KNSFV--GWS TDWSPY---- --------AD QSIKVRISLK SALGDKAYEW

Ace_2_Danio_ 599 QKNnrvpGWN VNVNPgvlts sfindaeiSE NAFKVRISLK SALGNEAYTW

Ace_2_Rattus 600 RNSTV--GWS TDWSPY---- --------AD QSIKVRISLK SALGKNAYEW

Ace_2_Felis_ 600 RNSFV--GWN TDWRPY---- --------AD QSIKVRISLK SALGDEAYEW

Ace_2_Mus_mu 600 RNSFV--GWN TEWSPY---- --------AD QSIKVRISLK SALGANAYEW

Ace_2_Macaca 600 KNSFV--GWS TDWSPY---- --------AD QSIKVRISLK SALGDKAYEW

Ace_2_Homo_s 636 NDNEMYLFRS SVAYAMRQYF LKVKNQMILF GEEDVRVANL KPRISFNFFV

Ace_2_Danio_ 649 NANDIYLFKS TMAFAMRQYY LKEKNTDVNF TPENIHTYNE TARISFKFAV

Ace_2_Rattus 636 TDNEMYLFRS SVAYAMREYF SREKNQTVPF GEADVWVSDL KPRVSFNFFV

Ace_2_Felis_ 636 NDNEMYLFRS SVAYAMREYF SKVKNQTIPF VEDNVWVSNL KPRISFNFFV

Ace_2_Mus_mu 636 TNNEMFLFRS SVAYAMRKYF SIIKNQTVPF LEEDVRVSDL KPRVSFYFFV

Ace_2_Macaca 636 NDNEMYLFRS SVAYAMRTYF LEIKHQTILF GEEDVRVADL KPRISFNFYV

Ace_2_Homo_s 686 TAPKNVSDII PRTEVEKAIR MSRSRINDAF RLNDNSLEFL GIQPTLGPPN

Ace_2_Danio_ 699 MDPTKTGTVI PKAEVENAIW QERDRINGAF LLSDETLEFV GLMATLAPPK

Ace_2_Rattus 686 TSPKNVSDII PRSEVEEAIR MSRGRINDIF GLNDNSLEFL GIYPTLKPPY

Ace_2_Felis_ 686 TASKNVSDVI PRSEVEEAIR MSRSRINDAF RLDDNSLEFL GIQPTLSPPY

Ace_2_Mus_mu 686 TSPQNVSDVI PRSEVEDAIR MSRGRINDVF GLNDNSLEFL GIHPTLEPPY

Ace_2_Macaca 686 TAPKNVSDII PRTEVEEAIR ISRSRINDAF RLNDNSLEFL GIQTTLAPPY

90

Ace_2_Homo_s 736 QPPVSIWLIV FGVVMGVIVV GIVILIFTGI RDRKKKNKAR SGENPYASID

Ace_2_Danio_ 749 EEKITIWLVV FGVVMGVTVL AGIYLVTTGI LNRKKES--- ----------

Ace_2_Rattus 736 EPPVTIWLII FGVVMGTVVV GIVILIVTGI KGRKKKNETK REENPYDSMD

Ace_2_Felis_ 736 QPPVTIWLIV FGVVMGVVVV GIVLLIVSGI RNRRKNNQAR SEENPYASVD

Ace_2_Mus_mu 736 QPPVTIWLII FGVVMALVVV GIIILIVTGI KGRKKKNETK REENPYDSMD

Ace_2_Macaca 736 QSPVTTWLIV FGVVMGVIVA GIVVLIFTGI RDRKKKNQAR SEENPYASID

Ace_2_Homo_s 786 ISKGENNPGF QNTDDVQTSF

Ace_2_Danio_ 786 ---------- ----------

Ace_2_Rattus 786 IGKGESNAGF QNSDDAQTSF

Ace_2_Felis_ 786 LSKGENNPGF QHADDVQTSF

Ace_2_Mus_mu 786 IGKGESNAGF QNSDDAQTSF

Ace_2_Macaca 786 INKGENNPGF QNTDDVQTSF

Finding Evolutionarily Conserved Regions in ACE2

Figure A-1. Evolutionarily conserved regions in ACE2

Active site

Target site

Subdomain II

Subdomain I

least conserved

most

conserved

91

APPENDIX B STRUCTURAL ANALYSIS

ACE2 Protein Surface Analysis

Table B-1. ACE2 protein surface parameters The surface area of 1R42

(open conformation) Å2

The surface area of 1R4L (closed confor-mation) Å2

% dif-ference

Total Accessible Surface Area (ASA)

28984.38 27073.25 -6.59

Surface area

% total Surface area

%total

Polar ASA 13132.69 45.31 11919.26 44.03 -1.28

Non-polar ASA 15851.69 54.69 15153.99 55.97 1.28

Polar side chain ASA 9788.68 33.77 8803.13 32.52 -1.26

Non-polar side chain ASA 13840.80 47.75 12920.93

47.73 -0.03

Positively charged ASA 2353.06 8.12 2049.46 7.57 -0.55

Negatively charged ASA 3040.30 10.49 2656.55 9.81 -0.68

Number of sphere clusters (potentially druggable sites)

22 39

total number of spheres identified 2924 total number of clusters 135

Probe radius=1.40 The surface area of 1R42 (open conformation) Structure contains 22 cavities The surface area of 1R4L (closed conformation) Structure contains 39 cavities

* Accessible Surface (AS) is defined as all positions adopted by the center of the probe

sphere in contact with atoms of the macromolecule. Molecular surface (MS) is defined

as the surface traced by the part of the probe sphere that faces the macromolecule,

joining the concave elements smoothly (113).

92

Analysis of Hinge Regions in ACE2

CCP4 DynDom Version 1.5

cutoff for definition for effective hinge axis and mechanical hinge: 5.5 A

distance criterion for a pair of atoms to be in contact: 3.5 A

FOUND 3 DOMAINS

=============================================================================

FIXED DOMAIN 1

RESIDUE NUMBERS: 21-96, 98-100, 293-390, 401-403, 407-423, 544-560

SIZE: 214 RESIDUES

BACKBONE RMSD ON THIS DOMAIN: 0.814A

-----------------------------------------------------------------------------

MOVING DOMAIN 2

RESIDUE NUMBERS: 97-97, 101-287, 435-520, 566-578, 581-613

SIZE: 320 RESIDUES

BACKBONE RMSD ON THIS DOMAIN: 0.726A

RATIO INTERDOMAIN TO INTRADOMAIN DISPLACEMENT: 4.938

PERCENTAGE EXTERNAL DISPLACEMENT IN MOVING DOMAIN: 99.639

ANGLE OF ROTATION: 22.108 DEGREES

TRANSLATION ALONG AXIS: -1.077 A

BENDING RESIDUES: 94 - 114

BENDING RESIDUES: 287 - 293

BENDING RESIDUES: 422 - 435

ACTS AS A MECHANICAL HINGE: DISTANCE FROM AXIS:

LYS 94 4.3 A

LEU 95 2.1 A

GLN 96 1.8 A

LEU 97 0.5 A

GLN 98 3.5 A

ALA 99 4.4 A

EFFECTIVE HINGE AXIS ANGLE: 69.037

DISTANCE BETWEEN SCREW AXIS AND CENTRES OF MASS: 13.101 A

PERCENTAGE CLOSURE MOTION: 87.201

-----------------------------------------------------------------------------

MOVING DOMAIN 3

RESIDUE NUMBERS: 393-400, 404-406, 521-543, 561-565, 579-580

SIZE: 41 RESIDUES

BACKBONE RMSD ON THIS DOMAIN: 0.751A

RATIO INTERDOMAIN TO INTRADOMAIN DISPLACEMENT: 1.145

PERCENTAGE EXTERNAL DISPLACEMENT IN MOVING DOMAIN: 79.119

ANGLE OF ROTATION: 10.269 DEGREES

TRANSLATION ALONG AXIS: -0.447 A

BENDING RESIDUES:

BENDING RESIDUES: 390 - 393

BENDING RESIDUES: 400 - 401

BENDING RESIDUES: 403 - 404

BENDING RESIDUES: 406 - 407

BENDING RESIDUES: 543 - 547

BENDING RESIDUES: 560 - 561

93

ACTS AS A MECHANICAL HINGE: DISTANCE FROM AXIS:

PHE 390 0.8 A

LEU 391 4.1 A

LEU 392 3.3 A

ARG 393 1.8 A

LEU 560 4.1 A

GLY 561 1.1 A

EFFECTIVE HINGE AXIS ANGLE: 29.894

DISTANCE BETWEEN SCREW AXIS AND LINE JOINING CENTRES OF MASS: 1.908 A

PERCENTAGE CLOSURE MOTION: 24.840

=============================================================================

FIXED DOMAIN 3

RESIDUE NUMBERS: 97-97, 101-287, 435-520, 566-578, 581-613

SIZE: 320 RESIDUES

BACKBONE RMSD ON THIS DOMAIN: 0.726A

MOVING DOMAIN 3

RESIDUE NUMBERS: 393-400, 404-406, 521-543, 561-565, 579-580

SIZE: 41 RESIDUES

BACKBONE RMSD ON THIS DOMAIN: 0.751A

RATIO INTERDOMAIN TO INTRADOMAIN DISPLACEMENT: 1.467

PERCENTAGE EXTERNAL DISPLACEMENT IN MOVING DOMAIN: 90.175

ANGLE OF ROTATION: 13.022 DEGREES

TRANSLATION ALONG AXIS: -0.111 A

BENDING RESIDUES:

BENDING RESIDUES: 520 - 521

BENDING RESIDUES: 565 - 566

BENDING RESIDUES: 578 - 581

ACTS AS A MECHANICAL HINGE: DISTANCE FROM AXIS:

LEU 520 2.2 A

TYR 521 1.1 A

PRO 565 4.7 A

TRP 566 3.5 A

MET 579 5.3 A

EFFECTIVE HINGE AXIS ANGLE: 88.955

DISTANCE BETWEEN SCREW AXIS AND CENTRES OF MASS: 2.788 A

PERCENTAGE CLOSURE MOTION: 99.967

94

Defining the “Druggable” Surface in ACE2: Open Conformation Sphere Clusters

DOCK receptor sphere coordinates for clusters of 10-50 spheres

cluster 1 number of spheres in cluster 45

2059 42.77444 89.46559 33.94810 1.401 2085 0 0

2060 41.00746 90.18408 36.75004 3.020 2116 0 0

2062 38.87259 90.02457 37.56086 2.828 2116 0 0

2085 42.87594 89.72588 36.71462 2.397 2114 0 0

2088 50.38366 93.16967 37.16918 3.994 2176 0 0

2091 43.02426 87.15175 34.83333 1.400 2085 0 0

2096 42.98539 87.13660 34.79240 1.400 2090 0 0

2108 47.71457 89.12929 36.61616 1.401 2085 0 0

2111 48.64960 87.10100 39.47860 1.400 2141 0 0

2113 46.98714 87.22206 41.49746 1.400 2140 0 0

2114 43.05800 89.69858 36.79001 2.316 2085 0 0

2115 45.96776 90.40842 43.15339 3.333 2142 0 0

2116 37.81494 86.91676 44.82018 3.638 2840 0 0

2119 42.80749 81.02161 42.11775 1.400 3765 0 0

2140 46.98724 87.22231 41.49753 1.400 2113 0 0

2142 45.69212 90.19088 43.74100 3.303 2115 0 0

2146 43.77354 86.31195 44.65400 2.051 2115 0 0

2175 46.93311 90.68468 39.15800 3.645 2114 0 0

2192 55.21155 91.17426 33.38964 1.401 2223 0 0

2223 54.59832 92.59683 34.11713 2.673 2194 0 0

2811 36.96270 75.43557 50.67264 2.335 3709 0 0

2839 37.22883 78.83105 46.01588 2.472 2808 0 0

2840 36.37400 84.34240 47.58345 3.209 3772 0 0

2847 40.38524 82.84127 43.30809 2.391 3773 0 0

3689 40.23067 79.35124 53.86060 3.781 3770 0 0

3690 47.47174 73.19514 58.81463 2.727 3789 0 0

3691 46.65794 77.90715 60.79079 3.748 3792 0 0

3709 38.38146 75.89149 50.78113 3.046 4352 0 0

3747 39.63773 72.77711 48.61534 1.400 3698 0 0

3753 37.07917 78.56560 43.81148 1.401 4471 0 0

3765 38.02634 79.86422 46.06602 2.498 2839 0 0

3771 39.85871 79.89067 46.87792 1.400 3765 0 0

3773 39.00661 85.46351 44.92632 2.913 2840 0 0

3776 43.71869 79.59330 55.11783 3.425 3689 0 0

3783 47.10312 80.30591 56.11868 2.371 3792 0 0

3789 47.45455 74.87672 60.19167 3.178 3691 0 0

3792 45.75859 78.65750 59.09189 3.307 3691 0 0

4340 40.09470 77.27481 46.56104 1.400 3752 0 0

4342 40.42094 76.72115 48.55995 1.401 4340 0 0

4352 39.10030 78.98721 52.99000 3.807 3770 0 0

4353 46.28281 75.07384 56.69391 1.401 3690 0 0

4467 37.65105 86.82247 43.23132 2.809 2116 0 0

4564 54.20008 89.59903 35.21508 1.400 2223 0 0

4574 45.23302 85.70716 43.38984 1.401 2146 0 0

4578 47.49438 87.28042 37.43866 1.401 2105 0 0

95

cluster 2 number of spheres in cluster 33

1492 51.93953 41.72435 41.24348 2.062 1528 0 0

1528 53.51908 38.81583 42.76700 3.830 1494 0 0

1549 43.83287 43.40616 37.60675 1.675 4199 0 0

1551 48.15509 41.71426 37.43188 1.400 3330 0 0

1552 48.48756 36.47943 39.56147 3.098 3323 0 0

1556 42.95193 40.44431 44.93200 3.348 1529 0 0

1569 44.48161 46.11073 37.99704 1.400 1519 0 0

1571 43.20405 44.52927 38.22903 1.400 1549 0 0

1582 40.18498 42.57849 43.22657 1.401 1556 0 0

1735 36.30678 27.94917 33.87958 3.988 4221 0 0

3075 51.91860 40.30484 40.08856 2.795 1528 0 0

3076 52.22320 43.47956 40.11910 1.400 1492 0 0

3104 54.04809 40.20367 34.88795 1.401 3331 0 0

3106 53.97357 38.17118 35.38103 2.813 3331 0 0

3149 55.35647 38.03387 33.24369 1.401 3256 0 0

3256 52.11902 34.96772 34.73099 3.141 3321 0 0

3287 50.12657 35.26786 31.80695 1.401 3321 0 0

3296 51.43257 36.64014 33.78438 2.147 3320 0 0

3320 50.68328 38.32300 33.46320 1.400 3294 0 0

3321 52.25711 34.89560 35.21060 3.415 3256 0 0

3323 43.99019 37.50946 39.60177 3.421 1552 0 0

3324 42.08884 32.24931 36.12156 3.407 4279 0 0

3330 49.67600 39.17297 38.15095 2.262 1552 0 0

3331 53.92563 39.72246 36.31422 2.065 3106 0 0

4178 33.76148 32.88515 34.79248 1.400 1735 0 0

4179 37.26082 32.14993 35.82614 2.371 4207 0 0

4192 40.97602 38.27960 36.22801 1.400 4201 0 0

4199 41.15273 40.28014 37.56207 1.400 1582 0 0

4201 40.73409 38.89259 36.60189 1.400 4192 0 0

4279 41.91044 37.25808 38.33199 3.188 1584 0 0

4403 45.92328 43.12691 37.40400 1.400 1551 0 0

4404 45.41800 39.12652 38.23089 2.109 1552 0 0

4660 51.38252 43.89179 40.89101 1.400 1517 0 0

cluster 3 number of spheres in cluster 31

475 84.01600 51.00230 39.96045 3.839 1254 0 0

479 79.93836 55.58245 45.76100 3.023 541 0 0

481 80.04042 56.46213 49.00480 3.519 541 0 0

508 80.06149 62.58800 47.67356 1.400 497 0 0

519 77.04732 59.97920 50.70148 1.401 1382 0 0

521 77.11100 60.98649 55.65477 3.375 1394 0 0

537 79.22481 62.31286 47.98199 1.400 4796 0 0

539 79.96444 59.54577 48.90563 2.036 4797 0 0

541 79.82191 54.72034 46.04600 3.545 481 0 0

568 77.80595 55.90480 37.94361 2.913 476 0 0

571 76.72486 54.40215 43.11930 3.045 1378 0 0

587 79.63701 57.62302 37.98663 1.924 476 0 0

593 78.07312 55.72353 32.99317 1.400 1246 0 0

595 75.23800 57.84710 36.59217 2.143 568 0 0

605 84.85033 52.96289 32.47083 2.678 1254 0 0

623 84.37436 51.61709 31.43173 2.663 1254 0 0

96

1243 80.94141 52.55634 32.35926 1.402 1254 0 0

1246 80.49707 53.26260 31.23630 1.400 605 0 0

1253 79.72025 55.17880 35.49100 2.564 4784 0 0

1254 84.90000 52.54043 38.41257 3.142 475 0 0

1265 77.23878 55.30545 38.45839 3.142 568 0 0

1363 72.98411 57.01611 43.01100 1.853 571 0 0

1364 73.92861 55.98261 42.44400 1.783 571 0 0

1442 77.36140 52.40786 42.19075 4.000 476 0 0

4676 76.20657 58.81089 54.67034 2.102 1394 0 0

4692 74.90980 50.90967 38.31681 1.400 1441 0 0

4784 80.15900 55.08165 35.03684 2.449 1253 0 0

4787 78.11365 58.62949 35.88097 1.400 4784 0 0

4796 79.22474 62.31288 47.98223 1.400 537 0 0

4797 80.28939 57.65600 51.12630 3.744 1382 0 0

4804 87.07900 56.61154 37.95551 2.565 475 0 0

cluster 4 number of spheres in cluster 29

2909 26.97403 66.94158 43.63709 1.400 2943 0 0

2940 30.41932 61.08192 43.71769 1.400 4077 0 0

2941 30.48300 62.15667 45.92577 2.119 4077 0 0

2943 26.47619 66.65806 45.35202 2.259 4457 0 0

2951 27.60641 57.66715 46.11832 3.039 4100 0 0

3649 41.68387 63.39003 50.25916 1.400 3722 0 0

3650 42.56938 62.87103 52.77957 1.400 3675 0 0

3651 41.23949 62.08376 49.98102 1.400 3722 0 0

3667 42.18166 67.09991 51.80524 1.400 3673 0 0

3671 41.50579 69.12125 54.76156 1.400 4350 0 0

3673 40.32405 65.29687 52.26675 2.765 4348 0 0

3687 41.40239 69.24454 54.96980 1.401 4350 0 0

3694 41.50610 69.12199 54.76116 1.400 3671 0 0

3696 41.42211 68.10085 52.82273 1.400 3671 0 0

3701 41.00069 67.20035 51.02244 1.401 3667 0 0

3716 27.93840 61.98721 48.37600 3.729 2919 0 0

3718 35.46800 59.89425 51.93249 3.644 4068 0 0

3722 40.10700 63.21228 52.73419 3.475 3675 0 0

3732 31.38612 64.69784 45.64862 1.401 3716 0 0

3742 26.93299 65.16754 48.14690 3.946 2919 0 0

4040 39.14986 62.18125 52.98747 3.700 4348 0 0

4068 34.24306 59.81469 51.87155 3.848 3716 0 0

4069 38.37595 58.10063 50.40095 1.401 4043 0 0

4077 28.26805 60.05641 46.60692 2.939 2919 0 0

4078 31.80624 56.47245 48.19989 1.400 4101 0 0

4101 33.12144 56.27009 48.50393 1.400 4073 0 0

4105 28.32032 55.37812 45.07375 1.400 4100 0 0

4114 23.91011 56.22187 44.29557 2.586 2922 0 0

4350 40.80968 66.46809 55.55991 2.360 3673 0 0

cluster 5 number of spheres in cluster 28

1927 18.46240 65.00662 33.57958 2.150 1938 0 0

1937 20.02830 64.78652 33.18787 1.400 1933 0 0

1938 18.01724 68.00176 36.55200 2.520 2899 0 0

1942 21.53782 70.51902 35.49351 2.496 2897 0 0

97

2807 31.06719 74.36014 44.41165 1.400 2834 0 0

2808 31.32770 74.87859 45.43886 1.401 2835 0 0

2810 33.92926 71.68334 45.04346 1.401 3738 0 0

2876 25.78168 76.05338 41.36039 1.400 4459 0 0

2881 18.24424 74.69088 40.35965 2.654 2900 0 0

2885 20.87533 72.39813 34.85364 3.428 1943 0 0

2889 23.63720 72.15705 32.60426 1.401 1943 0 0

2893 24.40293 75.43414 41.49400 1.400 4461 0 0

2895 23.61400 75.15588 43.47464 2.348 4461 0 0

2897 19.30047 72.03952 34.57521 3.985 1943 0 0

2899 19.55100 68.91811 35.93482 2.521 1938 0 0

2900 16.41120 74.38093 39.02731 3.713 2881 0 0

2906 27.38336 73.96591 45.54023 2.296 2835 0 0

2908 28.62394 74.07723 44.73432 1.400 2835 0 0

2913 19.51695 67.01472 38.46378 1.400 2899 0 0

3705 30.91339 71.91940 50.38929 3.889 3741 0 0

3712 31.56211 69.34304 49.75980 2.079 3741 0 0

3738 34.32778 71.03949 43.35672 1.400 2800 0 0

3740 32.01050 70.22194 42.86555 1.400 2800 0 0

3741 31.83300 72.27138 47.64252 2.315 2809 0 0

4344 34.38790 71.07373 43.34456 1.401 2800 0 0

4458 22.58506 69.34586 37.12382 1.401 2914 0 0

4459 25.78240 76.05358 41.36022 1.400 2876 0 0

4461 21.95762 75.37964 43.56911 2.637 2896 0 0

cluster 6 number of spheres in cluster 20

966 37.73955 39.38731 15.57582 1.401 1000 0 0

1663 24.97183 43.40020 25.59701 1.400 1672 0 0

1665 25.93273 38.84678 25.19375 3.000 1697 0 0

1696 26.17831 40.77142 24.47266 2.013 1663 0 0

1697 25.30244 35.71539 24.72171 3.850 1726 0 0

1705 32.85279 36.19894 17.20900 2.457 4273 0 0

1712 28.99303 41.45096 23.61822 1.401 1664 0 0

1720 31.89760 35.76680 23.74540 1.400 4233 0 0

1725 27.81710 35.51714 24.00677 3.064 1697 0 0

3365 38.79750 39.02653 16.46295 1.401 4244 0 0

3369 40.15433 35.97717 13.35741 2.580 4249 0 0

3373 41.69394 35.48345 15.03517 1.400 4249 0 0

4231 31.72924 36.58837 23.07558 1.400 1718 0 0

4233 30.47054 33.41145 22.28884 2.948 4267 0 0

4244 37.84400 37.09711 14.67929 2.715 1000 0 0

4249 37.92374 36.42603 13.33277 3.097 1000 0 0

4266 31.71152 35.22477 15.46503 3.500 1706 0 0

4273 36.27000 36.88533 13.82016 3.365 1000 0 0

4395 38.50215 39.87982 16.12556 1.400 966 0 0

4633 29.40100 33.69926 20.92799 2.917 4267 0 0

cluster 7 number of spheres in cluster 20

1178 67.02700 39.35715 25.28398 2.235 4424 0 0

1184 72.99232 37.92648 25.15428 3.690 1235 0 0

1207 70.28596 40.02491 25.00048 2.233 1184 0 0

1235 71.26657 38.38900 28.18500 3.066 3177 0 0

98

1236 75.03081 38.62994 23.88012 2.942 1185 0 0

1271 71.54900 38.71217 34.75673 3.395 4422 0 0

1275 69.75700 38.22368 36.50159 3.699 4422 0 0

3171 68.60672 41.52420 35.93535 1.400 1275 0 0

3173 67.48980 41.57813 38.57178 1.400 1275 0 0

3196 66.19531 31.74081 29.63776 2.507 4421 0 0

3198 67.20796 34.86929 26.50262 2.274 4421 0 0

3203 66.97668 37.65728 25.00253 2.721 3177 0 0

3205 69.72563 36.21078 25.13874 3.857 3177 0 0

3233 65.19229 38.14315 23.81036 1.401 3203 0 0

4419 65.13262 38.26720 26.64614 1.401 4424 0 0

4421 68.23800 35.80288 31.54944 1.401 4422 0 0

4422 72.28998 37.29429 29.61001 3.723 1235 0 0

4424 64.47588 38.96794 25.95669 1.400 4419 0 0

4690 70.56289 41.44764 33.10939 1.401 1282 0 0

4702 74.81793 42.43481 24.10006 1.400 1215 0 0

cluster 8 number of spheres in cluster 20

259 61.13698 72.56328 6.64668 2.832 274 0 0

264 61.11518 75.84849 7.35536 1.400 259 0 0

266 59.32872 82.45781 4.58573 3.844 2292 0 0

271 59.80704 75.73552 3.25310 3.981 2306 0 0

2038 45.04777 88.39051 21.31581 2.821 2263 0 0

2050 46.87900 88.25831 21.71172 2.522 2263 0 0

2263 41.96822 85.11201 19.22948 3.460 2461 0 0

2267 44.21679 80.75331 17.50904 2.462 4525 0 0

2270 45.97974 74.27999 14.28700 3.242 2455 0 0

2288 52.82086 78.57792 6.35993 2.121 2304 0 0

2289 54.76420 79.53367 5.41228 2.029 2306 0 0

2303 49.76293 76.56902 10.74100 2.982 2270 0 0

2304 52.02848 78.63633 7.14772 1.957 2288 0 0

2305 55.48192 80.37233 7.16429 1.401 2289 0 0

2306 60.17279 76.51403 4.17856 3.336 271 0 0

2441 45.45787 77.22166 17.89951 1.400 2455 0 0

2453 44.25952 79.35967 16.97739 2.260 2267 0 0

2455 44.85988 76.00698 14.12618 2.831 2270 0 0

4525 44.05668 82.90071 19.88141 1.401 2263 0 0

4545 51.24802 74.15620 10.65635 2.963 2308 0 0

cluster 9 number of spheres in cluster 18

3244 50.18022 34.55439 23.82523 1.400 3274 0 0

3245 54.12400 30.79822 19.50930 2.992 3388 0 0

3246 48.07058 31.12670 22.59239 3.745 4254 0 0

3248 54.05500 29.22376 18.87445 3.690 3389 0 0

3274 49.16700 33.22094 22.99747 2.670 3246 0 0

3288 47.05185 30.30989 29.95332 3.000 4213 0 0

3290 46.63448 29.72052 26.06001 3.654 3316 0 0

3310 47.13216 34.68398 29.71147 1.401 3286 0 0

3312 46.73212 32.25169 28.52841 2.183 3288 0 0

3315 47.47302 30.17823 23.58400 3.847 3249 0 0

3316 46.71343 29.65206 25.69700 3.738 3290 0 0

3360 48.15912 35.12499 20.23176 1.401 3378 0 0

99

3378 48.66609 36.39070 20.44361 1.400 3283 0 0

3397 55.02091 32.07587 17.96925 1.400 3388 0 0

3400 53.77628 34.10318 19.72504 1.400 4392 0 0

4213 47.69291 30.63088 31.40700 2.950 3288 0 0

4392 49.78507 34.46466 21.56065 2.383 3274 0 0

4416 55.74292 31.69079 21.32920 1.400 3245 0 0

cluster 10 number of spheres in cluster 18

3652 48.02568 59.83367 55.01354 3.029 4015 0 0

3655 48.50100 63.34139 53.19363 2.231 3676 0 0

3656 51.01554 66.95657 53.58303 1.400 3814 0 0

3672 49.26663 68.34182 55.57113 2.402 3813 0 0

3814 50.63978 66.71704 57.09780 3.423 3676 0 0

3815 53.07486 66.93386 56.54739 2.175 3967 0 0

3944 57.77454 60.69091 56.47835 1.400 4310 0 0

3962 49.97795 60.78078 51.40644 2.097 4020 0 0

3963 51.16177 60.66526 50.17308 1.400 3622 0 0

3966 53.23707 66.78807 54.50723 1.400 3815 0 0

3970 57.78491 57.57301 57.08798 2.746 3978 0 0

3971 52.89908 56.47689 57.01019 3.151 4015 0 0

3999 49.53000 59.86037 53.17041 2.890 3652 0 0

4010 43.56831 56.37303 52.27827 1.400 4043 0 0

4011 43.49260 56.14712 52.27568 1.401 4043 0 0

4020 50.17990 60.54780 51.37467 2.128 3962 0 0

4042 44.81839 57.55959 54.16425 2.652 4013 0 0

4311 50.04838 59.95796 55.94891 3.366 4015 0 0

cluster 11 number of spheres in cluster 16

752 58.09792 51.04716 7.12602 1.400 1104 0 0

777 58.13400 51.81475 4.33088 3.177 1105 0 0

780 57.40544 51.88798 7.46632 1.400 752 0 0

785 55.29974 50.23894 3.10619 3.229 1105 0 0

786 54.75315 54.80699 3.73690 1.401 4757 0 0

796 48.42412 47.26656 4.67515 1.400 1078 0 0

798 48.80791 45.37709 1.46200 3.236 1078 0 0

799 51.07598 47.81393 2.19463 2.456 1077 0 0

1044 46.43660 45.63235 5.87100 1.400 1080 0 0

1077 51.30700 47.54703 1.75389 2.751 799 0 0

1080 47.50285 47.13704 5.13847 1.400 796 0 0

1086 53.66800 50.00020 6.48903 1.400 4755 0 0

1104 59.03700 51.98870 4.92974 2.858 777 0 0

1105 55.72788 50.93792 3.46325 3.108 785 0 0

4753 46.96776 44.69646 3.31400 2.671 1078 0 0

4755 54.51645 49.65733 3.98178 2.599 1105 0 0

cluster 12 number of spheres in cluster 14

1450 60.53727 47.81009 48.81110 1.400 1474 0 0

1474 57.25488 45.73651 50.33715 2.875 1505 0 0

1478 55.52397 48.95343 48.18029 1.400 1482 0 0

1482 56.14421 48.47262 51.02958 2.723 1505 0 0

1502 54.71968 50.92804 48.40700 2.059 4306 0 0

1503 54.96924 51.33830 51.72248 3.259 3983 0 0

100

1505 57.36110 44.99356 51.20846 3.284 1474 0 0

1509 53.77178 50.38843 47.04134 1.400 1502 0 0

3986 56.66965 51.46404 49.03960 1.400 1482 0 0

4000 52.02784 51.48405 49.82800 1.893 1503 0 0

4006 50.57175 51.60481 47.53978 1.400 1509 0 0

4016 50.61355 48.77985 54.43827 3.273 1504 0 0

4306 54.16500 51.54994 50.94501 2.723 1503 0 0

4662 54.48314 49.95072 47.26940 1.401 1502 0 0

cluster 13 number of spheres in cluster 11

901 43.56949 55.90601 18.20319 1.400 1789 0 0

905 44.43113 55.99682 18.67846 1.400 1790 0 0

1787 40.77921 55.95609 24.09594 1.400 1857 0 0

1789 44.17565 55.70141 18.29529 1.400 905 0 0

1790 45.48071 58.67862 19.93487 2.578 903 0 0

1808 47.48000 58.72125 22.34706 2.910 1835 0 0

1809 48.10567 57.41367 18.95297 1.400 905 0 0

1830 41.72432 56.76973 24.97921 1.401 4613 0 0

1857 41.20570 56.51030 24.78556 1.400 4613 0 0

1861 43.56248 59.01147 23.62124 2.518 1836 0 0

4613 41.55612 55.94214 23.93525 1.400 1787 0 0

cluster 14 number of spheres in cluster 11

197 69.43369 80.73021 16.42033 1.400 308 0 0

198 70.78019 80.35228 16.62321 1.401 308 0 0

222 69.33806 83.33565 15.47497 2.079 310 0 0

308 69.43408 80.72989 16.42049 1.400 197 0 0

310 70.30700 83.06256 16.65563 2.669 222 0 0

329 77.87480 78.10980 13.88500 2.804 354 0 0

332 74.02501 77.90237 12.84418 1.400 311 0 0

335 76.59537 77.95573 16.55568 2.443 352 0 0

336 74.53685 80.88869 16.38970 3.469 311 0 0

343 78.75376 72.53745 14.93654 2.511 353 0 0

354 79.36696 74.66535 12.67094 3.096 331 0 0

cluster 15 number of spheres in cluster 11

1067 50.59304 47.25755 21.25990 1.400 4386 0 0

1804 48.28741 47.33416 23.32779 1.400 3306 0 0

3058 50.95154 49.24268 23.28989 1.400 3449 0 0

3267 51.18208 48.22175 22.94810 1.779 1813 0 0

3279 47.34799 45.49368 22.88818 1.400 3305 0 0

3305 48.66100 46.11500 22.57110 1.579 4872 0 0

3306 49.02662 47.59537 23.56707 1.401 1804 0 0

3346 46.01745 44.91777 23.92271 1.400 3279 0 0

3448 52.81329 48.38447 23.37989 1.400 3090 0 0

4386 52.22746 48.20566 22.13864 1.400 3448 0 0

4616 46.55922 46.70935 22.05578 1.401 1067 0 0

cluster 16 number of spheres in cluster 11

1555 41.09504 48.89764 44.85543 1.400 4655 0 0

1562 38.83681 52.40254 44.40427 1.401 4089 0 0

1564 38.84304 52.84800 44.31671 1.400 4089 0 0

101

4032 39.66207 53.36332 44.54943 1.401 1564 0 0

4034 39.95035 50.28158 46.87325 1.835 4092 0 0

4035 40.20333 49.84743 49.57335 3.024 4092 0 0

4072 38.69030 52.91935 45.35687 1.401 4089 0 0

4091 39.28933 50.90339 45.08949 1.400 1562 0 0

4092 40.80761 48.73135 48.15282 2.819 4035 0 0

4300 38.46472 54.19068 44.67290 1.400 4089 0 0

4655 41.09483 48.89846 44.85547 1.400 1560 0 0

cluster 17 number of spheres in cluster 11

1591 30.01452 42.45725 45.18862 3.276 4157 0 0

1592 34.07252 41.97547 42.98726 1.401 4185 0 0

4099 29.76594 50.22524 47.77113 1.401 4128 0 0

4125 29.31795 44.23877 46.35800 2.441 4159 0 0

4128 30.24316 43.70813 49.45098 3.563 4159 0 0

4130 27.85049 45.94091 45.41303 1.400 4157 0 0

4133 27.84426 48.57342 48.13349 2.757 4128 0 0

4157 27.89916 45.87371 45.39282 1.400 4289 0 0

4285 30.10885 39.96764 44.11292 3.325 4186 0 0

4286 30.47269 43.34485 42.20084 1.400 4186 0 0

4292 29.24538 49.32497 46.98309 1.400 4128 0 0

cluster 18 number of spheres in cluster 10

118 80.68204 77.53233 26.87280 1.400 146 0 0

119 82.55200 82.13863 27.63453 3.539 140 0 0

140 81.62068 79.18747 26.04300 2.667 119 0 0

146 80.95512 78.73229 26.60830 2.061 119 0 0

147 83.48693 79.13520 23.66800 3.884 121 0 0

148 82.78253 76.71367 25.21500 2.109 121 0 0

351 83.75980 73.83613 22.01053 3.533 408 0 0

386 84.71011 73.46104 25.50935 2.180 408 0 0

407 86.11817 72.29339 27.40593 1.400 417 0 0

417 86.94391 71.33871 28.31691 1.401 407 0 0

102

APPENDIX C MOLECULAR DOCKING AND FUNCTIONAL TESTING

Docking Algorithm Parameters

limit_max_ligands no

read_mol_solvation no

write_orientations no

write_conformations no

skip_molecule no

calculate_rmsd no

rank_ligands yes

max_ranked_ligands 1000

scored_conformer_output_override no

orient_ligand yes

automated_matching yes

max_orientations 100

critical_points no

chemical_matching no

use_ligand_spheres no

flexible_ligand yes

bump_filter yes

max_bumps 3

score_molecules yes

contact_score_primary yes

contact_score_secondary yes

grid_score_primary yes

grid_score_secondary yes

grid_score_vdw_scale 1.0

grid_score_es_scale 1.0

minimize_ligand yes

simplex_max_iterations 200

simplex_max_cycles 3

simplex_score_converge 0.1

simplex_cycle_converge 1.0

simplex_trans_step 1.0

simplex_rot_step 0.1

simplex_tors_step 10.0

simplex_final_min_add_internal no

simplex_secondary_minimize_pose yes

use_advanced_secondary_simplex_parameters yes

simplex_secondary_max_iterations 200

simplex_secondary_min_add_internal no

simplex_random_seed 1

atom_model all

103

Sphere Set Coordinates Defining the Target Site

ATOM 1 C SPH 651 74.129 59.451 36.614

ATOM 2 C SPH 652 74.596 59.184 36.373

ATOM 4 C SPH 654 76.872 55.174 38.768

ATOM 5 C SPH 655 76.021 54.749 39.952

ATOM 6 C SPH 656 71.554 59.944 37.445

ATOM 7 C SPH 657 75.358 54.972 40.262

ATOM 9 C SPH 674 78.434 55.809 36.935

ATOM 11 C SPH 681 76.928 57.222 36.519

ATOM 13 C SPH 685 75.640 58.457 36.128

ATOM 15 C SPH 1423 78.523 55.398 36.386

ATOM 17 C SPH 1426 79.431 55.075 34.109

ATOM 19 C SPH 1434 79.312 55.217 36.115

ATOM 23 C SPH 1448 77.122 54.128 39.245

ATOM 24 C SPH 1449 77.771 55.705 37.151

ATOM 26 C SPH 1494 67.936 56.749 34.028

ATOM 28 C SPH 1499 70.429 55.960 33.886

ATOM 30 C SPH 1501 70.097 55.572 33.407

ATOM 32 C SPH 1529 70.789 55.843 35.499

ATOM 33 C SPH 1530 69.989 57.553 34.592

ATOM 35 C SPH 1533 70.296 60.231 36.693

ATOM 37 C SPH 1557 72.182 56.906 40.520

ATOM 38 C SPH 1558 75.284 54.956 41.474

ATOM 39 C SPH 1559 76.316 54.220 40.531

104

Clinical Uses of Selected FDA-approved Compounds

Table C-1. Clinical uses of selected FDA-approved compounds

NSC number

Chemical name Common name

Mol. weight

Drug category

NSC 354677 (XNT)

1-[[2-(dimethylamino)ethyl] amino]-4-(hydroxymethyl)-7-[[(4-methylphenyl) sul-fonyl]oxy]-9H-xanthen-9-one

XNT 482.54 -

NSC 290956 (ESP)

8-[3-(2-chlorophenothiazin-10-yl)propyl]-4-thia-1,8-diazaspiro[4.5]decan-2-one hydrochloride

Spicloma-zine

482.48 Phenothiazine antipsychotic

NSC 357775 (DMZ)

4-[2-(4-carbamimidoyl phenyl) iminohydrazinyl] benzenecarboximidamide Dihydrochloride

Diminazene 354.23 Veterinary anti-bacterial and an-ti-protozoal agent

NSC 169188 (HXZ)

2-[2-[4-[(4-chlorophenyl)-phenylmethyl] piperazin-1-yl]ethoxy]ethanol

Hydroxyz-ine

374.90 Histamine H1 re-ceptor antago-nist, antipruriptic, antiemetic

NSC 169899 (CTX)

(3Z)-3-(2-chlorothioxanthen-9-ylidene)-N,N-dimethylpropan-1-amine hy-drochloride

Minithixen 352.32 Thioxanthene antipsychotic

NSC 134434 (HCT)

1-(2-diethylaminoethyl amino)-4-(hydroxymethyl) thioxanthen-9-one

Hycanthone 356.48 Thioxanthene antischistosomal agent

NSC 293901 (FMB)

N-[4-chloro-2-[[methyl-(2-morpholin-4-yl-2-oxoethyl)amino]methyl] phenyl] benzamide hydro-chloride

Fominoben hydrochlo-

ride

438.34 Antitussive, res-piratory stimulant

NSC 284614 (APR)

N'-(2,3-dihydro-1H-inden-2-yl)-N,N-diethyl-N'-phenylpropane-1,3-diamine hydrochloride

Aprindine 358.94 Anti-arrhythmic, cardiac depres-sant

105

Table C-1 continued.

NSC number

Chemical name Common name

Mol. weight

Drug category

NSC 289337 (TIA)

5-chloro-3-[2-[4-(2-hydroxyethyl)piperazin-1-yl]-2-oxoethyl]-1, 3-benzothiazol-2-one hydro-chloride

Tiaramide, 392.30 Non-steroid anti-inflammatory agent, anti-asthmatic

NSC 290312 (LAB)

2-hydroxy-5-[1-hydroxy-2-(4-phenylbutan-2-ylamino)ethyl]benzamide Hydrochloride

Labetalol 364.86 Anti-hypertensive, al-pha- and beta-adrenergic re-ceptor blocker

106

Effect of DMZ on Catalytic Activity of ACE

Since ACE2 is nearly 42% identical to Angiotensin Converting Enzyme (ACE) –

counter-regulatory member of RAS, compounds capable of binding ACE2 might also

affect ACE (14) (26). Understanding drug specificity is key to predicting potential safety

risks: drugs that are promiscuous in binding to molecular targets may exert unwanted or

even opposing effects (114) (115) (116). Based on our results, 100 µM DMZ (just as

XNT (40)) does not produce any significant effect on ACE. This is also consistent with

previous observations that ACE inhibitors do not inhibit ACE2, and ACE2 inhibitors do

not inhibit ACE (117) (118).

Figure C-1. Effect of DMZ on catalytic activity of ACE

0

50

100

150

200

250

ACE2 ACE

% V

ma

x / K

m

107

Analysis of Ang II Cleavage by MALDI-TOF

Figure C-2. MALDI mass-spectrum of Angiotensin 1-7 generated by cleavage of Ang II

108

APPENDIX D DESIGNING DERIVATIVE COMPOUNDS

Chemical Structures of Derivative Compounds (Library 1)

Figure D-1. Chemical structures of Library 1 derivative compounds.

109

Docking Scores of Top-scoring Derivative Compounds

Table D-1. Docking scores of 35 top-scoring derivative compounds (Library 1)

Name Grid

score

vdW Electro-

static

4,4'-(carbonylbis(azanediyl))dibenzimidamide -33.3139 -31.1365 -2.1774

4-(4-(2,4,5-trimethylphenyl)-1H-1,2,3-triazol-1-yl)

benzimidamide

-31.9523 -30.1424 -1.8099

4-(4-(3,4-difluorophenyl)-1H-1,2,3-triazol-1-yl)

benzimidamide

-31.6230 -30.6701 -0.9528

4-(4-(2,5-dimethylphenyl)-1H-1,2,3-triazol-1-yl)

benzimidamide

-31.4681 -30.8745 -0.5936

4-(4-(4-ethylphenyl)-1H-1,2,3-triazol-1-yl) benzim-

idamide

-31.4003 -31.2356 -0.1646

4,4'-(1H-1,2,3-triazole-1,4-diyl)bis(2-

hydroxybenzamide)

-30.9993 -29.6407 -1.3585

4-(4-(3-methoxyphenyl)-1H-1,2,3-triazol-1-yl) ben-

zimidamide

-30.7815 -29.5614 -1.2200

4-(4-(4-(trifluoromethoxy)phenyl)-1H-1,2,3-triazol-

1-yl)benzimidamide

-30.4265 -32.1386 1.7121

(E)-4-((((4-carbamimidoylphenyl)amino)methyl-

ene)amino)benzimidamide

-30.1709 -28.4991 -1.6717

4,4'-(1H-imidazole-1,4-diyl)dibenzimidamide -30.1156 -27.8578 -2.2578

(S)-4,4'-(oxazolidine-3,5-diyl)bis(2-

hydroxybenzamide)

-29.8907 -28.6648 -1.2258

4,4'-(oxazole-3,5(2H)-diyl)dibenzimidamide -29.5827 -27.5711 -2.0115

4-(4-(o-tolyl)-1H-1,2,3-triazol-1-yl)benzimidamide -29.5630 -28.1657 -1.3972

4-(4-(4-(dimethylamino)phenyl)-1H-1,2,3-triazol-1-

yl)benzimidamide

-29.3596 -29.1830 -0.1765

110

Table D-1. Continued.

Name Grid

score

vdW Electro-

static

4-(4-(4-methoxyphenyl)-1H-1,2,3-triazol-1-yl) ben-

zimidamide

-28.6684 -28.3956 -0.2727

4-(4-(3,5-dimethoxyphenyl)-1H-1,2,3-triazol-1-yl)

benzimidamide

-28.6006 -29.1321 0.5314

4-(4-(3-hydroxyphenyl)-1H-1,2,3-triazol-1-yl) ben-

zimidamide

-28.4097 -27.2876 -1.1221

(S)-4,4'-(oxazolidine-3,5-diyl)dibenzimidamide -28.2427 -28.4471 0.2043

4,4'-(1H-1,2,3-triazole-1,4-diyl)dibenzimidamide -27.9892 -27.0702 -0.9189

N-(4-(1-(4-carbamimidoylphenyl)-1H-1,2,3-triazol-

4-yl)phenyl)-N-oxohydroxylammonium

-27.8798 -29.2290 1.3492

(E)-4-(2-(4-carbamimidoylbenzylidene)hydrazinyl)

benzimidamide

-27.7685 -27.3755 -0.3930

4-(4-(m-tolyl)-1H-1,2,3-triazol-1-yl)benzimidamide -27.1166 -27.1847 0.0680

4-(4-(3,4-dichlorophenyl)-1H-1,2,3-triazol-1-yl)

benzimidamide

-26.9646 -26.5035 -0.4611

1,3-bis(4-carbamimidoylphenyl)-1H-imidazol-3-ium

chloride

-26.8625 -26.0548 -0.8077

4,4'-(1H-imidazole-1,4-diyl)bis(2-

hydroxybenzamide)

-26.2645 -26.1872 -0.0772

4-(4-(4-hydroxyphenyl)-1H-1,2,3-triazol-1-yl) ben-

zimidamide

-25.8977 -26.1618 0.2641

4,4'-(methylenebis(oxy))dibenzimidamide -25.8389 -24.1129 -1.7260

4-(4-phenyl-1H-1,2,3-triazol-1-yl)benzimidamide -25.5076 -24.1546 -1.3530

4,4'-(1H-pyrrole-2,5-diyl)dibenzimidamide -24.6917 -24.7236 0.0318

(S)-4-(1-hydroxy-2-((2-morpholino-2-

oxoethyl)amino)ethyl)benzimidamide

-22.9294 -22.5395 -0.3898

111

Table D-1. Continued.

Name Grid

score

vdW Electro-

static

4-((2-(4-carbamimidoylphenyl)-2-hydroxyethyl)

amino)benzimidamide

-22.3873 -22.9902 0.6028

(S)-4-(3-(2-morpholino-2-oxoethyl)oxazolidin-5-

yl)benzimidamide

-20.3591 -20.3368 -0.0222

4-(4-(4-methoxy-2-methylphenyl)-1H-1,2,3-triazol-

1-yl)benzimidamide

-18.3364 -18.1847 -0.1517

N-(4-(1-(4-carbamimidoylphenyl)-1H-1,2,3-triazol-

4-yl)phenyl)-N-oxohydroxylammonium

-17.1572 -17.2965 0.1393

112

1H NMR Spectra of New Triazolic Derivatives of Diminazene

113

114

115

116

117

118

119

120

121

122

123

124

125

REFERENCES

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BIOGRAPHICAL SKETCH

Lidia Vladimirovna Kulemina was born in 1984 in a small town of Sarapul, Russia.

Upon graduating from high-school, she moved to Saint-Petersburg to study chemistry

and English at Herzen University and completed her specialist degree in the summer of

2005. Several months later, Lidia joined graduate program in chemistry at the University

of Florida and was working in the lab of Dr. Thomas Lyons. In the spring of 2008, she

joined the lab of Dr. David Ostrov where she studied X-ray crystallography and struc-

ture-based methods in drug discovery. In 2009 she started collaborating and then joined

the lab of Dr. Sukwon Hong to continue working on the development of novel activators

for Angiotensin-Converting enzyme 2. After graduation, Lidia will be joining the lab of

Dr. Maria Zajac-Kaye to work on the development of novel allosteric inhibitors of thymi-

dylate synthase as a basis for new chemotherapy.