m.tech divison - dissertation grade/parikshit totawar... · 2015. 4. 24. · m.tech. in information...

Structure‐based discovery of a novel PRMT1 inhibitor: New leads to combat cardiovascular

risk factors

DISSERTATION

SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF TECHNOLOGY IN INFORMATION TECHNOLOGY (BIOINFORMATICS)

INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD 2007

Under the Supervision of Dr. C.V.S. Siva Prasad

Assistant Professor Bioinformatics Division IIIT‐

Allahabad

Submitted By Parikshit Totawar

MB200513 M. Tech. IT (Bio‐Informatics)

IIIT‐Allahabad

Structure‐based discovery of a novel PRMT1 inhibitor: New leads to combat

cardiovascular risk factors

DISSERTATION

SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF TECHNOLOGY IN INFORMATION TECHNOLOGY (BIOINFORMATICS)

M. Tech. IT (Bio‐Informatics)

INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD 2007

Under the Supervision of Dr. C.V.S. Siva Prasad

Assistant Professor Bioinformatics Division IIIT‐

Allahabad

Submitted By Parikshit Totawar

MB200513

IIIT‐Allahabad

Date _______________

WE DO HEREBY RECOMMEND THAT THE THESIS WORK

PREPARED UNDER OUR SUPERVISION BY PARIKSHIT

TOTAWAR ENTITLED STRUCTURE BASED DISCOVERY Of A

NOVEL PRMT1 INHIBITOR: NEW LEADS TO COMBAT

CARDIOVASCULAR RISK FACTORS BE ACCEPTED IN

PARTIAL FULFILMENT OF THE REQUIREMENTS OF THE

DEGREE OF MASTER OF TECHNOLOGY IN INFORMATION

TECHNOLOGY (BIOINFORMATICS) FOR EXAMINATION.

COUNTERSIGNED:

DEAN (ACADEMIC)

THESIS ADVISOR

IINNDDIIAANN IINNSSTTIITTUUTTEE OOFF IINNFFOORRMMAATTIIOONN TTEECCHHNNOOLLOOGGYY AALLLLAAHHAABBAADD

(A CENTRE OF EXCELLENCE IN INFORMATION TECHNOLOGY ESTABLISHED BY GOVT. OF INDIA)

CERTIFICATE OF APPROVAL*

The foregoing thesis is hereby approved as a creditable study in the area of

Information Technology carried out and presented in a manner satisfactory to

warrant its acceptance as a pre-requisite to the degree for which it has been

submitted. It is understood that by this approval the undersigned do not necessarily

endorse or approve any statement made, opinion expressed or conclusion drawn

therein but approve the thesis only for the purpose for which it is submitted.

COMMITTEE ON

FINAL EXAMINATION

FOR EVALUATION

OF THE THESIS

*Only in case the recommendation is concurred in

IINNDDIIAANN IINNSSTTIITTUUTTEE OOFF IINNFFOORRMMAATTIIOONN TTEECCHHNNOOLLOOGGYY AALLLLAAHHAABBAADD

(A CENTRE OF EXCELLENCE IN INFORMATION TECHNOLOGY ESTABLISHED BY GOVT. OF INDIA)

iStructure‐based discovery of a novel PRMT1 inhibitor: New leads to combat cardiovascular risk factors

Declaration

This is to certify that this thesis work entitled “Structure-based discovery of a

novel PRMT1 inhibitor: New leads to combat cardiovascular risk factors” which is

submitted by me in partial fulfillment of the requirement for the completion of

M.Tech. in Information Technology specialization in Bioinformatics to Indian

Institute of Information Technology, Allahabad comprises only my original work and

due acknowledgement has been made in the text to all other material used.

I understand that my thesis may be made electronically available to the public.

Parikshit Totawar

Indian Institute of Information Technology, Allahabad. 2007

iiStructure‐based discovery of a novel PRMT1 inhibitor: New leads to combat cardiovascular risk factors

Acknowledgement

I am highly grateful to the honorable Director, IIIT-Allahabad, Prof. M. D. Tiwari,

for his ever helping attitude and encouraging us to excel in studies. Besides, he has

been a source of inspiration during my entire period of M.Tech. at, IIITA.

I am thankful to Prof. U. S. Tiwari, Dean Academics, IIIT Allahabad for

providing all the necessary requirements and for his moral support for this dissertation

work as well during the whole course of M. Tech.

The most notable source of guidance was my advisor, Dr. C.V.S. Siva Prasad, I

owe him a great deal of thanks for taking me under his wing and allowing me to soak

up some of his knowledge and insight. He has not only made us to work but guided us

to orient towards research.

It gives me great pleasure to express my deep sense of gratitude and my humble

gratefulness to Prof. C. M. Bhandari, Coordinator of Indo Russian Center for

Biotechnology for his honest dedication towards our education and carrier and for

being with us in various levels of academic pursuits.

I am also grateful to Prof. Krishna Mishra, Dr T. Lahiri, Mr. Vikram Katju and

Mr. Pritish Varadwaj for their support and motivation; through out my research

project work.

I would like to mention thanks to my friends Ashutosh, Ojas, Shrikant, Manish

Mishra, Manish Singh, Anil, and Kapil for not only helping me in studies but also for

making this batch a house of learning through their hard work and dedication.

I am also thankful to rest of the classmates for their cooperation during my work. I

am also thankful to them for helping me in my project work and also some kind of

discussion regarding my work which helps me to understand the concept regarding

my work.

This acknowledgement will not complete until I pay my respectful homage to my

family especially my parents and my sister, whose enthusiasm to see this work

complete was as infectious as their inspiration.

Parikshit Totawar


iiiStructure‐based discovery of a novel PRMT1 inhibitor: New leads to combat cardiovascular risk factors

Contents

DECLARATION i ACKNOWLEDGEMENT ii LIST OF FIGURES iv LIST OF TABLES v ABBREVIATIONS vi ABSTRACT vii 1 INTRODUCTION 1

1.1 Motivation of the project 1 1.2 Thesis outline 3

2 LITERATURE REVIEW 5

2.1 Background 5 2.2 Post-translational modifications 5 2.3 Arginine methylation 2.4 Cellular processes regulated by arginine methylation 6 2.4.1 RNA processing 6 2.4.2 Transcriptional regulation 7 2.4.3 Signal transduction 7 2.4.4 DNA repair 8 2.4.5 Protein-protein interactions 8 2.4.6 Arginine protection 9 2.5 Regulation of arginine methylation 9 2.5.1 Oligomerization of PRMT’s 9 2.5.2 Regulation of arginine methylation by acetylation 9 2.5.3 Regulation of arginine methylation by PRMT binding proteins 9 2.6 ADMA and cardiovascular pathophysiology 10 2.7 Protein arginine methyltransferases 10 2.8 Structural analysis of PRMT1 12 2.9 Computer-aided drug design 14 2.9.1 Introduction 14

2.9.2 The drug design cycle 16 2.9.3 Design of combinatorial library 17 2.9.4 Pre-filtering 18 2.9.5 Virtual screening 19 2.9.6 Docking 20

3 EXPERIMENTAL DESIGN AND ANALYSIS 21

3.1 Generation of diverse combinatorial library based on a known bioactive inhibitor 21 3.1.1 Ilib diverse working principle 21 3.1.2 Template structure 21 3.1.3 Fragment set selection 22 3.1.4 Filter settings 22 3.1.5 Steps involved in random library generation 22 3.2 Conversion of 2D to 3D 23 3.3 Molegro working principle 24 3.3.1 Scoring function 24 3.3.2 Search algorithm 26 3.3.3 Pose clustering 26 3.4 Structure preparation 27 3.5 Cavity prediction 29 3.6 Defining binding region 29 3.7 Adding ligand atom constraints 29 3.8 Setting up side-chain flexibility 30


ivStructure‐based discovery of a novel PRMT1 inhibitor: New leads to combat cardiovascular risk factors

3.9 Setting up template docking 31 3.10 Running docking simulation 33 3.11 Analyzing docking results 34 3.11.1 Importing docking results 34 3.11.2 Detailed energy analysis 34 3.11.3 Visualizing the results 36 3.11.4 Sidechain minimization of the complex structure 36 3.11.5 Saving the results 37 3.11.6 Exporting molecules 37

4 RESULTS AND DISCUSSION 38

4.1 Results 38 4.1.1 Docking results of AMI-1 with 1ORI 38 4.1.2 Top 10 ligands found by docking 38 4.1.3 Comparison of energy score of AMI-1complex with drug like top 10 ligands

after Docking 44

4.1.4 Protein-Ligand Interactions 45 4.2 Discussion 46

5 CONCLUSION AND FUTURE WORK 49

6 BIBLIOGRAPHY 50

7 APPENDIX 58


vStructure‐based discovery of a novel PRMT1 inhibitor: New leads to combat cardiovascular risk factors

List of Figures

Figure 1 Synthesis of methylated forms of arginines. Arginine residues (circled R) that lie within appropriate consensus sequences in proteins can be post-translationally methylated by the action of PRMTs. S-adenosylmethionine (SAM) is the methyl donor in these reactions, and S-adenosylhomocysteine (SAH) is produced. After proteolysis of arginine-methylated proteins, free L-NMMA, ADMA, and SDMA are released into the cytosol. L-NMMA and ADMA are competitive inhibitors of all 3 isoforms of NOS; SDMA has no inhibitory activity [19].

3

Figure 2 The N-terminal helix Y is shown in red, and the AdoMet binding domain in green. The bound AdoHcy is shown in a stick model with the sulfur atom (where the transferable methyl group would be attached in AdoMet) shown in yellow. The barrel structure is shown in yellow, and the dimerization arm (which is inserted into the barrel) is in light blue (see Figure 1A). The bound arginine (blue) in the S14-AdoHcy-R3 ternary complex defines the active site, located between the AdoMet binding domain (green) and the barrel (yellow) [106].

13

Figure 3 3D structure of rat PRMT1 along with co-factor and arginine residue

13

Figure 4 PRMT1 active site with bound Arg [106]. 14

Figure 5 Features extracted from AMI-1 taken as template for similarity scoring of the ligands

32

Figure 6 Complex structure of AMI-1 with rat PRMT1 (PDB code 1ORI). 38

Figure 7 The top ten ligands found on the basis of energy score after docking 39

Figure 8 Bar graph showing energy comparison of top 10 ligands on docking 44

Figure 9 Diagrams featuring the key protein-ligand interactions for the top 3 ligands generated using LIGPLOT

45


viStructure‐based discovery of a novel PRMT1 inhibitor: New leads to combat cardiovascular risk factors

List of Tables

Table 1 The parameters for approximating the steric term adopted from GEMDOCK.

25

Table 2 Scheme for assigning charges while preparing the molecules 28

Table 3 Energy split-up terms 35

Table 4 Energy score of top 10 ligand conformations obtained by docking. 44


viiStructure‐based discovery of a novel PRMT1 inhibitor: New leads to combat cardiovascular risk factors

Abbreviations

ADMA Asymmetric DiMethylArginine

NOS Nitric Oxide Synthases

L-NMMA NG-Monomethyl L-Arginine

SDMA Symmetric DiMethylArginine

DDAH Dimethylarginine DimethylAminoHydrolase

PRMT Protein Arginine MethylTransferases

SAM, AdoMet S-AdenosylMethionine

SAH, AdoHyc S-AdenosylHomocysteine

RMSD Root Mean Squared Deviation

PDB Protein Data Bank

MW Molecular Weight

TPSA Total Solvent Accesible Surface Area

HBa Hydrogen Bond Acceptor

HBd Hydrogen Bond Donor

AMI Arginine Methyltransferase Inhibitor



viiiStructure‐based discovery of a novel PRMT1 inhibitor: New leads to combat cardiovascular risk factors

Abstract

Type I Protein Arginine Methyltransferases catalyze the formation of Asymmetric

Dimethyl Arginine (ADMA) residues by transferring methyl groups from S-

Adenosyl-L-methionine to the guanidine groups of arginine residues in variety of

eukaryotic proteins. In the previous studies it has been concluded that PRMT1

contributes the major type I protein arginine methyltransferases enzyme activity

present in mammalian cells. ADMA is a naturally occurring inhibitor of NOS. It is

clear that it is generated by many different cell types in the cardiovascular system and

affects vascular and cardiac function. Correlation of ADMA with endothelial

dysfunction and cardiovascular risk, together with the associations between cardiac

risk factors and ADMA levels, suggest that ADMA is linked to cardiovascular

disease, but strong causal relationships have yet to be established which qualifies it as

a potential therapeutic target.

The induced fit of the active site upon binding of a known inhibitor was analyzed.

The derived pharmacophore features were used to dock about 80000 molecules

generated by ilib diverse in silico to the active site of the protein structure with

Molegro Virtual Docker (MVD) program. Binding affinity of the lead candidates

obtained by docking were compared to that of the known inhibitor and found to be

much lower.

In this work a series of ten novel small molecules lead compounds were identified

which could be developed into more effective therapeutic agents to modulate

hypertension and control atherogenic diseases of heart associated with raised ADMA

levels.

1Structure‐based discovery of a novel PRMT1 inhibitor: New leads to combat cardiovascular risk factors

CHAPTER 1

Introduction

1.1. Motivation of this project

Asymmetric dimethylarginine (ADMA) is a naturally occurring amino acid that

circulates in plasma, is excreted in urine, and is found in tissues and cells [1-3]. It has

aroused interest because it inhibits nitric oxide synthases (NOSs)1 and therefore has

the potential to produce considerable biological effects, particularly in the

cardiovascular system. Recently, several studies have suggested that the plasma

concentrations of ADMA provide a marker of risk for endothelial dysfunction and

cardiovascular disease [1, 4–6]. ADMA is synthesized when arginine residues in

proteins are methylated by the action of protein arginine methyltransferases

(PRMTs) [7, 8]. Protein arginine methylation is a posttranslational modification that

adds either 1 or 2 methyl groups to the guanidine nitrogens of arginine incorporated

into proteins. There are 2 broad types of PRMTs: type 1 catalyze the formation of

ADMA, whereas type 2 methylate both of the guanidino nitrogens and so result in

the formation of symmetric dimethylarginine. Both types of PRMT, of which there

are several isoforms, can also monomethylate, leading to the formation of G-

monomethyl L-arginine (L-NMMA) [7, 8]. Once the proteins are hydrolyzed, free

methylarginines appear in the cytosol. The asymmetrically methylated arginines

(ADMA and L-NMMA) are inhibitors of NOS, whereas SDMA is not. There is

potentially a very broad range of substrate proteins for type 1 PRMTs [9] and the

enzymes and their substrates are widely distributed throughout the body [10]. The

role of protein arginine methylation is unclear, but this process has been implicated

in regulation of RNA binding, transcriptional regulation, DNA repair, protein

localization, protein–protein interaction, signal transduction, and recycling or

desensitization of receptors. The amount of ADMA generated within a cell is

dependent on the extent of arginine methylation in proteins and the rates of protein

turnover. Recently, studies with relatively nonspecific and low-potency PRMT

inhibitors have suggested that PRMT activity over 24 to 48 hours contributes to rates



of generation of free ADMA and that there is a relationship between PRMT

expression levels and free ADMA production [11]. In the cardiovascular system,

type 1 PRMTs are expressed in the heart, smooth muscle cells, and endothelial cells.

The expression of PRMT-1 in endothelial cells is increased in response to shear

stress and this effect can be blocked either by suppression of IκB kinase or by the

peroxisome proliferator-activated receptor (PPAR) γ activator troglitozone [12] This

altered expression of PRMT-1 was associated with corresponding changes in ADMA

release, suggesting that rates of ADMA generation in the vessel wall may be

regulated in part through alteration in PRMT expression. PRMT-1 expression is also

increased by lowdensity lipoprotein (LDL) expression, and again the effect seems to

correlate with altered ADMA generation [11]. ADMA inhibits all 3 isoforms of NOS

and is approximately equipotent with L-NMMA [1, 13]. In addition to blocking NO

formation, L-NMMA treatment may uncouple NOS and lead to the generation of

superoxide [14, 15], and it is likely that ADMA can do the same. It elevates blood

pressure, causes vasoconstriction, impairs endothelium-dependent relaxation, and

increases endothelial cell adhesiveness [1, 16, 17].

For cardiovascular pathology the most obvious treatment would be to reverse the

effects of increased ADMA or reduce the ADMA levels. Arginine has been reported

to displace ADMA and restore NOS activity thus improve endothelial function in

patients of hypercholesterolemia and peripheral vascular diseases [20]. An

alternative approach would be to increase DDAH expression or activity. At this

stage, agents that alter DDAH expression are likely to be useful experimental tools to

probe the biology of ADMA and DDAH, but it is far too early to know whether

increasing DDAH activity is indeed a potentially useful therapeutic goal. Inhibition

of methyltransferase activity affects cellular methylation of phospholipids, proteins,

DNA, and RNA thus decreasing the production of ADMA. But due to lack of

specificity of the current methyltransferase, all the enzymes using AdoMet are

indiscriminately targeted. A currently known inhibitor identified by Cheng et al

(2004) [18] using high-throughput screening is found to selectively inhibit arginine

methyltransferases family and not lysine methyltransferases. The AMIs displays no

specificity for individual PRMTs, demonstrating that further primary or analog

screens are required to identify PRMT-specific inhibitors.



Our aim is to develop a new drug-like lead inhibitors of PRMT1 by structure-

based drug discovery approach. During our research we employed docking based

virtual screening of a combinatorial library to identify drug like lead that is selective

for arginine methyltransferases and specifically inhibit PRMT1 of the family. We

generated a combinatorial library taking the known bioactive inhibitor AMI-1 as a

seed structure.

Figure 1: Synthesis of methylated forms of arginines. Arginine residues (circled R) that lie within appropriate consensus sequences in proteins can be post-translationally methylated by the action of PRMTs. S-adenosylmethionine (SAM) is the methyl donor in these reactions, and S-adenosylhomocysteine (SAH) is produced. After proteolysis of arginine-methylated proteins, free L-NMMA, ADMA, and SDMA are released into the cytosol. L-NMMA and ADMA are competitive inhibitors of all 3 isoforms of NOS; SDMA has no inhibitory activity [19].

The library was pruned using Lipinski parameters. We also found a shared

pharamacophore based on the structural alignment of the top scoring poses for lead

optimization and further drug development. This work has lead to identification of 10

top-scoring drug-like leads that may potentially inhibit PRMT1 and be developed

into a drug to combat a new cardiovascular risk factor, i.e., ADMA.

1.2. Thesis Outline The remaining of this thesis is organized as follows. In chapter 2 we described

previous work and background including structure-based drug discovery and virtual



screening. Chapter 3 describes experimental data preparation, experimental design

and virtual screening of PRMT1 active site. Chapter 4 presents and discusses

experimental results and the virtual screening results. In chapter 5 we conclude our

work and present the avenues for future work. Chapter 6 contains Appendix for

detailed energy split of the top 10 ligands.



CHAPTER 2

Literature Review

2.1 Background Recently the role of arginine methylation in manifold cellular processes like

signaling, RNA processing, transcription, and sub-cellular transport has been

extensively investigated. Here we describe recent works and findings that yielded new

insights into the cellular functions of arginine-methylation.

2.2 Post-translational modifications Post-translational modification of proteins is observed in all known living

organisms and is a mechanism by which a function of a protein can be modified.

Nearly every protein in the cell is chemically modified after its sytnthesis on a

ribosome. Such modifications, which may alter the activity, life span, or cellular

location of proteins, entail linkage of a chemical group to the free —NH2 or —COOH

group at either end of a protein or to a reactive sidechain group in an internal residue.

Acetylation, the addition of acetyl group to the amino group of the N-terminal

reesidue is the most common form of chemical modification, affecting an 80% of all

proteins. Acetyl groups can also be added to specific internal residues in proteins. An

important modification is phosphorylation of serine, threonine, tyrosine, and histidine

residues. The sidechains of asparagine, serine, and threonine are sites for

glycosylation, the attatchment of linear and branched carbohydrate chains. Other post-

translational modifications found in selected proteins include the hydroxylation, the

methylation, and the γ-carboxylation.

2.3 Arginine methylation One type of post-translational modification has gained recognition recently in

terms of its importance in cellular function. The methylation of residues in proteins

has been observed for nearly 30 years [21-24] but only recently has significant



progress been made in how arginine methylation of proteins influence cellular

function. Methylation occurs on many types of atoms in biology. Because the methyl

group itself is chemically inert, methylation requires that the methylated atom be

sufficiently nucleophilic to remove the methyl group from the chemical moiety

(usually S-adenosyl methionine or N5-methyltetrahydrofolate) to which the methyl

group is covalently bound. Methylation of sulfur atoms is required to generate

methionine from homocysteine in nearly all microorganisms that synthesize

methionine. Methylation of fully or partially aromatic nitrogen atoms occurs on

guanidinoacetate and nucleotide bases as well as on histidine, glutamine, asparagine,

and arginine residues of proteins [22, 24-26]. In this report, we focus on enzymes that

methylate the nitrogen atoms on guanidinium side chains of arginine residues within

proteins. These proteins are called protein arginine methyltransferases (PRMT).

2.4 Cellular processes regulated by arginine methylation

2.4.1 RNA Processing

Arginine methylation is known to affect many mechanisms, mostly involving

modulation of processes involving nucleic acids. RNA binding proteins (RBPs) fulfill

numerous tasks to ensure the proper processing and folding as well as the stabilization

and localization of RNAs and mRNA. Translation RBPs represent major targets for

PRMTs because most hnRNPs (A1, A2, K, R, and U) have been identified to be

arginine methylated [27]. It was proposed that arginine methylation might serve as a

maturation signal, as several RBPs including Sam68 are mislocalized in their

hypomethylated state [28]. It is also considered that the methylation of RBPs,

mediates their assembly into mature small nuclear ribonucleoprotein particles

(snRNPs) [29, 30]. The extensive secondary and tertiary structure of RNA, with its

numerous non-Watson-Crick base pairing, represents unique interfaces for

recognition by RBPs (Jones et al., 2001). Arginines within the active sites of RBPs

have been recognized as key amino acids in RNA-protein interactions (Calnan et al.,

1991). The guanidino nitrogens of arginine favor hydrogen bonding and Van Der

Waal contacts (Jones et al., 2001). The affinity of a particular RBP for its RNA

targets may be altered negatively, as the methyl group would prevent hydrogen

bonding by sterically hindering interactions. However, it remains to be determined



whether arginine methylation will dramatically influence the specific RNA binding

activity of RBPs. The extensive secondary and tertiary structures of RNA, with its

numerous non-Watson-Crick base pairing, represent unique interfaces for recognition

by RBPs [31]. Arginines within the active sites of RBPs have been recognized as key

amino acids in RNA-protein interactions [32]. The guanidino nitrogens of arginine

favor hydrogen bonding and Van Der Waal contacts [31]. The affinity of a particular

RBP for its RNA targets may be altered negatively, as the methyl group would

prevent hydrogen bonding by sterically hindering interactions. Alternatively, arginine

methylation may positively regulate RNA-protein interactions, as the arginine

becomes more “hydrophobic” in nature with methyl groups, and this may facilitate

stacking with the bases of the RNA. However, it remains to be determined whether

arginine methylation will dramatically influence the specific RNA binding activity of

RBPs.

2.4.2 Transcriptional regulation

It was recognized early on that histones were substrates of methyltransferases

[23], and it is now known that histones are substrates of PRMT1, CARM1, and

PRMT5 [8]. The posttranslational modification of histones is known to regulate gene

expression and contribute to the histone code [34]. There are numerous transcription

factors, including p53, YY1, and NF-κB, that contribute to the recruitment of the

PRMTs to promoters [33]. In addition to the histones, PRMTs have been shown to

methylate coactivators including CBP/p300 [35] as well as transcriptional elongation

factors SPT5 [36], HIV Tat [37], and hnRNPs to promote the packaging of mRNPs

[38, 39]. Thus, arginine methylation regulates the initiation and elongation steps of

transcription and may synchronize with mRNP packaging and export. It has been

proposed, but not yet demonstrated, that the methylation of AT hooks may regulate

protein-DNA or protein-protein interactions [40].

2.4.3 Signal transduction

Arginine methylation is used to mediate signal transduction downstream of the

TCR (T-cell antigen receptor), cytokine (including interferon), and NGF receptors

[41, 42]). A role of protein arginine methylation in regulating the expression of

interleukin 2 in T lymphocytes has been reported. The recruitment of methylarginine-



specific protein(s) to cytokine promoter regions was shown to regulate the cytokine

gene expression [43]. The JAK-STAT pathway downstream of cytokine receptors has

been reported to be regulated through arginine methylation of STAT1, STAT3 and

STAT6 transcription factors [44-46]. Signaling is governed by posttranslational

modifications that alter protein function in part by altering protein-protein

interactions. Methylated arginines have been shown to block some interactions and to

promote others [47]. Interactions mediated by SH3 domains are observed to be

sensitive to arginine methylation, whereas WW and Tudor domain interactions are

unaffected or are enhanced by arginine methylation [30, 48, 49].

2.4.4 DNA repair

It has been demonstrated that arginine methylation plays a significant role in DNA

damage. GAR motif of MRE11 is methylated by PRMT1. Substitution of arginine

residues in GAR motifs inhibited the exonuclease activity of MRE11 by regulating its

association with nuclear structures such as nuclear bodies and the sites of DNA

damage [50]. 53BP1 is yet another mediator of DNA damage checkpoint, which when

methylated is rapidly recruited to the sites of DNA double strand breaks and forms

characteristic nuclear foci, demonstrating its role in the early events of detection,

signaling, and repair of damaged DNA [51].

2.4.5 Protein-protein interactions

Arginine methylation can increase or decrease protein-protein interactions. The

methylation of an arginine residue of the HIV-1 Nef protein blocks the interaction

with tyrosine kinase Fyn. The interaction between the nuclear factor of activated T

cells (NF-AT) and the NF-AT-interacting protein of 45 kDa, NIP45, decreased in the

presence of methylase inhibitors [41] . Methylation of arginine residues by PRMT1 in

hnRNP K did not influence RNA-binding activity, the translation inhibitory function

and the cellular localization of the protein, but reduced the interaction of hnRNP K

with the tyrosine kinase c-Src leading to inhibition of c-Src activation and hnRNP K

phosphorylation [53]. There is evidence that methylated arginines serve as

physiological ligands for the Tudor domain. The Tudor domains of the splicing factor

SPF30 and the Tudor domain containing protein TDRD3 interact with GAR motifs in



a methylarginine-dependent manner, and with methylargininecontaining cellular

proteins [54].

2.4.6 Arginine protection

It was proposed that arginine methylation might protect crucial arginine residues

against attack by endogenous reactive dicarbonyl agents, such as methylglyoxal and

other natural by-products of normal metabolic pathways [55].

2.5 Regulation of arginine methylation

2.5.1 Oligomerization of PRMT’s

Formation of homo-dimers or larger homo-oligomers has also been linked to the

activity of Hmt1, PRMT1, and PRMT5 [56]. Enzymic activity of PRMT1 or

Hmt1/Rmt1 is abolished when dimerization of these enzymes is prevented [57,58].

However, later it was reported that PRMT1 and PRMT5 are catalytically active only

in the form of multimers, but not as a dimer or tetramer of the expressed subunit [59].

2.5.2 Regulation of arginine methylation by acetylation

It was reported that acetylation of the histone H3 at Lys 18 and Lys 23 tethers

recombinant CARM1 to the H3 tail and induces methylation of Arg 17 [60].

Particularly, acetylation of Lys 9 prevents the methylation of Arg 8 in H3 [61].

Taking into account these and other results, Bedford and Richard suggested that

preexisting modifications close to a site of methylation can alter the recognition motif

of protein methyltransferases.

2.5.3 Regulation of arginine methylation by PRMT-binding proteins

PRMTs bind not only their substrates to be methylated, but also several other

proteins and protein complexes, which can affect the methyltransferase activity of the

PRMTs under certain conditions. The related proteins BTG1 and TIS21/BTG2 bind

PRMT1 and stimulate its activity toward selected substrates [62]. The Binding of

tumor suppressor DAL-1 to PRMT3 acts as an inhibitor of enzymatic activity, both in

in vitro reactions and in cells [63]. DAL-1 also regulates PRMT5 activity by either



inhibiting or enhancing it in a substrate-specific manner [64]. CARM1 is found in a

complex of at least ten proteins called the nucleosomal methylation activator complex

(NUMAC) [65]. CARM1 within NUMAC is able to methylate nucleosomal histone

H3, whereas recombinant CARM1 preferentially methylates free histone H3. Nuclear

PRMT5 forms a complex with the hSWI/SNF chromatin remodelers BRG and BRM,

and this association enhances PRMT5 methyltransferase activity [61]. Protein

phosphatase 2A (PP2A) inhibits PRMT1 enzymatic activity and therefore not only

increases the helicase activity of NS3 (a hepatitis C virus protein, which is inhibited

by methylation) but also interferes with the cellular defense against viruses by

inhibiting interferon-induced signaling through STAT1 [66].

2.6 ADMA and cardiovascular pathophysiology

Nitrous oxide (NO), which is synthesized from L-arginine by nitric oxide synthase

(NOS), plays multiple roles in the cardiovascular system. The arginine analogs MMA

and ADMA, but not SDMA, inhibit the activity of NOS. It elevates blood pressure,

causes vasoconstriction, impairs endothelium-dependent relaxation, and increases

endothelial cell adhesiveness [1, 16, 17]. Long-term exposure to ADMA is expected

to enhance atherogenesis and produce sustained hypertensive damage to end organs

[68, 69]. NOS knockout study suggest that prolonged inhibition of NOS predisposes

to aneurysm formation [70], but it is unclear whether the same would be true for

prolonged “pharmacological” inhibition of the enzyme by high-circulating levels of

ADMA. In the heart, ADMA reduces heart rate and cardiac output, and other NOS

inhibitors have similar effects [67, 71]. Left ventricular hypertrophy is also a feature

of prolonged NOS inhibition. Renal effects of NOS inhibition include reduced sodium

excretion [72,73] and this may also contribute to the hypertension. High-salt diet may

be associated with increased pressor responses to NOS inhibitors, particularly in

individuals who are most salt sensitive [74]. ADMA also inhibits angiogenesis in

animal models [75]. Furthermore, DDAH over-expression promotes angiogenic

processes in cells in culture [76] and in experimental tumors in vivo [77,78]. DDAH

over-expression is also associated with an increase in vascular endothelial growth

factor expression, and this seems to be important in promoting the angiogenesis [76].



2.7 Protein Arginine Methyltransferases

PRMT belong to the methyltransferases family and catalyze the protein arginine

N-methylation reactions. Currently 9 proteins in humans are known to possess PRMT

activity. PRMT5, 7, 9 are type II PRMT while PRMT1, 2, 3, 4, 6, 8 belong to type I

category [79]. Type I PRMT lead to the formation of ADMA whereas types II

PRMTs produce SDMA. PRMT1 is the most prevalent PRMT isoform. PRMT8 is a

highly similar variant of PRMT1 that is myristoylated and is expressed primarily in

the vertebrate brain. PRMT3, possessing an NH2 terminal zinc finger, may function in

nucleic acid-related events, such as ribosome function. PRMT4, another histone

methyltransferases, also functions to promote transcription of steroid hormone-

sensitive promoters. PRMT2 and 6 are related to PRMT4, but their functions are far

less understood. PRMT7 is a covalent dimer of PRMT that confers sensitivity to

various types of drugs, and may play a role in carcinogenesis. PRMT5 functions also

in histone methylation but also modulates RNA polymerase II and spliceosome

functions. Finally, PRMT9 is a structurally distinct protein whose function in cells has

not yet been defined [79]. PRMT1 is one of the smallest members of this family,

encoded in only 324 amino acids from the MTSKDY motif that immediately precedes

the first known α-helix to the last amino acid. The PRMT1 gene is found on human

chromosome 19q13.3 [80]. PRMT1 in humans has at least 3 major transcript variants

(NCBI accession numbers: NM_001536, NM_198319, NM_198318, AY775289)

[81], that vary in employment of the second and third exons of the 12-exon gene. The

only difference in these isoforms with respect to the polypeptide sequence is a

potential alternate use of a translation initiation site 42, 28, or 24 amino acids

upstream of a YFDSY motif that forms most of the first helix of the 3-helix motif that

covers the AdoMet binding site [82] that appears like a lid over the AdoMet binding

pocket.

PRMT1 is well conserved in both structure and function among eukaryotic

species. Easily identifiable homolog’s of PRMT1 are found in metazoans, fungi, and

green plants. Because mammalian as well as yeast PRMT1 is the most prominent

PRMT in the cell [83, 84], a means of regulating its expression or its activity seems

necessary. PRMT1 is so abundant in mammalian cells with respect to other PRMT

that it was the first human PRMT isolated, cloned, purified, and characterized with

respect to its substrates and its pattern of methylation. It is interesting that PRMT1 has



no extra polypeptide sequence after its essential core sequence, and yet it plays a role

in so many processes. PRMT1 is known to methylate asymmetrically Arg-3 of histone

H4 in mammalian cells [86, 87]. Methylation of Arg-3 on histone H3 promotes

histone acetylation by CBP/ p300 that is a prerequisite for assembling a functional

promoter complex. In both yeast and mammals, PRMT1 or HMT1p methylated poly-

A binding protein or NAB2p and NPL3p and consequentially promoted mRNA

export and stability [88, 89, 90].

2.8 Structural analysis of PRMT1

The overall monomeric structure of PRMT1 can be divided into four parts (Figure

1): N-terminal (red), Ado-Met binding (green), barrel (yellow), and dimerization arm

(light blue). The AdoMet binding domain has the consensus fold conserved in other

AdoMet-dependent methyltransferases, whereas the barrel domain is unique to the

PRMT family [106]. The AdoMet binding domain has the consensus fold conserved

in other AdoMet-dependent methyltransferases [126, 127], whereas the barrel domain

is unique to the PRMT family [128]. Besides the N terminus, the only size

differences between PRMT1 and PRMT3 are in the barrel domain—a single-residue

deletion in the loop between strands 10 and 11 and an 8 residue insertion between

strands 14 and 15. The additional 8 residues near the C terminus result in longer

strands 14 and 15, while maintaining the exact position of the carboxyl group COO of

the C-terminal residue next to the active site.

Besides the N terminus, the only size differences between PRMT1 and PRMT3

are in the barrel domain—a single-residue deletion in the loop between strands 10 and

11 and an 8 residue insertion between strands 14 and 15. The additional 8 residues

near the C terminus result in longer strands 14 and 15, while maintaining the exact

position of the carboxyl group COO of the C-terminal residue next to the active site.

Interestingly, the position of the carboxyl terminus is also the same in the yeast RMT1

structure [107], which has an even larger insertion between strands 14 and 15. This

raises the possibility that the negatively charged C-terminal carboxyl group has an

important role for binding positively charged substrate and/or for catalysis [106].



Figure 1: Structure of PRMT1. The N-terminal helix Y is shown in red, and the AdoMet binding domain in green. The bound AdoHcy is shown in a stick model with the sulfur atom (where the transferable methyl group would be attached in AdoMet) shown in yellow. The barrel structure is shown in yellow, and the dimerization arm (which is inserted into the barrel) is in light blue (see Figure 1A). The bound arginine (blue) in the S14-AdoHcy-R3 ternary complex defines the active site, located between the AdoMet binding domain (green) and the barrel (yellow) [106].

Figure 2: 3D structure of rat PRMT1 along with co-factor and arginine residue

The cofactor product, AdoHcy, is observed in a deep pocket on the carboxyl ends

of the parallel strands 1– 5, surrounded by residues that are highly conserved in the

PRMT family. The interactions can be grouped according to the three moieties of

AdoHcy: (1) the Gly-rich loop (G78 and G80) after strand 1 makes the backbone Van



Der Waals contacts to the AdoHcy homocysteine and adenosine ribose moieties; (2)

the acidic residue at the car-boxyl end of strand 2 (E100) forms bifurcated hydrogen

bonds with the ribose hydroxyl oxygens; and (3) the acidic residue from the loop after

strand 3 (E129) hydrogen bonds with the amino group of adenine. These three

interactions are conserved among many structurally characterized consensus AdoMet-

dependent methyltransferases, and define the structural context of the

AdoMet/AdoHcy binding site [126, 127].

Figure 3: PRMT1 active site with bound Arg.

The target arginine is situated in a deep pocket between. The residues that make

up the active site are conserved across the PRMT family, and it contains two invariant

glutamates (E144 and E153; Xang and Cheng noted that the negative charges on both

E153 and E144 are critical for catalysis, while the length of the side chain (Glu

vs.Asp) is also important. When active site residues of PRMT1 onto PRMT3 (PDB

ID: 1F3J) were superimposed, the largest deviation was observed between E153 and

S154 of PRMT1 and the corresponding residues E335 and S336 of PRMT3. The

superimposition placed the target arginine in between E326 and E335 of PRMT3; the

side chain of E335 could form a bifurcated hydrogen bond with the guanidino group.

2.9 Computer-aided drug design

2.9.1 Introduction

Structure-based ligand or inhibitor design, or rational drug design, as it is

sometimes called, aims to identify chemical compounds or peptides that bind strongly

to key regions of biologically relevant molecules, e.g. enzymes or receptors, for which



three-dimensional structures are known. Designed compounds should be able to

inhibit or stimulate the biological activity of their target molecules. The rapid progress

of the human genome project is providing an ever-increasing number of potential

protein drug targets. Together with advances in structural determination techniques

such as nuclear magnetic resonance, crystallography and even homology modeling,

structure-based design of ligands or inhibitors has emerged as an important tool in

drug discovery and pharmaceutical research [91]. Computational methods are

required to extract all of the relevant information from the available structures and to

use it in an efficient and intelligent manner to design improved ligands for the target.

Due to genome sequencing projects, the number of known sequences is increasing at a

rapid rate [92]. New target identification strategies and associated bioinformatic

technologies are being developed to categorize this vast body of information [93].

Today, many scientists are working on ways to try to predict the three-dimensional

structure of a protein from its one-dimensional amino acid sequence [94, 95]. There is

also a worldwide effort in functional genomics to determine as many three-

dimensional structures of proteins as possible or to develop computational approaches

to cluster sequences into families of related proteins and then select and solve the

three-dimensional structure of a representative sequence.

Computational methods are needed to exploit the structural information to understand

specific molecular recognition events and to elucidate the function of the target

macromolecule. This information should ultimately lead to the design of small

molecule ligands for the target, which will block its normal function and thereby act

as improved drugs. Most of the drugs currently on the market have been found

through large-scale random screening of compounds for activity against a target, for

which no three-dimensional structural information was available. That is, thousands

of compounds (in the company database) are screened for activity. High-throughput

robotic screening methods [96] accelerate this process. In the end, it is hoped that at

least a small number of compounds will be active against the target. A good lead

compound is active at concentrations of 10 mM or less [97].

The first example of structure-based design was reported by the group of Beddell

and Goodford in 1976 at Wellcome Laboratories in the United Kingdom [98].

Hemoglobin was selected as a target, which at the time was the only example of

pharmacological relevance with a known crystal structure. The goal of the studies was



to develop a ligand that acts similarly to the natural allosteric effector

diphosphoglycerate. This endogenous ligand binds to hemoglobin and regulates its

oxygen affinity. Taking this molecule as a reference, the Wellcome group designed

dialdehyde derivatives and related bisulfite adducts which, as expected, modify the

oxygen affinity to hemoglobin. Several years later the antihypertensive captopril,

which inhibits the angiotensin-converting enzyme, was introduced onto the market;

this was the first drug to be developed using structural information. The past 20 years

of drug design have witnessed the structural characterization of a tremendously

number of therapeutically important targets. The increasing number of successful

applications of drug design has led to the discovery of new therapeutics. The recent

development of human immunodeficiency virus (HIV) protease inhibitors has

convincingly demonstrated the impact and the relevance of structure-based

approaches to the development of new drugs [99].

2.9.2 The drug design cycle

The process of structure-based drug design is an iterative one and often proceeds

through multiple cycles before an optimized lead goes into phase I clinical trials. The

first cycle includes the cloning, purification and structure determination of the target

protein or nucleic acid by one of three principal methods: X-ray crystallography,

NMR, or homology modeling. Using computer algorithms, compounds or fragments

of compounds from a database are positioned into a selected region of the structure.

These compounds are scored and ranked based on their steric and electrostatic

interactions with the target site and the best compounds are tested with biochemical

assays. In the second cycle, structure determination of the target in complex with a

micromolar inhibition in vitro, reveals sites on the compound that can be optimized to

increase potency. Additional cycles include synthesis of the optimized lead, structure

determination of the new target-lead complex, and further optimization of the lead

compound. After several cycles of the drug design process, the optimized compounds

usually show marked improvement in binding and, often, specificity for the target

[100].

Information available about the protein structure and the ligand binding to a

particular target guide determine the approach for drug design. A lead structure can be

discovered by serendipity. If the 3D structure of the target of interest is known from



X-ray crystallography, NMR spectroscopy, or homology modeling, structure-based

drug designing methods can be applied. If the experimental structure of the protein

with a ligand is known, the binding mode of the ligand can be analyzed. Docking of

new ligand with the same binding mode, and ranking of these ligands by scoring

functions, guide further drug development. Otherwise one has to apply de novo design

to perform a 3D search in a ligand database for a compound with a complementary

shape and surface properties to the binding site of the receptor.

2.9.4 Design of combinatorial library

The large amount of possible compounds that can be synthesized is still a small

portion of possible compounds that may exist. Biological screening of billions of

compounds are too high for experimental methods, and, therefore, computer aided

approaches have emerged as a promising tool for helping medicinal chemists to

decide what to synthesize [108]. Major goal of computer aided ligand discovery is to

identify a small subgroup from large groups of chemical compounds. HTS data as

well as virtual screening can guide and direct design of combinatorial libraries. The

number of compounds accessible by a combinatorial synthesis often exceeds the

number of compounds which can be synthesized experimentally. To reduce the

number of compounds in the library a subset of relevant fragments has to be chosen.

Sheridian and Kearsley have demonstrated the selection of a subset of amines for the

construction of tripeptioid target as a scoring function [105].

Combinatorial libraries can contain several 1000–100,000 compounds as already

demonstrated [109], and, furthermore, libraries with a size of 109 or more molecules

can be assembled. Up to this, the existing virtual chemistry space may contain

perhaps 1060 possible molecules. The filtering of large databases or libraries of

candidate compounds through the use of computational approaches based on

discrimination functions that permit the selection of series of compounds to be tested

for biological activity has been termed “virtual screening” (VS). There are problems

encountered with the generation and screening of very large virtual libraries, however,

within the next years all the necessary components for processing virtual libraries

with as many as 1015 compounds will be in place. State-of-the-art virtual screening

strategy is able to reduce the number of candidates to be examined experimentally by

at least 9 orders of magnitude, thus ending up with some 1000 of compounds to be



assayed for their biological activity. Moreover, the main goal is to develop

compounds that do not fail in later research phases. Therefore, the prediction of side-

effects and ADMET properties is crucial. Side effects, however, are often linked to

low target specificity.

2.9.5 Pre-filtering

The first step in virtual screening is the filtering the library by the application of

Lipinski's "Rule of Five"[113]. Lipinski's work was based on the results of profiling

the calculated physical property data in a set of 2245 compounds chosen from the

World Drug Index. Statistical analysis of this dataset showed that approximately 90%

of the remaining compounds have:

A molecular weight less than 500 g/mol;

A calculated lipophilicity (log P) of less than 5;

Fewer than five H-bond donors;

Fewer than 10 H-bond acceptors (sum of all nitrogen and oxygen atoms).

Sometimes the "Rule of Five" is extended by a fifth condition:

The number of rotatable bonds less than 10 or one of the four rules can be

violated.

In a more recent study it was shown that molecular weight and lipophilicity are

the properties showing the clearest influence on the successful passage of a orally

administered drug [114]. Other filters used for pre-filtering account for lead- [115] or

drug-likeness, an appropriate ADMET profile [119-121], or favorable properties

concerning receptor binding. Drug-likeness can be defined as a complex balance of

various molecular properties, which determine whether particular molecule is drug or

not. These properties influence the behavior of molecule in an organism, including

characteristics such as transport, affinity to proteins, reactivity, toxicity, metabolic

stability and many others. A large number of definitions for drug-likeness exist [116-

118]. Lead-like ligands typically exhibit suboptimal target binding affinity. Leads that

are to be taken in consideration for further drug development are characterized by the

following properties:

chemical features capable of optimization and development by combinatorial and



medicinal chemistry

good ADME properties

no reactive/toxic substructures and

favorable patent situation

Thus, lead structures are the starting point for medicinal chemistry research. The

definition of leads requires at least one marketed drug based on this particular lead

structure. In contrast to drugs, leads should be characterized by a lower molecular

weight and a lower logP.

2.9.5 Virtual screening

The term "virtual screening" or "in silico screening" is defined as the selection of

compounds by evaluating their desirability in a computational model [122]. The

desirability comprises high potency, selectivity, appropriate pharmacokinetic

properties, and favorable toxicity. Virtual screening assists the selection of

compounds for screening in-house libraries and compounds from external suppliers.

Two different strategies can be applied:

Diverse libraries can be used for lead finding by screening against several

different targets. The selected compound should cover the biological activity well.

Targeted or focused libraries are suited for both lead finding and optimization. If

knowledge of a lead compound is available, compounds with similar structure are

selected for the targeted library. Targeted libraries are focused on a single target.

With the help of computational approach a virtual library can be generated that

fulfils the criteria for generating a valuable virtual library like - large diversity; high

degree of lead-likeness; low chance of affecting targets responsible for side-effects;

favorable ADMET properties; and, last but not least, synthetic accessibility. Diversity

in this case, however, reflects scaffold diversity of compounds all matching the same

pharmacophore space. Diversity estimation is still a field of large interest, and a

considerable number of new algorithms have been developed and used for assessing

the structural diversity of libraries. Clearly, multidimensional optimization has to be

performed for obtaining valuable virtual compound libraries [123]. It is considered

that genetic algorithms combined with optimizing heuristics are the most appropriate

and flexible approach to achieve such a goal. It is possible to generate structurally



diverse compound databases containing more than 90 % of drug-like molecules as

assessed by Sadowski and Kubinyi using their neural network described in ref. [124].

Considering the fact that structural diversity alone does not represent the key to

success for a compound library, it is suggested that by mimicking certain distribution

properties of natural compounds (e.g., the prevalence of aromatic rings, the number of

complex ring systems, the degree of saturation), combinatorial libraries might be

designed that are substantially more diverse and have greater biological relevance,

which is still an issue in this area [125].

2.9.6 Docking

The application of computational methods to study the formation of

intermolecular complexes has been the subject of intensive research during the last

decade. It is widely accepted that drug activity is obtained through the molecular

binding of one molecule (the ligand) to the pocket of another, which is commonly a

protein. In the binding conformations of a complex of a protein with a therapeutic

drug, the molecules exhibit geometric and chemical complementarities, both of which

are essential for successful drug activity. The computational process of searching for a

ligand that is able to fit both geometrically and energetically to the binding site of a

protein is called molecular docking. The docking problem is analogous to an

assembly-planning problem where the parts are actuated by molecular force fields and

have thousands of degrees of freedom.

In general docking process can be divided in to two phases. One is the searching

algorithm, which finds possible binding geometries of the protein and its ligand. The

other is the scoring function, which ranks the searching results and selects out the best

binding geometry based on the energies of the of the complexes or, more theoretical

value, ∆Gbind, the binding free energy difference between the bound and unbound

states of the ligand and protein [111]. Ligand docking and screening algorithms are

now frequently used in the drug-design process, and have additional application in the

elucidation of fundamental biochemical processes. The purpose of docking algorithms

is now expanding beyond the original goal of fitting a given ligand into a specific

protein structure. Newer applications include database screening, lead generation and

de novo drug design [112].

CHAPTER 3



Experimental Design and Analysis

Software programs and database were used for molecular docking, virtual screening

of ligand database and building ligand molecules:

ACD ChemSketch: for drawing small molecules and visualizations.

OpenBabel: for file format conversions.

Chemfile Browser: for handling of sdf files.

CORINA: for 2D to 3D structure conversion.

iLib diverse: for automatic combinatorial library generation and filtering.

Molegro Virtual Docker (MVD): for visualization, docking based virtual screening,

side chain minimization.

LIGPLOT: for sketching protein ligand interactions

3.1 Generation of diverse combinatorial library based on a known bioactive inhibitor

3.1.1 iLib diverse working principle

ilib diverse is a flexible tool for creating libraries of drug-like organic molecules

suitable for rational lead structure discovery in a fast and efficient way. ilib diverse

was built upon CombiGen, which has been designed for obtaining compound libraries

with an optimal molecular diversity [133].

3.1.2 Template structure

Recently small molecules that specifically inhibit PRMT activity were reported by

Cheng et al [134]. He identified a primary compound, AMI-1, that specifically

inhibits arginine, but not lysine, methyltransferase activity in vitro and does not

compete for the AdoMet binding site. AMI-1 inhibits arginine methylation by

inserting into the arginine-binding pocket, which can explain its high degree of PRMT

specificity. AMI-1, which is a symmetrical sulfonated urea, was identified as the lead.



It contains urea group, hydrophobic naphthelene ring system and the sulfonic acid.

These fragments were drawn in ACD/ChemSketch and saved into mol file format.

AMI-1 was drawn using ACD/ChemSketch and saved as a mol file. Then it was

converted to sdf file using OpenBabel. Further 3D structure of AMI-1 was generated

using CORINA.

3.1.3 Fragment set selection

The above fragments representing the characteristics of AMI’s were imported into

the ilib diverse fragment set and the weights set to 100 (% probability). Other

fragments of all groups were assigned at default weights. Random library generation

mode was selected as it provides highest diversity for the generated libraries.

3.1.4 Filter settings

Lipinski’s “Rule of 5” filter was selected with following constraints:

Molecular weight MAX: 500

LogP MAX: 5

Hydrogen bond donor MAX: 5

Hydrogen bond acceptor MAX: 10

3.1.5 Steps involved in random library generation

Step 1 The number of fragments used to define the size of the generated molecule

was set to 3.

Step 2 Default groups and their fragments were selected for drug-like criteria.

Step 3 Default weights of drug-like set were selected for the fragment set.

Step 4 Default reactivity setting of drug-like set was selected.

Step 5 Lipinski’s “Rule of 5” was selected from the filter set to adjust the properties

of generated molecules.

Step 6 The stereo chemistry setting was set to “assign mixed”

Step 7 The number of molecules generated was set to 80000 and the out \put format

was set to MDL SD file.



Step 8 Results were stored in the specified output directory. The generated library

contained drug-like compounds that almost share the pharmacophoric features

of the known AMI molecules.

3.2 Conversion of 2D to 3D

CORINA is a rule and a data based program system that automatically generates

three-dimensional atomic co-ordinates of a molecule as expressed by a connection

table or linear code, it can convert large databases of several thousand or even

millions of compounds.

The generated library in SDFile format to be converted into 3D for screening was

taken as input. Implicit hydrogen atoms were added, small fragments (e.g. counter

ions in salts) were removed and all molecules neutralized. Structures that couldn't be

converted were excluded from the 3D output file but written to a seperate error file.

The output file was also formatted in MDL SDFile format.

Command line:

Corina –d wh, rs, neu, r2d, errorfile = errors.sdf input_file.sdf output_file.sdf

-d: CORINA driver options

wh: CORINA adds missing or implicitly given hydrogen atoms before the

generation of 3D coordinates in order to obtain structures. This option writes the

added hydrogen atoms to the output file.

rs: Removes small fragments. Remove all but the largest fragments from multi-

component records (e.g., counter-ions in salts, solvent molecules).

neu: Neutralizes formal charges at [C,S,P]-[O-] and [NH+]. This option can be used to

achieve the same protonation state for acids, alcoholates and basic nitrogen atoms by

adding or removing protons. This option can be used together with the driver option rs

(see above) in order to remove counter-ions from salts.

r2d: Removes 2D records from the output. If the input and the output file type are

both set to MDL SDFile (default), CORINA by default writes the original 2D

structure to the output file in cases where no 3D structure is or could be generated.



This option is useful for database purposes in order to obtain consistent input and

output files. This sub option prevents the output of 2D structures.

Further output file containing crude 3D structures with missing stereo information

was corrected. The stereo information was derived from the crude input geometries

and the generated 3D structures were oriented by their moments of inertia.

Command line:

corina -d 3dst,ori input_file.sdf output_file.sdf 3dst: Forces stereo descriptors from the 3D structure. If this option is switched on and

there is a discrepancy between the stereo descriptors and the 3D structure in the input

file, CORINA takes the configuration derived from the 3D coordinates.

ori: Orients the 3D structure according to the moments of inertia. This option is

useful when the structure is directly forwarded to a graphical viewer. The molecule

then appears more often in an orientation that shows as much of it as possible on one

sight.

3.3 Molegro Working principle

Molegro Virtual Docker (MVD) is an integrated environment for studying and

predicting how ligands interact with macromolecules. The identification of ligand

binding modes is done by iteratively evaluating a number of candidate solutions

(ligand conformations) and estimating the energy of their interactions with the

macromolecule. The highest scoring solutions are returned for further analysis. MVD

requires a three-dimensional structure of both protein and ligand (usually derived

from X-ray/NMR experiments or homology modeling). MVD performs flexible

ligand docking, so the optimal geometry of the ligand will be determined during the

docking.

3.3.1 Scoring function

The MolDock scoring function (MolDock Score) used by MVD is derived from

the PLP scoring functions originally proposed by Gehlhaar et al [130] and later

extended by Yang et al [131]. The MolDock scoring function further improves these



scoring functions with a new hydrogen bonding term and new charge schemes. The

docking scoring function, Escore, is defined by the following energy terms:

int intscore er raE E E= +

Where Einter is the ligand-protein interaction energy:

int 2( ) 332.04

i jer PLP ij

i ligand j protein ij

q qE E r

r∈ ∈

⎡ ⎤= +⎢ ⎥

⎢ ⎥⎣ ⎦∑ ∑

The summation runs over all heavy atoms in the ligand and all heavy atoms in the

protein including any cofactor atoms and water molecule atoms that might be present.

The EPLP term is a piecewise linear potential. The second term describes the

electrostatic interactions between charged atoms. It is a Coulomb potential with a

distance-dependent dielectric constant given by: D(r) = 4r. The numerical value of

332.0 fixes the units of the electrostatic energy to kilocalories per mole. To ensure that

no energy contribution can be higher than the clash penalty the electrostatic energy is

set to a cut-off level corresponding to a distance of 2.0 Å for distances less than 2.0 Å.

Although the electrostatic energy contribution has the theoretically predicted

magnitude, the other energy terms are empirically motivated and the total energy does

not necessarily correlate with the true binding affinity. EPLP is a “piecewise linear

potential” using two different sets of parameters:

One set for approximating the steric (Van Der Waals) term between atoms, and

another stronger potential for hydrogen bonds. The linear potential is defined by the

following functional form:

EPLP(0) = A0, EPLP(R1) = 0, EPLP(R2) = EPLP(R3) = A1, EPLP(r) = 0 for r ≥ R4 and

is linearly interpolated between these values. The parameters used here (see table

be low) were adopted from GEMDOCK [131].

Table1: The parameters for approximating the steric term adopted from GEMDOCK.



Search algorithm

Guided Differential Evolution

The guided differential evolution algorithm (MolDock Optimizer) used in MVD is

based on an evolutionary algorithm (EA) variant called differential evolution (DE).

The DE algorithm was introduced by Storn and Price in 1995 [132]. Compared to

more widely known EA-based techniques (e.g. genetic algorithms, evolutionary

programming, and evolution strategies), DE uses a different approach to select and

modify candidate solutions (individuals). The main innovative idea in DE is to create

offspring from a weighted difference of parent solutions. The DE works as follows:

First, all individuals are initialized and evaluated according to the MolDock Score

(fitness function). Afterwards, the following process will be executed as long as the

termination condition is not fulfilled: For each individual in the population, an

offspring is created by adding a weighted difference of the parent solutions, which are

randomly selected from the population. Afterwards, the offspring replaces the parent,

if and only if it is more fit. Otherwise, the parent survives and is passed on to the next

generation (iteration of the algorithm).

DE works well because the variation operator exploits the population diversity in

the following manner: Initially, when the candidate solutions in the population are

randomly generated the diversity is large. Thus, when offspring are created the

differences between parental solutions are big, resulting in large step sizes being used.

As the algorithm converges to better solutions, the population diversity is lowered,

and the step sizes used to create offspring are lowered correspondingly. Therefore, by

using the differences between other individuals in the population, DE automatically

adapts the step sizes used to create offspring as the search process converges toward

good solutions.

3.3.2 Pose clustering

The multiple poses returned from a docking run are identified using the following

procedure:

During the docking run, new candidate solutions (poses) scoring better than

parental solutions are added to a temporary pool of docking solutions. If the number

of poses in the pool is higher than 300, a clustering algorithm is used to cluster all the

solutions in the pool. The clustering is performed online during the docking search



and when the docking run terminates. Because of the limit of 300 poses, the clustering

process is fast. The members of the pool are replaced by the new cluster

representatives found (limited by the Max number of poses returned option).

The clustering procedure works as follows:

1. The pool of solutions is sorted according to energy scores (starting with the best-

scoring pose).

2. The first member of the sorted pool of solutions is added to the first initial cluster

and the member is assigned to be the cluster representative.

3. The remainder of the pool members are added to the most similar cluster available

(using the common RMSD measure) if and only if the RMSD between the

representative of the most similar cluster and the member is below a user-

specified RMSD threshold. Otherwise, a new cluster is created and the member is

assigned to be the cluster representative.

4. The clustering procedure is terminated when the total number of clusters created

exceeds maximum number of poses returned (user-defined parameter) or when all

members of the pool have been assigned to a cluster.

5. When the cluster procedure has terminated, the set of representatives (one from

each cluster) is returned.

3.4 Structure preparation

Crystal structure of rat PRMT1 along with the co-factor was downloaded from

RCSB Protein Data Bank (PDB ID: 1ORI). The structure was deposited in March

2003 with the resolution of 2.50 A and R-factor of 0.199. Ramachandran plot analysis

show about 87% residues falling in the most favorable region, 12.4% residues falling

in the allowed region while 0.3% in the disallowed region. Although some structures

contain information about bond types and bond orders, and have explicit hydrogens

assigned, sometimes PDB files often have poor or missing assignment of explicit

hydrogens, and the PDB file format cannot accommodate bond order information. In

order to make accurate predictions it is important that structure is prepared properly

before docking. The structure in PDB format was prepared using Molegro Virtual

Docker (MVD). Using MVD, the water molecules were removed and co-factor was

incorporated into the protein structure was automatically prepared by using following

options:



Assigning bonds

This option allows to determine which atoms are connected (covalently bound). Two

atoms are connected if their distance is more than 0.4Å and less than the sum of their

covalent radii plus a threshold of 0.45Å (the threshold is set to 0.485Å if one of the

atoms is Phosphorus).

Assigning bond order and hybridization

This option allows recognition of bond orders (whether bonds are single, double or

triple ...), the number of hydrogens attached to the atoms, and their hybridization (SP,

SP2, and SP3). Also aromatic rings will be detected. The algorithm only assigns the

number of implicit hydrogens to each atom. No actual atoms will be added.

Adding explicit hydrogens

Adds hydrogens matching the predicted number of hydrogens in the step above. The

hydrogens are placed according to geometric criteria (i.e. SP3 hybridized atoms are

kept at 109 degrees geometry). The hydrogens are placed at standard distances

according to the atom they are connected to. No energy minimization is performed.

Assigning charges

The charges are set according to the scheme listed in table below. Metal ions are

assigned a charge of +1 (e.g. Na) or +2 (e.g. Zn, Ca, Fe).

Charge Ligand atoms Protein atoms

0.5 N atoms in –C(NH2)2 His (ND1/NE2) Arg (NH1/NH2)

1.0 N atoms in –N(CH3)2, – (NH3) Lys (N) -0.5 O atoms in –COO, –SO4, Asp (OD1/OD2)

Glu (OE1/OE2) -0.66 O atoms in –PO3 -0.33 O atoms in –SO3 -1.0 N atoms in –SO2NH

Table 2: Scheme for assigning charges



Detecting Flexible Torsions In Ligands

This option determines which bonds that should be considered flexible during

docking.

Hydrogen Bonding Type

Atom hydrogen bonding types (acceptor, donor, both or non-polar) were set during

preparation.

3.5 Cavity Prediction

Cavity was predicted by MVD at the active site by using following settings:

Minimum cavity volume: 10 Å

Probe size: 1.20 Å

Max number of ray checks: 16

Min number of ray hits: 12

Grid resolution 0.80 Å

The predicted cavity was positioned at X (10.5199), Y (31.418), and Z (29.9533) with

volume of 209.92 Å3 and surface of 647.68 Å2.

3.6 Defining binding region

Since the active site of the protein is known the binding region was defined

manually. Centre of the grid was fixed at X (9.81), Y (42.43), and Z (28.12) co-

ordinates with a resolution of 0.30 Å and radius of 10 Å. The binding region

contained residues - D51, R54, E144, Y148, Y152, E153, S154, M155, and H293.

3.7 Adding ligand atom constraints

Constraints are limitations imposed on the molecular system based on chemical

insight or knowledge. Constraints can dramatically increase docking accuracy and

speed, as they often limit the search space considerably. Ligand Atom Constraints are

always soft constraints. It is possible to choose whether the chosen atoms in the ligand

should be rewarded or penalized for contacts with the target molecules (proteins,



cofactors and water). The criteria for contact used here are purely based on the

distance between the chosen ligand atoms and the closest atom in any target molecule.

Constraints are useful if something about the system is known in advance. If

perhaps a hydrogen bond from a hydrogen donor was known to be present – a

distance constraint could be set up at the position of the protein hydrogen donor, and a

hard constraint could force hydrogen acceptors in the ligand to satisfy the hydrogen

bond.

Ligand constraint setup

Ligand constraint is bound to ligand 0: ARG 700

List of constrained atoms: 2, 0,1,3,4

Soft constraint

Penalize chosen atoms for making contacts. Energy penalty = 500

Atom contact threshold: 4A

3.8 Setting up side-chain flexibility

It is possible to work with sidechain conformational changes in two ways: By

softening the potentials (the steric, hydrogen bonding, and electrostatic forces) used

during the docking simulation. This is done in order to simulate flexibility in the

binding pocket ('induced fit'). By defining which residues should be considered

flexible during the docking simulation. The backbone is kept rigid, but the torsional

angles in the sidechains are allowed to change. When sidechain flexibility was setup,

the following steps are applied during the docking simulation:

The ligands was docked with the softened potentials. At this point the receptor is

kept rigid at its default conformation.

After each ligand was docked, the sidechains chosen for minimization will be

minimized with respect to the found pose. This repositioning of the sidechains

will be performed using the standard non- softened potentials.

Side-chain flexibility setup

26 residues closest to active ligand were added. (This will choose all sidechain

which are close enough to the active ligand to interact with it. More precisely: for



each given sidechain, a sphere bounding all possible configurations of the

sidechain is calculated, and it is tested whether any atom in the active ligand is

close enough to make a steric contact with an atom in this bounding sphere. For

the 'MolDock' potential, all steric contacts are cut off at a distance of 6.0Å).

Adjust potentials for side chains

Tolerance: 0.90

(The Tolerance of a potential refers to the size of the region between a ligand

atom and a receptor atom where the interaction energy is optimal. For nonpolar

steric interactions (such as two carbon atoms) the interaction is optimal between

3.6 Å and 4.5 Å.)

Strength: 1.0

(The Strength factor is multiplied onto all interaction energies for the sidechain).

3.9 Setting up template docking

Information about the 3D conformation of the AMI-1 in complex with 1ORI

obtained by its docking was used to create a template with features expected to be

relevant to docking. Templates are used as scoring functions rewarding poses similar

to the extracted pattern.

A template is a collection of groups, where each group represents a chemical

feature for an atom (e.g. 'hydrogen acceptors atoms' form a template group). Each

template group contains a number of centers: optimal 3D positions for the group

feature. If an atom matches a group definition (e.g. is a hydrogen acceptor), it will be

rewarded depending on its distance to the group centers by using the following

(Gaussian) formula for each center:

e = ω*exp (-d2/r02)

Where d is the distance from the position of the atom to the center in the group. ω

is a weight (importance) factor for the template group, and r0 is a distance parameter,

specifying a characteristic distance for the template group (when d is equal to this

characteristic distance, the interaction is at e-1 ~ 36% of its maximum value). ω and r0

can be customized for each template group.



The following strategy applies when evaluating ligands during docking: For each

atom in the ligand, score contributions from all centers in all matching groups are

taken into account, i.e. a single atom may contribute to several centers in several

groups - an atom is not restricted to the closest matching center or a single group. The

template score is normalized: the resulting score found using the procedure above is

divided by the score of a perfectly fitting ligand.

Setup for template docking

AMI-1 was selected from the workspace as the reference ligand to create docking

template. Following groups were selected for the similarity score:

Steric. The steric group matches all atoms. It is used for shape matching without taking any chemical groups into account.

Hydrogen Donor. Matches any hydrogen donor atom.

Hydrogen Acceptor. Matches any hydrogen acceptor atom.

Negative Charge. Matches negatively charged atoms. Notice that the magnitude of the partial atom charge does not matter, only the sign.

Ring. Matches all atoms which are part of rings (both aromatic and aliphatic).

Green: Hydrogen acceptor Yelllow: Ring Purple: Hydrogen donor

Red: Negative charge Gray: Steric

Figure 5: Features extracted from AMI-1 taken as template for similarity scoring of the ligands

In the docking wizard the overall strength determines the normalization of the

similarity score. A ligand perfectly matching the template gets an energy contribution

corresponding to the specified strength (e.g. per default a perfectly matching ligand

gets an energy contribution of -500).

3.10 Running docking simulation



Step 1: Choosing ligands to dock. Select the ligands imported in the workspace for

docking. Diverse combinatorial library of 80,000 molecules generated by iLib:diverse

was first split into sdf files of 10, 000 molecules each and imported into the

workspace containing 1ORI with the cofactor and substrate residue one at a time. All

the molecules were prepared for docking and then selected.

Step 2: Side chain flexibility setup. Following options were selected:

Soften potentials during the docking

Minimize receptor for each found pose (The receptor minimization is performed

using the Nelder- Mead simplex algorithm):

Max minimization steps per residue: 2000

Max global minimization steps: 2000

Step 3: Template docking. The template scoring is enabled with following

parameters:

Overall strength: -500

Energy grid resolution: 0.40

Step 4: Choosing scoring function and define binding site. Following parameters were

selected:

Scoring function: MolDockScore[GRID]

Grid resolution: .30A

Binding site: User-defined

Centre: X[9.81]; Y[40.18]; Z[28.12]

Radius: 8A

Step 5: Customizing search algorithm. Following parameter settings were done –

Algorithm: MolDock Optimizer

Number of runs: 5

Constrain poses to cavity: yes

Population size: 50

Max iterations: 2000

Scaling factor: 0.50

Crossover rate: 0.90



Offspring scheme: Scheme 1

Termination scheme: Variance based

Step 6: Pose clustering setup. Tabu clustering was selected for returning one pose for

each run. Following parameters were used:

RMSD threshold: 2.00

RMSD calculation: By atom id (fast)

Energy penalty: 100.0

3.11 Analyzing docking results

3.11.1 Importing docking results

The DockingResults.mvdresults file is located in the output directory together

with a docking log file and the poses found (in mol2 file format). After importing the

DockingResults.mvdresults file, the poses found appeared in the Pose Organizer.

These poses were added to the workspace containing the protein structure along with

the co-factor, ligands and the constraints. Newly found poses closest to active ligand

were inspected, edited and saved with the help of pose organizer window of MVD. By

enabling the Dynamic update option, the individual poses were inspected one at a

time (single pose view mode).

3.11.2 Detailed energy analysis

The pose organizer was used to rerank the ligands (using a rank score), and view

their detailed energy contributions split up into different categories (shown in the

table below). It also allowed for recalculation of scoring functions including MolDock

Score, Binding Affinity score, and re-ranking score. These scoring function values are

already calculated if the poses are imported from an mvdresults file. The ranking

score function is computationally more expensive than the scoring function used

during the docking simulation but it is generally better than the docking score

function at determining the best pose among several poses originating from the same

ligand.

The binding affinity is believed to be the best choice when trying to identify the

best binder to a given target between a set of different ligands. The binding affinity of



a given pose is given by: Ebinding = -5.68*pKi and is measured in kJ/mol (the

numerical factor corresponds to a temperature of 297K). The pKi is predicted using a

combination of energy terms and molecular descriptors and then converted to a

binding affinity estimate using the equation above. The coefficients for the binding

affinity terms were derived using multiple linear regressions. The pKi estimator has

been calibrated using a data set of more than 200 complexes with known binding

affinities. The correlation coefficient was 0.60 when doing 10-fold cross validation.

Column name Description

Name Pose name

Ligand The name of the ligand this pose was created from

Filename The name this file was stored as (if any)

MolDockScore The energy score used during docking (arbitrary units)

Affinity The estimated binding affinity (kJ/mol)

Rerank Score The reranking score (arbitrary units)

RMSD The RMS deviation from a reference ligand (if available)

Interaction The total interaction energy between the pose and the target molecule(s)

Cofactor The interaction energy between the pose and the cofactors

Protein The interaction energy between the pose and the protein

Water The interaction energy between the pose and the water

Molecules

Internal The internal energy of the pose

Solvation The energy calculated from the implicit solvation model

Protein VdW Protein steric interaction energy from an alternative 12-6

VdW potential

Torsions The number of (chosen) rotatable bonds in the pose

Soft Constraints The energy contributions from soft constraints

Electro The electrostatic energy in the model

HBond Hydrogen bonding energy

Heavy Atoms Number of ligand heavy atoms

LE1 Ligand Efficiency 1: MolScore divided by Heavy Atoms count



LE2 Ligand Efficiency 2: Binding Affinity divided by Heavy Atoms count

LE3 Ligand Efficiency 3: Rerank Score divided by Heavy Atoms count

Table 3: Energy split-up terms

3.11.3 Visualizing the results

The poses and their interactions with the protein were visualized and studied with

the help of MVD visualization window. Each pose was individually visualized by

unchecking every other pose in the workspace. The graphical settings for the 3D

visualization were adjusted by selecting various rendering options for styles (ball and

stick, scale, stick, spacefill, wireframe) and color (fixed, element, chain, carbons only,

hydrogen bond type, partial charges). Extended Van Der Waals surface was added to

the protein structure with electrostatic type coloring. Ball & stick rendering was added

to the co-factor while ligand was kept in stick style for better vision and study of close

interactions. The views were changed according to the purpose – docking view,

preparation view, hydrophobicity, electrostatic interactions, and secondary structure

view

3.11.4 Sidechain minimization of the complex structure

Protein was minimized with respect to itself and other structures in the workspace.

The minimization was performed using a fairly simple forcefield (it uses the PLP-

potentials for steric and hydrogen bonding interactions, and the Coulomb potential for

the electrostatic forces.Only torsion angles in the sidechains were modified during the

minimization – all other properties (including bond lengths and backbone atom

positions) were held fixed.

Setup for sidechain minimization

Add Closest to Active Ligand:

All the sidechains which are close enough to the active ligand to interact with it.

More precisely: for each given sidechain, a sphere bounding all possible

configurations of the sidechain was calculated, and it was tested whether any atom

in the active ligand is close enough to make a steric contact with an atom in this

bounding sphere (for the 'MolDock' potential, all steric contacts are cut off at a

distance of 6.0 Å).



Minimization settings:

Maximum steps per residue: 1000

Maximum global steps: 1000

3.11.5 Saving results

After importing and preparing molecules, all information was saved in a MVD

Workspace (MVDML) file, which contains all relevant information (position of

atoms, charges, hybridization, bond orders, ligand flexibility ...). The top ranking 10

poses were selected in the pose organizer and their detailed energy analysis was

stored into csv datasheet. The selected poses were also exported into sdf file format.

Poses imported to the workspace were converted into ligand.

3.11.6 Exporting molecules

Finally, the protein, co-factor and the new found ligands were exported from the

workspace for each ligand into a single pdb file.



CHAPTER 4

Results and Discussion

4.1 Results

Virtual screening was carried out for finding novel inhibitors of PRMT1 with 80

000 drug-like molecules from generated diverse library. Top ten molecules are

presented here along with their energy score in terms of both Van Der Waals and

electrostatic components.

4.1.1 Docking results of AMI-1 with 1ORI

Figure 6: Complex structure of AMI-1 with rat PRMT1 (PDB code 1ORI) and 2D structure of AMI-1

IC50 (µM) for PRMT1 [18]: 8.81

Molecular Weight: 548.45

Calculated LogP by ACD/LogP: -0.95+/- 1.23

Energy Score on Docking: -76.8802 KJ/mol

4.1.2 Top 10 ligands found by docking

The top ten ligands found on the basis of energy score after docking the generated

combinatorial library are given below:



Rank 1: Molecule_411. 2-[6-(naphthalen-2-ylcarbamoylamino)-1,4-dihydronaphthalen-1-yl]-5-oxo-pyrazole-1-sulfonate

Figure 7 a

MolDockScore -130.818

Affinity -24.9064

Rerank Score -73.8632

Estimated LogP 4.91841

MW 477.512

TPSA 122.43

Torsions 6

HBa 8

HBd 3

Rank 2: Molecule_1092. 2-[[5-(carbamoyl-sulfonato-amino)-5,8-dihydronaphthalen-2- yl]carbamoylamino]naphthalene

Figure 7 b


Affinity -26.9504



MW 451.475

TPSA 141.87

Torsions 7

HBa 8

HBd 5



Rank 3: Molecule_9823. 2-[[7-[3-(sulfonatoamino)propylamino]-5,8-dihydronaphthalen-2-yl]carbamoylamino]naphthalene

Figure 7 c


Affinity -27.8622



MW 451.475

TPSA 119.56

Torsions 9

HBa 6

HBd 5

Rank 4: Molecule_5015. 4-[5,8-dihydronaphthalen-2-yl-(naphthalen-2-yl-sulfinatooxy-carbamoyl)amino]-2,5-dioxo-imidazolidine

Figure 7 d


Affinity -28.833



MW 493.512

TPSA 125.12

Torsions 7

HBa 12

HBd 2



Rank 5: Molecule_1108. 8-(carbamoylamino)-5-(phenoxycarbonylmethyl)naphthalene-2-sulfonate

Figure 7 e


Affinity -33.5155



MW 399.397

TPSA 135.83

Torsions 7

HBa 10

HBd 4

Rank 6: Molecule_30. 2-[(5,8-dihydronaphthalen-2-yl-sulfonato-carbamoyl)-naphthalen-2-yl-amino]acetate

Figure 7 f


Affinity -25.9365



MW 450.464

TPSA 115.22

Torsions 6

HBa 8

HBd 2



Rank 7: Molecule_6112. 2-(5,8-dihydronaphthalen-2-ylcarbamoylamino)-6-[(2-hydroxyacetyl)-sulfonato-amino]naphthalene

Figure 7 g

MolDockScore -92.83

Affinity -23.2169



MW 466.486

TPSA 136.04

Torsions 7

HBa 8

HBd 4

Rank 8: Molecule_12041. 8-tetrahydrofuran-3-yl-6-ureido-naphthalene-2-sulfonate

Figure 7 h


Affinity -37.864



MW 335.355

TPSA 118.76

Torsions 4

HBa 8

HBd 4



Rank 9: Molecule_8040. 2-[(2-carbamoylvinyl-(5, 8-dihydronaphthalen-2-yl) carbamoyl)-sulfonate-amino] naphthalene

Figure 7 i


Affinity -24.2512



MW 463.506

TPSA 121.05

Torsions 8

HBa 8

HBd 3

Rank 10: Molecule_102. 2-[2-carbamoylvinyl-(5,8-dihydronaphthalen-2-yl-sulfinatooxy-carbamoyl)amino]naphthalene

Figure 7 j


Affinity -31.107



MW 464.514

TPSA 118.76

Torsions 8

HBa 10

HBd 2



4.1.3 Comparison of energy score of AMI-1complex with drug like top 10 ligands after Docking

Name E-Inter* E-Intra* Energy score (KJ/mol)

AMI-1 -82.7695 5.88926 -76.8802 1 Molecule_411 -118.376 -12.4418 -130.818

2 Molecule_1092 -119.178 0.974894 -118.203

3 Molecule_9823 -122.425 12.7693 -109.656

4 Molecule_5015 -121.908 17.3808 -104.527

5 Molecule_1108 -125.826 23.7592 -102.067

6 Molecule_30 -102.377 6.29896 -96.0778

7 Molecule_6112 -96.0917 3.26173 -92.83

8 Molecule_12041 -98.3245 7.49798 -90.8265

9 Molecule_8040 -105.824 13.0615 -92.7625

10 Molecule_102 -105.169 15.9807 -89.1882

*split-up of E-Intra and E-Inter is given in the appendix.

Table 4: Energy score of top 10 ligand conformations obtained by docking.

Figure 8: Bar graph showing energy comparison of top 10 ligands on docking



4.1.4 Protein-Ligand Interactions

Following are the diagrams featuring the key protein-ligand

interactions for the top 3 ligands generated using LIGPLOT [137].

Figure 9 a: PRMT1 - Molecule_411 Interaction

Figure 9 b: PRMT1 - Molecule_1092 Interactions



Figure 9 c: PRMT1 - Molecule_9823 Interactions.

Residues taking part in hydrophobic interactions are indicated by an arc with radiating

spokes and dotted line toward the ligand atom they contact; those participating in the

hydrogen bonding are shown in ball-and-stick representations. Hydrogen bonds are

illustrated as dotted lines with the donor-acceptor distance given in Å.

4.2 Discussion

The X-ray crystal structure of rat PRMT1 along with co-factor was obtained from

Protein Data Bank (PDB Code: 1ORI) [106]. Recently arginine methyltransferases

specific inhibitors were identified using HTS by Cheng et al [134]. But AMI's

displays low binding affinity and no specificity for individual PRMT's. Hence we

have selected this protein structure for virtual screening to identify PRMT-specific

inhibitors that may have therapeutic potential against hypertension and other

cardiovascular disorders involving raised ADMA levels.

In this study we used a structure based approach to identify a novel inhibitor of

PRMT1. We first built a diverse combinatorial library incorporating the features of

known inhibitor, AMI-1. Naphthalene ring, urea and sulfonate moieties present in



AMI-1 were incorporated in the newly generated compounds. The library was pruned

using Lipinski's ("Rule of 5") drug-like filter. In order to address the accuracy of the

virtual screening we docked AMI-1, a PRMT specific inhibitor, into the binding

pocket. A rapid docking procedure with flexible ligand and a grid representation of

the active site was followed by extensive sidechain minimization with both ligand and

active site sidechains flexible. The ligand superimposed well with natural arginine

fragment of the substrate at the active site

The pharmacophore features of AMI-1 in complex with PRMT1 that were

relevant for binding were used as template and the search was focused on the ligands

matching the template features. Further ligand constraints were imposed on the

docking process such that ligand atoms making contact with target protein and the co-

factor were penalized.

There are now a number of drugs whose development was heavily influenced by

or based on structure-based design and screening strategies, such as HIV protease

inhibitors [135]. Similarly, we have carried out virtual screening for finding novel

ligands of PRMT1 with about 80000 ligands of drug-like molecules obtained by

randomly generating a diverse combinatorial library based on the pharmacophore

features of the known inhibitor – AMI-1. Each compound was automatically docked

into the defined grid representation as previously described, and assigned a score

according to the quality of fit. After careful visual examination 10 top ranking

candidates displaying the lowest predicted binding energy were selected and subjected

to more refined energy minimization of the active site residues (sidechains), and the

energy of the complex was predicted as previously described.

More negative the energy score (kcal/mol) more is the binding affinity. After

docking of AMI-1 ligand with PRMT1, the energy score was found to be -76.8802

kcal/mol. Top ten ligands which were obtained from virtual screening using Molegro

program had energy scores ranging from -89.1882 to -130.818 KJ/mol [see section

4.1.3]. Energy scores for the top ranked ligand Molecule_414 was -130.818 KJ/mol

followed by -118.203 and -109.656 KJ/mol for Molecule_9823 and Molecule_5015

respectively. Thus it is clearly evident that above top ten ligands has much higher

binding affinity than the AMI-1. This data signifies that after further optimization

process these probable leads can generate a potent therapeutic inhibitor for the



PRMT1 enzyme. After visualizing PRMT1 in complex with top ten ligands, it was

found that above ligands were best fitted in the cavity of receptor

The aromatic part contributed by naphthalene ring system in these ligands

facilitates hydrophobic interactions in the predominantly hydrophobic binding pocket

of the protein apart from electrostatic interactions. These hydrophobic interactions

provide major contribution to the binding free energy [See Section 4.1.4]. Although

strong nonpolar contributions occur in the binding pocket that have nothing to do with

the active site. Also it was observed that the net contribution of polar atoms to the free

energy was relatively small. Although the crystal seems to define a large binding site,

the simulation indicates a highly localized high affinity site that binds hydrophobic.

These results support the fact that entropy is an important contributing factor to the

binding free energy due to increased entropy of water released from the hydrophobic

surface [136].



CHAPTER 5

Conclusion and Future Work

This project was aimed at finding novel lead molecules for the inhibition of

PRMT1, an important enzyme playing crucial role in arginine methylation various

physiological responses. The X-ray crystal structure (Resolution 1.54 Å) of PPRMT1

(PDB Code: 1ORI) was considered as target receptor with known active site.

This protein was used for virtual screening for finding novel ligands having better

energy scores than known inhibitor AMI-1 using the program Molegro Virtual

Docker and diverse combinatorial library generated by ilib diverse. We have found

new ligands having better energy scores compared to AMI-1.

These novel lead compounds may act as a potent and specific inhibitor for

PRMT1 enzyme; though their efficacy, toxicity and pharmacokinetic properties need

to be studied experimentally. Molecular dynamics study of these putative PRMT1

inhibitor complexes reported herein, would also throw some more light on the

thermodynamic stability of the ligand receptor complexes. Experimental elucidation

of the PRMT1 inhibitor complex structure would answer the question of specificity of

these inhibitors in the body.



CHAPTER 6

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CHAPTER 6

Appendix

A. Detailed Energy Split of AMI-1 and the top ten ligand conformations after docking.

AMI-1 E-Inter total -82.7695

E-Inter (protein - ligand) -71.7271 Steric [544] -75.1319 VdW (LJ12-6) 144.564 HBond [5] -4.67756 NoHBond90 -5.57269 Electro [6] 6.03932 ElectroLong [924] 2.04303

E-Inter (cofactor - ligand) -11.0423 Cofactor (VdW) 21.2667 Cofactor (hbond) 0 Cofactor (elec) 0.905968

E-Inter (water - ligand) 0 E-Intra (tors, ligand atoms) 5.88926

E-Intra (tors) 10.7934 E-Intra (sp2-sp2) 0 E-Intra (hbond) 0 E-Intra (steric) -4.90419 E-Intra (vdw) 97.1343 E-Intra (elec) 10.3913

E-Soft Constraint Penalty 0

Molecule_411 E-Total -130.818 E-Inter total -118.376

E-Inter (protein - ligand) -101.584 Steric [551] -99.1074 VdW (LJ12-6) -38.6367 HBond [1] -2.5 NoHBond90 -2.5 Electro [0] 0 ElectroLong [465] 0.0233153

E-Inter (cofactor - ligand) -16.792 Cofactor (VdW) -6.03525 Cofactor (hbond) 0 Cofactor (elec) 0.623553

E-Inter (water - ligand) 0

E-Intra (tors, ligand atoms) -12.4418 E-Intra (tors) 0.354347 E-Intra (sp2-sp2) 19.1164 E-Intra (hbond) 0



E-Intra (steric) -12.7961 E-Intra (vdw) 104.026 E-Intra (elec) 4.69995



E-Inter (protein - ligand) -107.618 Steric [570] -103.913 VdW (LJ12-6) -35.9812 HBond [2] -4.10463 NoHBond90 -4.60667 Electro [0] 0

ElectroLong [465] 0.399304 E-Inter (cofactor - ligand) -11.56

Cofactor (VdW) 4.49259 Cofactor (hbond) 0 Cofactor (elec) 0.59118


E-Intra (tors) 0.955826 E-Intra (sp2-sp2) 26.3407 E-Intra (hbond) 0 E-Intra (steric) 0.0190679 E-Intra (vdw) 109.399 E-Intra (elec) 4.69975



E-Inter (protein - ligand) -108.614 Steric [561] -108.046 VdW (LJ12-6) -12.5424 HBond [3] -6.28214 NoHBond90 -7.5 Electro [3] 2.87404 ElectroLong [462] 2.83971




E-Soft Constraint Penalty 0 Molecule_5015 E-Total -104.527 E-Inter total -121.908

E-Inter (protein - ligand) -109.805 Steric [569] -100.997 VdW (LJ12-6) 60.6393



HBond [5] -8.80726 NoHBond90 -11.9395 Electro [0] 0 ElectroLong [0] 0

E-Inter (cofactor - ligand) -12.1032 Cofactor (VdW) -0.889695 Cofactor (hbond) 0 Cofactor (elec) 0


E-Intra (tors) 5.78921 E-Intra (sp2-sp2) 14.9374 E-Intra (hbond) 0 E-Intra (steric) 11.5915 E-Intra (vdw) 129.943 E-Intra (elec) 0


E-Inter (protein - ligand) -109.284 Steric [480] -102.086 VdW (LJ12-6) -39.8263 HBond [5] -7.80385 NoHBond90 -12.0824 Electro [2] -2.62147 ElectroLong [463] 3.22693





E-Inter (protein - ligand) -79.4152 Steric [479] -82.0731 VdW (LJ12-6) -34.1382 HBond [2] -2.73832 NoHBond90 -4.84032 Electro [7] 2.64371 ElectroLong [768] 2.7525



E-Intra (tors) 2.79532 E-Intra (sp2-sp2) 16.6488



E-Intra (hbond) 0 E-Intra (steric) 3.50364 E-Intra (vdw) 135.813 E-Intra (elec) 11.2104


E-Inter (protein - ligand) -81.5693 Steric [471] -79.6561 VdW (LJ12-6) -15.2448 HBond [1] -2.48506 NoHBond90 -2.5 Electro [0] 0 ElectroLong [465] 0.571863





E-Inter (protein - ligand) -86.6887 Steric [418] -80.479 VdW (LJ12-6) -28.7751 HBond [4] -7.51981 NoHBond90 -7.53587 Electro [3] -1.45409 ElectroLong [462] 2.76423

E-Inter (cofactor - ligand) -19.1352 Cofactor (VdW) -3.3081 Cofactor (hbond) -2.5 Cofactor (elec) 0.79679




E-Inter (protein - ligand) -73.6372 Steric [544] -77.9984 VdW (LJ12-6) -5.35387




HBond [0] 0 NoHBond90 -0.739527 Electro [4] 3.37143 ElectroLong [461] 0.989777

E-Inter (cofactor - ligand) -24.6873 Cofactor (VdW) 11.8732 Cofactor (hbond) 0 Cofactor (elec) 1.59514




E-Inter (protein - ligand) -94.1918 Steric [493] -82.909 VdW (LJ12-6) -23.877 HBond [6] -11.2829 NoHBond90 -17.5279 Electro [0] 0 ElectroLong [0] 0

E-Inter (cofactor - ligand) -10.9771 Cofactor (VdW) 8.70681 Cofactor (hbond) 0 Cofactor (elec) 0


E-Intra (tors) 5.7185 E-Intra (sp2-sp2) 22.0794 E-Intra (hbond) 0 E-Intra (steric) 10.2622 E-Intra (vdw) 129.585 E-Intra (elec) 0


m.tech divison - dissertation grade/parikshit totawar... · 2015. 4. 24. · m.tech. in information...

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