FINAl3

Homology Modeling of Mycobacterium tuberculosis H37Rv gyrA Protein :Design and Evaluation of novel gyrA Inhibitors

1

1.Introduction

1.1 Motivation

Mycobacterium tuberculosis (MTB) is an obligate aerobic prokaryote, can survive only in an

environment containing oxygen[1]

. First discovered by the Robert Koch in 1882. M. tuberculosisbacteria are not stained by any bacteriological stain (such as Methylene blue) due to high contentof lipid in its wall. So, staining of Mycobacteria requires specific staining techniques, such asZiehl-Neelsen staining and acid-fast, staining is used. M. tuberculosis comes from thegenus Mycobacterium, which is composed of approximately 100 recognized and proposedspecies. Its unusual cell wall, rich in lipids (e.g., mycolic acid), is likely responsible for this

resistance and is a key virulence factor[2]

. The most familiar of the species are Mycobacterium

tuberculosis and Mycobacterium leprae (leprosy)[3]

.

There are number of different type of inhibitor available, the Trovafloxacin and Moxifloxacininhibitors one of them best inhibitor. Trovafloxacin and Moxifloxacin are an antibiotic of thefourth generation flouroquinolone family. Trovafloxacin (sold Trovan by Pfizer and Turvelby Almirall Laboratories) is a broad spectrum antibiotic that inhibits the uncoilingof supercoiled DNA in various bacteria by blocking the activity of DNA

gyrase and topoisomerase IV[4]

. Moxifloxacin is marketed worldwide under the brandnames Avelox, Avalox, and Avelon. Moxifloxacin is a broad-spectrum antibiotic that is activeagainst both Gram-positive and Gram-negative bacteria. It functions by inhibiting DNA gyrase a

type II topoisomerase and topoisomerase IV,[5]

enzymes necessary to separate bacterial DNA,thereby inhibiting cell replication.

Computer aided drug design like structure based approach can be used effectively againstresistant Mycobacterium species. To develop novel drug which bind to target and inhibit thesynthesis of DNA gyrase of Mycobacteria. We have designed computationally newTrovafloxacin and Moxifloxacin derivative which bind to active site of the receptor lead toinhibition of DNA gyrase activity.

Homology Modeling of Mycobacterium tuberculosis H37Rv gyrA Protein :Design and evaluation of novel gyrA inhibitors

School of Biotechnology and Center for Bioinformatics2

This project will help in designing the new Trovafloxacin and Moxifloxacin derivatives drug.

There is need for designing new bactericides agents as the problem of resistance developed in thestrains of the pathogens and also sensitivity of some patients with some drugs. Particularly thisproject is important for me as it is giving me opportunity to work on live projects whosepredictions will be validated in wet-lab with collaborations with some Laboratories around theworld.

1.2 Problem statement

Tuberculosis is the second-largest cause of death from an infectious agent worldwidekillingapproximately 1.7 million people in 2003. The number of new cases appears to be growing, withan estimated 8.8 million new cases in 2003,[6].Various antibiotics have been used to control thediseases caused by the Mycobacterium tuberculosis species. The fluoroquinolones(Trovafloxacin and Moxifloxacin) are the most important group of antibiotics that are availableas bactericidal.. They are used to control the diseases caused by the bacteria. Single genetic

changes usually produce highly resistant strains of pathogens[7]

. Disease-causing organisms also

know as pathogen, they reproduce and mutate quickly. These organisms acquired genetic

resistance to chemical controls and have the capability to pass on a disease to new hybrids[8]

.

Currently most of the Mycobacterium organisms have become resistant to marketed Quinolonederivatives because their site of action (active site) has mutated loosing the sensitivity fordifferent Quinolone. Some of the Quinolone based drugs thus disappeared from the market. Inthis project work we try to develop novel Quinolone derivative which effective against variousMycobacterium species which becomes resistant. They become resistance because the targetsites become change so, we have to find new target site and related drug. Quinolone derivative

drugs has severe adverse effect[9]

. These are marketed Quinolone drug Flerofloxacin,Lemofloxacin, Pefloxacin, Levofloxacin, Sparfloxacin, OfLoxacin, Moxifloxacin, Norfloxacin,Enoxacin, Dextrofloxacin, Nalidixic acid, Trovafloxacin, Ciprofloxacin etc.



1.3 Introduction to Trovafloxacin

Trovafloxacin is a broad spectrum antibiotic that inhibits the uncoiling of supercoiled DNA in

various bacteria by blocking the activity of DNA gyrase and topoisomerase IV[10]

. It had bettergram-positive bacterial coverage and less gram-negative coverage than the previous

fluoroquinolones[11]

.

F

F

F

O

O

O

N

N

NN H

H

H

H

HH

H H

H

H

H

H

H

H H

Figure 1. Stucture of Trovafloxacin

1.4 Introduction to Moxifloxacin

Moxifloxacin is a synthetic fluoroquinolone antibiotic agent[12]

. The bactericidal action ofMoxifloxacin results from inhibition of the enzymes topoisomerase II (DNA gyrase) and

topoisomerase IV[13]

.

Figure 2. Structure of Moxifloxacin



1.4 Target of Trovafloxacin and Moxifloxacin

The main target enzyme of Trovafloxacin and Moxifloxacin antibiotics , the DNA gyrase andtopoisomerase II. DNA gyrase is a replicative enzyme which is responsible for replication ofDNA. These agents inhibit the enzyme DNA gyrase and topoisomerase II[14]. Therefore,replication of DNA is inhibited and bacteria gets kill.

1.5 DNA gyrase

DNA gyrase is an ATP-dependent enzyme that acts by creating a transient double-stranded DNAbreak. It is unique in catalyzing the negative supercoiling of DNA and is essential for efficientDNA replication, transcription, and recombination. DNA gyrase is a tetrameric A2B2 protein.The A subunit carries the breakage-reunion active site, whereas the B subunit promotes ATPhydrolysis [15].

2.Literature review

2.1. ntroduction to Mycobacterium tuberculosis Genome

The genome of the H37Rv strain was published [16] and sequenced in 1998[17]. Its size is 4million base pairs, with 3959 genes; 40% of these genes have had their function characterised,with possible function postulated for another 44%. Within the genome are also 6 pseudogenes.

The genome contains 250 genes involved in fatty acid metabolism, with 39 of these involved inthe polyketide metabolism generating the waxy coat. Such large numbers of conserved genesshow the evolutionary importance of the waxy coat to pathogen survival.

About 10% of the coding capacity is taken up by two clustered gene families that encode acidic,glycine-rich proteins. These proteins have a conserved N-terminal motif, deletion of whichimpairs growth in macrophages and granulomas.[18].Nine noncoding sRNAs have been

characterised in M. tuberculosis,[19] with a further 56 predicted in a bioinformatics screen[20]

.



.

2.1.1. Disease caused by Mycobacterium

Tuberculosis

TB is responsible for 25% of adult deaths in the developing world- more than those caused bydiarrhea, malaria and AIDS combined. Tuberculosis (TB) is a bacterial infection caused by a

germ called Mycobacterium tuberculosis[21]

. The bacteria usually attack the lungs, but they canalso damage other parts of the body. TB spreads through the air when a person with TB of the

lungs or throat coughs, sneezes or talks[22]

.In 2007 there were an estimated 13.7 million chronic

active cases, 9.3 million new cases, and 1.8 million deaths, mostly in developing countries[23]

.

In this project I worked on Mycobacterium tuberculosis which causes tuberculosisdisease, and is one of the most important throat and lung disease over the world.Now days Mycobacterium tuberculosis has become highly resistant to availablefluoroquinolone. The reason behind the resistance was pointed to mutation in the gyrA gene,

encoding a replacement of Alanine for Serine at position 74(A74 S), with resistance[24]

.

Figure 3: Mycobacterium tuberculosis colonies



2.1.2 Symptoms and Sign

The symptoms of disease does not notice until the disease is quite advance. In active pulmonaryTB, patients may have no symptoms, except not feeling well, anorexia, fatigue, and weight

loss, which develop gradually over several weeks, or they may have more specific symptoms.Cough is the first symbol of the infection with M.tuberculosis [25]. At first, it may be minimallyproductive of yellow or green sputum, usually on rising, but cough may become more productiveas the disease progresses. Hemoptysis occurs only with cavitary TB (sometimes due to fungalgrowth in a cavity). Drenching night sweats are a classic symptom but are neither common innor specific for TB. Dyspnea may result from lung parenchymal damage, spontaneouspneumothorax, or pleural TB with effusion.

Figure 4: Symptoms of M. tuberculosis in Human lung



2.2. Computer aided drug design

2.2.1 Introduction

Structure-based ligand or inhibitor design, or rational drug design, as it is sometimes called, aimsto identify chemical compounds or peptides that bind strongly to key regions of biologicallyrelevant 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 targetmolecules. The rapid progress of the human genome project is providing an ever-increasingnumber of potential protein drug targets. Together with advances in structural determinationtechniques 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[26]

.

Computational methods are required to extract all of the relevant information from the availablestructures and to use it in an efficient and intelligent manner to design improved ligands for thetarget. Due to genome sequencing projects, the number of known sequences is increasing at arapid rate

[27].

New target identification strategies and associated bioinformatics technologies are being

developed to categorize this vast body of information[28]

. Today, many scientists are working onways to try to predict the three-dimensional structure of a protein from its one-dimensional

amino acid sequence[29]

. There is also a worldwide effort in functional genomics to determine asmany three-dimensional structures of proteins as possible or to develop computationalapproaches to cluster sequences into families of related proteins and then select and solve thethree-dimensional structure of a representative sequence. Computational methods are needed toexploit the structural information to understand specific molecular recognition events and toelucidate the function of the target macromolecule. This information should ultimately lead tothe 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 foundthrough large-scale random screening of compounds for activity against a target, for which nothree-dimensional structural information was available. That is, thousands of compounds (in thecompany database) are screened for activity. High-throughput robotic screening methodsaccelerate 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[30]

.

The first example of structure-based design was reported by the group of Beddell and Goodfordin 1976 at Wellcome Laboratories in the United Kingdom [98]. Hemoglobin was selected as atarget, 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 naturalallosteric effector diphosphoglycerate. This endogenous ligand binds to hemoglobin andregulates its oxygen affinity. Taking this molecule as a reference, the Wellcome group designeddialdehyde derivatives and related bisulfite adducts which, as expected, modify the oxygenaffinity to hemoglobin. Several years later the antihypertensive captopril, which inhibits theangiotensin-converting enzyme, was introduced onto the market; this was the first drug to bedeveloped using structural information. The past 20 years of drug design have witnessed thestructural characterization of a tremendously number of therapeutically important targets. Theincreasing number of successful applications of drug design has led to the discovery of newtherapeutics. The recent development of human immunodeficiency virus (HIV) proteaseinhibitors has convincingly demonstrated the impact and the relevance of structure-based

approaches to the development of new drugs[31]

.

2.2.2. The drug design cycle

The process of structure-based drug design is an iterative one and often proceeds throughmultiple cycles before an optimized lead goes into phase I clinical trials. The first cycle includesthe cloning, purification and structure determination of the target protein or nucleic acid by oneof three principal methods: X-ray crystallography, NMR, or homology modeling. Usingcomputer algorithms, compounds or fragments of compounds from a database are positioned intoa 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 withbiochemical assays. In the second cycle, structure determination of the target in complex with amicromolar inhibition in vitro, reveals sites on the compound that can be optimized to increasepotency. Additional cycles include synthesis of the optimized lead, structure determination of thenew target-lead complex, and further optimization of the lead compound. After several cycles ofthe drug design process, the optimized compounds usually show marked improvement in binding

and, often, specificity for the target[32]

.

2.3. Homology modeling

Now days there are number of technique developed in molecular biology that provide easy toidentification, sequencing of DNA, RNA or proteins. To determine the three-Dimensionalstructure of protein is very difficult and time consuming task. In the field of structural biologythe main objective to find out the three dimensional structure of protein from the theresequences.

[33]. The ultimate goal of protein modeling is to predict a structure from its sequence

with an accuracy that is comparable to the best results achieved experimentally. This wouldallow users to safely use rapidly generated in silico protein models in all the contexts wheretoday only experimental structures provide a solid basis: structure-based drug design, analysis ofprotein function, interactions, antigenic behavior, and rational design of proteins with increased

stability or novel functions[34]

.

One method that can be applied to generate reasonable models of protein structures is homologymodelling. In protein structure prediction, homology modeling, also known as comparativemodeling, is a class of methods for constructing an atomic-resolution model of a protein from itsamino acid sequence (the "query sequence" or "target").most of the homology modelingtechnique based on the already known 3-D coordinates of the protein which we called templatestructure or parent structure, it should be likely to resemble to the query sequence (whosestructure is not known). The model of our target protein is produced by the use of both sequencealignment and template structure. Because protein structures are more conserved than proteinsequences, detectable levels of sequence similarity usually imply significant structural similarity



[35]. The quality of the homology model is dependent on the quality of the sequence alignment

and template structure[36]

.

2.3.1. General Procedures

The steps to creating a homology model are as follows[37]

.

Identify homologous proteins and determine the extent of their sequence similarity with one

another and the unknown Align the sequences

Identify structurally conserved and structurally variable regions Generate coordinates for core (structurally conserved) residues of the unknown structure from

those of the known structure(s) Generate conformations for the loops (structurally variable) in the unknown structure Build the side-chain conformations

Refine and evaluate the unknown structure.

2.4. Docking

The application of computational methods to study the formation of intermolecular complexeshas been the subject of intensive research during the last decade. It is widely accepted that drugactivity is obtained through the molecular binding of one molecule (the ligand) to the pocket ofanother, which is commonly a protein. In the binding conformations of a complex of a proteinwith a therapeutic drug, the molecules exhibit geometric and chemical complementarities, bothof which are essential for successful drug activity. The computational process of searching for aligand that is able to fit both geometrically and energetically to the binding site of a protein iscalled molecular docking. The docking problem is analogous to an assembly-planning problemwhere 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 scoringfunction, 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[38]

. Ligand dockingand screening algorithms are now frequently used in the drug-design process, and haveadditional application in the elucidation of fundamental biochemical processes. The purpose ofdocking algorithms is now expanding beyond the original goal of fitting a given ligand into aspecific protein structure. Newer applications include database screening, lead generation and de

novo drug design[39]

.



3. Materials and Tools Used

For our present study I used bioinformatics tools,biological databases like PubMed, Drug Bank,PDB (Protein Data Bank) and softwares like MOE, ACD ChemSketch,Modeller.

3.1 : ACD/ChemSkecth

ACD/ChemSketch is the powerful all-purpose chemical drawing and graphics package fromACD/Labs developed to help chemists quickly and easily draw molecules, and schematicdiagrams, calculate chemical properties, and design professional reports and presentations. ACDChemsketch can convert SMILES notations to Structure and vice versa.

3.2 : MOE(Molecular Operating Enviroment)

MOE is an Interactive Molecular Graphics Programing suite for calculating and displayingfeasible docking modes of pairs of protein and DNA molecules. MOE can also calculate Protein-Ligand Docking, assuming the ligand is rigid, and it can superpose pairs of molecules using onlyknowledge of their 3D shapes.MOE(Molecular Operating Environment), combines visualisation,simulation and methodology development into a single integrated package. MOEs collection of

built-in applications, which include tools for protein modeling, molecular modeling, structure-based drug design.

3.3 : Drug Bank

Drug Bank is a unique Bioinformatics/Chemoinformatics resource that combines detailed drug(i.e. chemical) data with comprehensive drug target (i.e. protein). Each Drug Card entry containsgreater than 80 data fields with half of the information being devoted to drug/chemical data and

the other half devoted to drug target or protein data[40]

.



3.4 : Protein Data Bank

The PDB (Protein Data Bank) is the single world wide archive of Structural data of Biologicalmacromolecules, established in Brookhaven National Laboratories (BNL) in 1971 [41].Itcontains Structural information of the macromolecules determined by X-ray crystallographic,NMR methods etc.

3.5 : Pubmed

PubMed is a free digital archive of biomedical and life sciences journal literature at the U.S.National Institutes of Health (NIH), developed and managed by NIH's National Center forBiotechnology Information (NCBI) in the National Library of Medicine (NLM). PubMed isa free search engine for accessing the MEDLINE database of citations and abstracts of

biomedical research articles[42]

.

3.6 : Modeller 9v7

The Modeller is the freely available computer program for the production of homology modelsof protein tertiary structures. Modeller was maintained and written by Andrej Sali at theUniversity of California, San Fransisco.



4. METHODOLGY

Bioinformatics is seen as an emerging field with the potential to significantly improve how drugsare found, brought to the clinical trials and eventually released to the marketplace. Computer Aided Drug Design (CADD) is a specialized discipline that uses computational methods tosimulate drug receptor interactions. CADD methods are heavily dependent on bioinformatics

tools, applications and databases[43]

.

4.1 :Sequence retrieval of gyrA gene

The protein sequence of Mycobacterium tuberculosis H37Rv gyrA, Accession NumberNP_2145201 was retrieved from NCBI Reference Sequence database and the FASTA sequenceis used for our studies.The protein sequence is mutated at 74 position from Ala to Ser.

4.2 : Screening for best homologous templates

The target protein sequence was blasted using BLASTP[44-45]

across Protein Data Bank to obtainthe most identical structures based on the percentage of identity, similarity, expectation valuesand alignment scores which could be considered as templates in the modeling procedure.

4.3 : In silico Comparative Modeling of gyrA Protein

An alignment between the target and temple is performed.An in silico comparative modeling of

the gyrA protein was carried out by the MODELLER 9v7[46]

. The best homolog identified earlierthrough BLASTp was used to generate alignment, atom and the script files for modeling. Themodeled protein was visualized by MOE.

4.4 : Model refinement, validation and evaluation

The modeled protein is validated by PROCHECK[47]

.The parameters include the covalent bonddistances, angles and torsion, stereocemical validation and atom nomenclature. The input toPROCHECK is a single file containing the coordinates of your protein structure. This must be inBrookhaven file format the input to PROCHECK is a single file containing the coordinates ofyour protein structure. This must be in Brookhaven file format.



The outputs comprise a number of plots, together with a detailed residue-by-residue listing. Theplots are output in PostScript format, it shows Ramachandran plot.

4.4 : Submission of Modeled structure to PMDB

The modeled gyrA Protein was deposited to the Protein Model Data Base[48]

.] and can bedownloaded with the PMID PM0077412.

4.5: Selection of Potential Drug Candidates against gyrA protein

The Pubchem[49]

, KEGG[50]

and Drug Bank databases provides collection of drugs that help toinhibit the function of DNA gyrase. The * .mol structure files of these drugs were obtained fromthe Drug Bank and used in our study. The DrugBank database has a wide collection of bacterialantibiotics and the drugs were directly obtained in .mol format for docking based on the literaturestudies.



Figure5. 3-D structure of gyrA protein modeled by MODELLER 9v7



Ramachandran plot: 88.7% core 10.1% allow 1.1% gener 0.0% disall

Figure 6: Ramachandran plot for gyrA protein of M.tuberculosis

4.6. Ligand

The existing known Quinolones drugs have become resistant, so there was need for newmodified Quinolines derivatives. The derivatives of Trovafloxacin and Moxifloxacin are usedas ligand.These ligands are sketched using ChemSkecth.



Trovafloxacin Derivative Structure

F

N

O

NH2

OH

N

NNH2

F

FH

H

Analog 1

F

N

O

OH

N

NNH2

F

FH

H

O

OH

Analog 2

F

N

OH N

N NH2

F

FH

H

O

OH

Analog 3

N

O

O

OH

N

N OH

OH

OHOH

OH

OH

OH

OH

OH

OH

Analog 4OH

N

O

O

OH

N

NOH

OH

F

H

H

Analog 5

N

O

O

OH

N

OH

O H

O H

OH

O H

O H

OH

Analog 6

Table 1



Moxifloxacin Derivative Structure

N CH3

O

OHOOC

F

N

NH

H

H

Analog 1

N

CH3

O

O

O CH3

F

N

NH

H

CH3

Analog 2

NO

O

O CH3

F

N

NHH

H

NH2

Analog 3

N

CH3

CH3

O

O

O

CH3

F

N

NHH

H

Analog 4

NCH3

NH2

O

O CH3

F

N

NHH

H

Analog 5

N O C H 3

F

N

NHH

H

Analog 6

Table 2



4.7. Docking in MOE

The purpose of the Dock application is to search for favorable binding configurations betweenone or more small, flexible ligands and a rigid macromolecular target, which is usually a protein.For each ligand, a number of configurations called poses are generated and scored in an effort todetermine favorable binding modes..

4.7.1 : Methodology Overview

The Dock application is divided into a number of stages. The stages are:

Conformational Analysis:

If ligand conformations are not supplied via a conformation database, Dock will generateconformations from a single 3D conformation by applying a collection of preferred torsionangles to the rotatable bonds. Bond lengths and bond angles will not be altered. For smallligands, a systematic search is conducted which generates all combinations of angles. For largerligands, a (non-deterministic) stochastic sampling of conformations is performed.

Placement :

A collection of poses is generated from the pool of ligand conformations using one of theplacement methods available.The methods are Alpha Triangle(default), Triangle Matcher, ProxyTriangle, Alpha PMI, None.

Pharmacore Filtering :

Optionally, the generated poses are constrained to satisfy an arbitrary pharmacophore query.Such a query is used to bias the search towards known important interactions.



Rescoring(l) :

Poses generated by the placement methodology could be rescored using one of the availablemethods. Typically, scoring functions emphasize favorable hydrophobic, ionic and hydrogenbond contacts. All scoring methods should assign low scores to good poses.

Refinment:

Poses resulted from the placement stage could be refined using one of the methods. Dependingon the choice of methodology, explicit molecular mechanics forcefields or grid-based energeticsmay be used.

Rescoring(2):

Poses resulted from the refinement stage could be rescored using one of the scoring schemes.



5. Results

Docking with proposed structure of Trovafloxacin and Moxifloxacin shows better H-bondingand binding energy with Target protein gyrA , while the docking with known or existingbactericides showing poor binding energy and H-bonding.

Trovafloxacin analog 6 and Moxifloxacin analog 6 show better H-bonding and binding energywith the target protein then its predecessor.



5.1. Docking result with Trovafloxacin derivatives

Active Site Ligand

Active SiteWith Ligand

Interaction Data

Final Score 9.9646

E_conf 0.0000

H-bond No

H-acc No

H-don No

Amino acid No

M.W 417.14

Log P 0.99Ligand Interaction

Table 3a



Table 3b

Active SiteLigand

Active Site with Ligand

Interaction Data

Final Score -9.6316

E_conf 1.0000

H-bond 1

H-acc No

H-don 1

Amino acid GLY(353)

M.W 446.12

Log P1.40

3D



Active Site Ligand


Interaction Data

Final Score 7.5610

E_conf 0.0000

H-bond 1

H-acc No

H-don 1

Amino acid MET(35)

M.W 418

Log p 1.98

:Ligand Interaction

Table 3c



Active Site Ligand


Interaction Data

Final Score -11.137

E_conf 2.4023

H-bond 1

H-acc No

H-don 1

Amino acidTYR(32)

M.W 447.06

Log P -0.20

Ligand InteractionTable 3d



Active Site Ligand


Interaction Data

Final Score -9.2662

E_conf 0.8000

H-bond1

H-acc1

H-donNo

Amino acid Ser14

M.W 413.10

Log P 0.99

Ligand Interaction

Table 3e



Active SiteLigand


Interaction Data

Final Score -12.4770

E_conf 2.6023

H-bond 4

H-acc No

H-doc 4

Amino acidAsp30 Val183Gly351 Gly351

M.W 378.03

Log P-0.43

Ligand Interaction

Table 3f



5.2. Docking result with Moxifloxacin derivatives

Active SiteLigand


Interaction Data

Final Score -9.7132

E_conf 0.6406

H-bond 1

H-accNo

H-don1

Amino acid Asp350

M.W 415.23

Log P 1.85

Ligand InteractionTable 4a



Active SiteLigand


Interaction Data

Final Score -9.0229

E_conf 1.4161

H-bond No

H-acc No

H-doc No

Amino acid No

M.W 415.23

Log P 2.47

Ligand InteractionTable 4b



Active SiteLigand


Interaction Data

Final Score -9.3648

E_conf 2.3992

H-bond No

H-acc No

H-doc NO

Amino acid No

M.W 400.19

Log P 1.57

Ligand InteractionTable 4c



Active Site Ligand


Interaction Data

Final Score -9.8935

E_conf 2.6224

H-bond 2

H-acc No

H-doc 2

Amino acid Ser54 Gly352

M.W 373.18

Log P 2.40

Ligand InteractionTable 4d



Active siteLigand


Interaction Data

Final Score -9.7361

E_conf 3.0156

H-bond 2

H-acc No

H-don 2

Amino acid Val283 Gly352

M.W 400.23

Log P 2.13

Ligand Interaction

Table 4e



Active SiteLigand


Interaction Data

Final Score -11.9563

E_conf 1.1755

H-bond 3

H-acc 1

H-don 2

Amino acid Gly351 Ser34Ser34

M.W 343.21

Log P 3.76

Ligand InteractionTable 4f



6. Discussion

The Mycobacterium tuberculosis belongs to the Kingdom Bacteria. M. tuberculosis is highlyaerobic and requires high level of oxygen for its survivel. M. tuberculosis is the causativeagent of Tuberculosis. Tuberculosis is a highly infectious disease and primarly affect lungs.Due to presence of waxy coating (mycolic acid) around the cell membrane, making its cellimpervious to Gram staining, so staining requires special techniques such as acid fast. So, M.tuberculosis is neither Gram positive or Gram negative.

The Trovafloxacin and Moxifloxacin drugs affect the replication process of tuberculosis bacteriaby the inhibition of DNA gyrase enzyme. DNA gyrase is an ATP dependent molecule (requiresATP), relaxes the negative supercoiling of DNA molecule. M .tuberculosis, DNA gyrase hasbecome resistant to the available drugs, because of the mutation at binding site. The possiblereason behind the mutation is prolonged use of the drugs. So, we design novel derivatives of thedrugs and perform in silico study of these drugs with the DNA gyrase.

The complete structure of gyrA is not available in protein data bank (PDB) so, the target proteinwas modeled by comparative homology modeling using modeller software (9v7 version). Theaccuracy of the model was 88.7 % of residue fall in core region and other 10.1 % in allowableregion in Ramachandran plot. We have screened out best two molecules on the basis ofHydrogen bonding and binding energy from above proposed derivative compounds using MOEdocking software.



7. Conclusions

The Protein-Ligand interaction plays a significant role in structural based drug designing.In thepresent work I have taken the DNA gyrase as receptor and identified the drugs that were used asinhibitor. When the receptor (DNA gyrase) was docked with the drug the energy valueobtained was; Trovafloxacin (-11.4397), Moxifloxacin (-11.3729 ). When the modified drugswere docked against the same receptor the energy value obtained was Trovafloxacin Analog 6 (-12.4770), Maxifloxacin Analog 6(-11.9563). From this we can conclude that some of themodified drugs are better than the commercial drugs available in the market.



8. Future work

In future research work the ADME/T (Absorption, Distribution, Metabolism, Excretion /Toxicity) properties of these compounds can be calculated using the commercial ADME/T toolsavailable thus reducing the time and cost in drug discovery process.



9. References

1. http://en.wikipedia.org/wiki/Mycobacteria.2. Murray PR, Rosenthal KS, Pfaller MA, Medical Microbiology. Elsevier Mosby,

(2005).3. Mahon, Lehman, Manuselis, Textbook of Diagnostic Microbiology , Page 576.4. Bloomberg News "2 Drug Companies Report Big Quarterly Profit Gains". The New

York Times: p. D.5. ISSN 03624331. (via ProQuest, Document ID 28630468), (April 15,1998).

5. Drlica K, Zhao X., "DNA gyrase, topoisomerase IV, and the 4- quinolones". MicrobiolMol Biol Rev. 61 (3): 37792. PMC 232616 PMID 9293187, ( September 1, 1997).

6. Bill & Melinda Gates Foundation ,Tuberculosis Is The Principal Cause Of Death From aCurable Infectious Disease,But Treatment Is Highly Cost Effective(PDF), (April 2006).

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