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www.wjpps.com Vol 4, Issue 09, 2015. 620

Kanakaraju et al. World Journal of Pharmacy and Pharmaceutical Sciences

COMPUTER AIDED LIGAND DESIGN AND MOLECULAR DOCKING

STUDIES ON A SERIES OF PYRIDINE-CHALCONE CONJUGATES

AGAINST SELECTED ANTITUBERCULAR DRUG TARGETS

Asst. Prof. A. Kanakaraju1,*

and Prof. Y. Rajendra Prasad2

1Pharmaceutical Chemistry Division, Vignan Institute of Pharmaceutical Technology, Beside

SEZ, Duvvada, Visakhapatnam, Andhra Pradesh, Pincode-5530049, India.

2Pharmaceutical Chemistry Division, A U College of Pharmaceutical Sciences, Andhra

University, Visakhapatnam, Andhra Pradesh, Pincode-530003, India.

ABSTRACT

Contemporary scenario of drug discovery and development there is an

accumulation of diseases through more-complex pathological

mechanisms, for which the classic „single target, single drug‟ pattern

has moderately or completely ineffective. In these conditions, drugs

acting on various targets could offer greater efficacy profiles compared

with single-target drugs. Hence in the present research as a part of our

ongoing search for new chemical entities as potential antitubercular

agents, we could design a series of pyridine-chalcone conjugates to

study their inherent mechanism against selected antitubercular drug

target by using molecular docking studies.

KEYWORDS: Pyridine-chalcone conjugates, antitubercular drug

target, molecular docking.

1. INTRODUCTION

In the recent past three decades, molecular docking methodologies have become well

integrated in the modern drug design process and have gained in influence. They have

dramatically revolutionized the way in which we approach drug discovery, leading to the

explosive growth in the amount of chemical and biological data that are typically

multidimensional in structure. A variety of advanced computational algorithms and methods

has been effectively applied recently in medicinal chemistry for dimensionality reduction and

W W OORRLLDD JJOOUURRNN A A LL OOFF PPHH A A RRMM A A CC Y Y A A NNDD PPHH A A RRMM A A CCEEUUTTIICC A A LL SSCCIIEENNCCEESS

SSJJIIFF IImmppaacctt FFaaccttoorr 55..221100

V V oolluummee 44,, IIssssuuee 0099,, 620-633 RReesseeaarrcchh A A rrttiiccllee IISSSSNN 2278 – 4357

Article Received on25 July 2015,

Revised on 16 July 2015,Accepted on 07 Aug 2015

*Correspondence for

Author

Prof. A. Kanakaraju

Pharmaceutical Chemistry

Division, Vignan Institute

of Pharmaceutical

Technology, Beside SEZ,

Duvvada, Visakhapatnam,

Andhra Pradesh, Pincode-

5530049, India


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visualization of the chemical data of different types and structure.[1-5] The majority of these

computational models are commonly based on the basic principles of dimensionality

reduction and mapping. In turn, dimensionality reduction is an essential computational

technique for the analysis of a large-scale, streaming and tangled data. In silico methods may

benefit drug discovery and development significantly by saving an average of $130 million

and 0.8 years per drug. In an industrial environment, the commonly used ligand-based and

receptor-based methods need to be computationally faster to return the utmost benefit.

Intelligent database searching using new fast feedback driven screening methods appears to

be particularly rewarding in terms of both cost and time benefits6. Consequently it was

considered worthwhile to discover a drug based on modern computational methods such as

molecular docking, pharmacophore modeling and quantitative structure activity relationship

studies etc. Homosapien‟s battle with tuberculosis (TB) dates back to ancient times. TB,

which is caused by Mycobacterium tuberculosis (Mtb), was a much more widespread disease

in the past than it is today, and it was responsible for the deaths of about one billion people

during the last two centuries. The introduction of TB chemotherapy in the 1950s, along with

the pervasive use of BCG vaccine, had a great impact on further reduction in TB incidence.

However, despite these advances, TB still remains a leading infectious disease worldwide.

The increasing emergence of drug-resistant TB, especially multidrug-resistant TB (MDR-TB,

resistant to at least two frontline drugs such as isoniazid and rifampin), is particularly

frightening. In view of this situation, the World Health Organization (WHO) in 1993 declared

TB a global emergency. There is an urgent need to develop new anti-TB drugs. However, no

new TB drugs have been developed in about 40 years. Although TB can be cured with the

current therapy, the six months needed to treat the disease is too long, and the treatment often

has significant toxicity.[7, 8] Therefore in the present study we could have developed a series

of some new pyridine-chalcone conjugates and established their binding orientation,

chemical reactivity and mechanism of binding against selected ten vital anti-TB drug targets

which are involved in the Mycobacterium tuberculosis cell cycle, cell growth and replication,

They are Shikimate Kinase (SK)[9], Chorismate Synthetase (CS)[10], Isocitratelyase (ICL)[11],

Pantothenate Synthetase (PS)[12], Enoyl-[Acyl-Carrier Protein] Reductase (InhA)[13], 3-

Oxoacyl-[Acyl-Carrier Protein] Reductase (MabA)[14], Ornithine Acetyltransferase (OAT)[15],

Lumazine Synthetase (LS)[16], Quinolinate Phosphoribosyl Transferase (QAPRT)[17] and

Glucosamine-1-Phosphate-N-Acetyl Transferase (GLmU)[18], respectively.


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2. MATERIALS AND METHODS

2.1. Software Methodology

In the present study, software Molegro Virtual Docker (MVD) v 5.0 (www.molegro.com)

along with Graphical User Interface (GUI), MVD tools was utilized to generate grid,

calculate dock score and evaluate conformers. Molecular docking was performed using

MolDock docking engine of software. The scoring function used by MolDock is derived from

the Piecewise Linear Potential (PLP) scoring functions. The active binding site region was

defined as a spherical region which encompasses all protein within 15.0 Ao of bound

crystallographic ligand atom with selected co-ordinates of X, Y and Z axes, respectively.

Default settings were used for all the calculations. Docking was performed using a grid

resolution of 0.30 Ao and for each of the 10 independent runs; a maximum number of 1500

iterations were executed on a single population of 50 individuals. Both the active binding

sites and ligands were treated as being flexible i.e. all non-ring torsions were allowed. [19]

2.2. Molecular Modeling

A set of some novel pyridine-chalcone conjugates listed in Table 3 and 4, were designed and

modeled based on the synthetic chalcones which earlier synthesized and reported from

Andhra University, Pharmaceutical Chemistry Research Laboratories as potential

antitubercular agents.[20]

Correspondingly, a database of thirty five pyridine-chalcone

conjugates were modeled by using ISIS DRAW 2.2 software and subjected for ligand

preparation protocol.

2.3. Ligand Preparation

The structures of pyridine-chalcone conjugates were converted into suitable chemical

information using Chemdraw ultra v 10.0 (Cambridge software), copied to Chem3D ultra v

10.0 to create a 3D model and, finally subjected to energy minimization using molecular

mechanics (MM2). The minimization was executed until the root mean square gradient value

reached a value smaller than 0.001kcal/mol. Such energy minimized structures are considered

for docking and corresponding pdb files were prepared using Chem3D ultra v 10.0 integral

option (save as /Protein Data Bank (pdb)) (Table 3 and 4).[21]

2.4. Protein Selection: The selection of target protein for molecular docking studies is based

upon several factors i.e. structure should be determined by X-ray diffraction, and resolution

should be between 2.0-2.5Ao

, it should contain a co-crystallized ligand; the selected protein

should not have any protein breaks in their 3D structure. However, we considered


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ramachandran plot statistics as the important filter for protein selection that none of the

residues present in disallowed regions.[22]

2.5. Protein Preparation

Table 1: Selected Antitubercular Drug Targets.

Name of the Antitubercular Target Protein PDB ID

Shikimate Kinase (SK) 1L4Y

Chorismate Synthetase (CS) 2QHF

Isocitratelyase (ICL) 1F8I

Pantothenate Synthetase (PS) 1N2H

Enoyl-[Acyl-Carrier Protein] Reductase (InhA) 2X22

3-Oxoacyl-[Acyl-Carrier Protein]

Reductase (MabA) 1UZN

Ornithine Acetyltransferase (OAT) 3IT4

Lumazine Synthetase (LS) 2C9D

Quinolinate Phosphoribosyl Transferase (QAPRT) 1QPN

Glucosamine-1-Phosphate-N-Acetyl Transferase (GLmU) 3D8V

All the X-ray crystal structures of the selected target proteins were obtained from the

Brookhaven Protein Data Bank (http://www.rcsb.org/pdb). Subsequent to screening for the

above specific standards the resultant protein targets (Table 1) were prepared for LPIIFD

simulation in such a way that all heteroatoms (i.e., non-receptor atoms such as water, ions,

etc.) were removed and Kollmann charges were assigned.[23]

2.6. Software Method Validation

Table 2: Software Validation Data.

PDB ID Co-Crystallized Ligand Code RMSD (Ao)

1L4Y CL191 1.42

2QHF NA602 1.22

1F8I GLV461 1.71

1N2H PAJ10002 1.27

2X22 NAD1270 0.92

1UZN NAP1249 1.323IT4 GOL500 1.22

2C9D PHR791 1.18

1QPN NCN2901 1.77

3D8V UD1496 1.24

Software method validation was performed in MVD using Protein Data Bank (PDB) proteins

1L4Y, 2QHF, 1F8I, 1N2H, 2X22, 1UZN, 3IT4, 2C9D, 1QPN and 3D8V. The x-ray crystal

structures of 1L4Y, 2QHF, 1F8I, 1N2H, 2X22, 1UZN, 3IT4, 2C9D, 1QPN and 3D8V

complex with co-crystallized ligands were recovered from http://www.rcsb.org/pdb. The bioactive co-crystallized bound ligands were docked with in the active site region of 1L4Y,


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2QHF, 1F8I, 1N2H, 2X22, 1UZN, 3IT4, 2C9D, 1QPN and 3D8V, respectively. The RMSD

of all atoms between the docked and X-ray crystallographic conformations are displayed in

the following Table 2. The results indicating that the parameters for docking simulation are

good in reproducing X-ray crystal structure.

2.7. Molecular Docking

In the present investigation, we make use of a docking algorithm called MolDock. MolDock

is based on a new hybrid search algorithm, called guided differential evolution. The guided

differential evolution algorithm combines the differential evolution optimization technique

with a cavity prediction algorithm.[24,25]

Table 3: Pyridine-chalcone conjugates (KR1-KR35) with their Moldock Scores(kcal/mol) against selected antitubercular drug targets.

Compound R SK CS ICL PS InhA

KR1 Phenyl -92.2807 -102.001 -80.3044 -93.8873 -88.9512

KR2 2-MeC6H4 -95.5268 -102.266 -90.2236 -96.1141 -90.8165

KR3 3-MeC6H4 -91.3618 -98.412 -95.9246 -92.8452 -97.7398

KR4 4-MeC6H4 -104.085 -121.486 -79.49 -104.77 -96.3855

KR5 2-OMeC6H4 -105.985 -107.264 -97.866 -107.809 -108.76

KR6 3-OMeC6H4 -87.8509 -87.7452 -83.2031 -81.7279 -77.141

KR7 4-OMeC6H4 -87.8155 -107.431 -83.9429 -92.1318 -93.5277

KR8 3-OHC6H4 -89.7754 -104.763 -83.3807 -102.515 -86.9096

KR9 4-OHC6H4 -93.1103 -96.7127 -89.8237 -94.769 -90.8683

KR10 3,5-diOHC6H3 -102.651 -106.11 -107.754 -105.176 -105.706KR11 4,5-diOHC6H3 -98.416 -108.464 -77.4452 -101.052 -101.227

KR12 2-Me,5-OHC6H3 -99.2531 -112.91 -103.73 -102.55 -106.509

KR13 2-NH2C6H4 -91.1482 -103.245 -84.0796 -99.8701 -89.7596

KR14 3-NH2C6H4 -87.3075 -92.7187 -76.6763 -94.6158 -86.0126

KR15 4-NH2C6H4 -105.811 -119.641 -104.233 -113.14 -112.809

KR16 2-NO2C6H4 -84.2757 -106.905 -86.3906 -88.5355 -84.0621

KR17 3-NO2C6H4 -86.9602 -100.191 -88.5964 -96.097 -93.0566

KR18 4-NO2C6H4 -92.4733 -106.617 -92.0656 -96.0399 -91.5438

KR19 2-ClC6H4 -106.128 -107.747 -98.1041 -96.6352 -92.3131

KR20 3-ClC6H4 -100.221 -103.777 -94.0206 -99.578 -95.0073KR21 4-ClC6H4 -98.3607 -96.9107 -95.8372 -97.0165 -92.9122


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KR22 2,4-diClC6H3 -101.99 -111.343 -90.6852 -94.8492 -97.7461

KR23 2-FC6H4 -91.9026 -109.302 -104.428 -99.0724 -102.379

KR24 3-FC6H4 -103.691 -82.9469 -95.449 -94.7426 -89.9811

KR25 4-FC6H4 -89.8776 -106.571 -73.136 -93.6172 -99.2324

KR26 2,4-diFC6H3 -94.7923 -109.934 -95.8531 -93.9541 -101.941

KR27 Furan-2yl -80.5848 -90.4753 -77.8077 -83.3594 -77.212KR28 Thiophen-3-yl -70.6235 -78.8087 -78.2365 -84.5833 -84.6069

KR29 Pyrrol-2yl -94.0145 -106.499 -82.1012 -95.8456 -95.599

KR30 Pyridin-2-yl -92.5894 -98.6213 -83.8498 -91.1919 -89.5359

KR31 Pyridin-3-yl -115.934 -121.365 -88.8189 -105.357 -108.92

KR32 Pyridin-4-yl -106.128 -107.747 -98.1041 -96.6352 -92.3131

KR33 Naphthalen-2-yl -108.057 -142.08 -94.6875 -112.943 -120.054

KR34 Naphthalen-3-yl -103.691 -82.9469 -95.449 -94.7426 -89.9811

KR35 Anthracen-9-yl -119.898 -115.102 -59.2644 -116.648 -104.342

Table 4: Pyridine-chalcone conjugates (KR1-KR35) with their Moldock Scores(kcal/mol) against selected antitubercular drug targets.

Compound R MabA OAT LS QAPRT GLmU

KR1 Phenyl -88.4086 -73.5491 -87.5657 -77.8365 -84.6719KR2 2-MeC6H4 -94.2264 -79.9528 -92.9645 -82.2714 -84.3382

KR3 3-MeC6H4 -102.57 -79.5769 -97.5413 -89.6043 -90.6182

KR4 4-MeC6H4 -106.656 -80.1541 -89.3755 -94.9893 -97.5183

KR5 2-OMeC6H4 -102.526 -88.2657 -101.537 -96.6126 -101.322

KR6 3-OMeC6H4 -74.0527 -62.7041 -80.0789 -74.8006 -82.5287

KR7 4-OMeC6H4 -82.2662 -79.5054 -95.6765 -77.2506 -90.506

KR8 3-OHC6H4 -104.579 -81.8349 -98.3754 -91.8257 -90.4049

KR9 4-OHC6H4 -88.6987 -74.6151 -93.9618 -86.4725 -83.9321

KR10 3,5-diOHC6H3 -91.7312 -68.6193 -92.1224 -80.0109 -83.2349

KR11 4,5-diOHC6H3 -93.5827 -86.4768 -96.8181 -98.3924 -86.9817KR12 2-Me,5-OHC6H3 -99.2442 -84.4368 -97.531 -96.2878 -102.491

KR13 2-NH2C6H4 -80.4549 -81.2998 -92.6002 -85.956 -83.1111

KR14 3-NH2C6H4 -86.082 -63.5364 -82.3916 -76.4916 -83.7712

KR15 4-NH2C6H4 -101.91 -90.8969 -107.937 -99.0164 -103.555

KR16 2-NO2C6H4 -99.2442 -84.4368 -97.531 -96.2878 -102.491

KR17 3-NO2C6H4 -79.2065 -77.1796 -87.2604 -80.8184 -88.5372

KR18 4-NO2C6H4 -98.0939 -71.2051 -101.304 -90.7012 -91.579

KR19 2-ClC6H4 -103.691 -82.9469 -95.449 -94.7426 -89.9811

KR20 3-ClC6H4 -99.4005 -70.7923 -85.7546 -88.5613 -92.0864

KR21 4-ClC6H4 -86.6383 -66.7165 -95.5683 -81.2432 -80.0666

KR22 2,4-diClC6H3 -86.828 -80.1743 -96.1634 -88.6799 -98.842KR23 2-FC6H4 -102.644 -74.3106 -104.703 -99.3127 -89.327


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KR24 3-FC6H4 -102.192 -80.8224 -97.7032 -95.2263 -95.4188

KR25 4-FC6H4 -101.399 -81.2647 -96.5202 -91.8843 -92.8393

KR26 2,4-diFC6H3 -96.7815 -88.4274 -95.1317 -87.8335 -92.4578

KR27 Furan-2yl -79.0072 -52.7795 -76.68 -66.4658 -81.1523

KR28 Thiophen-3-yl -83.0322 -51.8248 -86.7161 -68.7265 -68.4562

KR29 Pyrrol-2yl -97.5773 -76.8254 -93.5449 -92.0795 -88.709KR30 Pyridin-2-yl -92.3867 -67.8127 -88.3799 -79.477 -92.0832

KR31 Pyridin-3-yl -103.691 -82.9469 -95.449 -94.7426 -89.9811

KR32 Pyridin-4-yl -102.644 -74.3106 -104.703 -99.3127 -89.327

KR33 Naphthalen-2-yl -94.0367 -94.8787 -109.504 -99.1087 -85.4469

KR34 Naphthalen-3-yl -110.443 -73.1929 -94.3755 -104.88 -100.449

KR35 Anthracen-9-yl -119.576 -92.136 -101.336 -103.883 -119.645

Molecular docking technique was employed to study the database of compounds pyridine-

chalcone conjugates KR1-KR35 listed in (Table 3 and 4) for their binding phenomenon

against selected potential antitubercular drug targets using MVD as well as to locate the

interactions between pyridine-chalcone conjugates KR1-KR35 and drug targets. MVD

requires the receptor and ligand coordinates in either Mol2 or PDB format. Non polar

hydrogen atoms were removed from the receptor file and their partial charges were added to

the corresponding carbon atoms. Molecular docking was performed using MolDock docking

engine of Molegro software. The binding site was defined as a spherical region which

encompasses all protein atoms within 15.0 Ao of bound crystallographic ligand with respect

to the individual target protein. The dimensions are showed in the atom X, Y and Z axes, as

seen in case individual targets such as SK = X [37.84]; Y [4.71]; Z [13.83], CS= X [54.14]; Y

[8.71]; Z [19.83], ICL = X [19.50]; Y [41.06]; Z [53.00], PS = X [33.65]; Y [33.50]; Z

[43.74], Inh A = X [-16.30]; Y [-35.37]; Z [16.93], MabA = X [1.06]; Y [15.38]; Z [16.89],

OAT= X [37.84]; Y [4.71]; Z [13.83], LS = X [-17.53]; Y [10.22]; Z [-25.15], QAPRT = X [-

14.63]; Y [44.13]; Z [17.74] and Glm U = X [29.15]; Y [-30.97]; Z [37.01], respectively.

Default settings were used for all the calculations. Molecular docking was performed using a

grid resolution of 0.3 Ao and for each of the 10 independent runs; a maximum number of

1500 iterations were executed on a single population of 50 individuals (Figure 1 and 2).


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Table 5: Summarized molecular docking results of Pyridine-Chalcone conjugates (KR1-

KR35) against selected antitubercular drug targets.

Target

Code

PDB

ID

Best Fit

Ligand

‘R’ Group

Substituent

Moldock

Score

(kcal/mol)

No. of

H-Bonds

H-bond Interacting

Residues

SK 1L4Y KR35 Anthracen-9-yl -119.898 7 Thr 17, Gly 12, Gly 14, Lys 15

CS 2QHF KR33 Naphthalen -2-yl -142.08 10 Arg 49, Ser 137, Ser 10

ICL 1F8I KR10 3,5-diOHC6H3 -107.754 13Ser 317, Ser 191, Glu 285, Asp153, Ser 91, Trp 93, Gly 92

PS 1N2H KR35 Anthracen-9-yl -116.648 1 Val 187

InhA 2X22 KR33 Naphthalen -2-yl -120.054 1 Val 187

MabA 1UZN KR35 Anthracen-9-yl -119.576 2 Gly 90

OAT 3IT4 KR33 Naphthalen -2-yl -94.8787 5 Lys 189, Thr 127

LS 2C9D KR33 Naphthalen -2-yl -109.504 6 Gly 85, Thr 87, Phe 90

QAPRT 1QPN KR34 Naphthalen-3-yl -104.88 2 Ser 248GLmU 3D8V KR35 Anthracen-9-yl -119.645 6 Ser 112, Gly 15, Ala 14

.

Figure 1: a) Active binding site, binding mode and H-bond interactions of KR35 against

SK b) Active binding site, binding mode and H-bond interactions of KR33 against CS c)

Active binding site, binding mode and H-bond interactions of KR10 against ICL d)

Active binding site, binding mode and H-bond interactions of KR35 against PS e) Active

binding site, binding mode and H-bond interactions of KR33 against InhA.


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Figure 2: a) Active binding site, binding mode and H-bond interactions of KR35 against

MabA b) Active binding site, binding mode and H-bond interactions of KR33 against

OAT c) Active binding site, binding mode and H-bond interactions of KR33 against LS

d) Active binding site, binding mode and H-bond interactions of KR34 against QAPRT

e) Active binding site, binding mode and H-bond interactions of KR35 against Glmu.

3. RESULTS AND DISCUSSION

Molecular docking approach has been used as an essential means in facilitating drug-target

search. The compound with least binding energy against each target protein is considered as

„Best fit‟. By this means, it is possible to understand how the compounds interact with the

receptor. The results emerging out of this study can be used to establish the possible inherent

mechanism of action of pyridine-chalcone conjugates KR1-KR35 as potential antitubercular

agents. The Molecular docking simulation technique was performed using MVD program

with 35 designed compounds assumed to be having antitubercular activity. Each compound

was docked into 10 different targets shown in Table 3 and 4. The lowest energy docked

conformation of the most populated cluster (the best cluster) was selected and then taken into

account. Table 5 summarizes the result of the docking interactions of the selected compounds


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against each target. From the results, KR35 was accomplished best binding efficiency against

SK, PS, MabA and GLmU with Moldock scores -119.898, -116.648, -119.576 and -119.645

kcal/mol respectively. Similarly, compound KR33 against CS, InhA, OAT and LS with

Moldock scores -142.08, -120.054, -94.8787 and -109.504 kcal/mol respectively.

Correspondingly compound KR10 and KR34 showed good binding efficiency against the

remaining two targets ICL and QAPRT with Moldock scores -107.754 and -104.88 kcal/mol

respectively. Among 35 molecules belongs to 1,3,5-triaizine-Chalcone basic scaffold the

“best fit” molecule showed a good binding efficiency against each protein target is identified

on the basis of their Moldock scores (kcal/mol). The results are used to understand the status

of binding capacity of this class of molecules assumed to be having antitubercular activity

with established mechanism of action. However for strengthen this approach our studies

carried forward by examining the binding orientation and H-bond interacting residues of the

best scored molecule and co-crystallized ligand of active binding site region of individual

protein target selected for Molecular docking methodology (Figure 1 and 2).

Molecular docking studies on a series of pyridine-chalcone conjugates (KR1-KR35) against

1L4Y showed that KR35 scored least binding energy with 7 hydrogen bond interactions and

the corresponding interacting residues are Thr 17, Gly 12, Gly 14, Lys 15, these hydrogen

bonds not only relevant for the binding KR35 to 1L4Y to exhibit highly selective and potent

binding affinity. The residues that participate in H-bond interactions with KR35 are may

contribute for further improvement of the binding efficiency. Similarly, pyridine-chalcone

conjugates (KR1-KR35) against 2QHF showed that KR33 scored least binding energy with

10 hydrogen bond interactions and the corresponding interacting residues are Arg 49, Ser

137, Ser 10 these hydrogen bonds not only relevant for the binding KR33 to 2QHF to exhibit

highly selective and potent binding affinity. The residues that participate in H-bond

interactions with KR33 are may contribute for further improvement of the binding efficiency.Similarly, pyridine-chalcone conjugates (KR1-KR35) against 1F8I showed that KR10 scored

least binding energy with 13 hydrogen bond interactions and the corresponding interacting

residues are Ser 317, Ser 191, Glu 285, Asp 153, Ser 91, Trp 93, Gly 92 these hydrogen

bonds not only relevant for the binding KR10 to 1F8I to exhibit highly selective and potent

binding affinity. The residues that participate in H-bond interactions with KR10 are may


conjugates (KR1-KR35) against 1N2H showed that KR35 scored least binding energy with 1

hydrogen bond interaction and the corresponding interacting residue is Val 187 this hydrogen


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bond not only relevant for the binding KR35 to 1N2H to exhibit highly selective and potent

binding affinity. The residue that participates in H-bond interaction with KR35 may


conjugates (KR1-KR35) against 2X22 showed that KR33 scored least binding energy with 1

hydrogen bond interaction and the corresponding interacting residue is Val 187 this hydrogen

bond not only relevant for the binding KR33 to 2X22 to exhibit highly selective and potent

binding affinity. The residue that participates in H-bond interaction with KR33 may


conjugates (KR1-KR35) against 1UZN showed that KR35 scored least binding energy with 2

hydrogen bond interactions and the corresponding interacting residue is Gly 90 these

hydrogen bonds not only relevant for the binding KR35 to 1UZN to exhibit highly selective

and potent binding affinity. The residue that participates in H-bond interactions with KR35

may contribute for further improvement of the binding efficiency. Similarly, pyridine-

chalcone conjugates (KR1-KR35) against 3IT4 showed that KR33 scored least binding

energy with 5 hydrogen bond interactions and the corresponding interacting residues are Lys

189, Thr 127 these hydrogen bonds not only relevant for the binding KR33 to 3IT4 to exhibit

highly selective and potent binding affinity. The residues that participate in H-bond

interactions with KR33 are may contribute for further improvement of the binding efficiency.

Similarly, pyridine-chalcone conjugates (KR1-KR35) against 2C9D showed that KR33

scored least binding energy with 6 hydrogen bond interactions and the corresponding

interacting residues are Gly 85, Thr 87, Phe 90 these hydrogen bonds not only relevant for

the binding KR33 to 2C9D to exhibit highly selective and potent binding affinity. The

residues that participate in H-bond interactions with KR33 are may contribute for further

improvement of the binding efficiency. Similarly, pyridine-chalcone conjugates (KR1-KR35)

against 1QPN showed that KR34 scored least binding energy with 2 hydrogen bond

interactions and the corresponding interacting residue is Ser 248 these hydrogen bonds not

only relevant for the binding KR34 to 1QPN to exhibit highly selective and potent binding

affinity. The residue that participates in H-bond interactions with KR34 may contribute for

further improvement of the binding efficiency. Similarly, pyridine-chalcone conjugates

(KR1-KR35) against 3D8V showed that KR35 scored least binding energy with 6 hydrogen

bond interactions and the corresponding interacting residues are Ser 112, Gly 15, Ala 14

these hydrogen bonds not only relevant for the binding KR35 to 3D8V to exhibit highly

selective and potent binding affinity. The residues that participate in H-bond interactions with

KR35 are may contribute for further improvement of the binding efficiency (Figure 1 and 2).


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4. CONCLUSIONS

This article presents molecular docking technique currently applied to database of

compounds assumed to be potential antitubercular drugs with pre-defined biological targets.

In summary, we could hypothesize KR35 as a potential modulator of SK, PS, MabA and

GlmU, similarly KR33 against CS, InhA, OAT and LS, KR10 and KR34 against the

remaining two targets ICL and QAPRT respectively.

ACKNOWLEDGMENTS

One of the authors Asst. Prof. A. Kanakaraju is thankful to Dr. Rene Thomsen, M/S Molegro

Aps, Denmark for providing academic software (one month trail license) to Andhra

University during the course of our research work.

CONFLICTS OF INTEREST

We declare that, we all authors have no conflict of interest.

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