International Journal of Innovative Pharmaceutical ...ijipsr.com/sites/default/files/articles/IJIPSRMNR-234.pdf · INSILICO DESIGN AND MOLECULAR DOCKING STUDIES OF NOVEL PYRIDYL TRIAZOLE

RESEARCH ARTICLE Shiny George et.al / IJIPSR / 2 (12), 2014, 3032-3039

Department of Pharmaceutical Chemistry ISSN (online) 2347-2154

Available online: www.ijipsr.com December Issue 3032

INSILICO DESIGN AND MOLECULAR DOCKING STUDIES OF

NOVEL PYRIDYL TRIAZOLE DERIVATIVES AS CYP-51

INHIBITORS

1Shiny George*,

2Salina.S,

3Shameeja.K,

4Sruthiprabha.K,

5Vineeth.M.T

Department of Pharmaceutical Chemistry, Devaki Amma Memorial College of Pharmacy,

Chelembra, Malappuram, Kerala-673634, INDIA

Corresponding Author

Dr. Shiny George

Asst.Professor,

Devaki Amma Memorial College of Pharmacy,

Chelembra, Malappuram,

Kerala – 673634, INDIA

Email: [email protected]

Mobile: +919656860305

International Journal of Innovative

Pharmaceutical Sciences and Research www.ijipsr.com

Abstract

Molecular docking is one of the best data-based screening methodology of virtual screening for ligand

which minimize the work cost by filtering and also helps to predicted the toxicity study for designing the

formulation or synthesis of New Chemical Entity (NCE) in pharmaceutical research developments. In

the present investigation a new series of benzothiazole incorporated pyridyl triazole derivatives were

designed as cytochrome P450 inhibitors based on docking studies and oral bioavailability scores based

on Lipinski’s rule evaluation. Insilico molecular docking was carried out using ArgusLab. To identify

potential anti-fungal lead compounds among compounds 6a1-6j3, docking calculations were performed

into the 3D structure of the catalytic site of CYP 51 enzyme (pdb code: 1EA1). Docking score of the

novel compounds showed good fit against CYP 51 while compared with antifungal drug fluconazole.

Keywords: Benzothiazole, 1,2,4-triazole, pyridine, CYP 51, Docking.

mailto:[email protected]




INTRODUCTION

Since last few decades, there is tremendous growth of research in the synthesis of nitrogen

containing heterocyclic derivatives because of their utility in various applications such as

pharmaceuticals, propellants, explosives, pyrotechnics and especially in chemotherapy. Molecular

docking has a wide variety of uses and applications in drug discovery, including structure-activity

studies, lead optimization, finding potential leads by virtual screening, providing binding

hypothesis to facilitate predictions for mutagenesis, assisting x-ray crystallography in the fitting

of substrate and inhibitors to electron density, chemical mechanism studies and combinatorial

library design. Computer-based molecular modeling aims to speed up drug discoveries by

predicting potential effectiveness of ligand-protein interactions prior to laborious experiments and

costly preclinical trials. Pyridine is associated with diverse biological activities [1-3].

Azoles exert antifungal activity through inhibition of cytochrome P450 14α-demethylase

(CYP51), which is crucial in the process of biosynthesis of ergosterol by a mechanism in which

the heterocyclic nitrogen atom (N-4 of 1,2,4-triazole) binds to the heme iron atom [4]. Selective

inhibition of CYP51 would cause depletion of ergosterol and accumulation of lanosterol and other

14-methyl sterols resulting in the growth inhibition of fungal cells [5-7].

Our recent docking experiments for pyridyl triazole into the catalytic site of CYP51 (pdb code:

1EA1) as template showed that the molecules bind to the catalytic site adopting the similar

bioactive conformation as observed in the crystallized complex of ligand with the enzyme. The

aim of this study is a comparison of pharmacophore features of active conformation obtained via

docking of the drug in CYP51, with the pharmacophore model proposed for the antifungal drugs.

In order to define more precisely the structure-activity relationship within the investigated

compounds a molecular modeling study was undertaken.

The present study aimed to develop molecules with improved antimicrobial activity. The possible

effective molecules were designed by incorporating the triazole and pyridine nucleus in to

benzothiazole moiety. The objectives are to screen the 1,2,4-triazole derivatives by using Lipinski

rule of 5 for oral bioavailability and carry out docking simulation by using ArgusLab and find out

the derivative with higher docking scores.

MATERIALS AND METHODS

All the compounds were constructed using Chem Draw Ultra software, Cambridge Soft

Corporation, USA. Version-8.0 April 23, 2003. It is a Chem Tech tool used for the drawing of




ligand molecules. The crystal structure of cyp 51 receptor used for docking was recovered from

the Brookhaven Protein Data Bank (http://www.rcsb.org/pdb/home) (entry code: 1EA1).

Docking study

Lead optimization

Lead optimization was done through insilico Lipinski filter. Molinspiration server was used for

this purpose [8]. The structure drawn in the JME editor was subjected to calculate the

druglikeness score through calculate the properties module. The datas are given in the table 2.

Input File Preparations for Energy Minimization of Protein

For each of the protein-ligand complexes chosen for the study, a “clean input file” was generated

by removing water molecules, ions, ligands, and subunits not involved in ligand binding from the

original structure file. Water molecules were removed because ArgusLab sometimes failed to

dock the compounds having water molecules at their binding sites. All hydrogen atoms in the

protein were allowed to optimize. The hydrogen locations are not specified by the X-ray structure

but these are necessary to improve the hydrogen bond geometries, at the same time maintaining

the protein conformation very close to that observed in the crystallographic model. The resulting

receptor model was saved to a PDB file. Minimization was performed by geometry convergence

function of ArgusLab software performed according to Hartree-Fock calculation method [9].

Ligand Input File Preparation and Optimization

Ligand input structure was drawn using Chem Draw software. The structure was cleaned in 3D

format and energy was minimized. The resulting structure was then saved in “mdl mol” format

for molecular docking studies.

Docking Methodology

After the preparation of the protein and ligand, molecular docking studies were performed by

ArgusLab 4.0.1 to evaluate the interactions. The active site of protein was obtained from CASTp

[10].

ArgusLab 4.0.1

ArgusLab is implemented with shapebased search algorithm. Docking has been done using

“Argus Dock” exhaustive search docking function of ArgusLab with grid resolution of 0.40 ˚ A.

Docking precision was set to “Regular precision” and “Flexible” ligand docking mode was

employed for each docking run. The stability of each docked pose was evaluated using ArgusLab

energy calculations and the number of hydrogen bonds formed.




Molecular Docking Study

To perform docking one first needs to define atoms that make up the ligand and the binding sites

of the protein where the ligand should bind. The prepared 3D structures of 1EA1 protein was

downloaded into the ArgusLab program and binding sites were made by choosing “Make binding

site for this protein” option. The ligand was then introduced and docking calculation was allowed

to run using shape-based search algorithm and AScore scoring function. The scoring function is

responsible for evaluating the energy between the ligand and the protein target. Flexible docking

was allowed by constructing grids over the binding sites of the protein and energy-based rotation

is set for that ligand’s group of atoms that do not have rotatable bonds. For each rotation, torsions

are created and poses (conformations) are generated during the docking proces. For each complex

10 independent runs were conducted and one pose was returned for each run. The best docking

model was selected according to the lowest AScore calculated by ArgusLab and the most suitable

binding conformation was selected on the basis of hydrogen bond interactions between the ligand

and protein near the substrate binding site. The lowest energy poses indicate the highest binding

affinity as high energy produces the unstable conformations [11].

RESULTS AND DISCUSSION

The least binding energy exhibits the highest activity which has been observed by the ranking of

poses generated by AScore scoring function of ArgusLab and is given in Table 3. ARG 96 of

1EA1 form hydrogen bonding with 6f3 with a bond length of 3.218 A0. ALA 256 of 1EA1 form

hydrogen bonding with 6d2 with bond length 2.721 A0.Ligand 6i1 shows best binding energy of -

12.6216 when comparing with standard drug fluconazole.

N

N

N

N

NH

S

N R1

R2




Table 1: List of substituents used

S.No Compound code R1 R2 S No Compound code R1 R2

1 6a1 H H 16 6f2 NO2 CH3

2 6b1 H NH2 17 6g2 NO2 OCH3

3 6c1 H NH2 18 6h2 NO2 F

4 6d1 H Br 19 6i2 NO2 CH3

5 6e1 H Cl 20 6j2 NO2 Cl

6 6f1 H CH3 21 6a3 NH2 H

7 6g1 H OCH3 22 6b3 NH2 NH2

8 6h1 H F 23 6c3 NH2 NH2

9 6i1 H CH3 24 6d3 NH2 Br

10 6j1 H Cl 25 6e3 NH2 Cl

11 6a2 NO2 H 26 6f3 NH2 CH3

12 6b2 NO2 NH2 27 6g3 NH2 OCH3

13 6c2 NO2 NH2 28 6h3 NH2 F

14 6d2 NO2 Br 29 6i3 NH2 CH3

15 6e2 NO2 Cl 30 6j3 NH2 Cl

Table 2 Lipinski Rule Analysis

S .No Compound

code Log p

H donor

(nON)

H acceptor

(nOHNH)

Mol.

Wt No of violation

1 6a1 4.634 6 1 370.441 0

2 6b1 4.069 7 3 385.456 0

3 6c1 4.069 7 3 385.456 0

4 6d1 5.419 6 1 449.337 1

5 6e1 5.264 6 1 404.886 1

6 6f1 5.58 6 1 384.468 1

7 6g1 4.667 7 1 400.467 0

8 6h1 4.75 6 1 388.431 0

9 6i1 5.034 6 1 384.468 1

10 6j1 5.288 6 1 404.886 1

11 6a2 4.593 9 1 415.438 0

12 6b2 4.028 10 3 430.453 0

13 6c2 3.645 10 3 430.453 0

14 6d2 5.378 9 1 494.334 1

15 6e2 5.223 9 1 449.883 1

16 6f2 5.017 9 1 429.465 1

17 6g2 4.625 10 1 445.464 0

18 6h2 4.708 9 1 433.428 0

19 6i2 4.993 9 1 429.465 0

20 6j2 5.247 9 1 449.883 1

21 6a3 3.71 7 3 385.456 0

22 6b3 3.145 8 5 400.471 0

23 6c3 2.762 8 5 400.471 0

24 6d3 4.495 7 3 464.352 0

25 6e3 4.34 7 3 419.901 0

26 6f3 4.134 7 3 399.483 0

27 6g3 3.742 8 3 415.482 0

28 6h3 3.826 7 3 403.446 0

29 6i3 4.11 7 3 399.483 0

30 6j3 4.364 7 3 419.901 0




Table 3 binding energy of designed analogues

Fig.1: binding interaction of 6c2 with 1EA1. Fig 2: CYS 394 of 1EA1 form hydrogen

bonding with 6a2

Fig 3: 1EA1 form hydrogen bonding with 6f3 Fig 4: 1EA1 form hydrogen bonding with 6d2

CONCLUSION

Preliminary in-silico molecular modeling was carried out with the help of available softwares.

Most of the proposed analogs obeyed Lipinski’s Rule of Five. Docking studies were carried out

on the proposed analogue to determine the affinity with the enzyme CYP 51 using Argus lab. The

analogue 6d3 was found to have higher docking score and significant binding interaction.

S. No Compound

code

Binding energy

(kcal/mol) S. No

Compound

code

Binding energy

(kcal/mol)

1 6a1 -10.9077 12 6d3 -11.0399

2 6a2 -9.21338 13 6h2 -11.3267

3 6a3 -9.74558 14 6h3 -9.66509

4 6b1 -9.64901 15 6f3 -10.2645

5 6b2 -8.88428 16 6g1 -10.8102

6 6b3 -9.06004 17 6g2 -9.39291

7 6c1 -10.4343 18 6g3 -8.88844

8 6c2 -9.62976 19 6i1 -12.6216

9 6c3 -9.32983 20 6i2 -9.04603

10 6e3 -11.8979 21 6j3 -10.0724

11 6h1 -8.55756 22 Fluconazole -8.58




Molecular docking studies shows that hydrogen bond interaction and hydrophobic interaction

plays a crucial role in the biological activity of novel compounds. From the present study it can be

concluded that the benzothiazole incorporated pyridyl triazole derivatives were found to possess

good CYP-51 inhibition. Further experimental approaches can be adopted to prove the effect of

structural alterations of pyridyl triazole in the catalytic site of cytochrome P450 14α-demethylase.

REFERENCE

1. Bruno Leal, Ilidio Afonso F, Carlos Rodrigues, Paula Abreu A, Rafael Garrett, Luiz

Carlos Pinheiro S, Alexandre Azevedo R, Julio Borges C, Percilene Vegi F, Claudio

Santos C C, Francisco da Silveira C A., Antibacterial profile against drug-resistant

staphylococcus epidermidis clinical strain and structure-activity relationship studies of

1H-Pyrazolo [3, 4-b] pyridine and thieno [2, 3-b] pyridine derivatives, Bioorg Med

Chem., 2008, 16, 8196-8204.

2. Jong-Keun Son, Long-Xuan Zhao, Arjun Basnet, Pritam Thapa, Radha Karki, Younghwa

Na, Yurngdong Jahng, Tae Cheon Jeong , Byeong-Seon Jeong, Chong-Soon Lee, Eung-

Seok Lee., Synthesis of 2, 6-diaryl-substituted pyridine and their anti tumor activities, Eur

J Med Chem., 2008, 43, 675-682.

3. Vera Klimesova, Petra Herzigova, Karel Palat, Milos Machacek, Jirina Stolaríkova, Hans-

Martin Dahse and Ute Mollmann., The synthesis and antimycobacterial properties of 4-

(substitutedbenzylsulfanyl)pyridine-2-carboxamides, ARKIVOC., 2012, (iii) 90-103.

4. Georgopapadakou N.H., Walsh T.J, Antifungal agents: chemotherapeutic targets and

immunologic strategies. Antimicrob. Agents Chemother; 40:279–291, 1996.

5. Sheng C.Q., Zhang W.N., Ji H.T., Zhang M., Song Y.L., Xu H., Zhu J., Miao Z.Y., Jiang

Q.F., Yao J.Z., Zhou Y.J., Zhu J., Lv J.G, Structure-based optimization of azole antifungal

agents by CoMFA, CoMSIA, and molecular docking. J Med Chem; 49:2512–2525, 2006.

6. Weerasak Samee1 and Opa Vajragupta, Antifungal, cytotoxic activities and docking

studies of 2,5-dimercapto-1,3,4-thiadiazole derivatives, African Journal of Pharmacy and

Pharmacology Vol. 5(4), pp. 477-485, April 2011.

7. Soghra Khabnadideh, Zahra Rezaei, Ali Khalafi-Nezhad, Keyvan Pakshir, Mohammad

Javad Heiran, Hesamedin Shobeiri, Design and Synthesis of 2-Methyl and 2-Methyl-4-

Nitro Imidazole Derivatives as Antifungal Agents, Iranian Journal of Pharmaceutical

Sciences, 2009: 5(1): 31-36




8. Sonia George and Ravi T. K, design, synthesis and antitubercular screening of certain

novel thiadiazolyl pyrrolidine carboxamides as enoyl acp reductase inhibitors, Int J

Pharm Pharm Sci, Vol 3, Suppl 4, 280-284 , 2011.

9. S. Joy, P. S. Nair, R. Hariharan, and M. R. Pillai, “Detailed comparison of the protein-

ligand docking efficiencies of GOLD, a commercial package and arguslab, a licensable

freeware,” In Silico Biology, vol. 6, no. 6, pp. 601–605, 2006.

10. Joe Dundas, Zheng Ouyang, Jeffery Tseng, Andrew Binkowski, Yaron Turpaz, and Jie

Liang. CASTp: computed atlas of surface topography of proteins with structural and

topographical mapping of functionally annotated resiudes. Nucleic Acid Research,

34:W116-W118, 2006.

11. Ambreen Hafeez, Zafar Saied Saify, Afshan Naz, Farzana Yasmin and Naheed Akhtar,

Molecular Docking Study on the Interaction of Riboflavin (Vitamin B2) and

Cyanocobalamin (Vitamin B12) Coenzymes, Journal of Computational Medicine, Volume

2013, Article ID 312183, 5 pages.

Documents

International Journal of Innovative Pharmaceutical ...ijipsr.com/sites/default/files/articles/IJIPSRMNR-234.pdf · INSILICO DESIGN AND MOLECULAR DOCKING STUDIES OF NOVEL PYRIDYL TRIAZOLE