Grid based docking study of some 2-(4-methylsulfonyl phenyl) pyrimidine derivatives (designed after QSAR studies) with Cyclooxygenase-2

Year : 2024 | Volume :01 | Issue : 01 | Page : 23-41
By

Satish Sarankar,

A.K Pathak,

Abstract

Molecular docking helps in studying drug/ligand or receptor/ protein interactions by identifying the suitable active sites in protein, obtaining the best geometry of ligand-receptor complex and calculating the energy of interaction for different ligands to design more effective ligands. In Grid based docking, after unique conformers of the ligand are generated, the receptor cavity of interest is chosen and a grid is generated around the cavity. Cavity points are found and the centre of mass of the ligand is moved to each cavity point. All rotations of ligand are scanned at each cavity point where ligand is placed. Efficiency and precision of docking rely upon scoring function. Scoring functions are based on force fields that are used to simulate the functions of proteins. With known 3D structure of receptor molecule or protein, the binding affinity of the protein ligand complex was calculated and it is termed as Dock Score. In our research work, to know the biding affinity of some 2-(4-methylsulfonylphenyl) pyrimidine derivatives (designed after QSAR studies) with Cyclooxygenase-2, the compounds were docked with three different receptors namely Cyclooxygenase-2 (Prostaglandin synthase-2), Uninhibited Mouse Cyclooxygenase-2 (Prostaglandin Synthase-2) and Membrane Protein Prostaglandin H2 Synthase-1) using VLifeMDS software. Among all compounds, three compounds having 2,2-dibromoethanamine, 2,2-diiodoethanamine and 2,2,2-triiodoethanamine substituted pyrimidine derivatives showed best docking score with all three COX-2 receptors. With Prostaglandin synthase-2 after successful completion of docking process, the minimum score obtained was -6.199069, -6.180829 and -6.049794 for three compounds respectively. The compounds were docked with a different receptor Uninhibited Mouse Cyclooxygenase-2 and the same three compounds showed minimum score of -6.293567, -6.202445, -6.100384 respectively. The docking score was found -6.345966, -6.245738, -6.150805 with Membrane Protein Prostaglandin H2 Synthase-1 with the same substituted pyrimidine derivatives. The present research work opened a new path to fulfill the increasing demand of COX-2 inhibitors which can be used in the treatment and cure of various ailments those are mediated through COX-2 pathway.

Keywords: Docking, Pyrimidine derivatives, Cyclooxygenase-2, Docking score, VLifeMDS software

[This article belongs to International Journal of Antibiotics(ijab)]

How to cite this article: Satish Sarankar, A.K Pathak. Grid based docking study of some 2-(4-methylsulfonyl phenyl) pyrimidine derivatives (designed after QSAR studies) with Cyclooxygenase-2. International Journal of Antibiotics. 2024; 01(01):23-41.
How to cite this URL: Satish Sarankar, A.K Pathak. Grid based docking study of some 2-(4-methylsulfonyl phenyl) pyrimidine derivatives (designed after QSAR studies) with Cyclooxygenase-2. International Journal of Antibiotics. 2024; 01(01):23-41. Available from: https://journals.stmjournals.com/ijab/article=2024/view=131738



References

T. Cronin, J. C. Dearden, Quantitative structure-activity relationships for human health effects: Commonalities with other endpoints, Environ. Toxicol. Chem. 22 (2003) 1829-1843.
L. Podlogar, D. M. Ferguson, QSAR and CoMFA: A perspective on the practical application to drug discovery. Drug Des Discov 1 (2000) 4-12.
Z. Dudek, O. Arodz, J. Galvez, Computational methods in developing quantitative structure-activity relationships (QSAR): A Review. Combinatorial Chemistry & High Throughput Screening, 9(3) (2006) 213.
Vedani, M. Dobler, et. al., Combining protein modeling and 6D-QSAR: Simulating the binding of structurally diverse ligands to the estrogen receptor, J. Med. Chem.,48 (2005) 3700-3703.
Todeschini, V. Consonni, Descriptors from molecular geometry. In: Gasteiger J (ed) Handbook of Chemoinformatics, Wiley-VCH, Weinheim (2003)
S. Du, R. B. Huang, et. al., Fragment-based quantitative structure-activity relationship (FB-QSAR) for fragment-based drug design, Journal of Computational Chemistry,30(2) (2009) 295-304.
Z. Myint, C. Ma, et. al., Fragment-Similarity-Based QSAR (FS-QSAR) algorithm for ligand biological activity predictions, SAR and QSAR in Environmental Research 22 (3-4) (2010) 1-26.
S. Reddy, S. P. Pati et. al., Virtual screening in drug discovery – A computational perspective, Current protein and peptide science, 8 (2007) 329-351.
A. Lewis, S. D. Pickett, D. E Clark, Computer-aided molecular diversity analysis and combinatorial library design. In: Reviews in Computational Chemistry, Edited by Kenny B. Lipkowitz DBB (2007) 1-51.
Bansal, B. Sinha et. al., QSAR and docking-based computational chemistry approach to novel GABA-AT inhibitors: kNN-MFA-based 3DQSAR model for phenyl-substituted analogs of phenylethylidene hydrazine, Medicinal Chemistry Research, 20 (5)(2011) 549-553.
B. Kitchen, H. Decornez et. al., Docking and scoring in virtual screening for drug discovery: methods and applications, Nature Reviews Drug Discovery, 3(11) (2004) 935.
Danjuma, Abdullahi, A. M. Abdualkader, Application of group-based QSAR and molecular docking in the design of insulin-like growth factor antagonists, Tropical Journal of Pharmaceutical Research,14 (6) (2015) 941-951.
N. Jain, Effects of protein conformation in docking : Improved pose prediction through protein pocket adaptation, J Comput Aided Mol Des, 23 (2009) 355–374.
Hippisley-Cox, C. Coupland et. al., Risk of adverse gastrointestinal outcomes in patients taking cyclo-oxygenase-2 inhibitors or conventional non-steroidal anti-inflammatory drugs: population based nested case-control analysis, BMJ, 331 (2005) 1310-1316.
Richard, J. Barbara, Do cox-2 inhibitors worsen renal function? The Journal of Family Practice, 56 (11) (2007).
RS B. Cardiovascular events associated with rofecoxib in a colorectal adenoma chemoprevention trial. N Engl J         2005;352:1092-102.
Nofal ZM, Fahmy HH, Zarea ES, El-Eraky W. Synthesis of new pyrimidine derivatives with evaluation of their anti-inflammatory and analgesic activities. Acta Pol Pharm. 2011 Jul 1;68(4):507-17.
Tirlapur VK, Narasimha G, Raga B, Prasad YR. Synthesis, characterization and biological activities of some new pyrimidines and isoxazoles bearing benzofuran moiety. International Journal of ChemTech Research. 2010;2(3):1434-40.
Singour PK, Khare A, Dewangan H, Pawar RS. Synthesis and biological evaluation of novel pyrimidine derivatives as anti-inflammatory agents. J Pharm Res. 2012 Sep;5:4853-8.
Ghorab MM, Alsaid MS. Synthesis of some tricyclic indeno [1, 2-d] pyrimidine derivatives as a new class of anti-breast cancer agents. Biomedical Research. 2015 Jan 1;26(3):420-5.
Pathak AK, Sarankar SK, Mehta P. Two dimensional QSAR study of novel 2-(4-methyl sulfonylphenyl) pyrimidine derivatives as highly potent and specific COX-2 inhibitors. Journal of Pharmacy Research. 2011 Jun;4(6):1592-5.
Jain SK, Bharti SK, Jagan BG, Gupta AK. 3D-QSAR and Pharmacophoric study on 2, 6-Disubstituted Thiazolo [4, 5-b] Pyridines as H3 Receptor Antagonists. Research Journal of Pharmacy and Technology. 2023 Oct 1;16(10):4575-82.
Orjales A, Mosquera R, Lopez B, Olivera R, Labeaga L, Núñez MT. Novel 2-(4-methylsulfonylphenyl) pyrimidine derivatives as highly potent and specific COX-2 inhibitors. Bioorganic & medicinal chemistry. 2008 Mar 1;16(5):2183-99.
Tervo AJ, Nyrönen TH, Rönkkö T, Poso A. Comparing the quality and predictiveness between 3D QSAR models obtained from manual and automated alignment. Journal of chemical information and computer sciences. 2004 May 24;44(3):807-16.
Tervo AJ, Nyrönen TH, Rönkkö T, Poso A. Comparing the quality and predictiveness between 3D QSAR models obtained from manual and automated alignment. Journal of chemical information and computer sciences. 2004 May 24;44(3):807-16.
Asikainen A, Kolehmainen M, Ruuskanen J, Tuppurainen K. Structure-based classification of active and inactive estrogenic compounds by decision tree, LVQ and kNN methods. Chemosphere. 2006 Jan 1;62(4):658-73.
Burley SK, Berman HM, Bhikadiya C, Bi C, Chen L, Di Costanzo L, Christie C, Dalenberg K, Duarte JM, Dutta S, Feng Z. RCSB Protein Data Bank: biological macromolecular structures enabling research and education in fundamental biology, biomedicine, biotechnology and energy. Nucleic acids research. 2019 Jan 8;47(D1):D464-74.


Regular Issue Subscription Original Research
Volume 01
Issue 01
Received January 19, 2024
Accepted January 23, 2024
Published January 31, 2024