Join us   Log in   pharmaspire@isfcp.org  

Abstract


PHARMASPIRE - Volume 14,Issue 2, 2022 , April-June

Pages: 76-84

Computational screening for the identification of potent tubulin inhibitors as anticancer agents

Priyanka Sharma, Vivek Asati

Category: P'Ceutical Chemistry

Download PDF

Abstract:

Tubulin is a biological target for several multiple clinically used anticancer drugs which is responsible forn chromosome segregation, cell shape maintenance, transport, motility, and organelle dispersion, among other things. For decades, anticancer medicines that target the microtubule, such as taxanes and vinca alkaloids, have formed the cornerstone of many chemotherapy regimens. However, these medicines have substantial drawbacks, prompting the development of new microtubule targeting compounds. The pyrazole ring system is a critical component of various tubulin inhibitors discovered in recent years. In the present study, a dataset of tubulin inhibitors have been downloaded from PubMed database and included for screening study by Glide module. The Lipinski rule of 5, high-throughput virtual screening, standard precision, and extra precision methodologies have been used for final screening of potent compounds against tubulin protein (PDB ID: 3E22). The docking studies of these inhibitors revealed a complementary fit in the allosteric site of Tubulin protein. Among all the selected inhibitors, Centaureldin and Chalcones MDL showed the highest docking scores of -7.76 and -6.21 kcal/mol, respectively, when compared with the cocrystal ligands of PDB-3E22. Post molecular mechanics/generalized born surface area analysis of these potent inhibitors showed dG binding values -37.83 and -28.37 kcal/mol, respectively. On the basis of final screened compounds, we can further develop pharmacophore model and screened potential compounds against tubulin protein.

Keywords: Extra precision, High throughput virtual screen, Pharmacophore model, Standard precision, Tubulin inhibitors, Virtual screening

References:

1. Sheng L, Hao SL, Yang WX, Sun Y. The multiple functions of kinesin-4 family motor protein KIF4 and its clinical potential. Gene 2018;678:90-9.
2. Bates D, Eastman A. Microtubule destabilising agents: Far more than just antimitotic anticancer drugs. Br J Clin Pharmacol 2017;83:255-68.
3. Jordan MA, Wilson L. Microtubules as a target for anticancer drugs. Nat Rev Cancer 2004;4:253-65.
4. Khattab M, Al-Karmalawy A. Computational repurposing of benzimidazole anthelmintic drugs as potential colchicine binding site inhibitors. Future Med Chem 2021;13:1623-38.
5. Mukhtar E, Adhami VM, Mukhtar H. Targeting microtubules by natural agents for cancer therapy. Mol Cancer Ther 2014;13:275-84.
6. Wu X, Wang Q, Li W. Recent advances in heterocyclic tubulin inhibitors targeting the colchicine binding site. Anticancer Agents Med Chem 2016;16:1325-38.
7. Brenner DR, Weir HK, Demers AA, Ellison LF, Louzado C, Shaw A, et al. Projected estimates of cancer in Canada in 2020. CMAJ 2020;192:E199-205.
8. Shim JS, Liu JO. Recent advances in drug repositioning for the discovery of new anticancer drugs. Int J Biol Sci 2014;10:654-63.
9. Parvathaneni V, Kulkarni NS, Muth A, Gupta V. Drug repurposing: A promising tool to accelerate the drug
discovery process. Drug Discov Today 2019;24:2076-85.
10. Abdullahi M, Adeniji SE. In-silico molecular docking and ADME/pharmacokinetic prediction studies of some novel carboxamide derivatives as anti-tubercular agents. Chem Afr 2020;3:989-1000.
11. Anant A, Ali A, Ali A, Gupta GD. A Computational approach to discover potential quinazoline derivatives against CDK4/6 kinase. J Mol Struct 2021;1245:131079.
12. Simon L, Imane A, Srinivasan KK, Pathak L, Daoud I. In silico drug-designing studies on flavanoids as anticolon cancer agents: Pharmacophore mapping, molecular docking, and Monte Carlo method-based QSAR modeling. Interdiscip Sci 2017;9:445-58.
13. .Arooj M, Sakkiah S, Kim S, Arulalapperumal V, Lee KW. A combination of receptor-based pharmacophore modeling
& QM techniques for identification of human chymase inhibitors. PLoS One 2013;8:e63030.
14. Anand SA, Chandrasekaran L, Kuppusamy S, Kabilan S. Comparison of molecular docking and molecular dynamics simulations of 1, 3-thiazin-4-one with MDM2 protein. Int Lett Chem Phys Astron 2015;60:161-7.
15. Chen H, Lyne PD, Giordanetto F, Lovell T, Li J. On evaluating molecular-docking methods for pose prediction and enrichment factors. J Chem Inf Model 2006;46:401-15.
16. Elokely KM, Doerksen RJ. Docking challenge: Protein sampling and molecular docking performance. J Chem Inf Model 2013;53:1934-45.
17. Bode W, Turk D, Karshikov A. The refined 1.9-Å X-ray crystal structure of D-Phe-Pro-Arg chloromethylketoneinhibited human α-thrombin: Structure analysis, overall structure, electrostatic properties, detailed active-site geometry, and structure-function relationships. Protein Sci 1992;1:426-71.
18. Tripathi SK, Singh SK, Singh P, Chellaperumal P, Reddy KK, Selvaraj S. Exploring the selectivity of a ligand complex with CDK2/CDK1: A molecular dynamics simulation approach. J Mol Recognit 2012;25:504-12.
19. Stanzione F, Giangreco I, Cole JC. Use of molecular docking computational tools in drug discovery. Prog Med Chem 2021;60:273-343.
20. Sotriffer C, Klebe G. Identification and mapping of smallmolecule binding sites in proteins: Computational tools for structure-based drug design. Farmaco 2002;57:243-51.
21. Hu X, Li H. Force spectroscopy studies on protein-ligand interactions: A single protein mechanics perspective. FEBS Lett 2014;588:3613-20.
22. Tran-Nguyen VK, Bret G, Rognan D. True accuracy of fast scoring functions to predict high-throughput screening data from docking poses: The simpler the better. J Chem Inf Model 2021;61:2788-97.
23. Morris GM, Lim-Wilby M. Molecular docking. Methods Mol Biol 2008;443:365-82.
24. Kirchmair J, Markt P, Distinto S, Schuster D, Spitzer GM, Liedl KR, et al. The protein data bank (PDB), its related services and software tools as key components for in silico guided drug discovery. J Med Chem 2008;51:7021-40.
25. Totrov M, Abagyan R. Flexible ligand docking to multiple receptor conformations: A practical alternative. Curr Opin Struct Biol 2008;18:178-84.
26. Huang SY, Zou X. Advances and challenges in proteinligand docking. Int J Mol Sci 2010;11:3016-34.
27. Hou T, Wang J, Li Y, Wang W. Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J Chem Inf Model 2011;51:69-82.
28. Li J, Abel R, Zhu K, Cao Y, Zhao S, Friesner RA. The VSGB 2.0 model: A next generation energy model for high resolution protein structure modeling. Proteins 2011;79:2794-812.
29. Kumar BK, Faheem, Sekhar KV, Ojha R, Prajapati VK, Pai A, et al. Pharmacophore based virtual screening, molecular docking, molecular dynamics and MM-GBSA approach for identification of prospective SARS-CoV-2 inhibitor from natural product databases. J Biomol Struct Dyn 2022;40:1363-86.
30. Prasanthi GJ. In-silico estimation on oral bioavailability and drug-likeness of mono and bis-mannich bases of piperazine derivatives. J Glob Trends Pharm Sci 2014;5:1485-8.
31. Asati V, Bharti SK. Design, synthesis and molecular modeling studies of novel thiazolidine-2, 4-dione derivatives as potential anti-cancer agents. J Mol Struct 2018;1154:406-17.
32. Tantawy MA, Shaheen S, Kattan SW, Alelwani W, Barnawi IO, Elmgeed GA, et al. Cytotoxicity, in silico predictions and molecular studies for androstane heterocycle compounds revealed potential antitumor agent against lung cancer cells. J Biomol Struct Dyn 2020;40:1-14.

DOI: 10.56933/Pharmaspire.2022.14209

DOI URL: http://isfcppharmaspire.com