Computation in BioInformatics. Группа авторов

motifs using DeepView/Swiss-PdbViewer. BMC Bioinf., 13, 173, 2012.

      23. Combet, C., Jambon, M., Deléage, G., Geourjon, C., Geno3D: automatic comparative molecular modelling of protein. Bioinformatics, 18, 213–214, 2002.

      24. Schwede, T., Kopp, J., Guex, N., Peitsch, M.C., SWISS-MODEL: an automated protein homology-modeling server. Nucleic Acids Res., 31, 13, 3381–3385, 2003.

      25. Winn, M.D., Ballard, C.C., Cowtan, K.D., Dodson, E.J., Emsley, P. et al., Overview of the CCP4 suite and current developments. Acta Crystallogr. D Biol. Crystallogr., 67, 235–242, 2011.

      26. Qureshi, R.H. and Noman, B., SZABIST., Is Abalone, Bio designer and Fold it, the best software for Protein Structure Prediction of AIDS Virus? J. Indep. Stud. Res. – Comput., 9, 2, 36–43, 2011.

      27. Rackers, J.A., Wang, Z., Lu, C., Laury, M.L., Lagardère, L., Schnieders, M.J., Schnieders, M.J., Tinker 8: Software Tools for Molecular Design. J. Chem. Theory Comput., 14, 10, 5273–5289, 2018.

      28. Eswar, N., Webb, B., Marti-Renom, M.A., Madhusudhan, M.S., Eramian, D., Shen, M.-y., Pieper, U., Sali, A., Comparative Protein Structure Modeling Using Modeller. Curr. Protoc. Bioinf., 15, 1, 5.6.1–5.6.30, 2006.

      29. Kaplan, W. and Littlejohn, T.G., Swiss-PDB Viewer (Deep View). Brief Bioinform., 2, 2, 195–7, 2001.

      30. Rester, U., From virtuality to reality - Virtual screening in lead discovery and lead optimization: a medicinal chemistry perspective. Curr. Opin. Drug Discovery Devel., 11, 559–568, 2008.

      31. Walters, W.P., Stahl, M.T., Murcko, M.A., Virtual screening – an overview. Drug Discovery Today, 3, 160–178, 1998.

      32. Laurie, A.T. and Jackson, R.M., Q-SiteFinder: An energy-based method for the prediction of protein-ligand binding sites. Bioinformatics, 21, 1908–1916, 2005.

33. Malathi, K., Anbarasu, A., Ramaiah, S., Ethyl Iso-allocholate from a medicinal rice karungkavuni inhibits dihydropteroate synthase in escherichia coli: A molecular docking and dynamics study. Indian J. Pharm. Sci., 78, 780–788, 2016.

      34. Forli, S., Huey, R., Pique, M.E., Sanner, M., Goodsell, D.S., Olson, A.J., Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nat. Protoc., 11, 5, 905–919, 2016.

      35. Hart, T.N. and Read, R.J., A multiple-start Monte Carlo docking method. Proteins, 13, 206–22, 1992.

      36. Hart, T.N., Ness, S.R., Read, R.J., Critical evaluation of the research docking program for the CASP2 challenge. Proteins, Suppl 1, 29, 205–9, 1997.

      37. Pagadala, N.S., Syed, K., Tuszynski, J., Software for molecular docking: a review. Biophys. Rev., 9, 2, 91–102, 2017.

      38. Bikadi, Z. and Hazai, E., Application of the PM6 semi-empirical method to modeling proteins enhances docking accuracy of AutoDock. J. Cheminform., 1, 15, 2009.

      39. Grosdidier, A., Zoete, V., Michielin, O., SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Res., 39, Web Server issue, W270–W277, 2011.

      40. Singh, K.D. and Muthusamy, K., Molecular modeling, quantum polarized ligand docking and structure-based 3D-QSAR analysis of the imidazole series as dual AT1 and ETA receptor antagonists. Acta Pharmacol. Sin., 34, 12, 1592–1606, 2013.

      41. Rizvi, S.M.D., Shakil, S., Haneef, M., A simple click by click protocol to perform docking: Autodock 4.2 made easy for non- Bioinformaticians. Excli J., 12, 831–857, 2013.

      42. Schuttelkopf, A.W. and van Aalten, D.M., PRODRG: A tool for high-throughput crystallography of protein-ligand complexes. Acta Crystallogr. Sect. D: Biol. Crystallogr., 60, 1355–1363, 2004.

      43. Alder, B.J. and Wainwright, T.E., Studies in Molecular Dynamics. I. General Method. J. Chem. Phys., 27, 1208, 1957.

      44. Van der Spoel, D., Lindahl, E., Hess, B., Groenhof, G., Mark, A.E., Berendsen, H.J., GROMACS: Fast, flexible, and free. J. Comput. Chem., 26, 1701–1718, 2005.

      45. Gimenez, B.G., Santos, M.S., Ferrarini, M., Fernandes, J.P., Evaluation of blockbuster drugs under the rule-of-five. Pharmazie, 65, 148–152, 2010.

      46. Cheng, F., Li, W., Zhou, Y., Shen, J., Wu, Z., Liu, G., Tang, Y., AdmetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties. J. Chem. Inf. Model., 52, 3099–3105, 2012.

      47. Lagunin, A., Stepanchikova, A., Filimonov, D., Poroikov, V., PASS: Prediction of activity spectra for biologically active substances. Bioinformatics, 16, 747–748, 2000.

      48. Mychaleckyj, J.C., Genome Mapping Statistics and Bioinformatics. Methods Mol. Biol., 404, 461–488, 2007.

49. Senthilvel, P., Lavanya, P., Kalavathi Murugan, K., Swetha, R., Anitha, P. et al., Flavonoid from Carica papaya inhibits NS2B-NS3 protease and prevents Dengue 2 viral assembly. Bioinformation, 9, 18, 889–895, 2013.

      50. Kumar, K.M., Lavanya, P., Anbarasu, A., Ramaiah, S., Molecular dynamics and molecular docking studies on E166A point mutant, R274N/R276N double mutant, and E166A/R274N/R276N triple mutant forms of class A β-lactamases. J. Biomol. Struct. Dyn., 32, 12, 1953–1968, 2014.

      51. Joshi, S.D., More, U.A., Dixit, S.R., Korat, H.H., Aminabhavi, T.M., Badiger, A.M., Synthesis, characterization, biological activity, and 3D-QSAR studies on some novel class of pyrrole derivatives as antitubercular agents. Med. Chem. Res., 23, 3, 1123–1147, 2013.

      52. Malathi, K. and Ramaiah, S., Molecular Docking and Molecular Dynamics Studies to Identify Potential OXA-10 Extended Spectrum β-Lactamase Non-hydrolysing Inhibitors for Pseudomonas aeruginosa. Cell Biochem. Biophys., 74, 2, 141–55, 2016.

      53. Lavanya, P., Ramaiah, S., Anbarasu, A., A Molecular Docking and Dynamics Study to Screen Potent Anti-Staphylococcal Compounds Against Ceftaroline Resistant MRSA. J. Cell Biochem., 117, 2, 542–8, 2016.

      54. Malathi, K., Anbarasu, A., Ramaiah, S., Exploring the resistance mechanism of imipenem in carbapenem hydrolysing class D beta-lactamases OXA-143 and its variant OXA-231 (D224A) expressing Acinetobacter baumannii: An in-silico approach. Comput. Biol. Chem., 67, 1–8, 2017.

      55. Baskaran, G., Salvamani, S., Ahmad, S.A., Shaharuddin, N.A., Pattiram, P.D., Shukor, M.Y., HMG-CoA reductase inhibitory activity and phytocomponent investigation of Basella alba leaf extract as a treatment for hypercholesterolemia. Drug Des. Devel. Ther., 14, 9, 509–17, 2014.

      56. Kist, R., Timmers, L.F.S.M., Caceres, R.A., Searching for potential mTOR inhibitors: Ligand-based drug design, docking and molecular dynamics studies of rapamycin binding site. J. Mol. Graph Model., 80, 251–263, 2018.

      1 *Corresponding author: [email protected]

      2

      New Strategies in Drug Discovery

       Vivek Chavda1, Yogita Thalkari2* and Swati Marwadi3

       ¹ Formulation and Protein Characterization Lab, Dr. Reddys Laboratory, Hyderabad, India

       ² Analytical Research and Development Lab, Lupin Research Park, Pune, India

       ³ Formulation and Protein Characterization Lab, Lupin Research Park, Pune, India

       Abstract

      The procedure involved in drug discovery is intricate, tedious, and cost incurring and requires multi-disciplinary expertize and inventive methodologies.