Abstract
Due to accumulating protein structure information and advances in computational methodologies, it has now become possible to predict protein-compound interactions. In biology, the classic strategy for drug discovery has been to manually screen multiple compounds (small scale) to identify potential drug compounds. Recent strategies have utilized computational drug discovery methods that involve predicting target protein structures, identifying active sites, and finding potential inhibitor compounds at large scale. In this protocol article, we introduce an in silico drug discovery protocol. Since multi-drug resistance of pathogenic bacteria remains a challenging problem to address, UDP-N-acetylmuramate-L-alanine ligase (murC) of Acinetobacter baumannii was used as an example, which causes nosocomial infection in hospital setups and is responsible for high mortality worldwide. This protocol should help microbiologists to expand their knowledge and research scope.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Ahmad, S., Murtaza, U.A., Raza, S., and Azam, S.S. 2019. Blocking the catalytic mechanism of MurC ligase enzyme from Acinetobacter baumannii: An in silico guided study towards the discovery of natural antibiotics. J. Mol. Liq.281, 117–133.
Apweiler, R., Bairoch, A., Wu, C.H., Barker, W.C., Boeckmann, B., Ferro, S., Gasteiger, E., Huang, H., Lopez, R., Magrane, M., et al. 2004. UniProt: the universal protein knowledgebase. Nucleic Acids Res.32, D115–D119.
Arlaud, G.J., Gaboriaud, C., Garnier, G., Circolo, A., Thielens, N.M., Budayova-Spano, M., Fontecilla-Camps, J.C., and Volanakis, J.E. 2002. Structure, function and molecular genetics of human and murine C1r. Immunobiology205, 365–382.
Ayers, M. 2012. ChemSpider: The free chemical database. Ref. Rev.26, 45–46.
Banerjee, P., Eckert, A.O., Schrey, A.K., and Preissner, R. 2018. Pro-Tox-II: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Res.46, W257–W263.
Berman, H.M., Battistuz, T., Bhat, T.N., Bluhm, W.F., Bourne, P.E., Burkhardt, K., Feng, Z., Gilliland, G.L., Iype, L., Jain, S., et al. 2002. The protein data bank. Acta Crystallogr. Sect. D Biol.58, 899–907.
Bertoni, M., Kiefer, F., Biasini, M., Bordoli, L., and Schwede, T. 2017. Modeling protein quaternary structure of homo- and heterooligomers beyond binary interactions by homology. Sci. Rep.7, 10480.
Bharath, E.N., Manjula, S.N., and Vijaychand, A. 2011. In silico drug design tool for overcoming the innovation deficit in the drug discovery process. Int. J. Pharm. Pharm. Sci.3, 8–12.
Buchan, D.W.A. and Jones, D.T. 2019. The PSIPRED protein analysis workbench: 20 years on. Nucleic Acids Res.47, W402–W407.
Burley, S.K., Berman, H.M., Kleywegt, G.J., Markley, J.L., Nakamura, H., and Velankar, S. 2017. Protein data bank (PDB): The single global macromolecular structure archive. Methods Mol. Biol.1607, 627–641.
Conchúir, S.Ó., Barlow, K.A., Pache, R.A., Ollikainen, N., Kundert, K., O’Meara, M.J., Smith, C.A., and Kortemme, T. 2015. A web resource for standardized benchmark datasets, metrics, and rosetta protocols for macromolecular modeling and design. PLoS One10, e134033.
Daina, A., Michielin, O., and Zoete, V. 2017. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep.7, 42717.
Dallakyan, S. and Olson, A.J. 2015. Small-molecule library screening by docking with PyRx. Methods Mol. Biol.1263, 243–250.
Davies, M., Nowotka, M., Papadatos, G., Dedman, N., Gaulton, A., Atkinson, F., Bellis, L., and Overington, J.P. 2015. ChEMBL web services: Streamlining access to drug discovery data and utilities. Nucleic Acids Res.43, W612–W620.
Edwards, Y.J. and Cottage, A. 2001. Prediction of protein structure and function by using bioinformatics. Methods Mol. Biol.175, 341–375.
Eisenberg, D., Luthy, R., and Bowie, J.U. 1997. VERIFY3D: assessment of protein models with three-dimensional profiles. Methods Enzymol.277, 396–404.
Fernandez-Recio, J., Totrov, M., Skorodumov, C., and Abagyan, R. 2005. Optimal docking area: A new method for predicting protein-protein interaction sites. Proteins58, 134–143.
Friesner, R.A., Murphy, R.B., Repasky, M.P., Frye, L.L., Greenwood, J.R., Halgren, T.A., Sanschagrin, P.C., and Mainz, D.T. 2006. Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J. Med. Chem.49, 6177–6196.
Göbel, U., Sander, C., Schneider, R., and Valencia, A. 1994. Correlated mutations and residue contacts in proteins. Proteins18, 309–317.
Gola, J., Obrezanova, O., Champness, E., and Segall, M. 2006. ADMET property prediction: the state of the art and current challenges. Qsar Comb. Sci.25, 1172–1180.
Irwin, J.J. and Shoichet, B.K. 2005. Zinc — a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model.45, 177–182.
Jahn, A., Hinselmann, G., Fechner, N., and Zell, A. 2009. Optimal assignment methods for ligand-based virtual screening. J. Cheminform.1, 14.
Johnson, M.S., Srinivasan, N., Sowdhamini, R., and Blundell, T.L. 1994. Knowledge-based protein modeling. Crit. Rev. Biochem. Mol. Biol.29, 1–68.
Jones, G., Willett, P., Glen, R.C., Leach, A.R., and Taylor, R. 1997. Development and validation of a genetic algorithm for flexible docking. J. Mol. Biol.267, 727–748.
Kalyaanamoorthy, S. and Chen, Y.P. 2011. Structure-based drug design to augment hit discovery. Drug Discov. Today16, 831–839.
Kelley, L.A., Mezulis, S., Yates, C.M., Wass, M.N., and Sternberg, M.J. 2015. The Phyre2 web portal for protein modeling, prediction and analysis. Nat. Protoc.10, 845–858.
Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B.A., Thiessen, P.A., Yu, B., et al. 2018. PubChem 2019 update: Improved access to chemical data. Nucleic Acids Res.47, D1102–D1109.
Kopp, J. and Schwede, T. 2004. The SWISS-MODEL repository of annotated three-dimensional protein structure homology models. Nucleic Acids Res.32, D230–D234.
Kubinyi, H. 1999. Chance favors the prepared mind—from serendipity to rational drug design. J. Recept. Signal Transduct. Res.19, 15–39.
Lavecchia, A. and Di Giovanni, C. 2013. Virtual screening strategies in drug discovery: a critical review. Curr. Med. Chem.20, 2839–2860.
Lee, S.K., Chang, G.S., Lee, I.H., Chung, J.E., Sung, K.Y., and No, K.T. 2004. The PreADME: PC-based program for batch prediction of ADME properties. EuroQSAR 2004.9, 5–10.
Lipinski, C.A. 2004. Lead- and drug-like compounds: The rule-of-five revolution. Drug Discov. Today Technol.1, 337–341.
Ma, J., Wang, S., Zhao, F., and Xu, J. 2013. Protein threading using context-specific alignment potential. Bioinformatics29, i257–i265.
Mcconkey, B.J., Sobolev, V., and Edelman, M. 2002. The performance of current methods in ligand-protein docking. Curr. Sci.83, 845–856.
Meng, X.Y., Zhang, H.X., Mezei, M., and Cui, M. 2011. Molecular docking: a powerful approach for structure-based drug discovery. Curr. Comput. Aided Drug Des.7, 146–157.
Moal, I.H., Chaleil, R.A.G., and Bates, P.A. 2018. Flexible protein-protein docking with swarmdock. Methods Mol. Biol.1764, 413–428.
Myers, S. and Baker, A. 2001. Drug discovery — an operating model for a new era. Nat. Biotechnol.19, 727–730.
Nisius, B., Sha, F., and Gohlke, H. 2012. Structure-based computational analysis of protein binding sites for function and drugg-ability prediction. J. Biotechnol.159, 123–134.
Pieper, U., Webb, B.M., Dong, G.Q., Schneidman-Duhovny, D., Fan, H., Kim, S.J., Khuri, N., Spill, Y.G., Weinkam, P., Hammel, M., et al. 2014. Modbase, a database of annotated comparative protein structure models and associated resources. Nucleic Acids Res.42, D336–D346.
Reddy, M.R. 2012. Use of computer aided drug design methods in the discovery of a new class of clinical candidates for diabetes. Abstr. Pap. Am. Chem. S.243.
Roy, R., Tiwari, M., Donelli, G., and Tiwari, V. 2018. Strategies for combating bacterial biofilms: a focus on anti-biofilm agents and their mechanisms of action. Virulence9, 522–554.
Sali, A. and Blundell, T.L. 1993. Comparative protein modeling by satisfaction of spatial restraints. J. Mol. Biol.234, 779–815.
Schmidtke, P., Bidon-Chanal, A., Luque, F.J., and Barril, X. 2011. MDpocket: open-source cavity detection and characterization on molecular dynamics trajectories. Bioinformatics27, 3276–3285.
Schneidman-Duhovny, D., Inbar, Y., Nussinov, R., and Wolfson, H.J. 2005. PatchDock and SymmDock: servers for rigid and symmetric docking. Nucleic Acids Res.33, W363–W367.
Schuster, D., Waltenberger, B., Kirchmair, J., Distinto, S., Markt, P., Stuppner, H., Rollinger, J.M., and Wolber, G. 2010. Predicting cyclooxygenase inhibition by three-dimensional pharmacophoric profiling. Part I: Model generation, validation and applicability in ethnopharmacology. Mol. Inform.29, 75–86.
Shoichet, B.K. 2004. Virtual screening of chemical libraries. Nature432, 862–865.
Sliwoski, G., Kothiwale, S., Meiler, J., and Lowe, E.W. Jr. 2014. Computational methods in drug discovery. Pharmacol. Rev.66, 334–395.
Song, C.M., Lim, S.J., and Tong, J.C. 2009. Recent advances in computer-aided drug design. Brief Bioinform.10, 579–591.
Tang, Y., Zhu, W., Chen, K., and Jiang, H. 2006. New technologies in computer-aided drug design: Toward target identification and new chemical entity discovery. Drug Discov. Today Technol.3, 307–313.
Tian, W., Chen, C., Lei, X., Zhao, J.L., and Liang, J. 2018. CASTp 3.0: computed atlas of surface topography of proteins. Nucleic Acids Res.46, W363–W367.
Topliss, J.G. 1995. Computer-aided drug design in industrial research — a management perspective. Ernst Schering Res. Found. Workshop15, 11–38.
Vilar, S., Cozza, G., and Moro, S. 2008. Medicinal chemistry and the molecular operating environment (MOE): Application of QSAR and molecular docking to drug discovery. Curr. Top. Med. Chem.8, 1555–1572.
Villoutreix, B.O., Renault, N., Lagorce, D., Sperandio, O., Montes, M., and Miteva, M.A. 2007. Free resources to assist structure-based virtual ligand screening experiments. Curr. Protein Pep. Sci.8, 381–411.
Wass, M.N., Kelley, L.A., and Sternberg, M.J.E. 2010. 3DLigandSite: predicting ligand-binding sites using similar structures. Nucleic Acids Res.38, W469–W473.
Yang, J.M. and Chen, C.C. 2004. GEMDOCK: a generic evolutionary method for molecular docking. Proteins55, 288–304.
Yang, J. and Zhang, Y. 2015. I-TASSER server: New development for protein structure and function predictions. Nucleic Acids Res.43, W174–W181.
Zhang, Z., Li, Y., Lin, B., Schroeder, M., and Huang, B. 2011. Identification of cavities on protein surface using multiple computational approaches for drug binding site prediction. Bioinformatics27, 2083–2088.
Zizalova, J., Rrahmaniova, D., Svorcikova, J., and Vrubel, F. 2015. The relation between real costs of drugs temporarily reimbursed in mode of coverage with evidence development and budget impact analysis submitted as a mandatory requirement of the application. Value Health18, A567.
Acknowledgments
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (No. NRF-2018R1A5A1025077 and No. NRF-2019M3-E5D4065682). This work was also supported by the National Research Foundation of Korea (NRF) and the Center for Women in Science, Engineering and Technology (WISET) Grant funded by the Ministry of Science and ICT (MSIT) under the Program for Returners into R&D.
Author information
Authors and Affiliations
Corresponding author
Additional information
Conflicts of Interest
There’s no conflict of interest.
Supplemental material for this article may be found at http://www.springerlink.com/content/120956
Electronic supplementary material
Rights and permissions
About this article
Cite this article
Shaker, B., Yu, MS., Lee, J. et al. User guide for the discovery of potential drugs via protein structure prediction and ligand docking simulation. J Microbiol. 58, 235–244 (2020). https://doi.org/10.1007/s12275-020-9563-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12275-020-9563-z