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In silico identification of potential inhibitors of key SARS-CoV-2 3CL hydrolase (Mpro) via molecular docking, MMGBSA predictive binding energy calculations, and molecular dynamics simulation.
PLoS One. 2020; 15(7):e0235030.Plos

Abstract

The incidence of 2019 novel corona virus (SARS-CoV-2) has created a medical emergency throughout the world. Various efforts have been made to develop the vaccine or effective treatments against the disease. The discovery of crystal structure of SARS-CoV-2 main protease has made the in silico identification of its inhibitors possible. Based on its critical role in viral replication, the viral protease can prove to be a promising "target" for antiviral drug therapy. We have systematically screened an in-house library of 15,754 natural and synthetic compounds, established at International Center for Chemical and Biological Sciences, University of Karachi. The in silico search for potential viral protease inhibitors resulted in nine top ranked ligands (compounds 1-9) against SARS-CoV-2 main protease (PDB ID: 6LU7) based on docking scores, and predictive binding energies. The in silico studies were updated via carrying out the docking, and predictive binding energy estimation, with a recently reported crystal structure of main protease (PDB ID: 6Y2F) at a better resolution i.e., 1.95 Å. Compound 2 (molecular bank code AAA396) was found to have highest negative binding energy of -71.63 kcal/mol for 6LU7. While compound 3 (molecular bank code AAD146) exhibited highest negative binding energy of -81.92 kcal/mol for 6Y2F. The stability of the compounds- in complex with viral protease was analyzed by Molecular Dynamics simulation studies, and was found to be stable over the course of 20 ns simulation time. Compound 2, and 3 were predicted to be the significant inhibitors of SARS-CoV-2 3CL hydrolase (Mpro) among the nine short listed compounds.

Authors+Show Affiliations

Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan. H. E. J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan.Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan.Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan.Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan. H. E. J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

32706783

Citation

Choudhary, M Iqbal, et al. "In Silico Identification of Potential Inhibitors of Key SARS-CoV-2 3CL Hydrolase (Mpro) Via Molecular Docking, MMGBSA Predictive Binding Energy Calculations, and Molecular Dynamics Simulation." PloS One, vol. 15, no. 7, 2020, pp. e0235030.
Choudhary MI, Shaikh M, Tul-Wahab A, et al. In silico identification of potential inhibitors of key SARS-CoV-2 3CL hydrolase (Mpro) via molecular docking, MMGBSA predictive binding energy calculations, and molecular dynamics simulation. PLoS ONE. 2020;15(7):e0235030.
Choudhary, M. I., Shaikh, M., Tul-Wahab, A., & Ur-Rahman, A. (2020). In silico identification of potential inhibitors of key SARS-CoV-2 3CL hydrolase (Mpro) via molecular docking, MMGBSA predictive binding energy calculations, and molecular dynamics simulation. PloS One, 15(7), e0235030. https://doi.org/10.1371/journal.pone.0235030
Choudhary MI, et al. In Silico Identification of Potential Inhibitors of Key SARS-CoV-2 3CL Hydrolase (Mpro) Via Molecular Docking, MMGBSA Predictive Binding Energy Calculations, and Molecular Dynamics Simulation. PLoS ONE. 2020;15(7):e0235030. PubMed PMID: 32706783.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - In silico identification of potential inhibitors of key SARS-CoV-2 3CL hydrolase (Mpro) via molecular docking, MMGBSA predictive binding energy calculations, and molecular dynamics simulation. AU - Choudhary,M Iqbal, AU - Shaikh,Muniza, AU - Tul-Wahab,Atia-, AU - Ur-Rahman,Atta-, Y1 - 2020/07/24/ PY - 2020/03/14/received PY - 2020/06/07/accepted PY - 2020/7/25/entrez PY - 2020/7/25/pubmed PY - 2020/8/4/medline SP - e0235030 EP - e0235030 JF - PloS one JO - PLoS ONE VL - 15 IS - 7 N2 - The incidence of 2019 novel corona virus (SARS-CoV-2) has created a medical emergency throughout the world. Various efforts have been made to develop the vaccine or effective treatments against the disease. The discovery of crystal structure of SARS-CoV-2 main protease has made the in silico identification of its inhibitors possible. Based on its critical role in viral replication, the viral protease can prove to be a promising "target" for antiviral drug therapy. We have systematically screened an in-house library of 15,754 natural and synthetic compounds, established at International Center for Chemical and Biological Sciences, University of Karachi. The in silico search for potential viral protease inhibitors resulted in nine top ranked ligands (compounds 1-9) against SARS-CoV-2 main protease (PDB ID: 6LU7) based on docking scores, and predictive binding energies. The in silico studies were updated via carrying out the docking, and predictive binding energy estimation, with a recently reported crystal structure of main protease (PDB ID: 6Y2F) at a better resolution i.e., 1.95 Å. Compound 2 (molecular bank code AAA396) was found to have highest negative binding energy of -71.63 kcal/mol for 6LU7. While compound 3 (molecular bank code AAD146) exhibited highest negative binding energy of -81.92 kcal/mol for 6Y2F. The stability of the compounds- in complex with viral protease was analyzed by Molecular Dynamics simulation studies, and was found to be stable over the course of 20 ns simulation time. Compound 2, and 3 were predicted to be the significant inhibitors of SARS-CoV-2 3CL hydrolase (Mpro) among the nine short listed compounds. SN - 1932-6203 UR - https://www.unboundmedicine.com/medline/citation/32706783/In_silico_identification_of_potential_inhibitors_of_key_SARS_CoV_2_3CL_hydrolase__Mpro__via_molecular_docking_MMGBSA_predictive_binding_energy_calculations_and_molecular_dynamics_simulation_ L2 - https://dx.plos.org/10.1371/journal.pone.0235030 DB - PRIME DP - Unbound Medicine ER -