Novel covalent and non-covalent complex-based pharmacophore models of SARS-CoV-2 main protease (Mpro) elucidated by microsecond MD simulations
pmid: 35982147
pmc: PMC9386674
Novel covalent and non-covalent complex-based pharmacophore models of SARS-CoV-2 main protease (Mpro) elucidated by microsecond MD simulations
AbstractAs the world enters its second year of the pandemic caused by SARS-CoV-2, intense efforts have been directed to develop an effective diagnosis, prevention, and treatment strategies. One promising drug target to design COVID-19 treatments is the SARS-CoV-2 Mpro. To date, a comparative understanding of Mprodynamic stereoelectronic interactions with either covalent or non-covalent inhibitors (depending on their interaction with a pocket called S1’ or oxyanion hole) has not been still achieved. In this study, we seek to fill this knowledge gap using a cascade in silico protocol of docking, molecular dynamics simulations, and MM/PBSA in order to elucidate pharmacophore models for both types of inhibitors. After docking and MD analysis, a set of complex-based pharmacophore models was elucidated for covalent and non-covalent categories making use of the residue bonding point feature. The highest ranked models exhibited ROC-AUC values of 0.93 and 0.73, respectively for each category. Interestingly, we observed that the active site region of Mproprotein–ligand complex undergoes large conformational changes, especially within the S2 and S4 subsites. The results reported in this article may be helpful in virtual screening (VS) campaigns to guide the design and discovery of novel small-molecule therapeutic agents against SARS-CoV-2 Mproprotein.
- Icesi University Colombia
- Virginia State University United States
- Universidad de Los Andes Colombia
- Virginia Tech United States
Computational chemistry, Organic chemistry, Combinatorial chemistry, Infectious disease (medical specialty), FOS: Health sciences, Biochemistry, Gene, Computational biology, Stereochemistry, Pathology, Disease, Coronavirus 3C Proteases, Heterocyclic Compounds for Drug Discovery, Q, R, Molecular Docking, Molecular Docking Simulation, Cysteine Endopeptidases, Chemistry, Infectious Diseases, Computational Theory and Mathematics, Physical Sciences, Medicine, Computational Methods in Drug Discovery, Virtual screening, Science, Docking (animal), Nursing, Molecular Dynamics Simulation, Coronavirus Disease 2019 Research, Molecular dynamics, Antiviral Agents, Article, Covalent bond, FOS: Chemical sciences, Health Sciences, Humans, Protease Inhibitors, Biology, Pharmacophore, SARS-CoV-2, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Organic Chemistry, In silico, COVID-19 Drug Treatment, Coronavirus disease 2019 (COVID-19), Computer Science, Small molecule
Computational chemistry, Organic chemistry, Combinatorial chemistry, Infectious disease (medical specialty), FOS: Health sciences, Biochemistry, Gene, Computational biology, Stereochemistry, Pathology, Disease, Coronavirus 3C Proteases, Heterocyclic Compounds for Drug Discovery, Q, R, Molecular Docking, Molecular Docking Simulation, Cysteine Endopeptidases, Chemistry, Infectious Diseases, Computational Theory and Mathematics, Physical Sciences, Medicine, Computational Methods in Drug Discovery, Virtual screening, Science, Docking (animal), Nursing, Molecular Dynamics Simulation, Coronavirus Disease 2019 Research, Molecular dynamics, Antiviral Agents, Article, Covalent bond, FOS: Chemical sciences, Health Sciences, Humans, Protease Inhibitors, Biology, Pharmacophore, SARS-CoV-2, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Organic Chemistry, In silico, COVID-19 Drug Treatment, Coronavirus disease 2019 (COVID-19), Computer Science, Small molecule
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