شناسایی مهارکننده بالقوه کرونا ویروس جدید با استفاده از دینامیک مولکولی و آنالیز آمینو اسیدی

عنوان انگليسی

Identification of a potential SARS-CoV2 inhibitor via molecular dynamics simulations and amino acid decomposition analysis

خلاصه انگلیسی

Considering lack of validated therapeutic drugs or vaccines against contagious SARS-CoV2, various efforts have been focused on repurposing of existing drugs or identifying new agents. In an attempt to identify new and potential SARS-CoV2 inhibitors targeting specific enzyme of the pathogen, a few induced fit models of SARS-CoV2 main protease (Mpro) including N-aryl amide and aryl sulfonamide based fragments were subjected to a multi-step in silico strategy. Sub-structure query of co-crystallographic fragments provided numerous ZINC15 driven commercially available compounds that entered molecular docking stage to find binding interactions/modes inside Mpro active site. Docking results were reevaluated through time dependent stability of top-ranked ligand-protease complexes by molecular dynamics (MD) simulations within 50 ns. Relative contribution of interacted residues in binding to the most probable binding pose was estimated through amino acid decomposition analysis in B3LYP level of theory with Def2-TZVPP split basis set. In confirmation of docking results, MD simulations revealed less perceptible torsional distortions (more stable binding mode) in binding of ZINC_252512772 (DGb �9.18 kcal/mol) into Mpro active site. H-bond interactions and hydrophobic contacts were determinant forces in binding interactions of in silico hit. Quantum chemical calculations confirmed MD results and proved the pivotal role of a conserved residue (Glu166) in making permanent hydrogen bond (98% of MD simulations time) with ZINC_252512772. Drug-like physicochemical properties as well as desirable target binding interactions nominated ZINC_252512772 as a desirable in silico hit for further development toward SARS-CoV2 inhibitors.

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