RESUMO
The use of computer simulation methods has become an indispensable component in identifying drugs against the SARS-CoV-2 coronavirus. There is a huge body of literature on application of molecular modelling to predict inhibitors against target proteins of SARS-CoV-2. To keep our review clear and readable, we limited ourselves primarily to works that use computational methods to find inhibitors and test the predicted compounds experimentally either in target protein assays or in cell culture with live SARS-CoV-2. Some works containing results of experimental discovery of corresponding inhibitors without using computer modelling are included as examples of a success. Also, some computational works without experimental confirmations are also included if they attract our attention either by simulation methods or by databases used. This review collects studies that use various molecular modelling methods: docking, molecular dynamics, quantum mechanics, machine learning, and others. Most of these studies are based on docking, and other methods are used mainly for post-processing to select the best compounds among those found through docking. Simulation methods are presented concisely, information is also provided on databases of organic compounds that can be useful for virtual screening, and the review itself is structured in accordance with coronavirus target proteins.
Assuntos
COVID-19 , SARS-CoV-2 , Humanos , Antivirais/farmacologia , Inibidores de Proteases/farmacologia , Simulação de Acoplamento Molecular , Relação Quantitativa Estrutura-Atividade , Desenho de Fármacos , Simulação de Dinâmica MolecularRESUMO
Docking and quantum-chemical methods have been used for screening of drug-like compounds from the own database of the Voronezh State University to find inhibitors the SARS-CoV-2 main protease, an important enzyme of the coronavirus responsible for the COVID-19 pandemic. Using the SOL program more than 42000 3D molecular structures were docked into the active site of the main protease, and more than 1000 ligands with most negative values of the SOL score were selected for further processing. For all these top ligands, the protein-ligand binding enthalpy has been calculated using the PM7 semiempirical quantum-chemical method with the COSMO implicit solvent model. 20 ligands with the most negative SOL scores and the most negative binding enthalpies have been selected for further experimental testing. The latter has been made by measurements of the inhibitory activity against the main protease and suppression of SARS-CoV-2 replication in a cell culture. The inhibitory activity \of the compounds was determined using a synthetic fluorescently labeled peptide substrate including the proteolysis site of the main protease. The antiviral activity was tested against SARS-CoV-2 virus in the Vero cell culture. Eight compounds showed inhibitory activity against the main protease of SARS-CoV-2 in the submicromolar and micromolar ranges of the IC50 values. Three compounds suppressed coronavirus replication in the cell culture at the micromolar range of EC50 values and had low cytotoxicity. The found chemically diverse inhibitors can be used for optimization in order to obtain a leader compound, the basis of new direct-acting antiviral drugs against the SARS-CoV-2 coronavirus.