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Structure-based virtual screening against SARS-3CL(pro) to identify novel non-peptidic hits.

ABSTRACT: Severe acute respiratory syndrome is a highly infectious upper respiratory tract disease caused by SARS-CoV, a previously unidentified human coronavirus. SARS-3CL(pro) is a viral cysteine protease critical to the pathogen's life cycle and hence a therapeutic target of importance. The recently elucidated crystal structures of this enzyme provide an opportunity for the discovery of inhibitors through rational drug design. In the current study, Gold docking program was utilized to conduct extensive docking studies against the target crystal structure to develop a robust and predictive docking protocol. The validated docking protocol was used to conduct a structure-based virtual screening of the Asinex Platinum collection. Biological evaluation of a screened selection of compounds was carried out to identify novel inhibitors of the viral protease.

SUBMITTER: Mukherjee P

PROVIDER: S-EPMC7127700 | biostudies-literature | 2008 Apr

REPOSITORIES: biostudies-literature

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Structure-based virtual screening against SARS-3CL(pro) to identify novel non-peptidic hits.

Mukherjee Prasenjit P Desai Prashant P Ross Larry L White E Lucile EL Avery Mitchell A MA

Bioorganic & medicinal chemistry 20080111 7

Severe acute respiratory syndrome is a highly infectious upper respiratory tract disease caused by SARS-CoV, a previously unidentified human coronavirus. SARS-3CL(pro) is a viral cysteine protease critical to the pathogen's life cycle and hence a therapeutic target of importance. The recently elucidated crystal structures of this enzyme provide an opportunity for the discovery of inhibitors through rational drug design. In the current study, Gold docking program was utilized to conduct extensive ...[more]

PMID: 18343121

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Project description:BackgroundThe COVID-19 pandemic, caused by the SARS-CoV-2 virus, has ravaged lives across the globe since December 2019, and new cases are still on the rise. Peoples' ongoing sufferings trigger scientists to develop safe and effective remedies to treat this deadly viral disease. While repurposing the existing FDA-approved drugs remains in the front line, exploring drug candidates from synthetic and natural compounds is also a viable alternative. This study employed a comprehensive computational approach to screen inhibitors for SARS-CoV-2 3CL-PRO (also known as the main protease), a prime molecular target to treat coronavirus diseases.MethodsWe performed 100 ns GROMACS molecular dynamics simulations of three high-resolution X-ray crystallographic structures of 3CL-PRO. We extracted frames at 10 ns intervals to mimic conformational diversities of the target protein in biological environments. We then used AutoDock Vina molecular docking to virtual screen the Sigma-Aldrich MyriaScreen Diversity Library II, a rich collection of 10,000 druglike small molecules with diverse chemotypes. Subsequently, we adopted in silico computation of physicochemical properties, pharmacokinetic parameters, and toxicity profiles. Finally, we analyzed hydrogen bonding and other protein-ligand interactions for the short-listed compounds.ResultsOver the 100 ns molecular dynamics simulations of 3CL-PRO's crystal structures, 6LZE, 6M0K, and 6YB7, showed overall integrity with mean Cα root-mean-square deviation (RMSD) of 1.96 (±0.35) Å, 1.98 (±0.21) Å, and 1.94 (±0.25) Å, respectively. Average root-mean-square fluctuation (RMSF) values were 1.21 ± 0.79 (6LZE), 1.12 ± 0.72 (6M0K), and 1.11 ± 0.60 (6YB7). After two phases of AutoDock Vina virtual screening of the MyriaScreen Diversity Library II, we prepared a list of the top 20 ligands. We selected four promising leads considering predicted oral bioavailability, druglikeness, and toxicity profiles. These compounds also demonstrated favorable protein-ligand interactions. We then employed 50-ns molecular dynamics simulations for the four selected molecules and the reference ligand 11a in the crystallographic structure 6LZE. Analysis of RMSF, RMSD, and hydrogen bonding along the simulation trajectories indicated that S51765 would form a more stable protein-ligand complexe with 3CL-PRO compared to other molecules. Insights into short-range Coulombic and Lennard-Jones potentials also revealed favorable binding of S51765 with 3CL-PRO.ConclusionWe identified a potential lead for antiviral drug discovery against the SARS-CoV-2 main protease. Our results will aid global efforts to find safe and effective remedies for COVID-19.

Project description:COVID-19 caused by a novel coronavirus (SARS-CoV-2) has been spreading all over the world since the end of 2019, and no specific drug has been developed yet. 3C-like protease (3CLpro) acts as an important part of the replication of novel coronavirus and is a promising target for the development of anticoronavirus drugs. In this paper, eight machine learning models were constructed using naïve Bayesian (NB) and recursive partitioning (RP) algorithms for 3CLpro on the basis of optimized two-dimensional (2D) molecular descriptors (MDs) combined with ECFP_4, ECFP_6, and MACCS molecular fingerprints. The optimal models were selected according to the results of 5-fold cross verification, test set verification, and external test set verification. A total of 5766 natural compounds from the internal natural product database were predicted, among which 369 chemical components were predicted to be active compounds by the optimal models and the EstPGood values were more than 0.6, as predicted by the NB (MD + ECFP_6) model. Through ADMET analysis, 31 compounds were selected for further biological activity determination by the fluorescence resonance energy transfer (FRET) method and cytopathic effect (CPE) detection. The results indicated that (+)-shikonin, shikonin, scutellarein, and 5,3',4'-trihydroxyflavone showed certain activity in inhibiting SARS-CoV-2 3CLpro with the half-maximal inhibitory concentration (IC50) values ranging from 4.38 to 87.76 μM. In the CPE assay, 5,3',4'-trihydroxyflavone showed a certain antiviral effect with an IC50 value of 8.22 μM. The binding mechanism of 5,3',4'-trihydroxyflavone with SARS-CoV-2 3CLpro was further revealed through CDOCKER analysis. In this study, 3CLpro prediction models were constructed based on machine learning algorithms for the prediction of active compounds, and the activity of potential inhibitors was determined by the FRET method and CPE assay, which provide important information for further discovery and development of antinovel coronavirus drugs.

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Structure-based virtual screening against SARS-3CL(pro) to identify novel non-peptidic hits.

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Structure-based virtual screening against SARS-3CL(pro) to identify novel non-peptidic hits.

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