Dataset Information

Exploration of chalcones as 3-chymotrypsin-like protease (3CL^pro) inhibitors of SARS-CoV-2 using computational approaches.

ABSTRACT: The main protease 3CL^pro is one of the potential targets against coronavirus. Inhibiting this enzyme leads to the interruption of viral replication. Chalcone and its derivatives were reported to possess the ability to bind to 3CL^pro protease in the binding pocket. This study explored an in-house database of 269 chalcones as 3CL^pro inhibitors using in silico screening models, including molecular docking, molecular dynamics simulation, binding free energy calculation, and ADME prediction. C264 and C235 stand out as the two most potential structures. The top hit compound C264 was with the Jamda score of -2.8329 and the MM/GBSA binding energy mean value of -28.23 ± 3.53 kcal/mol, which was lower than the reference ligand. Despite the lower mean binding energy (-22.07 ± 3.39 kcal/mol), in-depth analysis of binding interaction suggested C235 could be another potential candidate. Further, in vitro and in vivo experiments are required to confirm the inhibitory ability.

Supplementary information

The online version contains supplementary material available at 10.1007/s11224-022-02000-3.

SUBMITTER: Lam TP

PROVIDER: S-EPMC9253262 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

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.

Dataset Information

Exploration of chalcones as 3-chymotrypsin-like protease (3CL^pro) inhibitors of SARS-CoV-2 using computational approaches.

Supplementary information

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

Exploration of chalcones as 3-chymotrypsin-like protease (3CLpro) inhibitors of SARS-CoV-2 using computational approaches.

Supplementary information

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Exploration of chalcones as 3-chymotrypsin-like protease (3CL^pro) inhibitors of SARS-CoV-2 using computational approaches.