Repositioning therapeutics for COVID-19: virtual screening of the potent synthetic and natural compounds as SARS-CoV-2 3CLpro inhibitors

Evaluation of natural products from virtual screenings as SARS-CoV-2 main protease inhibitors using combinational experiments

Recently, andrographolide, kaempferol, maslinic acid, rutin, and schaftoside have been identified as potent SARS-CoV-2 main protease (Mpro) inhibitors via molecular docking studies. However, no comprehensive in vitro testing of these compounds against Mpro has been conducted. In this study, we rigorously evaluated the in vitro inhibition of Mpro by these compounds using combinational experiments, including fluorescence resonance energy transfer (FRET), fluorescence polarization (FP), and dimerization-dependent red fluorescent protein (ddRFP) assays. Our data revealed that these compounds are not Mpro inhibitors based on the results from a set of in vitro assays. These results suggest that an efficient combination of a molecular docking approach and an experimental assay is essential for the discovery of Mpro inhibitors in the future.

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

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.

Indole alkaloids as potential candidates against COVID-19: an in silico study

COVID-19 has recently grown to be pandemic all around the world. Therefore, efforts to find effective drugs for the treatment of COVID-19 are needed to improve humans’ life quality and survival. Since the main protease (M^pro) of SARS-CoV-2 plays a crucial role in viral replication and transcription, the inhibition of this enzyme could be a promising and challenging therapeutic target to fight COVID-19. The present study aims to identify alkaloid compounds as new potential inhibitors for SARS-CoV-2 M^pro by the hybrid modeling analyses. The docking-based virtual screening method assessed a collection of alkaloids extracted from over 500 medicinal plants and sponges. In order to validate the docking process, classical molecular dynamic simulations were applied on selected ligands, and the calculation of binding free energy was performed. Based on the proper interactions with the active site of the SARS-CoV-2 M^pro, low binding energy, few side effects, and the availability in the medicinal market, two indole alkaloids were found to be potential lead compounds that may serve as therapeutic options to treat COVID-19. This study paves the way for developing natural alkaloids as stronger potent antiviral agents against the SARS-CoV-2.

High predictive QSAR models for predicting the SARS coronavirus main protease inhibition activity of ketone-based covalent inhibitors

In this research, a dataset including 29 ketone-based covalent inhibitors with SARS-CoV-1 3CL^pro inhibition activity was used to develop high predictive QSAR models. Twenty-two molecules were put in train set and seven molecules in test set. By using stepwise MLR method for molecules in train set, four molecular descriptors including Mor26p, Hy, GATS7p and Mor04v were selected to build QSAR models. MLR and ANN methods were used to create QSAR models for predicting the activity of molecules in both train and test sets. Both QSAR models were validated by calculating several statistical parameters. R² values for the test set of MLR and ANN models were 0.93 and 0.95, respectively, and RMSE values for their test sets were 0.24 and 0.17, respectively. Other calculated statistical parameters (especially \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Q_{F3}^{2}$$\end{document}QF32 parameter) show that created ANN model has more predictive power with respect to developed MLR model (with four descriptor). Calculated leverages for all molecules show that predicted pIC₅₀ (by both QSAR models) for all molecules is acceptable, and drawn residuals plots show that there is no systematic error in building both QSAR modes. Also, based on developed MLR model, used molecular descriptors were interpreted.