Virtual screening of anti-HIV1 compounds against SARS-CoV-2: machine learning modeling, chemoinformatics and molecular dynamics simulation based analysis

Abstract

COVID-19 caused by the SARS-CoV-2 is a current global challenge and urgent discovery of potential drugs to combat this pandemic is a need of the hour. 3-chymotrypsin-like cysteine protease (3CLpro) enzyme is the vital molecular target against the SARS-CoV-2. Therefore, in the present study, 1528 anti-HIV1compounds were screened by sequence alignment between 3CLpro of SARS-CoV-2 and avian infectious bronchitis virus (avian coronavirus) followed by machine learning predictive model, drug-likeness screening and molecular docking, which resulted in 41 screened compounds. These 41 compounds were re-screened by deep learning model constructed considering the IC50 values of known inhibitors which resulted in 22 hit compounds. Further, screening was done by structural activity relationship mapping which resulted in two structural clefts. Thereafter, functional group analysis was also done, where cluster 2 showed the presence of several essential functional groups having pharmacological importance. In the final stage, Cluster 2 compounds were re-docked with four different PDB structures of 3CLpro, and their depth interaction profile was analyzed followed by molecular dynamics simulation at 100 ns. Conclusively, 2 out of 1528 compounds were screened as potential hits against 3CLpro which could be further treated as an excellent drug against SARS-CoV-2.

Computational investigation of drug bank compounds against 3C-like protease (3CLpro) of SARS-CoV-2 using deep learning and molecular dynamics simulation

“…The presence of rings and aromatics at higher numbers in hit compounds indicates the chemical diversity and their drug-like property. In the recent study of Nand et al, 2020 [ 32 ], it was also found that R 2 NH, R 3 N, rings, and aromatic groups were higher in reference and screened inhibitors compounds against 3CL pro of COVID-19. Various studies also showed that the structure of screened inhibitors against COVID-19 similar to the current study screened compounds (Fig.…”

“…The functional groups that support the drug molecules’ lipid solubility are often known to as hydrophobic or lipophilic functional groups, e.g., Aromatic groups and rings. The present study showed that antiviral functional groups like R 2 NH (amine) are abundant in hits compounds, followed by carbonyl groups (RCOR), tertiary amines (R 3 N), rings, and aromatic [ 32 ]. Amines groups have a mildly acidic and alkaline pH in the intestine and are easily ionized in the blood, they are called poor bases, and the most drugs have functional classes.…”

An Overview of COVID-19 and the Potential Plant Harboured Secondary Metabolites against SARS-CoV-2: A Review

“…The computational techniques includes both 42 . The majority of machine learning drug discovery studies of SARS-CoV-2 focused on viral protiens such as spike (S) protein, MPro, 3CLPro etc., protein ligand interaction, binding effeciency, and binding free energy are also considered in the process 44,49,50 . The studies on SARS-CoV-2 drug discovery focused on the sequence of target proteins such as 3CLPro, S protein, ACE-2, Main Protease, RdRP and host TMPRSS2 protease ACE2 receptor are considered.…”

“…Later by adapting the deep docking, QSAR etc., are using to identify the protein inhibitors from available databases like FDA approved drugs, ZINC database etc. In majority of studies 3CLPro of SARS-CoV-2 is target protein, Deep learning model and molecular dynamic simulation methods approched to finilise the highest hit compound to target protein like 3CLPro of SARS-CoV-2 42,44,[49][50][51][52][53] . Some of the extracts of litchi seeds, Houttuynia cordata, Chinese Rhubarb extracts, Isatis indigotica plants (beta-sistosterol) root extract can inhibit the SARS enzymatic activity 54 .…”

Deep Learning Driven Drug Discovery: Tackling Severe Acute Respiratory Syndrome Coronavirus 2

“…Deep learning combined with multiple sequence alignment drug-likeness screening, molecular docking, chemical space mapping and molecular dynamics simulation was also used to identify drug candidates by screening 1528 anti-HIV-1 compounds against 3-chymotrypsin-like cysteine protease (3CLpro) of SARS-CoV-2 (Nand et al, 2020).…”