Discovering potent inhibitors against the Mpro of the SARS-CoV-2. A medicinal chemistry approach.

COVID-19 MD Simulation Mpro Respiratory syndrome Virtual screening

Journal

Computers in biology and medicine
ISSN: 1879-0534
Titre abrégé: Comput Biol Med
Pays: United States
ID NLM: 1250250

Informations de publication

Date de publication:
Apr 2022
Historique:
received: 27 08 2021
revised: 12 01 2022
accepted: 12 01 2022
medline: 6 2 2022
pubmed: 6 2 2022
entrez: 5 2 2022
Statut: ppublish

Résumé

The global pandemic caused by a single-stranded RNA (ssRNA) virus known as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still at its peak, with new cases being reported daily. Although the vaccines have been administered on a massive scale, the frequent mutations in the viral gene and resilience of the future strains could be more problematic. Therefore, new compounds are always needed to be available for therapeutic approaches. We carried out the present study to discover potential drug compounds against the SARS-CoV-2 main protease (Mpro). A total of 16,000 drug-like small molecules from the ChemBridge database were virtually screened to obtain the top hits. As a result, 1032 hits were selected based on their docking scores. Next, these structures were prepared for molecular docking, and each small molecule was docked into the active site of the Mpro. Only compounds with solid interactions with the active site residues and the highest docking score were subjected to molecular dynamics (MD) simulation. The post-simulation analyses were carried out using the in-built GROMACS tools to gauge the stability, flexibility, and compactness. Principal component analysis (PCA) and hydrogen bonding were also calculated to observe trends and affinity of the drugs towards the target. Among the five top compounds, C1, C3, and C6 revealed strong interaction with the target's active site and remained highly stable throughout the simulation. We believe the predicted compounds in this study could be potential inhibitors in the natural system and can be utilized in designing therapeutic strategies against the SARS-CoV-2.

Identifiants

pubmed: 35123137
pii: S0010-4825(22)00027-0
doi: 10.1016/j.compbiomed.2022.105235
pmc: PMC8789387
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

105235

Informations de copyright

Copyright © 2022 Elsevier Ltd. All rights reserved.

Auteurs

Aamir Mehmood (A)

Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China; Peng Cheng Laboratory, Shenzhen, Guangdong, 518055, China.

Sadia Nawab (S)

State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, PR China.

Yanjing Wang (Y)

Engineering Research Center of Cell & Therapeutic Antibody , School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, PR China.

Aman Chandra Kaushik (A)

Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China.

Dong-Qing Wei (DQ)

Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China; Peng Cheng Laboratory, Shenzhen, Guangdong, 518055, China. Electronic address: dqwei@sjtu.edu.cn.

Classifications MeSH