Structural stability of SARS-CoV-2 3CLpro and identification of quercetin as an inhibitor by experimental screening
Abstract
The global health emergency generated by coronavirus disease 2019 (COVID-19) has prompted the search for preventive and therapeutic treatments for its pathogen, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). There are many potential targets for drug discovery and development to tackle this disease. One of these targets is the main protease, Mpro or 3CLpro, which is highly conserved among coronaviruses. 3CLpro is an essential player in the viral replication cycle, processing the large viral polyproteins and rendering the individual proteins functional. We report a biophysical characterization of the structural stability and the catalytic activity of 3CLpro from SARS-CoV-2, from which a suitable experimental in vitro molecular screening procedure has been designed. By screening of a small chemical library consisting of about 150 compounds, the natural product quercetin was identified as reasonably potent inhibitor of SARS-CoV-2 3CLpro (Ki ~ 7 μM). Quercetin could be shown to interact with 3CLpro using biophysical techniques and bind to the active site in molecular simulations. Quercetin, with well-known pharmacokinetic and ADMET properties, can be considered as a good candidate for further optimization and development, or repositioned for COVID-19 therapeutic treatment.
Read the paper
Related studies
- Quercetin Influences Quorum Sensing in Food Borne Bacteria: In-Vitro and In-Silico Evidence
- Phenolic-protein interactions: insight from in-silico analyses – a review
- In Silico Discovery of Candidate Drugs against Covid-19
- Neuropharmacological Effects of Quercetin: A Literature-Based Review
- In vitro and in silico anti-dengue activity of compounds obtained from Psidium guajava through bioprospecting
- Effect of Quercetin Rich Onion Extracts on Bacterial Quorum Sensing
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.