Issue 13, 2022

Computationally driven discovery of SARS-CoV-2 Mpro inhibitors: from design to experimental validation

Abstract

We report a fast-track computationally driven discovery of new SARS-CoV-2 main protease (Mpro) inhibitors whose potency ranges from mM for the initial non-covalent ligands to sub-μM for the final covalent compound (IC50 = 830 ± 50 nM). The project extensively relied on high-resolution all-atom molecular dynamics simulations and absolute binding free energy calculations performed using the polarizable AMOEBA force field. The study is complemented by extensive adaptive sampling simulations that are used to rationalize the different ligand binding poses through the explicit reconstruction of the ligand–protein conformation space. Machine learning predictions are also performed to predict selected compound properties. While simulations extensively use high performance computing to strongly reduce the time-to-solution, they were systematically coupled to nuclear magnetic resonance experiments to drive synthesis and for in vitro characterization of compounds. Such a study highlights the power of in silico strategies that rely on structure-based approaches for drug design and allows the protein conformational multiplicity problem to be addressed. The proposed fluorinated tetrahydroquinolines open routes for further optimization of Mpro inhibitors towards low nM affinities.

Graphical abstract: Computationally driven discovery of SARS-CoV-2 Mpro inhibitors: from design to experimental validation

Supplementary files

Article information

Article type
Edge Article
Submitted
25 10 2021
Accepted
03 2 2022
First published
10 2 2022
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2022,13, 3674-3687

Computationally driven discovery of SARS-CoV-2 Mpro inhibitors: from design to experimental validation

L. El Khoury, Z. Jing, A. Cuzzolin, A. Deplano, D. Loco, B. Sattarov, F. Hédin, S. Wendeborn, C. Ho, D. El Ahdab, T. Jaffrelot Inizan, M. Sturlese, A. Sosic, M. Volpiana, A. Lugato, M. Barone, B. Gatto, M. L. Macchia, M. Bellanda, R. Battistutta, C. Salata, I. Kondratov, R. Iminov, A. Khairulin, Y. Mykhalonok, A. Pochepko, V. Chashka-Ratushnyi, I. Kos, S. Moro, M. Montes, P. Ren, J. W. Ponder, L. Lagardère, J. Piquemal and D. Sabbadin, Chem. Sci., 2022, 13, 3674 DOI: 10.1039/D1SC05892D

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