Computationally driven discovery of SARS-CoV-2 Mpro inhibitors. From design to experimental validation
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Authors
El Khoury, Léa
Jing, Zhifeng
Cuzzolin, Alberto
Deplano, Alessandro
Loco, Daniele
Sattarov, Boris
Hédin, Florent
Ho, Chris
El Ahdab, Dina
Jaffrelot Inizan, Theo
Author (Corporation)
Publication date
01.01.2022
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Type
01A - Journal article
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Parent work
Chemical Science
Special issue
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Volume
13
Issue / Number
Pages / Duration
3674-3687
Patent number
Publisher / Publishing institution
Royal Society of Chemistry
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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.
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ISBN
ISSN
2041-6520
2041-6539
2041-6539
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Gold
License
Citation
El Khoury, L., Jing, Z., Cuzzolin, A., Deplano, A., Loco, D., Sattarov, B., Hédin, F., Ho, C., El Ahdab, D., Jaffrelot Inizan, T., Sturlese, M., Sosic, A., Volpiana, M., Lugato, A., Barone, M., Gatto, B., Macchia, M. L., Bellanda, M., Battistutta, R., et al. (2022). Computationally driven discovery of SARS-CoV-2 Mpro inhibitors. From design to experimental validation. Chemical Science, 13, 3674–3687. https://doi.org/10.1039/D1SC05892D