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

We report a fast-track computationally-driven discovery of new SARS-CoV2 Main Protease (M\(^{pro}\)) inhibitors whose potency range from mM for initial non-covalent ligands to sub-\(\mu\)M for the final covalent compound (IC50=830 +/- 50 nM). The project extensively relied on high-resolution all-ato...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2022-01
Main Authors: L El Khoury, Jing, Z, Cuzzolin, A, Deplano, A, Loco, D, Sattarov, B, Hédin, F, Wendeborn, S, C Ho, D El Ahdab, Inizan, T Jaffrelot, Sturlese, M, Sosic, A, Volpiana, M, Lugato, A, Barone, M, Gatto, B, Macchia, M Ludovica, Bellanda, M, Battistutta, R, Salata, C, Kondratov, I, Iminov, R, Khairulin, A, Mykhalonok, Y, Pochepko, A, Chashka-Ratushnyi, V, Kos, I, Moro, S, Montes, M, Ren, P, Ponder, J W, Lagardère, L, J -P Piquemal, Sabbadin, D
Format: Article
Language:English
Subjects:
Adaptive sampling
Amoeba
Binding
Free energy
Ligands
Machine learning
Molecular dynamics
NMR
Nuclear magnetic resonance
Optimization
Protease inhibitors
Proteins
Severe acute respiratory syndrome coronavirus 2
Supercomputers
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We report a fast-track computationally-driven discovery of new SARS-CoV2 Main Protease (M\(^{pro}\)) inhibitors whose potency range from mM for initial non-covalent ligands to sub-\(\mu\)M 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 ligands binding poses through the explicit reconstruction of the ligand-protein conformation spaces. Machine Learning predictions are also performed to predict selected compound properties. While simulations extensively use High Performance Computing to strongly reduce time-to-solution, they were systematically coupled to Nuclear Magnetic Resonance experiments to drive synthesis and to in vitro characterization of compounds. Such study highlights the power of in silico strategies that rely on structure-based approaches for drug design and allows to address the protein conformational multiplicity problem. The proposed fluorinated tetrahydroquinolines open routes for further optimization of M\(^{pro}\) inhibitors towards low nM affinities.
ISSN:2331-8422
DOI:10.48550/arxiv.2110.05427