Computationally driven discovery of targeting SARS-CoV-2 Mpro inhibitors: from design to experimental validation - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Chemical Science Année : 2022

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

Alberto Cuzzolin
  • Fonction : Auteur
Alessandro Deplano
  • Fonction : Auteur
Chris Ho
  • Fonction : Auteur
Theo Jaffrelot Inizan
  • Fonction : Auteur
  • PersonId : 1336006
  • IdHAL : tjaffrel2
Mattia Sturlese
  • Fonction : Auteur
Alice Sosic
  • Fonction : Auteur
Martina Volpiana
  • Fonction : Auteur
Barbara Gatto
  • Fonction : Auteur
Maria Ludovica Macchia
  • Fonction : Auteur
Massimo Bellanda
  • Fonction : Auteur
Roberto Battistutta
  • Fonction : Auteur
Cristiano Salata
  • Fonction : Auteur
Ivan Kondratov
  • Fonction : Auteur
Rustam Iminov
  • Fonction : Auteur
Andrii Khairulin
  • Fonction : Auteur
Yaroslav Mykhalonok
  • Fonction : Auteur
Anton Pochepko
  • Fonction : Auteur
Iaroslava Kos
  • Fonction : Auteur

Résumé

We report a fast-track computationally-driven discovery of new SARS-CoV2 Main Protease (Mpro) inhibitors whose potency range from mM for 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 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 Mpro inhibitors towards low nM affinities.

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hal-03361062 , version 1 (01-10-2021)

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Léa El Khoury, Zhifeng Jing, Daniele Loco, Alberto Cuzzolin, Alessandro Deplano, et al.. Computationally driven discovery of targeting SARS-CoV-2 Mpro inhibitors: from design to experimental validation. Chemical Science, 2022, ⟨10.1039/D1SC05892D⟩. ⟨hal-03361062⟩
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