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Computationally driven discovery of SARS-CoV-2 M^pro inhibitors: from design to experimental validation.

ABSTRACT: We report a fast-track computationally driven discovery of new SARS-CoV-2 main protease (M^pro) 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 M^pro inhibitors towards low nM affinities.

SUBMITTER: El Khoury L

PROVIDER: S-EPMC8966641 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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

El Khoury Léa L Jing Zhifeng Z Cuzzolin Alberto A Deplano Alessandro A Loco Daniele D Sattarov Boris B Hédin Florent F Wendeborn Sebastian S Ho Chris C El Ahdab Dina D Jaffrelot Inizan Theo T Sturlese Mattia M Sosic Alice A Volpiana Martina M Lugato Angela A Barone Marco M Gatto Barbara B Macchia Maria Ludovica ML Bellanda Massimo M Battistutta Roberto R Salata Cristiano C Kondratov Ivan I Iminov Rustam R Khairulin Andrii A Mykhalonok Yaroslav Y Pochepko Anton A Chashka-Ratushnyi Volodymyr V Kos Iaroslava I Moro Stefano S Montes Matthieu M Ren Pengyu P Ponder Jay W JW Lagardère Louis L Piquemal Jean-Philip JP Sabbadin Davide D

Chemical science 20220210 13

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 sampli ...[more]

PMID: 35432906

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Project description:Among the biomedical efforts in response to the current coronavirus (COVID-19) pandemic, pharmacological strategies to reduce viral load in patients with severe forms of the disease are being studied intensively. One of the main drug target proteins proposed so far is the SARS-CoV-2 viral protease 3CLpro (also called Mpro), an essential component for viral replication. Ongoing ligand- and receptor-based computational screening efforts would be facilitated by an improved understanding of the electrostatic, hydrophobic, and steric features that characterize small-molecule inhibitors binding stably to 3CLpro and by an extended collection of known binders. Here, we present combined virtual screening, molecular dynamics (MD) simulation, machine learning, and in vitro experimental validation analyses, which have led to the identification of small-molecule inhibitors of 3CLpro with micromolar activity and to a pharmacophore model that describes functional chemical groups associated with the molecular recognition of ligands by the 3CLpro binding pocket. Experimentally validated inhibitors using a ligand activity assay include natural compounds with the available prior knowledge on safety and bioavailability properties, such as the natural compound rottlerin (IC50 = 37 μM) and synthetic compounds previously not characterized (e.g., compound CID 46897844, IC50 = 31 μM). In combination with the developed pharmacophore model, these and other confirmed 3CLpro inhibitors may provide a basis for further similarity-based screening in independent compound databases and structural design optimization efforts to identify 3CLpro ligands with improved potency and selectivity. Overall, this study suggests that the integration of virtual screening, MD simulations, and machine learning can facilitate 3CLpro-targeted small-molecule screening investigations. Different receptor-, ligand-, and machine learning-based screening strategies provided complementary information, helping to increase the number and diversity of the identified active compounds. Finally, the resulting pharmacophore model and experimentally validated small-molecule inhibitors for 3CLpro provide resources to support follow-up computational screening efforts for this drug target.

Dataset Information

Computationally driven discovery of SARS-CoV-2 M^pro inhibitors: from design to experimental validation.

Publications

Computationally driven discovery of SARS-CoV-2 M<sup>pro</sup> inhibitors: from design to experimental validation.

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