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

International audienceWe 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

Spatial Averaging: Sampling Enhancement for Exploring Configurational Space of Atomic Clusters and Biomolecules

Spatial averaging Monte Carlo (SA-MC) is an efficient algorithm dedicated to the study of rare-event problems. At the heart of this method is the realization that from the equilibrium density a related, modified probability density can be constructed through a suitable transformation. This new density is more highly connected than the original density, which increases the probability for transitions between neighboring states, which in turn speeds up the sampling. The first successful investigations included the diffusion of small molecules in condensed phase environments and characterization of the metastable states of the migration of the CO ligand in myoglobin. In the present work, a general and robust implementation including rotational and torsional moves in the CHARMM molecular modeling software is introduced. Also, a procedure to estimate unbiased properties is proposed in order to compute thermodynamic observables. These procedures are suitable to study a range of topical systems including Lennard-Jones clusters of different sizes and the blocked alanine dipeptide (Ala)(2) in implicit and explicit solvent. In all cases, SA-MC is found to outperform standard Metropolis simulations in sampling configurational space at little extra computational expense. The results for (Ala)(2) in explicit solvent are in good agreement with previous umbrella sampling simulations