Computational Prediction of covalent Inhibitors
Covalent Inhibitors are an increasingly important class of therapeutic agents.
A computational pipeline has been described by the London lab to predict suggest covalent analogs of non-covalent ligands DOI.
Designing covalent inhibitors is increasingly important, although it remains challenging. Here, we present covalentizer, a computational pipeline for identifying irreversible inhibitors based on structures of targets with non-covalent binders. Through covalent docking of tailored focused libraries, we identify candidates that can bind covalently to a nearby cysteine while preserving the interactions of the original molecule. We found ∼11,000 cysteines proximal to a ligand across 8,386 complexes in the PDB. Of these, the protocol identified 1,553 structures with covalent predictions. In a prospective evaluation, five out of nine predicted covalent kinase inhibitors showed half-maximal inhibitory concentration (IC50) values between 155 nM and 4.5 μM. Application against an existing SARS-CoV Mpro reversible inhibitor led to an acrylamide inhibitor series with low micromolar IC50 values against SARS-CoV-2 Mpro. The docking was validated by 12 co-crystal structures. Together these examples hint at the vast number of covalent inhibitors accessible through our protocol.
RDKit was used for 2D molecular handling, conformation generation and RMSD calculation. RDKit: Open-source cheminformatics; version 2018.09.3; RDKit.org. Marvin was used in the process of preparing the molecules for docking, Marvin 17.21.0, ChemAxon (https://www.chemaxon.com). OpenBabel (http:// openbabel.org/wiki/Main_Page) was used to switch between molecular file formats. DOCKovalent (London et al., 2014) was used for virtual covalent docking. The Covalentizer code is available at https://github.com/LondonLab/Covalentizer.