An automatic pipeline for the design of irreversible derivatives identifies a potent SARS-CoV-2 Mpro inhibitor. Issue 12 (16th December 2021) - Zaidman, Daniel; Gehrtz, Paul; Filep, Mihajlo; Fearon, Daren; Gabizon, Ronen; Douangamath, Alice; Prilusky, Jaime; Duberstein, Shirly; Cohen, Galit; Owen, C. David; Resnick, Efrat; Strain-Damerell, Claire; et al.
Summary: 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 M pro reversible inhibitor led to an acrylamide inhibitor series with low micromolar IC50 values against SARS-CoV-2 M pro . The docking was validated by 12 co-crystal structures. Together these examples hint at the vast number of covalent inhibitors accessible through our protocol. Graphical abstract: Highlights: Covalentizer was developed to suggest covalent analogs of non-covalent binders Experimentally validated against kinase targets as well as SARS-CoV-2 M pro Application against the entire PDB uncovered numerous covalent opportunities A web server, as well as all the current predictions, are publicly available Abstract : Designing covalent inhibitors is a challenging task. Covalentizer is a computational pipeline for identifying irreversible inhibitors based on known non-covalent binders. Within the PDB, Zaidman and Gehrtz et al. identified >1, 553 structures with covalent predictions, leading to the discovery of covalent kinase inhibitors and an inhibitor of SARS-CoV-2 M pro .