HyperCys: A Structure- and Sequence-Based Predictor of Hyper-Reactive Druggable Cysteines.

ABSTRACT: The cysteine side chain has a free thiol group, making it the amino acid residue most often covalently modified by small molecules possessing weakly electrophilic warheads, thereby prolonging on-target residence time and reducing the risk of idiosyncratic drug toxicity. However, not all cysteines are equally reactive or accessible. Hence, to identify targetable cysteines, we propose a novel ensemble stacked machine learning (ML) model to predict hyper-reactive druggable cysteines, named HyperCys. First, the pocket, conservation, structural and energy profiles, and physicochemical properties of (non)covalently bound cysteines were collected from both protein sequences and 3D structures of protein-ligand complexes. Then, we established the HyperCys ensemble stacked model by integrating six d

SUBMITTER: Gao M 

PROVIDER: S-EPMC10054327 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature