A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes.

ABSTRACT: Chemoproteomics is a key technology to characterize the mode of action of drugs, as it directly identifies the protein targets of bioactive compounds and aids in the development of optimized small-molecule compounds. Current approaches cannot identify the protein targets of a compound and also detect the interaction surfaces between ligands and protein targets without prior labeling or modification. To address this limitation, we here develop LiP-Quant, a drug target deconvolution pipeline based on limited proteolysis coupled with mass spectrometry that works across species, including in human cells. We use machine learning to discern features indicative of drug binding and integrate them into a single score to identify protein targets of small molecules and approximate their binding sites

SUBMITTER: Piazza I 

PROVIDER: S-EPMC7442650 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature