Deciphering the dark cancer phosphoproteome using machine-learned co-regulation of phosphosites.

ABSTRACT: Mass spectrometry-based phosphoproteomics offers a comprehensive view of protein phosphorylation, yet our limited knowledge about the regulation and function of most phosphosites hampers the extraction of meaningful biological insights. To address this challenge, we integrate machine learning with phosphoproteomic data from 1195 tumor specimens spanning 11 cancer types to construct CoPheeMap, a network that maps the co-regulation of 26,280 phosphosites. By incorporating network features from CoPheeMap into a second machine learning model, namely CoPheeKSA, we achieve superior performance in predicting kinase-substrate associations. CoPheeKSA uncovers 24,015 associations between 9399 phosphosites and 104 serine/threonine kinases, shedding light on many unannotated phosphosites and understud

SUBMITTER: Jiang W 

PROVIDER: S-EPMC11926083 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature