Deep Learning-coupled Proximity Proteomics to Deconvolve Kinase Signaling In Vivo

Abstract

Deconvolving the substrates of hundreds of kinases linked to phosphorylation networks driving cellular behavior is a fundamental, unresolved biological challenge, largely due to the poorly understood interplay of kinase selectivity and substrate proximity. We introduce KolossuS, a deep learning framework leveraging protein language models to decode kinase-substrate specificity. KolossuS achieves superior prediction accuracy and sensitivity across mammalian kinomes, enabling proteome-wide predictions and evolutionary insights. By integrating KolossuS with CRISPR-based proximity proteomics in vivo, we capture kinase-substrate recognition and spatial context, obviating prior limitations. We show this combined framework identifies kinase substrates associated with physiological states such as sleep, revealing both known and novel Sik3 substrates during sleep deprivation. This novel integrated computational-experimental approach promises to transform systematic investigations of kinase signaling in health and disease.