Kinase-substrate prediction using an autoregressive model.

ABSTRACT: Kinase-specific phosphorylation plays a critical role in cellular signaling and various diseases. However, even in model organisms, the substrates of most kinases remain unidentified. Currently, there is no reliable method to predict kinase-substrate relationships. In this study, we introduce an innovative approach leveraging an autoregressive model to predict kinase-substrate pairs. Unlike traditional methods focused on predicting site-specific phosphorylation, our approach addresses kinase-specific protein substrate prediction at the protein level. We redefine this problem as a special type of protein-protein interaction prediction task. Our model integrates protein large language model ESM-2 as the encoder and employs an autoregressive decoder to classify protein-kinase interactions in

SUBMITTER: Esmaili F 

PROVIDER: S-EPMC11968300 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature