Using explainable machine learning to uncover the kinase-substrate interaction landscape.

ABSTRACT:

Motivation

Phosphorylation, a post-translational modification regulated by protein kinase enzymes, plays an essential role in almost all cellular processes. Understanding how each of the nearly 500 human protein kinases selectively phosphorylates their substrates is a foundational challenge in bioinformatics and cell signaling. Although deep learning models have been a popular means to predict kinase-substrate relationships, existing models often lack interpretability and are trained on datasets skewed toward a subset of well-studied kinases.

Results

Here we leverage recent peptide library datasets generated to determine substrate specificity profiles of 300 serine/threonine kinases to develop an explainable Transformer model for kinase-peptide interaction prediction. The mo

SUBMITTER: Zhou Z 

PROVIDER: S-EPMC10868336 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature