MetaDegron: Multimodal Feature-Integrated Protein Language Model for Predicting E3 Ligase Targeted Degrons

Authors

Mengqiu Zheng
Shaofeng Lin
Kunqi Chen
Ruifeng Hu
Liming Wang
Zhongming Zhao
Haodong Xu

Publication Date

9-23-2024

Journal

Briefings in Bioinformatics

Abstract

Protein degradation through the ubiquitin proteasome system at the spatial and temporal regulation is essential for many cellular processes. E3 ligases and degradation signals (degrons), the sequences they recognize in the target proteins, are key parts of the ubiquitin-mediated proteolysis, and their interactions determine the degradation specificity and maintain cellular homeostasis. To date, only a limited number of targeted degron instances have been identified, and their properties are not yet fully characterized. To tackle on this challenge, here we develop a novel deep-learning framework, namely MetaDegron, for predicting E3 ligase targeted degron by integrating the protein language model and comprehensive featurization strategies. Through extensive evaluations using benchmark datasets and comparison with existing method, such as Degpred, we demonstrate the superior performance of MetaDegron. Among functional features, MetaDegron allows batch prediction of targeted degrons of 21 E3 ligases, and provides functional annotations and visualization of multiple degron-related structural and physicochemical features. MetaDegron is freely available at http://modinfor.com/MetaDegron/. We anticipate that MetaDegron will serve as a useful tool for the clinical and translational community to elucidate the mechanisms of regulation of protein homeostasis, cancer research, and drug development.

Keywords

Ubiquitin-Protein Ligases, Proteolysis, Humans, Computational Biology, Deep Learning, Software, Databases, Protein, Degrons

Comments

Associated Data

PMID: 39431517