Precise prediction of hotspot residues in protein-RNA complexes using graph attention networks and pretrained protein language models.

ABSTRACT:

Motivation

Protein-RNA interactions play a pivotal role in biological processes and disease mechanisms, with hotspot residues being critical for targeted drug design. Traditional experimental methods for identifying hotspot residues are often inefficient and expensive. Moreover, many existing prediction methods rely heavily on high-resolution structural data, which may not always be available. Consequently, there is an urgent need for an accurate and efficient sequence-based computational approach for predicting hotspot residues in protein-RNA complexes.

Results

In this study, we introduce DeepHotResi, a sequence-based computational method designed to predict hotspot residues in protein-RNA complexes. DeepHotResi leverages a pretrained protein language model to predict prote

SUBMITTER: Shen S 

PROVIDER: S-EPMC12261489 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature