An interpretable deep geometric learning model to predict the effects of mutations on protein-protein interactions using large-scale protein language model.

ABSTRACT: Protein-protein interactions (PPIs) are central to the mechanisms of signaling pathways and immune responses, which can help us understand disease etiology. Therefore, there is a significant need for efficient and rapid automated approaches to predict changes in PPIs. In recent years, there has been a significant increase in applying deep learning techniques to predict changes in binding affinity between the original protein complex and its mutant variants. Particularly, the adoption of graph neural networks (GNNs) has gained prominence for their ability to learn representations of protein-protein complexes. However, the conventional GNNs have mainly concentrated on capturing local features, often disregarding the interactions among distant elements that hold potential important informatio

SUBMITTER: Zhang C 

PROVIDER: S-EPMC11927297 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature