DDMut-PPI: predicting effects of mutations on protein-protein interactions using graph-based deep learning.

ABSTRACT: Protein-protein interactions (PPIs) play a vital role in cellular functions and are essential for therapeutic development and understanding diseases. However, current predictive tools often struggle to balance efficiency and precision in predicting the effects of mutations on these complex interactions. To address this, we present DDMut-PPI, a deep learning model that efficiently and accurately predicts changes in PPI binding free energy upon single and multiple point mutations. Building on the robust Siamese network architecture with graph-based signatures from our prior work, DDMut, the DDMut-PPI model was enhanced with a graph convolutional network operated on the protein interaction interface. We used residue-specific embeddings from ProtT5 protein language model as node features, and

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC11223791 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature