Reliable prediction of protein-protein binding affinity changes upon mutations with Pythia-PPI.

ABSTRACT: Protein-protein interactions (PPIs) are essential for numerous biological functions and predicting binding affinity changes caused by mutations is crucial for understanding the impact of genetic variation and advancing protein engineering. Although machine-learning-based methods show promise in improving prediction accuracy, limited experimental data remain a significant bottleneck. In this study, we employed multitask learning and self-distillation to overcome the data limitation and improve the accuracy of protein-protein binding affinity prediction. By incorporating a mutation stability prediction task, our model achieved state-of-the-art accuracy on the SKEMPI dataset and was subsequently used to predict binding affinity changes for millions of mutations, generating an expanded dataset

SUBMITTER: Tao F 

PROVIDER: S-EPMC12199698 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature