Protein-protein interactions (PPIs) are critical for various biological
processes, and understanding their dynamics is essential for decoding molecular
mechanisms and advancing fields such as cancer research and drug discovery.
Mutations in PPIs can disrupt protein binding affinity and lead to functional
changes and disease. Predicting the impact of mutations on binding affinity is
valuable but experimentally challenging. Computational methods, including
physics-based and machine learning-based approaches, have been developed to
address this challenge. Machine learning-based methods, fueled by extensive PPI
datasets such as Ab-Bind, PINT, SKEMPI, and others, have shown promise in
predicting binding affinity changes. However, accurate predictions and
generalization of these models across different datasets remain challenging.
Geometric graph learning has emerged as a powerful approach, combining graph
theory and machine learning, to capture structural features of biomolecules. We
present GGL-PPI, a novel method that integrates geometric graph learning and
machine learning to predict mutation-induced binding free energy changes.
GGL-PPI leverages atom-level graph coloring and multi-scale weighted colored
geometric subgraphs to extract informative features, demonstrating superior
performance on three validation datasets, namely AB-Bind, SKEMPI 1.0, and
SKEMPI 2.0 datasets. Evaluation on a blind test set highlights the unbiased
predictions of GGL-PPI for both direct and reverse mutations. The findings
underscore the potential of GGL-PPI in accurately predicting binding free
energy changes, contributing to our understanding of PPIs and aiding drug
design efforts