Although considerable efforts have been made in this direction, predicting the effects of mutations on the protein-protein binding affinity is still a challenging research problem. Since the experimental measurement in wet-labs is labor-intensive and time-consuming, fast and accurate in silico approaches have received much attention. Įstimating the binding affinities of protein-protein interactions (PPIs) is crucial to understand protein function and design new functional proteins. These results demonstrate the potential of GeoPPI as a powerful and useful computational tool in protein design and engineering. Moreover, we show that GeoPPI can accurately estimate the difference of binding affinities between a few recently identified SARS-CoV-2 antibodies and the receptor-binding domain (RBD) of the S protein. In addition, through extensive experiments, we show that GeoPPI achieves new state-of-the-art performance in predicting the binding affinity changes upon both single- and multi-point mutations on six benchmark datasets.

We find that GeoPPI is able to learn meaningful features that characterize interactions between atoms in protein structures.

These representations are then used as features for training gradient-boosting trees to predict the changes of protein-protein binding affinity upon mutations. Based on the three-dimensional structure of a protein, GeoPPI first learns a geometric representation that encodes topology features of the protein structure via a self-supervised learning scheme.

In this study, we develop GeoPPI, a novel structure-based deep-learning framework to predict the change of binding affinity upon mutations. Modeling the impact of amino acid mutations on protein-protein interaction plays a crucial role in protein engineering and drug design.

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