Pre-training of Graph Neural Network for Modeling Effects of Mutations on Protein-Protein Binding Affinity
This provides a computational tool for protein engineering and drug design, though it is incremental as it builds on existing GNN methods with a novel pre-training scheme.
The study tackled predicting how mutations affect protein-protein binding affinity by developing GraphPPI, a deep learning framework that uses a pre-trained graph neural network and gradient-boosting trees, achieving state-of-the-art performance on five benchmark datasets for single- and multi-point mutations.
Modeling the effects of mutations on the binding affinity plays a crucial role in protein engineering and drug design. In this study, we develop a novel deep learning based framework, named GraphPPI, to predict the binding affinity changes upon mutations based on the features provided by a graph neural network (GNN). In particular, GraphPPI first employs a well-designed pre-training scheme to enforce the GNN to capture the features that are predictive of the effects of mutations on binding affinity in an unsupervised manner and then integrates these graphical features with gradient-boosting trees to perform the prediction. Experiments showed that, without any annotated signals, GraphPPI can capture meaningful patterns of the protein structures. Also, GraphPPI achieved new state-of-the-art performance in predicting the binding affinity changes upon both single- and multi-point mutations on five benchmark datasets. In-depth analyses also showed GraphPPI can accurately estimate the effects of mutations on the binding affinity between SARS-CoV-2 and its neutralizing antibodies. These results have established GraphPPI as a powerful and useful computational tool in the studies of protein design.