ProS-GNN: Predicting effects of mutations on protein stability using graph neural networks

Abstract

MotivationPredicting protein stability change upon variation through computational approach is a valuable tool to unveil the mechanisms of mutation-induced drug failure and help to develop immunotherapy strategies. However, some machine learning based methods tend to be overfitting on the training data or show anti-symmetric biases between direct and reverse mutations. Moreover, this field requires the methods to fully exploit the limited experimental data. ResultsHere we pioneered a deep graph neural network based method for predicting protein stability change upon mutation. After mutant part data extraction, the model encoded the molecular structure-property relationships using message passing and incorporated raw atom coordinates to enable spatial insights into the molecular systems. We trained the model using the S2648 and S3412 datasets, and tested on the Ssym and Myoglobin datasets. Compared to existing methods, our proposed method showed competitive high performance in data generalization and bias suppression with ultra-low time consumption. Furthermore, method was applied to predict the Pyrazinamides Gibbs free energy change for a real case study. Availabilityhttps://github.com/shuyu-wang/ProS-GNN. Contactvincentwang622@126.com