PMSPcnn: Predicting protein stability changes upon single point mutations with convolutional neural network

Xiaohan Sun; Shuang Yang; Zhixiang Wu; Jingjie Su; Fangrui Hu; Fubin Chang; Chunhua Li

doi:10.1016/j.str.2024.02.016

PMSPcnn: Predicting protein stability changes upon single point mutations with convolutional neural network

Structure. 2024 Mar 8:S0969-2126(24)00054-6. doi: 10.1016/j.str.2024.02.016. Online ahead of print.

Authors

Xiaohan Sun¹, Shuang Yang¹, Zhixiang Wu¹, Jingjie Su¹, Fangrui Hu¹, Fubin Chang¹, Chunhua Li²

Affiliations

¹ College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China.
² College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China. Electronic address: chunhuali@bjut.edu.cn.

PMID: 38508191
DOI: 10.1016/j.str.2024.02.016

Abstract

Protein missense mutations and resulting protein stability changes are important causes for many human genetic diseases. However, the accurate prediction of stability changes due to mutations remains a challenging problem. To address this problem, we have developed an unbiased effective model: PMSPcnn that is based on a convolutional neural network. We have included an anti-symmetry property to build a balanced training dataset, which improves the prediction, in particular for stabilizing mutations. Persistent homology, which is an effective approach for characterizing protein structures, is used to obtain topological features. Additionally, a regression stratification cross-validation scheme has been proposed to improve the prediction for mutations with extreme ΔΔG. For three test datasets: S^sym, p53, and myoglobin, PMSPcnn achieves a better performance than currently existing predictors. PMSPcnn also outperforms currently available methods for membrane proteins. Overall, PMSPcnn is a promising method for the prediction of protein stability changes caused by single point mutations.