PremPLI: a machine learning model for predicting the effects of missense mutations on protein-ligand interactions

• Published in: Communications biology 2021-11, Vol.4 (1), p.1311-11, Article 1311
• Main Authors: Sun, Tingting, Chen, Yuting, Wen, Yuhao, Zhu, Zefeng, Li, Minghui
• Format: Article
• Language: English
• Subjects: 631/114/1305; 631/114/2397; 631/114/469; Affinity; Biology; Biomedical and Life Sciences; Computer applications; Disease resistance; Drug resistance; Genetic disorders; Learning algorithms; Life Sciences; Ligands; Machine Learning; Missense mutation; Mutation; Mutation, Missense; Protein Binding; Proteins - chemistry
• ISSN: 2399-3642; 2399-3642
• DOI: 10.1038/s42003-021-02826-3

Resistance to small-molecule drugs is the main cause of the failure of therapeutic drugs in clinical practice. Missense mutations altering the binding of ligands to proteins are one of the critical mechanisms that result in genetic disease and drug resistance. Computational methods have made a lot of progress for predicting binding affinity changes and identifying resistance mutations, but their prediction accuracy and speed are still not satisfied and need to be further improved. To address these issues, we introduce a structure-based machine learning method for quantitatively estimating the effects of single mutations on ligand binding affinity changes (named as PremPLI). A comprehensive comparison of the predictive performance of PremPLI with other available methods on two benchmark datasets confirms that our approach performs robustly and presents similar or even higher predictive accuracy than the approaches relying on first-principle statistical mechanics and mixed physics- and knowledge-based potentials while requires much less computational resources. PremPLI can be used for guiding the design of ligand-binding proteins, identifying and understanding disease driver mutations, and finding potential resistance mutations for different drugs. PremPLI is freely available at https://lilab.jysw.suda.edu.cn/research/PremPLI/ and allows to do large-scale mutational scanning. Sun et al. present PremPLI, a machine learning approach and web tool to predict how missense mutations in a drug’s target protein will affect the drug’s binding affinity. PremPLI can be applied to identify drug resistance mechanisms in cancer cells and microorganisms and develop drugs to combat drug resistance.