PremPRI: Predicting the Effects of Single Mutations on Protein-RNA Interactions

Abstract

Protein-RNA interactions are crucial for many cellular processes, such as protein synthesis and regulation of gene expression. Missense mutations that alter protein-RNA interaction may contribute to the pathogenesis of many diseases. Here we introduce a new computational method PremPRI, which predicts the effects of single mutations occurring in RNA binding proteins on the protein-RNA interaction by calculating the binding affinity changes quantitatively. The multiple linear regression scoring function of PremPRI is composed of 11 sequence-and structure-based features, and is parameterized on 248 mutations from 50 protein-RNA complexes. Our model shows a good agreement between calculated and experimental values with Pearson correlation coefficient of 0.72 and the corresponding root-mean-square error of 0.76 kcal mol -1 , and outperforms three other available methods. PremPRI can be used for finding functionally important variants, understanding the molecular mechanisms underlying the effects, and designing new protein-RNA interaction inhibitors. PremPRI is freely available at