Title | PremPRI: Predicting the Effects of Missense Mutations on Protein-RNA Interactions. |
Publication Type | Journal Article |
Year of Publication | 2020 |
Authors | Zhang N, Lu H, Chen Y, Zhu Z, Yang Q, Wang S, Li M |
Journal | Int J Mol Sci |
Volume | 21 |
Issue | 15 |
Date Published | 2020 Aug 03 |
ISSN | 1422-0067 |
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 interactions by calculating the binding affinity changes quantitatively. The multiple linear regression scoring function of PremPRI is composed of three sequence- and eight 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 of binding affinity changes with a Pearson correlation coefficient of 0.72 and the corresponding root-mean-square error of 0.76 kcalĀ·mol, outperforming three other available methods. PremPRI can be used for finding functionally important variants, understanding the molecular mechanisms, and designing new protein-RNA interaction inhibitors. |
DOI | 10.3390/ijms21155560 |
Alternate Journal | Int J Mol Sci |
PubMed ID | 32756481 |
PubMed Central ID | PMC7432928 |
Grant List | 31701136 / / National Natural Science Foundation of China / BK20170335 / / Natural Science Foundation of Jiangsu Province, China / |