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PremPRI: Predicting the Effects of Missense Mutations on Protein–RNA Interactions

Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
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Int. J. Mol. Sci. 2020, 21(15), 5560; https://doi.org/10.3390/ijms21155560
Received: 29 June 2020 / Revised: 28 July 2020 / Accepted: 30 July 2020 / Published: 3 August 2020
(This article belongs to the Section Molecular Informatics)
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 kcal·mol−1, 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. View Full-Text
Keywords: Mutation; Protein–RNA interaction; binding affinity change; computational approach Mutation; Protein–RNA interaction; binding affinity change; computational approach
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Zhang, N.; Lu, H.; Chen, Y.; Zhu, Z.; Yang, Q.; Wang, S.; Li, M. PremPRI: Predicting the Effects of Missense Mutations on Protein–RNA Interactions. Int. J. Mol. Sci. 2020, 21, 5560.

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