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

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 predi...

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Bibliographic Details
Published in:International journal of molecular sciences 2020-08, Vol.21 (15), p.5560
Main Authors: Zhang, Ning, Lu, Haoyu, Chen, Yuting, Zhu, Zefeng, Yang, Qing, Wang, Shuqin, Li, Minghui
Format: Article
Language:English
Subjects:
Affinity
Binding
binding affinity change
Biophysical Phenomena
Cancer
computational approach
Computational Biology
Computer applications
Correlation coefficients
Datasets
Deoxyribonucleic acid
DNA
Gene expression
Humans
Machine learning
Methods
Missense mutation
Models, Molecular
Molecular modelling
Mutation
Mutation, Missense - genetics
Protein Binding - genetics
Protein biosynthesis
Protein synthesis
Proteins
Protein–RNA interaction
Regression analysis
Research methodology
RNA - genetics
RNA-binding protein
RNA-Binding Proteins - genetics
Software
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Summary: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 , 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.
ISSN:1422-0067
1661-6596
1422-0067
DOI:10.3390/ijms21155560