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 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.
Highlights
The interactions between protein and RNA are crucial for many cellular processes, such as protein synthesis and regulation of gene expression [1,2,3,4,5,6]
Developing reliable computational approaches provides an alternative way to investigate the effects of mutations on proteins and their interactions with other molecules on a large scale, and it will facilitate the identification of functionally important missense mutations and the discovery of the molecular mechanisms that cause diseases
We introduced a new computational method, PremPRI, for characterizing the effects of missense mutations on protein–RNA interactions by calculating the binding affinity changes quantitatively
Summary
The interactions between protein and RNA are crucial for many cellular processes, such as protein synthesis and regulation of gene expression [1,2,3,4,5,6]. Quantifying the effects of missense mutations on specific protein–RNA interactions requires assessing the binding affinity changes upon introducing mutations, which can be accurately measured by traditional mutagenesis technologies and recently developed high-throughput experimental methods. We developed a computational approach to estimate the impacts of missense mutations on protein–DNA interactions using molecular mechanics force fields and statistical potentials [34]. Some advancements have been made, the issue of predicting the effects of missense mutations on protein–RNA interactions is still at the initial stage To address this need, we introduced a new computational method, PremPRI, for characterizing the effects of missense mutations on protein–RNA interactions by calculating the binding affinity changes quantitatively. PremPRI is freely available at https://lilab.jysw.suda.edu.cn/ research/PremPRI/
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