RPiRLS: Quantitative Predictions of RNA Interacting with Any Protein of Known Sequence

Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry
Wen-Jun ShenJianzhen Xu

Abstract

RNA-protein interactions (RPIs) have critical roles in numerous fundamental biological processes, such as post-transcriptional gene regulation, viral assembly, cellular defence and protein synthesis. As the number of available RNA-protein binding experimental data has increased rapidly due to high-throughput sequencing methods, it is now possible to measure and understand RNA-protein interactions by computational methods. In this study, we integrate a sequence-based derived kernel with regularized least squares to perform prediction. The derived kernel exploits the contextual information around an amino acid or a nucleic acid as well as the repetitive conserved motif information. We propose a novel machine learning method, called RPiRLS to predict the interaction between any RNA and protein of known sequences. For the RPiRLS classifier, each protein sequence comprises up to 20 diverse amino acids but for the RPiRLS-7G classifier, each protein sequence is represented by using 7-letter reduced alphabets based on their physiochemical properties. We evaluated both methods on a number of benchmark data sets and compared their performances with two newly developed and state-of-the-art methods, RPI-Pred and IPMiner. On the non-redunda...Continue Reading

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Citations

Jun 20, 2019·Protein and Peptide Letters·Amit Sagar, Bin Xue
May 9, 2019·Wiley Interdisciplinary Reviews. RNA·Xiaoyong PanHong-Bin Shen
Jan 29, 2019·Database : the Journal of Biological Databases and Curation·Piyush AgrawalGajendra P S Raghava
Nov 11, 2019·Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry·Zhengfeng WangFang-Xiang Wu
Nov 12, 2020·Advanced Science·Li-Zhen ZhangShi-Mei Zhuang

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Methods Mentioned

BETA
interaction prediction

Software Mentioned

RPI
Pred
NPInter
IPMiner
catRAPID
RPiRLS

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