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Molecules 2018, 23(3), 540;

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

Department of Bioinformatics, Shantou University Medical College, Shantou 515000, Guangdong, China
Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
Author to whom correspondence should be addressed.
Received: 6 February 2018 / Revised: 24 February 2018 / Accepted: 25 February 2018 / Published: 28 February 2018
(This article belongs to the Special Issue Computational Analysis for Protein Structure and Interaction)
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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-redundant benchmark test sets extracted from the PRIDB, the RPiRLS method outperformed RPI-Pred and IPMiner in terms of accuracy, specificity and sensitivity. Further, RPiRLS achieved an accuracy of 92% on the prediction of lncRNA-protein interactions. The proposed method can also be extended to construct RNA-protein interaction networks. The RPiRLS web server is freely available at View Full-Text
Keywords: Protein-RNA interactions; lncRNA-protein interaction networks; derived kernel; regularized least squares Protein-RNA interactions; lncRNA-protein interaction networks; derived kernel; regularized least squares

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Shen, W.-J.; Cui, W.; Chen, D.; Zhang, J.; Xu, J. RPiRLS: Quantitative Predictions of RNA Interacting with Any Protein of Known Sequence. Molecules 2018, 23, 540.

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