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Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors
BACKGROUND: RNA-binding proteins participate in many important biological processes concerning RNA-mediated gene regulation, and several computational methods have been recently developed to predict the protein-RNA interactions of RNA-binding proteins. Newly developed discriminative descriptors will...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4897909/ https://www.ncbi.nlm.nih.gov/pubmed/27266516 http://dx.doi.org/10.1186/s12859-016-1110-x |
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author | Sun, Meijian Wang, Xia Zou, Chuanxin He, Zenghui Liu, Wei Li, Honglin |
author_facet | Sun, Meijian Wang, Xia Zou, Chuanxin He, Zenghui Liu, Wei Li, Honglin |
author_sort | Sun, Meijian |
collection | PubMed |
description | BACKGROUND: RNA-binding proteins participate in many important biological processes concerning RNA-mediated gene regulation, and several computational methods have been recently developed to predict the protein-RNA interactions of RNA-binding proteins. Newly developed discriminative descriptors will help to improve the prediction accuracy of these prediction methods and provide further meaningful information for researchers. RESULTS: In this work, we designed two structural features (residue electrostatic surface potential and triplet interface propensity) and according to the statistical and structural analysis of protein-RNA complexes, the two features were powerful for identifying RNA-binding protein residues. Using these two features and other excellent structure- and sequence-based features, a random forest classifier was constructed to predict RNA-binding residues. The area under the receiver operating characteristic curve (AUC) of five-fold cross-validation for our method on training set RBP195 was 0.900, and when applied to the test set RBP68, the prediction accuracy (ACC) was 0.868, and the F-score was 0.631. CONCLUSIONS: The good prediction performance of our method revealed that the two newly designed descriptors could be discriminative for inferring protein residues interacting with RNAs. To facilitate the use of our method, a web-server called RNAProSite, which implements the proposed method, was constructed and is freely available at http://lilab.ecust.edu.cn/NABind. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1110-x) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4897909 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-48979092016-06-10 Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors Sun, Meijian Wang, Xia Zou, Chuanxin He, Zenghui Liu, Wei Li, Honglin BMC Bioinformatics Research Article BACKGROUND: RNA-binding proteins participate in many important biological processes concerning RNA-mediated gene regulation, and several computational methods have been recently developed to predict the protein-RNA interactions of RNA-binding proteins. Newly developed discriminative descriptors will help to improve the prediction accuracy of these prediction methods and provide further meaningful information for researchers. RESULTS: In this work, we designed two structural features (residue electrostatic surface potential and triplet interface propensity) and according to the statistical and structural analysis of protein-RNA complexes, the two features were powerful for identifying RNA-binding protein residues. Using these two features and other excellent structure- and sequence-based features, a random forest classifier was constructed to predict RNA-binding residues. The area under the receiver operating characteristic curve (AUC) of five-fold cross-validation for our method on training set RBP195 was 0.900, and when applied to the test set RBP68, the prediction accuracy (ACC) was 0.868, and the F-score was 0.631. CONCLUSIONS: The good prediction performance of our method revealed that the two newly designed descriptors could be discriminative for inferring protein residues interacting with RNAs. To facilitate the use of our method, a web-server called RNAProSite, which implements the proposed method, was constructed and is freely available at http://lilab.ecust.edu.cn/NABind. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1110-x) contains supplementary material, which is available to authorized users. BioMed Central 2016-06-07 /pmc/articles/PMC4897909/ /pubmed/27266516 http://dx.doi.org/10.1186/s12859-016-1110-x Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Sun, Meijian Wang, Xia Zou, Chuanxin He, Zenghui Liu, Wei Li, Honglin Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors |
title | Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors |
title_full | Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors |
title_fullStr | Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors |
title_full_unstemmed | Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors |
title_short | Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors |
title_sort | accurate prediction of rna-binding protein residues with two discriminative structural descriptors |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4897909/ https://www.ncbi.nlm.nih.gov/pubmed/27266516 http://dx.doi.org/10.1186/s12859-016-1110-x |
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