Identification of Protein-RNA Interaction Sites Using the Information of Spatial Adjacent Residues

Overview

Journal Proteome Sci

Publisher Biomed Central

Date 2011 Dec 15

PMID 22165911

Citations 3

Authors

Wei Chen

Shao-Wu Zhang

Yong-Mei Cheng

Quan Pan

Affiliations

Soon will be listed here.

Abstract

Background: Protein-RNA interactions play an important role in numbers of fundamental cellular processes such as RNA splicing, transport and translation, protein synthesis and certain RNA-mediated enzymatic processes. The more knowledge of Protein-RNA recognition can not only help to understand the regulatory mechanism, the site-directed mutagenesis and regulation of RNA-protein complexes in biological systems, but also have a vitally effecting for rational drug design.

Results: Based on the information of spatial adjacent residues, novel feature extraction methods were proposed to predict protein-RNA interaction sites with SVM-KNN classifier. The total accuracies of spatial adjacent residue profile feature and spatial adjacent residues weighted accessibility solvent area feature are 78%, 67.07% respectively in 5-fold cross-validation test, which are 1.4%, 3.79% higher than that of sequence neighbour residue profile feature and sequence neighbour residue accessibility solvent area feature.

Conclusions: The results indicate that the performance of feature extraction method using the spatial adjacent information is superior to the sequence neighbour information approach. The performance of SVM-KNN classifier is little better than that of SVM. The feature extraction method of spatial adjacent information with SVM-KNN is very effective for identifying protein-RNA interaction sites and may at least play a complimentary role to the existing methods.

Citing Articles

Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors.

Sun M, Wang X, Zou C, He Z, Liu W, Li H BMC Bioinformatics. 2016; 17(1):231.

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Prediction of protein-protein interaction with pairwise kernel support vector machine.

Zhang S, Hao L, Zhang T Int J Mol Sci. 2014; 15(2):3220-33.

PMID: 24566145 PMC: 3958907. DOI: 10.3390/ijms15023220.

Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art.

Walia R, Caragea C, Lewis B, Towfic F, Terribilini M, El-Manzalawy Y BMC Bioinformatics. 2012; 13:89.

PMID: 22574904 PMC: 3490755. DOI: 10.1186/1471-2105-13-89.

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