Journal Article

Protein function annotation from sequence: prediction of residues interacting with RNA

R. V. Spriggs, Y. Murakami, H. Nakamura and S. Jones

in Bioinformatics

Volume 25, issue 12, pages 1492-1497
Published in print June 2009 | ISSN: 1367-4803
Published online April 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp257
Protein function annotation from sequence: prediction of residues interacting with RNA

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Motivation: All eukaryotic proteomes are characterized by a significant percentage of proteins of unknown function. Comp-utational function prediction methods are therefore essential as initial steps in the function annotation process. This article describes an annotation method (PiRaNhA) for the prediction of RNA-binding residues (RBRs) from protein sequence information. A series of sequence properties (position specific scoring matrices, interface propensities, predicted accessibility and hydrophobicity) are used to train a support vector machine. This method is then evaluated for its potential to be applied to RNA-binding function prediction at the level of the complete protein.

Results: The 5-fold cross-validation of PiRaNhA on a dataset of 81 RNA-binding proteins achieves a Matthews Correlation Coefficient (MCC) of 0.50 and accuracy of 87.2%. When used to predict RBRs in 42 proteins not used in training, PiRaNhA achieves an MCC of 0.41 and accuracy of 84.5%. Decision values from the PiRaNhA predictions were used in a second SVM to make predictions of RNA-binding function at the protein level, achieving an MCC of 0.53 and accuracy of 76.1%. The PiRaNhA RBR predictions allow experimentalists to perform more targeted experiments for function annotation; and the prediction of RNA-binding function at the protein level shows promise for proteome-wide annotations.

Availability and Implementation: Freely available on the web at www.bioinformatics.sussex.ac.uk/PIRANHA or http://piranha.protein.osaka-u.ac.jp.

Contact:s.jones@sussex.ac.uk.

Supplementary Information: Supplementary data are available at the Bioinformatics online.

Journal Article.  4337 words. 

Subjects: Bioinformatics and Computational Biology

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