Journal Article

Proteome-Wide Prediction of Novel DNA/RNA-Binding Proteins Using Amino Acid Composition and Periodicity in the Hyperthermophilic Archaeon <i>Pyrococcus furiosus</i>

Kosuke Fujishima, Mizuki Komasa, Sayaka Kitamura, Haruo Suzuki, Masaru Tomita and Akio Kanai

in DNA Research

Published on behalf of Kazusa DNA Research Institute

Volume 14, issue 3, pages 91-102
Published in print January 2007 | ISSN: 1340-2838
Published online June 2007 | e-ISSN: 1756-1663 | DOI: http://dx.doi.org/10.1093/dnares/dsm011

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Abstract

Proteins play a critical role in complex biological systems, yet about half of the proteins in publicly available databases are annotated as functionally unknown. Proteome-wide functional classification using bioinformatics approaches thus is becoming an important method for revealing unknown protein functions. Using the hyperthermophilic archaeon Pyrococcus furiosus as a model species, we used the support vector machine (SVM) method to discriminate DNA/RNA-binding proteins from proteins with other functions, using amino acid composition and periodicities as feature vectors. We defined this value as the composition score (CO) and periodicity score (PD). The P. furiosus proteins were classified into three classes (I–III) on the basis of the two-dimensional correlation analysis of CO score and PD score. As a result, approximately 87% of the functionally known proteins categorized as class I proteins (CO score + PD score > 0.6) were found to be DNA/RNA-binding proteins. Applying the two-dimensional correlation analysis to the 994 hypothetical proteins in P. furiosus, a total of 151 proteins were predicted to be novel DNA/RNA-binding protein candidates. DNA/RNA-binding activities of randomly chosen hypothetical proteins were experimentally verified. Six out of seven candidate proteins in class I possessed DNA/RNA-binding activities, supporting the efficacy of our method.

Keywords: DNA/RNA-binding protein; amino acid periodicity; support vector machine; archaea

Journal Article.  6880 words.  Illustrated.

Subjects: Genetics and Genomics

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