Genome-wide prediction of prokaryotic two-component system networks using a sequence-based meta-predictor. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Kara, Altan; Vickers, Martin; Swain, Martin; Whitworth, David; Fernandez-Fuentes, Narcis
• Is Part Of: BMC bioinformatics. Volume 16:Issue 1(2015)
• Page Start: 1
• Page End: 9
• Publication Date: 2015-12
• Subjects: Two-component signalling system -- Protein-protein interactions -- Protein-protein interaction predictions -- Meta-predictor -- Support vector machine -- Web server -- Genome context -- Co-evolution; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/s12859-015-0741-7
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract Background Two component systems (TCS) are signalling complexes manifested by a histidine kinase (receptor) and a response regulator (effector). They are the most abundant signalling pathways in prokaryotes and control a wide range of biological processes. The pairing of these two components is highly specific, often requiring costly and time-consuming experimental characterisation. Therefore, there is considerable interest in developing accurate prediction tools to lessen the burden of experimental work and cope with the ever-increasing amount of genomic information. Results We present a novel meta-predictor, MetaPred2CS, which is based on a support vector machine. MetaPred2CS integrates six sequence-based prediction methods:in-silico two-hybrid, mirror-tree, gene fusion, phylogenetic profiling, gene neighbourhood, and gene operon. To benchmark MetaPred2CS, we also compiled a novel high-quality training dataset of experimentally deduced TCS protein pairs for k-fold cross validation, to act as a gold standard for TCS partnership predictions. Combining individual predictions using MetaPred2CS improved performance when compared to the individual methods and in comparison with a current state-of-the-art meta-predictor. Conclusion We have developed MetaPred2CS, a support vector machine-based metapredictor for prokaryotic TCS protein pairings. Central to the success of MetaPred2CS is a strategy of integrating individual predictors that improves the overall prediction accuracy, with thein-silico two-hybrid method contributing most to performance. MetaPred2CS outperformed other available systems in our benchmark tests, and is available online athttp://metapred2cs.ibers.aber.ac.uk, along with our gold standard dataset of TCS interaction pairs.