Genome-wide prediction of prokaryotic two-component system networks using a sequence-based meta-predictor

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 the in-silico two-hybrid method contributing most to performance. MetaPred2CS outperformed other available systems in our benchmark tests, and is available online at http://metapred2cs.ibers.aber.ac.uk, along with our gold standard dataset of TCS interaction pairs. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0741-7) contains supplementary material, which is available to authorized users

MetaPred2CS:A sequence-based meta-predictor for protein-protein interactions of prokaryotic two-component system proteins

Abstract Motivation: Two-component systems (TCS) are the main signalling pathways of prokaryotes, and control a wide range of biological phenomena. Their functioning depends on interactions between TCS proteins, the specificity of which is poorly understood. Results: The MetaPred2CS web-server interfaces a sequence-based meta-predictor specifically designed to predict pairing of the histidine kinase and response-regulator proteins forming TCSs. MetaPred2CS integrates six sequence-based methods using a support vector machine classifier and has been intensively tested under different benchmarking conditions: (i) species specific gene sets; (ii) neighbouring versus orphan pairs; and (iii) k-fold cross validation on experimentally validated datasets. Availability and Implementation: Web server at: http://metapred2cs.ibers.aber.ac.uk/ , Source code: https://github.com/martinjvickers/MetaPred2CS or implemented as Virtual Machine at: http://metapred2cs.ibers.aber.ac.uk/download Contact:  [email protected] Supplementary information:  Supplementary data are available at Bioinformatics online.</jats:p

Additional file 2: of Genome-wide prediction of prokaryotic two-component system networks using a sequence-based meta-predictor

Diagrammatic representation of P+, P-, NP+, OP+, SP+ and SP- datasets. (PDF 76 kb