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

GP4: an integrated Gram-Positive Protein Prediction Pipeline for subcellular localization mimicking bacterial sorting

Subcellular localization is a critical aspect of protein function and the potential application of proteins either as drugs or drug targets, or in industrial and domestic applications. However, the experimental determination of protein localization is time consuming and expensive. Therefore, various localization predictors have been developed for particular groups of species. Intriguingly, despite their major representation amongst biotechnological cell factories and pathogens, a meta-predictor based on sorting signals and specific for Gram-positive bacteria was still lacking. Here we present GP⁴, a protein subcellular localization meta-predictor mainly for Firmicutes, but also Actinobacteria, based on the combination of multiple tools, each specific for different sorting signals and compartments. Novelty elements include improved cell-wall protein prediction, including differentiation of the type of interaction, prediction of non-canonical secretion pathway target proteins, separate prediction of lipoproteins and better user experience in terms of parsability and interpretability of the results. GP⁴ aims at mimicking protein sorting as it would happen in a bacterial cell. As GP⁴ is not homology based, it has a broad applicability and does not depend on annotated databases with homologous proteins. Non-canonical usage may include little studied or novel species, synthetic and engineered organisms, and even re-use of the prediction data to develop custom prediction algorithms. Our benchmark analysis highlights the improved performance of GP⁴ compared to other widely used subcellular protein localization predictors. A webserver running GP⁴ is available at http://gp4.hpc.rug.nl/

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

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: naf4@aber.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.