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SherLoc2: A High-Accuracy Hybrid Method for Predicting Subcellular Localization of Proteins

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Sebastian Briesemeister*^†, Torsten Blum^†, Scott Brady^‡, Yin Lam^‡, Oliver Kohlbacher^† and Hagit Shatkay^‡

Division for Simulation of Biological Systems, Center for Bioinformatics Tübingen, Eberhard-Karls-Universität Tübingen, Germany, and School of Computing, Queen’s University, Kingston, Ontario, Canada

J. Proteome Res., 2009, 8 (11), pp 5363–5366

DOI: 10.1021/pr900665y

Publication Date (Web): September 18, 2009

* To whom correspondence should be addressed. E-mail: briese@informatik.uni-tuebingen.de. Tel: +49 7071 2970462. Fax: +49 7071 295152., †

Eberhard-Karls-Universität Tübingen.

, ‡

Queen’s University.

Section:

Biochemical Methods

Abstract

SherLoc2 is a comprehensive high-accuracy subcellular localization prediction system. It is applicable to animal, fungal, and plant proteins and covers all main eukaryotic subcellular locations. SherLoc2 integrates several sequence-based features as well as text-based features. In addition, we incorporate phylogenetic profiles and Gene Ontology (GO) terms derived from the protein sequence to considerably improve the prediction performance. SherLoc2 achieves an overall classification accuracy of up to 93% in 5-fold cross-validation. A novel feature, DiaLoc, allows users to manually provide their current background knowledge by describing a protein in a short abstract which is then used to improve the prediction. SherLoc2 is available both as a free Web service and as a stand-alone version at http://www-bs.informatik.uni-tuebingen.de/Services/SherLoc2.

Keywords:

protein subcellular localization prediction; machine learning; text mining; Gene Ontology

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