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Nucleic Acids Research Advance Access published online on May 7, 2008
Nucleic Acids Research, doi:10.1093/nar/gkn193
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© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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FFPred: an integrated feature-based function prediction server for vertebrate proteomes

A. E. Lobley1, T. Nugent1, C. A. Orengo2 and D. T. Jones1,2,*

1Department of Computer Science, University College London and 2Institute of Structural and Molecular Biology, Division of Biosciences, University College London, Gower Street, London WC1E 6BT, United Kingdom

*To whom correspondence should be addressed. Tel: +44 020 7679 7982; Fax: +44 020 7387 1397; Email: d.jones{at}cs.ucl.ac.uk

Received January 27, 2008. Revised March 21, 2008. Accepted April 3, 2008.

One of the challenges of the post-genomic era is to provide accurate function annotations for large volumes of data resulting from genome sequencing projects. Most function prediction servers utilize methods that transfer existing database annotations between orthologous sequences. In contrast, there are few methods that are independent of homology and can annotate distant and orphan protein sequences. The FFPred server adopts a machine-learning approach to perform function prediction in protein feature space using feature characteristics predicted from amino acid sequence. The features are scanned against a library of support vector machines representing over 300 Gene Ontology (GO) classes and probabilistic confidence scores returned for each annotation term. The GO term library has been modelled on human protein annotations; however, benchmark performance testing showed robust performance across higher eukaryotes. FFPred offers important advantages over traditional function prediction servers in its ability to annotate distant homologues and orphan protein sequences, and achieves greater coverage and classification accuracy than other feature-based prediction servers. A user may upload an amino acid and receive annotation predictions via email. Feature information is provided as easy to interpret graphics displayed on the sequence of interest, allowing for back-interpretation of the associations between features and function classes.


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