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Bioinformatics 2005 21(Suppl 1):i47-i56; doi:10.1093/bioinformatics/bti1007
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

Protein function prediction via graph kernels

Karsten M. Borgwardt 1,*, Cheng Soon Ong 2, Stefan Schönauer 1, S. V. N. Vishwanathan 2, Alex J. Smola 2 and Hans-Peter Kriegel 1

1Institute for Computer Science, Ludwig-Maximilians-University Munich Oettingenstraßs e 67, 80538 Munich, Germany
2National ICT Australia Canberra, 0200 ACT, Australia

*To whom correspondence should be addressed.

Motivation: Computational approaches to protein function prediction infer protein function by finding proteins with similar sequence, structure, surface clefts, chemical properties, amino acid motifs, interaction partners or phylogenetic profiles. We present a new approach that combines sequential, structural and chemical information into one graph model of proteins. We predict functional class membership of enzymes and non-enzymes using graph kernels and support vector machine classification on these protein graphs.

Results: Our graph model, derivable from protein sequence and structure only, is competitive with vector models that require additional protein information, such as the size of surface pockets. If we include this extra information into our graph model, our classifier yields significantly higher accuracy levels than the vector models. Hyperkernels allow us to select and to optimally combine the most relevant node attributes in our protein graphs. We have laid the foundation for a protein function prediction system that integrates protein information from various sources efficiently and effectively.

Availability: More information available via www.dbs.ifi.lmu.de/Mitarbeiter/borgwardt.html.

Contact: borgwardt{at}dbs.ifi.lmu.de

Received on January 15, 2005; accepted on March 27, 2005

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