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Bioinformatics Advance Access published online on August 2, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti608
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received May 30, 2005
Revised July 23, 2005
Accepted July 29, 2005

Article

Motif-based protein ranking by network propagation

Rui Kuang 1, Jason Weston 2, William Stafford Noble 3, and Christina Leslie 4*

1 Department of Computer Science, Columbia University
2 NEC Labs, New Jersey
3 Department of Genome Sciences, University of Washington
4 Center for Computational Biology and Bioinformatics, Columbia University; Center for Computational Learning Systems, Columbia University

* To whom correspondence should be addressed.
Christina Leslie, E-mail: cleslie{at}cs.columbia.edu


  Abstract

Motivation: Sequence similarity often suggests evolutionary relationships between protein sequences that can be important for inferring similarity of structure or function. The most widely-used pairwise sequence comparison algorithms for homology detection, such as BLAST and PSI-BLAST, often fail to detect less conserved remotely-related targets.

Results: In this paper, we propose a new general graph-based propagation algorithm called MotifProp to detect more subtle similarity relationships than pairwise comparison methods. MotifProp is based on a protein-motif network, in which edges connect proteins and the k-mer based motif features that they contain. We show that our new motif-based propagation algorithm can improve the ranking results over a base algorithm, such as PSI-BLAST, that is used to initialize the ranking. Despite the complex structure of the protein-motif network, MotifProp can be easily interpreted using the top-ranked motifs and motif-rich regions induced by the propagation, both of which are helpful for discovering conserved structural components in remote homologies.

Availability: http://www.cs.columbia.edu/compbio/motifprop.


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