Paper Info
Title
Motif-based protein ranking by network propagation.
Abstract
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.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.
Year
DOI
Venue
2005
10.1093/bioinformatics/bti608
Bioinformatics
Keywords
Field
DocType
conserved remotely-related target,conserved structural component,sequence similarity,complex structure,motif-based protein ranking,new motif-based propagation algorithm,subtle similarity relationship,new general graph-based propagation,network propagation,base algorithm,protein-motif network,inferring similarity,protein sequence,protein motif
Sequence alignment,Pairwise comparison,Graph,Ranking,Pattern recognition,Computer science,Sequence motif,Motif (music),Homology (biology),Artificial intelligence,Bioinformatics,Peptide sequence
Journal
Volume
Issue
ISSN
21
19
1367-4803
Citations 
PageRank 
References 
12
0.74
11
Authors
4
Name
Order
Citations
PageRank
Rui Kuang148431.16
Jason Weston213068805.30
William Stafford Noble32907203.56
Christina Leslie4138977.99