DocumentCode :
2582668
Title :
Improved phylogenetic motif detection using parsimony
Author :
Roshan, Usman ; Livesay, Dennis R. ; La, David
Author_Institution :
New Jersey Inst. of Technol., Newark, NJ, USA
fYear :
2005
fDate :
19-21 Oct. 2005
Firstpage :
19
Lastpage :
26
Abstract :
We have recently demonstrated (La et al, Proteins, 58:2005) that sequence fragments approximating the overall familial phylogeny, called phylogenetic motifs (PMs), represent a promising protein functional site prediction strategy. Previous results across a structurally and functionally diverse dataset indicate that phylogenetic motifs correspond to a wide variety of known functional characteristics. Phylogenetic motifs are detected using a sliding window algorithm that compares neighbor joining trees on the complete alignment to those on the sequence fragments. In this investigation we identify PMs using heuristic maximum parsimony trees. We show that when using parsimony the functional site prediction accuracy of PMs improves substantially, particularly on divergent datasets. We also show that the new PMs found using parsimony are not necessarily conserved in sequence, and, therefore, would not be detected by traditional motif (information content-based) approaches.
Keywords :
biology computing; genetics; molecular biophysics; molecular configurations; proteins; trees (mathematics); heuristic maximum parsimony trees; neighbor joining trees; phylogenetic motif detection; protein functional site prediction; sequence fragments; sliding window algorithm; Accuracy; Biochemistry; Crystallography; Detection algorithms; Drugs; Large-scale systems; NP-hard problem; Phylogeny; Prediction methods; Protein engineering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Bioinformatics and Bioengineering, 2005. BIBE 2005. Fifth IEEE Symposium on
Print_ISBN :
0-7695-2476-1
Type :
conf
DOI :
10.1109/BIBE.2005.38
Filename :
1544444
Link To Document :
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