Title
seeMotif: exploring and visualizing sequence motifs in 3D structures.
Abstract
Sequence motifs are important in the study of molecular biology. Motif discovery tools efficiently deliver many function related signatures of proteins and largely facilitate sequence annotation. As increasing numbers of motifs are detected experimentally or predicted computationally, characterizing the functional roles of motifs and identifying the potential synergetic relationships between them are important next steps. A good way to investigate novel motifs is to utilize the abundant 3D structures that have also been accumulated at an astounding rate in recent years. This article reports the development of the web service seeMotif, which provides users with an interactive interface for visualizing sequence motifs on protein structures from the Protein Data Bank (PDB). Researchers can quickly see the locations and conformation of multiple motifs among a number of related structures simultaneously. Considering the fact that PDB sequences are usually shorter than those in sequence databases and/or may have missing residues, seeMotif has two complementary approaches for selecting structures and mapping motifs to protein chains in structures. As more and more structures belonging to previously uncharacterized protein families become available, combining sequence and structure information gives good opportunities to facilitate understanding of protein functions in large-scale genome projects. Available at: http://seemotif.csie.ntu.edu.tw, http://seemotif.ee.ncku.edu.tw or http://seemotif.csbb.ntu.edu.tw.
Year
DOI
Venue
2009
10.1093/nar/gkp439
NUCLEIC ACIDS RESEARCH
Keywords
Field
DocType
computer graphics,internet,sequence motif
Sequence alignment,Protein family,Genome project,Biology,Sequence motif,Computational biology,Bioinformatics,Genetics,Protein Data Bank,Protein Data Bank (RCSB PDB),Consensus sequence,Protein structure
Journal
Volume
Issue
ISSN
37
SUPnan
0305-1048
Citations 
PageRank 
References 
0
0.34
27
Authors
3
Name
Order
Citations
PageRank
Darby Tien-hao Chang122112.58
Ting-Ying Chien271.13
Chien-Yu Chen336729.24