STRALCP--structure alignment-based clustering of proteins.
Protein structural annotation and classification is an important and challenging problem in bioinformatics. Research towards analysis of sequence-structure correspondences is critical for better understanding of a protein's structure, function, and its interaction with other molecules. Clustering of protein domains based on their structural similarities provides valuable information for protein classification schemes. In this article, we attempt to determine whether structure information alone is sufficient to adequately classify protein structures. We present an algorithm that identifies regions of structural similarity within a given set of protein structures, and uses those regions for clustering. In our approach, called STRALCP (STRucture ALignment-based Clustering of Proteins), we generate detailed information about global and local similarities between pairs of protein structures, identify fragments (spans) that are structurally conserved among proteins, and use these spans to group the structures accordingly. We also provide a web server at http://as2ts.llnl.gov/AS2TS/STRALCP/ for selecting protein structures, calculating structurally conserved regions and performing automated clustering.
Computing Applications and Research, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA. adamz@llnl.gov
Adam
A
Zemla
Brian
B
Geisbrecht
Jason
J
Smith
Marisa
M
Lam
Bonnie
B
Kirkpatrick
Mark
M
Wagner
Tom
T
Slezak
Carol Ecale
CE
Zhou
D000465:Algorithms; D000595:Amino Acid Sequence; D016000:Cluster Analysis; D020407:Internet; D008958:Models, Molecular; D008969:Molecular Sequence Data; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000145:classification*; D016415:Sequence Alignment; D012984:Software; D040681:Structural Homology, Protein
D011506:Proteins