Domains, motifs and clusters in the protein universe.
The rapid growth of bio-sequence information has resulted in an increasing demand for reliable methods that group proteins. A few databases with curated alignments of protein families have demonstrated that expert-driven repositories can keep up with the data deluge in the genome era. These original resources implicitly identify domain-like modules in proteins. An increasing number of automatic methods have sprouted over the past few years that cluster the protein universe. Many of these implicitly dissect proteins into structural domain-like fragments. In a very coarse-grained evaluation, some of the automatic methods appear to be on par with expert-driven approaches. However, neither automatic nor manual methods are currently entirely up to the challenges of tasks such as target selection in structural genomics. Thus, we urgently need refined and sustained automatic clustering tools.
Liu
Jinfeng
J
CUBIC and North East Structural Genomics Consortium, Department of Biochemistry and Molecular Biophysics, Columbia University, 650 West 168th Street BB217, New York, NY 10032, USA.
Rost
Burkhard
B
D020816:Amino Acid Motifs; D016000:Cluster Analysis; D030562:Databases, Protein; D005103:Expert Systems; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000145:classification*; D017386:Sequence Homology, Amino Acid; D040681:Structural Homology, Protein
D011506:Proteins