The automatic detection of known beta-propeller structural motifs from protein tertiary structure.
Following our previous work on the analysis of 'structural plasticity' associated with the beta-propeller structural motifs, we have now developed a simple method that can automatically detect all the known beta-propellers in protein tertiary structure, given a list of Protein Data Bank (PDB) codes as input to the computer program. Our beta-propeller detection (BPD) method identifies the location of beta-propellers in the protein structure, specifies the beta-propeller type, the beta-sheet associated beta-strand pattern and the structurally similar beta-propellers observed in other proteins. When tested on 21,566 proteins in the PDB, the BPD method was capable of correctly identifying all the known 245 beta-propellers described in the structural classification of proteins (SCOP) with the number of false positives detected being less than 0.2%. Forty-one false positives were detected that correspond to eight known protein families. When compared with some of the popular web-based programs that can automatically detect 'structural similarities' between the query and target proteins, our method has the advantage of also being capable of detecting beta-propellers associated with 'structural plasticity' and in situations where the target and query proteins differ in amino acid sequence length.
Kotu
Archana
A
Bioinformatics, Centre for Cellular and Molecular Biology, Uppal Road, Hyderabad 500 007, India.
Guruprasad
Kunchur
K
D020816:Amino Acid Motifs; D000818:Animals; D001331:Automation; D002244:Carbon / Q000737:chemistry; D019295:Computational Biology; D003201:Computers; D030562:Databases, Protein; D006801:Humans; D046911:Macromolecular Substances / Q000737:chemistry*; D008958:Models, Molecular; D011487:Protein Conformation; D017510:Protein Folding; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D012984:Software
D046911:Macromolecular Substances; D002244:Carbon