Functional annotation by identification of local surface similarities: a novel tool for structural genomics.
BACKGROUND
Protein function is often dependent on subsets of solvent-exposed residues that may exist in a similar three-dimensional configuration in non homologous proteins thus having different order and/or spacing in the sequence. Hence, functional annotation by means of sequence or fold similarity is not adequate for such cases.


RESULTS
We describe a method for the function-related annotation of protein structures by means of the detection of local structural similarity with a library of annotated functional sites. An automatic procedure was used to annotate the function of local surface regions. Next, we employed a sequence-independent algorithm to compare exhaustively these functional patches with a larger collection of protein surface cavities. After tuning and validating the algorithm on a dataset of well annotated structures, we applied it to a list of protein structures that are classified as being of unknown function in the Protein Data Bank. By this strategy, we were able to provide functional clues to proteins that do not show any significant sequence or global structural similarity with proteins in the current databases.


CONCLUSIONS
This method is able to spot structural similarities associated to function-related similarities, independently on sequence or fold resemblance, therefore is a valuable tool for the functional analysis of uncharacterized proteins. Results are available at http://cbm.bio.uniroma2.it/surface/structuralGenomics.html.
Ferrè
Fabrizio
F
Boston College, Biology Department, Chestnut Hill MA, USA. ferref@bc.edu
Ausiello
Gabriele
G
Zanzoni
Andreas
A
Helmer-Citterich
Manuela
M
D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology; D030562:Databases, Protein; D005188:False Negative Reactions; D023281:Genomics / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods; D020407:Internet; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated; D011506:Proteins / Q000737:chemistry* / Q000145:classification* / Q000378:metabolism; D020539:Sequence Analysis, Protein / Q000379:methods*
D011506:Proteins