SitesIdentify: a protein functional site prediction tool.
BACKGROUND
The rate of protein structures being deposited in the Protein Data Bank surpasses the capacity to experimentally characterise them and therefore computational methods to analyse these structures have become increasingly important. Identifying the region of the protein most likely to be involved in function is useful in order to gain information about its potential role. There are many available approaches to predict functional site, but many are not made available via a publicly-accessible application.


RESULTS
Here we present a functional site prediction tool (SitesIdentify), based on combining sequence conservation information with geometry-based cleft identification, that is freely available via a web-server. We have shown that SitesIdentify compares favourably to other functional site prediction tools in a comparison of seven methods on a non-redundant set of 237 enzymes with annotated active sites.


CONCLUSIONS
SitesIdentify is able to produce comparable accuracy in predicting functional sites to its closest available counterpart, but in addition achieves improved accuracy for proteins with few characterised homologues. SitesIdentify is available via a webserver at http://www.manchester.ac.uk/bioinformatics/sitesidentify/
Bray
Tracey
T
Faculty of Life Sciences, The University of Manchester, Michael Smith Building, Oxford Road, Manchester M13 9PT, UK. t.bray@postgrad.manchester.ac.uk
Chan
Pedro
P
Bougouffa
Salim
S
Greaves
Richard
R
Doig
Andrew J
AJ
Warwicker
Jim
J
D001665:Binding Sites; D020134:Catalytic Domain; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D011506:Proteins / Q000737:chemistry*; D012984:Software
D011506:Proteins