title: [Computational method for prediction of protein functional sites using specificity determinants].
abstract: The current available data on protein sequences largely exceeds the experimental capabilities to annotate their function. So annotation in silico, i.e. using computational methods becomes increasingly important. This annotation is inevitably a prediction, but it can be an important starting point for further experimental studies. Here we present a method for prediction of protein functional sites, SDPsite, based on the identification of protein specificity determinants. Taking as an input a protein sequence alignment and a phylogenetic tree, the algorithm predicts conserved positions and specificity determinants, maps them onto the protein's 3D structure, and searches for clusters of the predicted positions. Comparison of the obtained predictions with experimental data and data on performance of several other methods for prediction of functional sites reveals that SDPsite agrees well with the experiment and outperforms most of the previously available methods. SDPsite is publicly available under http://bioinf.fbb.msu.ru/SDPsite.
authors: O V
authors: OV
authors: Kalinina
authors: R B
authors: RB
authors: Rassel
authors: A B
authors: AB
authors: Rakhmaninova
authors: M S
authors: MS
authors: Gel'fand
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D030562:Databases, Protein; D008958:Models, Molecular; D010802:Phylogeny; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*; D012680:Sensitivity and Specificity; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein; D017386:Sequence Homology, Amino Acid; D012984:Software
chemical_list: D011506:Proteins