title: A hidden Markov model for predicting protein interfaces.
abstract: Protein-protein interactions play a defining role in protein function. Identifying the sites of interaction in a protein is a critical problem for understanding its functional mechanisms, as well as for drug design. To predict sites within a protein chain that participate in protein complexes, we have developed a novel method based on the Hidden Markov Model, which combines several biological characteristics of the sequences neighboring a target residue: structural information, accessible surface area, and transition probability among amino acids. We have evaluated the method using 5-fold cross-validation on 139 unique proteins and demonstrated precision of 66% and recall of 61% in identifying interfaces. These results are better than those achieved by other methods used for identification of interfaces.
authors: Department of Computer Science and Engineering, University of Colorado at Denver and Health Sciences, Denver, CO 80217, USA. dcnguyen@ouray.cudenver.edu
authors: Cao
authors: C
authors: Nguyen
authors: Katheleen J
authors: KJ
authors: Gardiner
authors: Krzysztof J
authors: KJ
authors: Cios
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry; D001665:Binding Sites; D019295:Computational Biology; D030562:Databases, Protein; D008390:Markov Chains; D008958:Models, Molecular; D046912:Multiprotein Complexes; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*
chemical_list: D000596:Amino Acids; D046912:Multiprotein Complexes; D011506:Proteins