A two-stage classifier for identification of protein-protein interface residues.
BACKGROUND
The ability to identify protein-protein interaction sites and to detect specific amino acid residues that contribute to the specificity and affinity of protein interactions has important implications for problems ranging from rational drug design to analysis of metabolic and signal transduction networks.


RESULTS
We have developed a two-stage method consisting of a support vector machine (SVM) and a Bayesian classifier for predicting surface residues of a protein that participate in protein-protein interactions. This approach exploits the fact that interface residues tend to form clusters in the primary amino acid sequence. Our results show that the proposed two-stage classifier outperforms previously published sequence-based methods for predicting interface residues. We also present results obtained using the two-stage classifier on an independent test set of seven CAPRI (Critical Assessment of PRedicted Interactions) targets. The success of the predictions is validated by examining the predictions in the context of the three-dimensional structures of protein complexes.
Yan
Changhui
C
Artificial Intelligence Research Laboratory, Iowa State University, Ames, IA, 50010, USA. chhyan@iastate.edu
Dobbs
Drena
D
Honavar
Vasant
V
D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry*; D001665:Binding Sites; D003198:Computer Simulation; D008956:Models, Chemical; D008969:Molecular Sequence Data; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry*; D020539:Sequence Analysis, Protein / Q000379:methods*
D000596:Amino Acids; D011506:Proteins