Predicting binding sites of hydrolase-inhibitor complexes by combining several methods
1 L.H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, IA 50011, USA
2 Department of Biochemistry, Biophysics, and Molecular Biology, Iowa State University, Ames, IA 50011, USA
3 Department of Computer Science, Iowa State University, Ames, IA 50011, USA
4 Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50011, USA
5 Department of Physics and Astronomy, Iowa State University, Ames, IA 50011, USA
6 Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011, USA
BMC Bioinformatics 2004, 5:205 doi:10.1186/1471-2105-5-205Published: 17 December 2004
Protein-protein interactions play a critical role in protein function. Completion of many genomes is being followed rapidly by major efforts to identify interacting protein pairs experimentally in order to decipher the networks of interacting, coordinated-in-action proteins. Identification of protein-protein interaction sites and detection of specific amino acids that contribute to the specificity and the strength of protein interactions is an important problem with broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks.
In order to increase the power of predictive methods for protein-protein interaction sites, we have developed a consensus methodology for combining four different methods. These approaches include: data mining using Support Vector Machines, threading through protein structures, prediction of conserved residues on the protein surface by analysis of phylogenetic trees, and the Conservatism of Conservatism method of Mirny and Shakhnovich. Results obtained on a dataset of hydrolase-inhibitor complexes demonstrate that the combination of all four methods yield improved predictions over the individual methods.
We developed a consensus method for predicting protein-protein interface residues by combining sequence and structure-based methods. The success of our consensus approach suggests that similar methodologies can be developed to improve prediction accuracies for other bioinformatic problems.