Skip to main content
Predicting binding sites of hydrolase-inhibitor complexes by combining several methods
BMC Bioinformatics
  • Taner Z Sen, Iowa State University
  • Andrzej Kloczkowski, Iowa State University
  • Robert L Jernigan, Iowa State University
  • Changhui Yan, Iowa State University
  • Vasant Honovar, Iowa State University
  • Kai-Ming Ho, Iowa State University
  • Cai-Zhuang Wang, Iowa State University
  • Yungok Ihm, Iowa State University
  • Haibo Cao, Iowa State University
  • Xun Gu, Iowa State University
  • Drena Dobbs, Iowa State University
Document Type
Publication Version
Published Version
Publication Date


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. Results

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. Conclusions

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.


This article is from BMC Bioinformatics 5 (2005): 205, doi: 10.1186/1471-2105-5-205. Posted with permission.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright Owner
Sen et al
File Format
Citation Information
Taner Z Sen, Andrzej Kloczkowski, Robert L Jernigan, Changhui Yan, et al.. "Predicting binding sites of hydrolase-inhibitor complexes by combining several methods" BMC Bioinformatics Vol. 5 (2004) p. 205
Available at: