Campus Units

Genetics, Development and Cell Biology, Biochemistry, Biophysics and Molecular Biology, Computer Science, Bioinformatics and Computational Biology, Physics and Astronomy

Document Type

Article

Publication Version

Published Version

Publication Date

2004

Journal or Book Title

BMC Bioinformatics

Volume

5

First Page

205

DOI

10.1186/1471-2105-5-205

Abstract

Background

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.

Comments

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

Rights

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Copyright Owner

Sen et al

Language

en

File Format

application/pdf