Campus Units

Genetics, Development and Cell Biology, Bioinformatics and Computational Biology, Computer Science

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

6-2004

Journal or Book Title

Neural Computing & Applications

Volume

13

Issue

2

First Page

123

Last Page

129

DOI

10.1007/s00521-004-0414-3

Abstract

In this paper, we describe a machine learning approach for sequence-based prediction of proteinprotein interaction sites. A support vector machine (SVM) classifier was trained to predict whether or not a surface residue is an interface residue (i.e., is located in the protein-protein interaction surface), based on the identity of the target residue and its ten sequence neighbors. Separate classifiers were trained on proteins from two categories of complexes, antibody-antigen and protease-inhibitor. The effectiveness of each classifier was evaluated using leave-one-out (jack-knife) cross-validation. Interface and non-interface residues were classified with relatively high sensitivity (82.3% and 78.5%) and specificity (81.0% and 77.6%) for proteins in the antigen-antibody and protease-inhibitor complexes, respectively. The correlation between predicted and actual labels was 0.430 and 0.462, indicating that the method performs substantially better than chance (zero correlation). Combined with recently developed methods for identification of surface residues from sequence information, this offers a promising approach to predict residues involved in protein-protein interactions from sequence information alone.

Comments

This is a manuscript of an article from Neural Computing & Applications 13 (2004): 123. The final publication is available at Springer via http://dx.doi.org/10.1007/s00521-004-0414-3.

Copyright Owner

Springer-Verlag London Limited

Language

en

File Format

application/pdf

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