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Bioinformatics Advance Access published online on January 5, 2006
Bioinformatics, doi:10.1093/bioinformatics/bti804
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received August 4, 2005
Revised November 23, 2005
Accepted November 25, 2005

Article

A regularised discriminative model for predition of protein-peptide interactions

Wolfgang P. Lehrach 1 *, Dirk Husmeier 2, and Christopher K. I. Williams 3

1 University of Edinburgh, Edinburgh, EH1 2QL, UK; Biomathematics & Statistics Scotland, Edinburgh, EH9 3JZ, UK
2 Biomathematics & Statistics Scotland, Edinburgh, EH9 3JZ, UK
3 University of Edinburgh, Edinburgh, EH1 2QL, UK

* To whom correspondence should be addressed.
Wolfgang P. Lehrach, E-mail: wlehrach{at}ed.ac.uk


  Abstract

Motivation: Short well defined domains known as Peptide Recognition Modules (PRMs) regulate many important protein-protein interactions involved in the formation of macromolecular complexes and biochemical pathways. Since high-throughput experiments like yeast two-hybrid and phage display are expensive and intrinsically noisy, it would be desirable to more specifically target or partially bypass them with complementary in silico approaches. In the present paper, we present a probabilistic discriminative approach to predicting PRM-mediated protein-protein interactions from sequence data. The model is motivated by the discriminative model of Segal and Sharan (2004) as an alternative to the generative approach of Reiss and Schwikowski (2004). In our evaluation, we focus on predicting the interaction network. As proposed by Williams (1995), we overcome the problem of susceptibility to over-fitting by adopting a Bayesian a posteriori approach based on a Laplacian prior in parameter space.

Results: The proposed method was tested on two datasets of protein-protein interactions involving 28 SH3 domain proteins in Saccharmomyces cerevisiae, where the datasets were obtained with different experimental techniques. The predictions were evaluated with out-of-sample receiver operator characteristic (ROC) curves. In both cases, Laplacian regularisation turned out to be crucial for achieving a reasonable generalisation performance. While the Laplacian-regularised discriminative model showed a similar performance as the generative model of Reiss and Schwikowski (2004) on the phage display network, it obtained a larger area under the ROC curve for the yeast two-hybrid network.

Availability: http://lehrach.com/wolfgang/dmf.

Associate Editor: Keith A Crandall
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