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Bioinformatics Advance Access first published online on January 18, 2007
This version published online on January 19, 2007
Bioinformatics, doi:10.1093/bioinformatics/btl661
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Structure-based evaluation of in silico predictions of protein-protein interactions using Comparative Docking

Simon J. Cockell 1, Baldo Oliva 2 and Richard M. Jackson 1,*

1Institute of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, United Kingdom.
2Grup de Bioinformàtica Estructural (GRIB-IMIM), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona 08003, Catalonia, Spain.

*To whom correspondence should be addressed. Richard M. Jackson, E-mail: o.p.kuipers{at}rug.nl


  Abstract

Motivation: Due to the limitations in experimental methods for determining binary interactions and structure determination of protein complexes, the need exists for computational models to fill the increasing gap between genome sequence information and protein annotation. Here we describe a novel method that uses structural models to reduce a large number of in silico predictions to a high confidence subset that is amenable to experimental validation.

Results: A two stage evaluation procedure was developed, firstly, a sequence-based method assessed the conservation of protein interface patches used in the original in silico prediction method, both in terms of position within the primary sequence, and in terms of sequence conservation. When applying the most stringent conditions it was found that 20.5% of the dataset being assessed passed this test. Secondly, a high-throughput structure-based docking evaluation procedure assessed the soundness of three dimensional models produced for the putative interactions. 8,264 interactions or over 70% of the dataset being assessed could be modelled in this way, and 27% of these can be considered ‘valid’ by the applied criteria. In all, 6.9% of the interactions passed both the tests and can be considered to be a high confidence set of predicted interactions, several of which are described.

Availability: http://bioinformatics.leeds.ac.uk/~bmb4sjc.

Associate Editor: Dmitrij Frishman

Received on July 14, 2006; revised on November 13, 2006; accepted on December 19, 2006

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