Predicting the Strongest Domain-Domain Contact in Interacting Protein Pairs

Tom M. W. Nye, Carlo Berzuini, Walter R. Gilks, M. Madan Babu and Sarah Teichmann
Additional contact information
Tom M. W. Nye: Medical Research Council Biostatistics Unit
Carlo Berzuini: Medical Research Council Biostatistics Unit; Università di Pavia Dipartimento di Informatica e Systemistica
Walter R. Gilks: Medical Research Council Biostatistics Unit
M. Madan Babu: Medical Research Council Laboratory of Molecular Biology
Sarah Teichmann: Medical Research Council Laboratory of Molecular Biology

Statistical Applications in Genetics and Molecular Biology, 2006, vol. 5, issue 1

Abstract:

Experiments to determine the complete 3-dimensional structures of protein complexes are difficult to perform and only a limited range of such structures are available.

In contrast, large-scale screening experiments have identified thousands of pairwise interactions between proteins, but such experiments do not produce explicit structural information.

In addition, the data produced by these high through-put experiments contain large numbers of false positive results, and can be biased against detection of certain types of interaction.

Several methods exist that analyse such pairwise interaction data in terms of the constituent domains within proteins, scoring pairs of domain superfamilies according to their propensity to interact.

These scores can be used to predict the strongest domain-domain contact (the contact with the largest surface area) between interacting proteins for which the domain-level structures of the individual proteins are known.

We test this predictive approach on a set of pairwise protein interactions taken from the Protein Quaternary Structure (PQS) database for which the true domain-domain contacts are known.

While the overall prediction success rate across the whole test data set is poor, we shown how interactions in the test data set for which the training data are not informative can be automatically excluded from the prediction process, giving improved prediction success rates at the expense of restricted coverage of the test data.

Keywords: Computational Biology/Bioinformatics; protein interaction; domain; yeast 2-hybrid (search for similar items in EconPapers)
Date: 2006

Downloads: (external link)
http://www.bepress.com/cgi/viewcontent.cgi?article=1195&context=sagmb (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: http://EconPapers.repec.org/RePEc:bpj:sagmbi:v:5:y:2006:i:1:n:5

Access Statistics for this article

More articles in Statistical Applications in Genetics and Molecular Biology from Berkeley Electronic Press
Series data maintained by Avi Warner ().