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This article is part of the supplement: Articles selected from posters presented at the Tenth Annual International Conference on Research in Computational Biology

Open Access Research

Refining intra-protein contact prediction by graph analysis

Milana Frenkel-Morgenstern1, Rachel Magid1, Eran Eyal2 and Shmuel Pietrokovski1*

Author Affiliations

1 Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, 76100, Israel

2 Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA

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BMC Bioinformatics 2007, 8(Suppl 5):S6  doi:10.1186/1471-2105-8-S5-S6

Published: 24 May 2007

Abstract

Background

Accurate prediction of intra-protein residue contacts from sequence information will allow the prediction of protein structures. Basic predictions of such specific contacts can be further refined by jointly analyzing predicted contacts, and by adding information on the relative positions of contacts in the protein primary sequence.

Results

We introduce a method for graph analysis refinement of intra-protein contacts, termed GARP. Our previously presented intra-contact prediction method by means of pair-to-pair substitution matrix (P2PConPred) was used to test the GARP method. In our approach, the top contact predictions obtained by a basic prediction method were used as edges to create a weighted graph. The edges were scored by a mutual clustering coefficient that identifies highly connected graph regions, and by the density of edges between the sequence regions of the edge nodes. A test set of 57 proteins with known structures was used to determine contacts. GARP improves the accuracy of the P2PConPred basic prediction method in whole proteins from 12% to 18%.

Conclusion

Using a simple approach we increased the contact prediction accuracy of a basic method by 1.5 times. Our graph approach is simple to implement, can be used with various basic prediction methods, and can provide input for further downstream analyses.