This article is part of the supplement: BioSysBio 2007: Systems Biology, Bioinformatics, Synthetic Biology

Open Access Poster presentation

Discrimination of proteins using graph theoretic properties

Alper Küçükural* and O Ugur Sezerman

Author Affiliations

Biological Sciences and Bioengineering, Sabanci University, Tuzla, Istanbul, Turkey

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BMC Systems Biology 2007, 1(Suppl 1):P49 doi:10.1186/1752-0509-1-S1-P49


The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1752-0509/1/S1/P49


Published:8 May 2007

© 2007 Küçükural and Sezerman; licensee BioMed Central Ltd.

Background

Graph theoretic properties of proteins can be used to perceive the differences between correctly folded proteins and well designed decoy sets. Graphs are used to represent 3D protein structures. We used two different graph representations of protein structures which are Delaunay tessellations of proteins and contact map graphs. Graph theoretic properties for both graph types showed high classification accuracy to discrimination of proteins. Different type of linear classifiers and support vector classifier were used to classification of the protein structures. The best classifier accuracy was over 95% as shown in Table 1. The results showed that characteristic features of graph theoretic properties can be used many fields such as prediction of fold recognition, structure alignment and comparison, detection of similar domains and definition of structural motifs in high accuracy.

Table 1.

Conclusion

In this work we successfully showed that structural properties as well as potential scores can be used to discriminate native folds from the decoy sets. As far as graph types are concerned, the classification accuracy rates of the results obtained from contact map graphs are higher than the results obtained from Delaunay tessellated graphs for the same classification methods. Therefore contact map matrices are better representation method for protein structures. Support vector classifier and quadratic classifiers results are quite promising for the dataset which formed after outlier analysis. The accuracy rates are over 95%.

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