BioSysBio:abstracts/2007/Alper Kucukural

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Author(s): Alper Küçükural, Uğur Sezerman
Affiliations: Sabancı University
Contact:email: kucukural@su.sabanciuniv.edu
Keywords: 'Graph Theory' 'Bioinformatics' 'Delaunay Tesselation' 'Contact Maps'

Abstract

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 representation of 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. Fisher, linear, quadratic, neural network and support vector classifiers were used to classification of the protein structures. The best classifier accuracy was over %97.5. 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.

Results

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