Sevdanur Genc, Murat Gok and Osman Hilmi Kocal, Yalova University, Turkey

Bacterial virulence proteins, which have been classified on structure of virulence, causes several diseases.For instance, Adhesins play an important role in the host cells. They are inserted DNA sequences for a variety of virulence properties. Several important methods conducted for the prediction of bacterial virulence proteins for finding new drugs or vaccines. In this study, we propose a method for feature selection about classification of bacterial virulence protein. The features are constituted directly from the amino acid sequence of a given protein. Amino acids form proteins, which are critical to life, and have many important functions in living cells. They occurring with different physicochemical properties by a vector of 20 numerical values, and collected in AAIndex databases of known 544 indices. For all that, this approach have two steps. Firstly, the amino acid sequence of a given protein analysed with Lyapunov Exponentsthat they have a chaotic structure in accordance with the chaos theory. After that, if the results show characterization over the complete distribution in the phase space from the point of deterministic system, it means related protein will show a chaotic structure. Empirical results revealed that generated feature vectors give the best performance with chaotic structure of physicochemical features of amino acids with Adhesins and non-Adhesins data sets.

Bioinformatics, Virulence Protein Sequences, Attribute Encoding, Chaotic Structure, Classification.