Behaviour of protein secondary structure types in the sequence space of a certain length

Article type: Research Article

Authors: Siermala, Markku | Juhola, Martti

Affiliations: Department of Computer and Information Sciences, 33014 University of Tampere, Finland. Tel.: +358 3 2157566; Fax: +358 3 2156070; E-mail: Markku.Siermala@uta.fi

Abstract: We developed computational and theoretical methods to analyze the nature of experimental data. Our objective was to reveal how the protein secondary structure types behave in a space defined by a sequence of a certain length. Structure α-helix was only slightly more compact than the β-strand. The mean distance within the PPII structure class was the smallest, but the structure was not as compact as the others. This could be a consequence of the distance metric applied and the sensitivity of the structure to proline. In addition, this work describes some mathematical properties of the sequence space which explains the behaviour of secondary structure types in the space. This work gives an account of how prediction accuracy for conventional local prediction methods can be understood and explains why local prediction is so difficult.

Keywords: protein secondary structure prediction, properties of sequence space, statistical distribution, Gaussian kernels, hypersphere, pattern recognition, neural networks

DOI: 10.3233/IDA-2002-6503

Journal: Intelligent Data Analysis, vol. 6, no. 5, pp. 411-427, 2002