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This article is part of the supplement: Proceedings of the Sixth Annual MCBIOS Conference. Transformational Bioinformatics: Delivering Value from Genomes

Open Access Proceedings

Protein local 3D structure prediction by Super Granule Support Vector Machines (Super GSVM)

Bernard Chen* and Matthew Johnson

Author Affiliations

Department of Computer Science, University of Central Arkansas, 201 Donaghey Avenue. Conway, AR 72035, USA

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BMC Bioinformatics 2009, 10(Suppl 11):S15  doi:10.1186/1471-2105-10-S11-S15

Published: 8 October 2009

Abstract

Background

Understanding the relationship between the protein sequence and the 3D structure is a major research area in bioinformatics. The prediction of complete protein tertiary structure based only on sequence information is still an impractical work. This paper aims at revealing the hidden knowledge of the sequence motifs and the local tertiary structure.

Results

In this paper, we propose a Super Granule Support Vector Machine (Super GSVM) model to obtain the high quality protein sequence motifs and to predict local tertiary structure information based on purely sequence information.

Conclusion

The proposed model overcomes the innate shortcoming of using the SVM on such a large data set, which is the inherent computational complexity involved in training support vectors for huge datasets including half million of samples. The satisfactory prediction results show the Super GSVM model generates decent protein sequence clusters and has the ability to capture the hidden sequence-to-structure information. This model also has a strong potential in the application of SVMs on other research areas with huge datasets.