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This article is part of the supplement: Articles selected from posters presented at the Tenth Annual International Conference on Research in Computational Biology

Open Access Research

Learning biophysically-motivated parameters for alpha helix prediction

Blaise Gassend*, Charles W O'Donnell*, William Thies*, Andrew Lee, Marten van Dijk and Srinivas Devadas

Author Affiliations

Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

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BMC Bioinformatics 2007, 8(Suppl 5):S3  doi:10.1186/1471-2105-8-S5-S3

Published: 24 May 2007

Abstract

Background

Our goal is to develop a state-of-the-art protein secondary structure predictor, with an intuitive and biophysically-motivated energy model. We treat structure prediction as an optimization problem, using parameterizable cost functions representing biological "pseudo-energies". Machine learning methods are applied to estimate the values of the parameters to correctly predict known protein structures.

Results

Focusing on the prediction of alpha helices in proteins, we show that a model with 302 parameters can achieve a Qα value of 77.6% and an SOVα value of 73.4%. Such performance numbers are among the best for techniques that do not rely on external databases (such as multiple sequence alignments). Further, it is easier to extract biological significance from a model with so few parameters.

Conclusion

The method presented shows promise for the prediction of protein secondary structure. Biophysically-motivated elementary free-energies can be learned using SVM techniques to construct an energy cost function whose predictive performance rivals state-of-the-art. This method is general and can be extended beyond the all-alpha case described here.