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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



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.


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.


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.