Title |
Learning biophysically-motivated parameters for alpha helix prediction
|
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Published in |
BMC Bioinformatics, May 2007
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DOI | 10.1186/1471-2105-8-s5-s3 |
Pubmed ID | |
Authors |
Blaise Gassend, Charles W O'Donnell, William Thies, Andrew Lee, Marten van Dijk, Srinivas Devadas |
Abstract |
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 Qalpha value of 77.6% and an SOValpha 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. |
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Iran, Islamic Republic of | 1 | 7% |
Unknown | 13 | 93% |
Demographic breakdown
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Professor > Associate Professor | 2 | 14% |
Student > Ph. D. Student | 2 | 14% |
Student > Doctoral Student | 1 | 7% |
Student > Bachelor | 1 | 7% |
Other | 2 | 14% |
Unknown | 3 | 21% |
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Mathematics | 1 | 7% |
Other | 0 | 0% |
Unknown | 3 | 21% |