Learning biophysically-motivated parameters for alpha helix prediction

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. Conclusions: The method presented shows promise for the prediction of protein secondary structure. Biophysically-motivated elementary free-energies can be learned using SVM tech...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Blaise Gassend, Charles W. O'Donnell, William Thies, Andrew Lee, Marten van Dijk, Srinivas Devadas
• Keywords: Biophysically-motivated Energy Model, BMCBI 2007, Protein Secondary Structure, Secondary Structure Predictor