Learning Biophysically-Motivated Parameters for Alpha Helix Prediction
- Blaise Gassend ,
- Charles W. O'Donnell ,
- Bill Thies ,
- Andrew Lee ,
- Marten van Dijk ,
- Srinivas Devadas
Poster Session, International Conference on Research in Computational Molecular Biology (RECOMB 2006 - Poster Session). Venice, Italy |
Our goal is to develop a state-of-the-art protein secondary structure predictor, with an intuitive and biophysically-motivated energy model. The lack of experimentally determined free energy values makes it dicult to design accurate cost functions that can be optimized by predictors. Our technique uses a cost function comprised of unknown parameters, and applies Support Vector Machines (SVMs) to learn parameters that correctly predict known protein structures. So far, we have focused on the prediction of all-alpha proteins and have shown that a model with 302 parameters can achieve a Q value (percent of correctly predicted residues) of 77:6% and a SOV 99 (see [3]) value of 73:4%. As detailed in an accompanying technical report [1], these performance numbers are among the best for techniques that do not rely on multiple sequence alignments.