Predicting secondary structure of all-helical proteins using hidden markov support vector machines

Authors:
Blaise Gassend;Charles W. O'Donnell;William Thies;Andrew Lee;Marten van Dijk;Srinivas Devadas
Affiliations:
Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory;Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory;Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory;Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory;Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory;Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory
Venue:
PRIB'06 Proceedings of the 2006 international conference on Pattern Recognition in Bioinformatics
Year:
2006

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Abstract

Our goal is to develop a state-of-the-art secondary structure predictor with an intuitive and biophysically-motivated energy model through the use of Hidden Markov Support Vector Machines (HM- SVMs), a recent innovation in the field of machine learning. We focus on the prediction of alpha helices and show that by using HM-SVMs, a simple 7-state HMM with 302 parameters can achieve a Qα value of 77.6% and a SOVα value of 73.4%. As detailed in an accompanying technical report [11], these performance numbers are among the best for techniques that do not rely on external databases (such as multiple sequence alignments).