Secondary Structure Prediction of All-Helical Proteins Using Hidden Markov Support Vector Machines
Author(s)Gassend, B.; O'Donnell, C. W.; Thies, W.; Lee, A.; van Dijk, M.; Devadas, S.; ... Show more Show less
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Our goal is to develop a state-of-the-art 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 in proteins and show that using HM-SVMs, a simple 7-state HMM with 302 parameters can achieve a Q_alpha value of 77.6% and a SOV_alpha value of 73.4%. We briefly describe how our method can be generalized to predicting beta strands and sheets.
Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory