A Graphical Model for Protein Secondary Structure Prediction
Wei Chu, Zoubin Ghahramani and David L Wild
In: ICML 2004, July 4-8, 2004, Whistler, Canada.
In this paper, we present a graphical model for protein secondary structure
prediction. This model extends segmental semi-Markov models (SSMM) to exploit multiple sequence alignment profiles which contain information from evolutionarily related sequences. A novel parameterized model is proposed as the likelihood function for the SSMM to capture the segmental
conformation. By incorporating the information from long range interactions in beta-sheets, this model is capable of carrying out inference on contact maps. The numerical results on benchmark data sets
show that incorporating the profiles results in substantial improvements a
nd the generalization performance is promising.