Bayesian Segmental Models with Multiple Sequence Alignment Profiles for Protein Secondary Structure and Contact Map Prediction

Authors:
Wei Chu;Zoubin Ghahramani;Alexei Podtelezhnikov;David L. Wild
Affiliations:
-;-;-;-
Venue:
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Year:
2006

Citing 6
Cited 2

Protein Secondary-Structure Modeling with Probabilistic Networks

Proceedings of the 1st International Conference on Intelligent Systems for Molecular Biology
DIRICHLET MIXTURES: A METHOD FOR IMPROVING DETECTION OF WEAK BUT SIGNIFICANT PROTEIN SEQUENCE HOMOLOGY

DIRICHLET MIXTURES: A METHOD FOR IMPROVING DETECTION OF WEAK BUT SIGNIFICANT PROTEIN SEQUENCE HOMOLOGY
Statistical models and monte carlo methods for protein structure prediction

Statistical models and monte carlo methods for protein structure prediction
Protein secondary structure: entropy, correlations and prediction

Bioinformatics
Monte Carlo Strategies in Scientific Computing

Monte Carlo Strategies in Scientific Computing
The use of the area under the ROC curve in the evaluation of machine learning algorithms

Pattern Recognition

Combining segmental semi-Markov models with neural networks for protein secondary structure prediction

Neurocomputing
Protein secondary structure prediction using modular reciprocal bidirectional recurrent neural networks

Computer Methods and Programs in Biomedicine

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Abstract

In this paper, we develop a segmental semi-Markov model (SSMM) for protein secondary structure prediction which incorporates multiple sequence alignment profiles with the purpose of improving the predictive performance. The segmental model is a generalization of the hidden Markov model where a hidden state generates segments of various length and secondary structure type. A novel parameterized model is proposed for the likelihood function that explicitly represents multiple sequence alignment profiles to capture the segmental conformation. Numerical results on benchmark data sets show that incorporating the profiles results in substantial improvements and the generalization performance is promising. By incorporating the information from long range interactions in \beta{\hbox{-}}{\rm sheets}, this model is also capable of carrying out inference on contact maps. This is an important advantage of probabilistic generative models over the traditional discriminative approach to protein secondary structure prediction. The Web server of our algorithm and supplementary materials are available at http://public.kgi.edu/~wild/bsm.html.