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June 2010 A Dirichlet process mixture of hidden Markov models for protein structure prediction
Kristin P. Lennox, David B. Dahl, Marina Vannucci, Ryan Day, Jerry W. Tsai
Ann. Appl. Stat. 4(2): 916-942 (June 2010). DOI: 10.1214/09-AOAS296


By providing new insights into the distribution of a protein’s torsion angles, recent statistical models for this data have pointed the way to more efficient methods for protein structure prediction. Most current approaches have concentrated on bivariate models at a single sequence position. There is, however, considerable value in simultaneously modeling angle pairs at multiple sequence positions in a protein. One area of application for such models is in structure prediction for the highly variable loop and turn regions. Such modeling is difficult due to the fact that the number of known protein structures available to estimate these torsion angle distributions is typically small. Furthermore, the data is “sparse” in that not all proteins have angle pairs at each sequence position. We propose a new semiparametric model for the joint distributions of angle pairs at multiple sequence positions. Our model accommodates sparse data by leveraging known information about the behavior of protein secondary structure. We demonstrate our technique by predicting the torsion angles in a loop from the globin fold family. Our results show that a template-based approach can now be successfully extended to modeling the notoriously difficult loop and turn regions.


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Kristin P. Lennox. David B. Dahl. Marina Vannucci. Ryan Day. Jerry W. Tsai. "A Dirichlet process mixture of hidden Markov models for protein structure prediction." Ann. Appl. Stat. 4 (2) 916 - 942, June 2010.


Published: June 2010
First available in Project Euclid: 3 August 2010

zbMATH: 1194.62117
MathSciNet: MR2758427
Digital Object Identifier: 10.1214/09-AOAS296

Keywords: Bayesian nonparametrics , Density estimation , dihedral angles , Protein structure prediction , torsion angles , von Mises distribution

Rights: Copyright © 2010 Institute of Mathematical Statistics


Vol.4 • No. 2 • June 2010
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