Document Detail


A DIRICHLET PROCESS MIXTURE OF HIDDEN MARKOV MODELS FOR PROTEIN STRUCTURE PREDICTION.
MedLine Citation:
PMID:  21031154     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
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.
Authors:
Kristin P Lennox; David B Dahl; Marina Vannucci; Ryan Day; Jerry W Tsai
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Publication Detail:
Type:  JOURNAL ARTICLE    
Journal Detail:
Title:  The annals of applied statistics     Volume:  4     ISSN:  1941-7330     ISO Abbreviation:  -     Publication Date:  2010 Jun 
Date Detail:
Created Date:  2010-10-29     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101479511     Medline TA:  Ann Appl Stat     Country:  -    
Other Details:
Languages:  ENG     Pagination:  916-942     Citation Subset:  -    
Affiliation:
Department of Statistics, Texas A&M University, 3143 TAMU, College Station, Texas 77843-3143, USA, lennox@stat.tamu.edu.
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Descriptor/Qualifier:
Grant Support
ID/Acronym/Agency:
R01 GM081631-03//NIGMS NIH HHS; R01 HG003319-01A1//NHGRI NIH HHS

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