Document Detail

Evaluating mixture models for building RNA knowledge-based potentials.
MedLine Citation:
PMID:  22809345     Owner:  NLM     Status:  MEDLINE    
Ribonucleic acid (RNA) molecules play important roles in a variety of biological processes. To properly function, RNA molecules usually have to fold to specific structures, and therefore understanding RNA structure is vital in comprehending how RNA functions. One approach to understanding and predicting biomolecular structure is to use knowledge-based potentials built from experimentally determined structures. These types of potentials have been shown to be effective for predicting both protein and RNA structures, but their utility is limited by their significantly rugged nature. This ruggedness (and hence the potential's usefulness) depends heavily on the choice of bin width to sort structural information (e.g. distances) but the appropriate bin width is not known a priori. To circumvent the binning problem, we compared knowledge-based potentials built from inter-atomic distances in RNA structures using different mixture models (Kernel Density Estimation, Expectation Minimization and Dirichlet Process). We show that the smooth knowledge-based potential built from Dirichlet process is successful in selecting native-like RNA models from different sets of structural decoys with comparable efficacy to a potential developed by spline-fitting - a commonly taken approach - to binned distance histograms. The less rugged nature of our potential suggests its applicability in diverse types of structural modeling.
Adelene Y L Sim; Olivier Schwander; Michael Levitt; Julie Bernauer
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Publication Detail:
Type:  Evaluation Studies; Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
Title:  Journal of bioinformatics and computational biology     Volume:  10     ISSN:  1757-6334     ISO Abbreviation:  J Bioinform Comput Biol     Publication Date:  2012 Apr 
Date Detail:
Created Date:  2012-07-19     Completed Date:  2012-11-19     Revised Date:  2014-10-13    
Medline Journal Info:
Nlm Unique ID:  101187344     Medline TA:  J Bioinform Comput Biol     Country:  England    
Other Details:
Languages:  eng     Pagination:  1241010     Citation Subset:  IM    
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MeSH Terms
Computational Biology / methods*
Knowledge Bases
Models, Molecular
RNA / chemistry*,  metabolism
Grant Support
Reg. No./Substance:

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