Special Issue: Selected Papers from MCCMB 2011; Guest Editor: Mikhail Gelfand – Research PapersOpen Access

EVALUATING MIXTURE MODELS FOR BUILDING RNA KNOWLEDGE-BASED POTENTIALS

ADELENE Y. L. SIM

Department of Applied Physics, Stanford University, Stanford, CA 94305-4090, USA

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OLIVIER SCHWANDER

Combinatorial Models Team, Laboratoire d'Informatique (LIX), École Polytechnique, 91128 Palaiseau, France

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MICHAEL LEVITT

Department of Structural Biology, Stanford University, Stanford, CA 94305-5126, USA

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, and

JULIE BERNAUER

INRIA AMIB Bioinformatique, Laboratoire d'Informatique (LIX), École Polytechnique, 91128 Palaiseau, France

Corresponding author.

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https://doi.org/10.1142/S0219720012410107Cited by:6 (Source: Crossref)

Abstract

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.

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