Evaluating mixture models for building RNA knowledge-based potentials.

Adelene y L Sim 1 Olivier Schwander 2 Michael Levitt 1 Julie Bernauer 3
3 AMIB - Algorithms and Models for Integrative Biology
LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau], LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France
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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Submitted on : Tuesday, November 27, 2012 - 3:39:19 PM
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Adelene y L Sim, Olivier Schwander, Michael Levitt, Julie Bernauer. Evaluating mixture models for building RNA knowledge-based potentials.. Journal of Bioinformatics and Computational Biology, World Scientific Publishing, 2012, 10 (2), pp.1241010. ⟨10.1142/S0219720012410107⟩. ⟨hal-00757761⟩



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