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PLoS One. 2014 Jun 23;9(6):e100197. doi: 10.1371/journal.pone.0100197. eCollection 2014.

Bayesian weighting of statistical potentials in NMR structure calculation.

Author information

1
Institute for Mathematical Stochastics, Georg August University Göttingen, Göttingen, Germany; Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen, Germany.
2
Institute for Mathematical Stochastics, Georg August University Göttingen, Göttingen, Germany.

Abstract

The use of statistical potentials in NMR structure calculation improves the accuracy of the final structure but also raises issues of double counting and possible bias. Because statistical potentials are averaged over a large set of structures, they may not reflect the preferences of a particular structure or data set. We propose a Bayesian method to incorporate a knowledge-based backbone dihedral angle potential into an NMR structure calculation. To avoid bias exerted through the backbone potential, we adjust its weight by inferring it from the experimental data. We demonstrate that an optimally weighted potential leads to an improvement in the accuracy and quality of the final structure, especially with sparse and noisy data. Our findings suggest that no universally optimal weight exists, and that the weight should be determined based on the experimental data. Other knowledge-based potentials can be incorporated using the same approach.

PMID:
24956116
PMCID:
PMC4067304
DOI:
10.1371/journal.pone.0100197
[Indexed for MEDLINE]
Free PMC Article

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