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  Bayesian weighting of statistical potentials in NMR structure calculation

Mechelke, M., & Habeck, M. (2014). Bayesian weighting of statistical potentials in NMR structure calculation. PLoS One, 9(6): e0100197. doi:10.1371/journal.pone.0100197.

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 Creators:
Mechelke, M1, Author           
Habeck, M, Author           
Affiliations:
1Department Protein Evolution, Max Planck Institute for Developmental Biology, Max Planck Society, ou_3375791              

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 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.

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 Dates: 2014-06
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1371/journal.pone.0100197
PMID: 24956116
 Degree: -

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Title: PLoS One
  Abbreviation : PLoS One
Source Genre: Journal
 Creator(s):
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: 11 Volume / Issue: 9 (6) Sequence Number: e0100197 Start / End Page: - Identifier: ISSN: 1932-6203
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000277850