Evidence map›Paper›PMID 10921778›Full record

ArticleJournal of biomolecular NMR2000

RFAC, a program for automated NMR R-factor estimation.

W Gronwald, R Kirchhöfer, A Görler, W Kremer, B Ganslmeier, K P Neidig, H R Kalbitzer

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Article in Journal of biomolecular NMR, 2000. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

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0cells of the map it votes in
19citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

  1. Article
  2. Quality assessment of protein NMR structures.Current opinion in structural biology · 2013
    Review
  3. Article
  4. Structure-oriented methods for protein NMR data analysis.Progress in nuclear magnetic resonance spectroscopy · 2010
    Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Weighting of experimental evidence in macromolecular structure determination.Proceedings of the National Academy of Sciences of the United States of America · 2006
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. High-temperature solution NMR structure of TmCsp.Protein science : a publication of the Protein Society · 2004
    Article
  16. Rapid protein fold determination using unassigned NMR data.Proceedings of the National Academy of Sciences of the United States of America · 2003
    Article
  17. Article
  18. Article
  19. An NMR approach to structural proteomics.Proceedings of the National Academy of Sciences of the United States of America · 2002
    Article
4 · The record

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5 · Who and what money

Authors and funding

7 authors.

W GronwaldDepartment of Biophysics and Physical Biochemistry, University of Regensburg, Postfach, Germany.
R Kirchhöfer
A Görler
W Kremer
B Ganslmeier
K P Neidig
H R Kalbitzer

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A computer program (RFAC) has been developed, which allows the automated estimation of residual indices (R-factors) for protein NMR structures and gives a reliable measure for the quality of the structures. The R-factor calculation is based on the comparison of experimental and simulated 1H NOESY NMR spectra. The approach comprises an automatic peak picking and a Bayesian analysis of the data, followed by an automated structure based assignment of the NOESY spectra and the calculation of the R-factor. The major difference to previously published R-factor definitions is that we take the non-assigned experimental peaks into account as well. The number and the intensities of the non-assigned signals are an important measure for the quality of an NMR structure. It turns out that for different problems optimally adapted R-factors should be used which are defined in the paper. The program allows to compute a global R-factor, different R-factors for the intra residual NOEs, the inter residual NOEs, sequential NOEs, medium range NOEs and long range NOEs. Furthermore, R-factors can be calculated for various user defined parts of the molecule or it is possible to obtain a residue-by-residue R-factor. Another possibility is to sort the R-factors according to their corresponding distances. The summary of all these different R-factors should allow the user to judge the structure in detail. The new program has been successfully tested on two medium sized proteins, the cold shock protein (TmCsp) from Termotoga maritima and the histidine containing protein (HPr) from Staphylococcus carnosus. A comparison with a previously published R-factor definition shows that our approach is more sensitive to errors in the calculated structure.

Indexed as

Nuclear Magnetic Resonance, BiomolecularSoftwareAlgorithmsAmino Acid SequenceProtein Structure, SecondaryStaphylococcusThermotoga maritima

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.