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

W Gronwald1, R Kirchhöfer, A Görler

  • 1Department of Biophysics and Physical Biochemistry, University of Regensburg, Postfach, Germany.

Journal of Biomolecular NMR
|August 2, 2000
PubMed
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A new computer program, RFAC, automates the estimation of residual indices (R-factors) for protein NMR structures. This method improves structural quality assessment by analyzing experimental and simulated NOESY spectra, including unassigned peaks.

Area of Science:

  • Biochemistry
  • Structural Biology
  • Computational Biology

Background:

  • Protein Nuclear Magnetic Resonance (NMR) spectroscopy is crucial for determining 3D structures.
  • Assessing the quality of NMR-derived protein structures is essential for reliable biological interpretation.
  • Existing methods for R-factor calculation may not fully capture structural inaccuracies.

Purpose of the Study:

  • To develop an automated computational program (RFAC) for estimating R-factors in protein NMR structures.
  • To provide a reliable and sensitive measure for assessing the quality of protein NMR structures.
  • To incorporate non-assigned peaks in R-factor calculations for a more comprehensive quality assessment.

Main Methods:

  • Development of the RFAC computer program for automated R-factor estimation.

Related Experiment Videos

  • Utilizing 1H NOESY NMR spectra for comparison between experimental and simulated data.
  • Implementing automatic peak picking, Bayesian analysis, and automated structure-based assignment.
  • Accounting for non-assigned experimental peaks in the R-factor calculation.

    • Main Results:

    • The RFAC program enables automated R-factor calculation for protein NMR structures.
    • The R-factor estimation is based on comparing experimental and simulated 1H NOESY NMR spectra.
    • The inclusion of non-assigned peaks provides a more sensitive measure of structural quality.
    • RFAC allows for global, regional, and residue-specific R-factor calculations, offering detailed structural insights.

    Conclusions:

    • The developed RFAC program offers a reliable and automated method for assessing protein NMR structure quality.
    • RFAC's sensitivity to structural errors surpasses previous R-factor definitions.
    • The program's flexibility in calculating various R-factors aids in detailed structural evaluation.
    • RFAC was successfully validated on medium-sized proteins, demonstrating its practical utility.