Rapid prediction of multi-dimensional NMR data sets
 

Rapid prediction of multi-dimensional NMR data sets


Abstract We present a computational environment for Fast Analysis of multidimensional NMR DAta Sets (FANDAS) that allows assembling multidimensional data sets from a variety of input parameters and facilitates comparing and modifying such â??in silicoâ?? data sets during the various stages of the NMR data analysis. The input parameters can vary from (partial) NMR assignments directly obtained from experiments to values retrieved from in silico prediction programs. The resulting predicted data sets enable a rapid evaluation of sample labeling in light of spectral resolution and structural content, using standard NMR software such as Sparky. In addition, direct comparison to experimental data sets can be used to validate NMR assignments, distinguish different molecular components, refine structural models or other parameters derived from NMR data. The method is demonstrated in the context of solid-state NMR data obtained for the cyclic nucleotide binding domain of a bacterial cyclic nucleotide-gated channel and on membrane-embedded sensory rhodopsin II. FANDAS is freely available as web portal under WeNMR (http://www.wenmr.eu/services/FANDAS).

• Content Type Journal Article
• Category Article
• Pages 1-11
• DOI 10.1007/s10858-012-9681-y
• Authors
  • Sabine Gradmann, Faculty of Science, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
  • Christian Ader, Faculty of Science, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
  • Ines Heinrich, Department of Physical Biochemistry, Max Planck Institute for Molecular Physiology, Otto-Hahn-Strasse 11, 44227 Dortmund, Germany
  • Deepak Nand, Faculty of Science, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
  • Marc Dittmann, Department of Physical Biochemistry, Max Planck Institute for Molecular Physiology, Otto-Hahn-Strasse 11, 44227 Dortmund, Germany
  • Abhishek Cukkemane, Faculty of Science, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
  • Marc van Dijk, Faculty of Science, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
  • Alexandre M. J. J. Bonvin, Faculty of Science, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
  • Martin Engelhard, Department of Physical Biochemistry, Max Planck Institute for Molecular Physiology, Otto-Hahn-Strasse 11, 44227 Dortmund, Germany
  • Marc Baldus, Faculty of Science, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands

• • Journal Journal of Biomolecular NMR
  • Online ISSN 1573-5001
  • Print ISSN 0925-2738

Source: Journal of Biomolecular NMR