Rapid prediction of multi-dimensional NMR data sets
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 “in silico” 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).
KeywordsNMR Software Chemical shift Membrane Protein Solid-state NMR
This work was supported by the Netherlands Organization for Scientific Research (NWO) (grants 700.26.121 and 700.10.443 to M.B.) and by the European Community’s Seventh Framework Program, BioNMR project, contract number 211800, and the FP7 e-Infrastructure project WeNMR (contract no. 261572, www.wenmr.eu). We would like to thank Dieter Oesterhelt (Martinsried) for informing us that N. pharaonis can grow with acetate as sole carbon source. Initial work of J. Sauermann on the extraction of N. pharaonis lipids is gratefully acknowledged.
- Alber F, Forster F, Korkin D, Topf M, Sali A (2008) Integrating diverse data for structure determination of macromolecular assemblies. Annu Revi Biochem Palo Alto Annu Rev Palo Alto Annu Rev 77:443–477Google Scholar
- Brothers MC, Nesbitt A, Hallock M, Rupansinghe S, Tang M, Harris J, Baudry J, Schuler M, Rienstra C (2012). VITAL NMR: using chemical shift derived secondary structure information for a limited set of amino acids to assess homology model accuracy. J Biomol NMR 52:41–56Google Scholar
- Cukkemane A, Nand D, Gradmann S, Weingarth M, Kaupp B, Baldus M (2012) Solid-state NMR [13C, 15N] resonance assignments of the nucleotide-binding domain of a bacterial cyclic nucleotide-gated channel. Biomol NMR Assign 6:225Google Scholar
- Goddard TD, Kneller D (2006). SPARKY 3. University of California, CaliforniaGoogle Scholar
- Hohenfeld IP, Wegener AA, Engelhard M (1999). Purification of histidine tagged bacteriorhodopsin, pharaonis halorhodopsin and pharaonis sensory rhodopsin II functionally expressed in Escherichia coli. FEBS Lett 442:198–202Google Scholar
- Loquet A, Sgourakis NG, Gupta R, Giller K, Riedel D, Goosmann C, Griesinger C, Kolbe M, Baker D, Becker S, Lange A (2012) Atomic model of the type III secretion system needle. Nat Adv (online publication)Google Scholar
- Nand D, Cukkemane A, Becker S, Baldus M (2012) Fractional deuteration applied to biomolecular solid-state NMR spectroscopy. J Biomol NMR 52:91–101Google Scholar
- Seidel K, Etzkorn M, Heise H, Becker S, Baldus M (2005) High-resolution solid-state NMR studies on uniformly C-13, N-15 -labeled ubiquitin. Chem BioChem 6:1638–1647Google Scholar
- Wang Y, Jardetzky O (2002) Probability-based protein secondary structure identification using combined NMR chemical-shift data. Protein Sci 11:852–861Google Scholar