Rapid Prediction of Multi-dimensional NMR Data Sets Using FANDAS
Solid-state NMR (ssNMR) can provide structural information at the most detailed level and, at the same time, is applicable in highly heterogeneous and complex molecular environments. In the last few years, ssNMR has made significant progress in uncovering structure and dynamics of proteins in their native cellular environments [1–4]. Additionally, ssNMR has proven to be useful in studying large biomolecular complexes as well as membrane proteins at the atomic level . In such studies, innovative labeling schemes have become a powerful approach to tackle spectral crowding. In fact, selecting the appropriate isotope-labeling schemes and a careful choice of the ssNMR experiments to be conducted are critical for applications of ssNMR in complex biomolecular systems. Previously, we have introduced a software tool called FANDAS (Fast Analysis of multidimensional NMR DAta Sets) that supports such investigations from the early stages of sample preparation to the final data analysis . Here, we present a new version of FANDAS, called FANDAS 2.0, with improved user interface and extended labeling scheme options allowing the user to rapidly predict and analyze ssNMR data sets for a given protein-based application. It provides flexible options for advanced users to customize the program for tailored applications. In addition, the list of ssNMR experiments that can be predicted now includes proton (1H) detected pulse sequences. FANDAS 2.0, written in Python, is freely available through a user-friendly web interface at http://milou.science.uu.nl/services/FANDAS.
Key wordsBiomolecular NMR Labeling schemes Spectral prediction Spectral analysis and proton detection
This work was funded in part by the Netherlands Organization for Scientific Research (NWO) (grants 700.26.121 and 700.10.443 to M.B.). The development of the web portal was supported by a European H2020 e-Infrastructure grant West-Life (grant no. 675858 to A.B.). The authors would like to thank Panagiotis Koukos of the Computational Structural Biology Group for his humble assistance in hosting the webserver.
- 1.Renault M, Pawsey S, Bos MP, Koers EJ, Nand D, Tommassen-van Boxtel R, Rosay M, Tommassen J, Maas WE, Baldus M (2012) Solid-state NMR spectroscopy on cellular preparations enhanced by dynamic nuclear polarization. Angew Chem Int Ed Engl 51(12):2998–3001. https://doi.org/10.1002/anie.201105984 CrossRefPubMedGoogle Scholar
- 3.Kaplan M, Cukkemane A, van Zundert GC, Narasimhan S, Daniels M, Mance D, Waksman G, Bonvin AM, Fronzes R, Folkers GE, Baldus M (2015) Probing a cell-embedded megadalton protein complex by DNP-supported solid-state NMR. Nat Methods 12(7):649–652. https://doi.org/10.1038/nmeth.3406 CrossRefPubMedGoogle Scholar
- 4.Kaplan M, Narasimhan S, de Heus C, Mance D, van Doorn S, Houben K, Popov-Celeketic D, Damman R, Katrukha EA, Jain P, Geerts WJ, Heck AJ, Folkers GE, Kapitein LC, Lemeer S, van Bergen En Henegouwen PM, Baldus M (2016) EGFR dynamics change during activation in native membranes as revealed by NMR. Cell 167(5):1241–1251. e1211. https://doi.org/10.1016/j.cell.2016.10.038
- 13.Mance D, Sinnige T, Kaplan M, Narasimhan S, Daniels M, Houben K, Baldus M, Weingarth M (2015) An Efficient labelling approach to harness backbone and side-chain protons in 1H-detected solid-state NMR spectroscopy. Angew Chem Int Ed Engl 54(52):15799–15803 https://doi.org/10.1002/anie.201509170 CrossRefPubMedPubMedCentralGoogle Scholar
- 14.Goddard TD, Kneller DG SPARKY 3. University of California, San FranciscoGoogle Scholar