Abstract
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 [5]. 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 [6]. 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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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
Renault M, Tommassen-van Boxtel R, Bos MP, Post JA, Tommassen J, Baldus M (2012) Cellular solid-state nuclear magnetic resonance spectroscopy. Proc Natl Acad Sci USA 109(13):4863–4868 10.1073/pnas.1116478109
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
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
Kaplan M, Pinto C, Houben K, Baldus M (2016) Nuclear magnetic resonance (NMR) applied to membrane-protein complexes. Q Rev Biophys 49:e15. https://doi.org/10.1017/S003358351600010X
Gradmann S, Ader C, Heinrich I, Nand D, Dittmann M, Cukkemane A, van Dijk M, Bonvin AM, Engelhard M, Baldus M (2012) Rapid prediction of multi-dimensional NMR data sets. J Biomol NMR 54(4):377–387. https://doi.org/10.1007/s10858-012-9681-y
Sinnige T, Weingarth M, Renault M, Baker L, Tommassen J, Baldus M (2014) Solid-state NMR studies of full-length BamA in lipid bilayers suggest limited overall POTRA mobility. J Mol Biol 426(9):2009–2021. https://doi.org/10.1016/j.jmb.2014.02.007
Sinnige T, Houben K, Pritisanac I, Renault M, Boelens R, Baldus M (2015) Insight into the conformational stability of membrane-embedded BamA using a combined solution and solid-state NMR approach. J Biomol NMR 61(3–4):321–332. https://doi.org/10.1007/s10858-014-9891-6
Baker LA, Daniels M, van der Cruijsen EAW, Folkers GE, Baldus M (2015) Efficient cellular solid-state NMR of membrane proteins by targeted protein labeling. J Biomol NMR 62(2):199–208. https://doi.org/10.1007/s10858-015-9936-5
Renault M, Cukkemane A, Baldus M (2010) Solid-state NMR spectroscopy on complex biomolecules. Angew Chem Int Ed Engl 49(45):8346–8357. https://doi.org/10.1002/anie.201002823
Pauli J, Baldus M, van Rossum B, de Groot H, Oschkinat H (2001) Backbone and side-chain 13C and 15N signal assignments of the alpha-spectrin SH3 domain by magic angle spinning solid-state NMR at 17.6 Tesla. Chembiochem 2(4):272–281
Sinnige T, Daniels M, Baldus M, Weingarth M (2014) Proton clouds to measure long-range contacts between nonexchangeable side chain protons in solid-state NMR. J Am Chem Soc 136(12):4452–4455. https://doi.org/10.1021/ja412870m
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
Goddard TD, Kneller DG SPARKY 3. University of California, San Francisco
Wang Y, Jardetzky O (2002) Probability-based protein secondary structure identification using combined NMR chemical-shift data. Protein Sci 11(4):852–861. https://doi.org/10.1110/ps.3180102
Joosten RP, te Beek TA, Krieger E, Hekkelman ML, Hooft RW, Schneider R, Sander C, Vriend G (2011) A series of PDB related databases for everyday needs. Nucleic Acids Res 39(Database issue):D411–D419. https://doi.org/10.1093/nar/gkq1105
Frishman D, Argos P (1995) Knowledge-based protein secondary structure assignment. Proteins 23(4):566–579. https://doi.org/10.1002/prot.340230412
Drozdetskiy A, Cole C, Procter J, Barton GJ (2015) JPred4: a protein secondary structure prediction server. Nucleic Acids Res 43(W1):W389–W394. https://doi.org/10.1093/nar/gkv332
Jones DT (1999) Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 292(2):195–202. https://doi.org/10.1006/jmbi.1999.3091
Han B, Liu Y, Ginzinger SW, Wishart DS (2011) SHIFTX2: significantly improved protein chemical shift prediction. J Biomol NMR 50(1):43–57. https://doi.org/10.1007/s10858-011-9478-4
LeMaster DM, Kushlan DM (1996) Dynamical mapping of E. coli thioredoxin via 13C NMR relaxation analysis. J Am Chem Soc 118(39):9255–9264 doi:https://doi.org/10.1021/ja960877r
Hong M, Jakes K (1999) Selective and extensive 13C labeling of a membrane protein for solid-state NMR investigations. J Biomol NMR 14(1):71–74
Castellani F, van Rossum B, Diehl A, Schubert M, Rehbein K, Oschkinat H (2002) Structure of a protein determined by solid-state magic-angle-spinning NMR spectroscopy. Nature 420(6911):98–102. https://doi.org/10.1038/nature01070
Nand D, Cukkemane A, Becker S, Baldus M (2012) Fractional deuteration applied to biomolecular solid-state NMR spectroscopy. J Biomol NMR 52(2):91–101. https://doi.org/10.1007/s10858-011-9585-2
Weingarth M, Demco DE, Bodenhausen G, Tekely P (2009) Improved magnetization transfer in solid-state NMR with fast magic angle spinning. Chem Phys Lett 469(4–6):342–348. https://doi.org/10.1016/j.cplett.2008.12.084
Acknowledgments
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Science+Business Media LLC
About this protocol
Cite this protocol
Narasimhan, S., Mance, D., Pinto, C., Weingarth, M., Bonvin, A.M.J.J., Baldus, M. (2018). Rapid Prediction of Multi-dimensional NMR Data Sets Using FANDAS. In: Ghose, R. (eds) Protein NMR. Methods in Molecular Biology, vol 1688. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7386-6_6
Download citation
DOI: https://doi.org/10.1007/978-1-4939-7386-6_6
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-7385-9
Online ISBN: 978-1-4939-7386-6
eBook Packages: Springer Protocols