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Protein NMR pp 111-132 | Cite as

Rapid Prediction of Multi-dimensional NMR Data Sets Using FANDAS

  • Siddarth Narasimhan
  • Deni Mance
  • Cecilia Pinto
  • Markus Weingarth
  • Alexandre M. J. J. Bonvin
  • Marc Baldus
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1688)

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.

Key words

Biomolecular NMR Labeling schemes Spectral prediction Spectral analysis and proton detection 

Notes

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.

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Copyright information

© Springer Science+Business Media LLC 2018

Authors and Affiliations

  • Siddarth Narasimhan
    • 1
  • Deni Mance
    • 1
  • Cecilia Pinto
    • 1
  • Markus Weingarth
    • 1
  • Alexandre M. J. J. Bonvin
    • 2
  • Marc Baldus
    • 1
  1. 1.NMR SpectroscopyBijvoet Center for Biomolecular Research, Utrecht UniversityUtrechtThe Netherlands
  2. 2.Computational Structural BiologyBijvoet Center for Biomolecular Research, Utrecht UniversityUtrechtThe Netherlands

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