The NMRSuite science gateway is designed for comprehensive on-line analysis of various types of Nuclear Magnetic Resonance (NMR) data. It combines and integrates software tools developed over the years by David Fushman's group at the University of Maryland (see references [1-12]) NMRSuite is built using the GenApp framework for scientific gateways designed and developed by Emre Brookes at UT San Antonio & University of Montana [13]. The computer tools and capabilities currently available in NMRSuite are grouped by the following sections/categories::

Analyze: analysis of experimental data, including spin-relaxation data, residual dipolar couplings (RDCs), and paramagnetic effects such as pseudo-contact shifts (PCSs), paramagnetic relaxation enhancements (PREs), and residual dipolar couplings (RDCs).

Predict: prediction of various types of NMR data, including those listed above, as well as rotational diffusion tensors of proteins.

Ensemble: determination of conformational/structural ensembles from experimental data (NMR, SAS, etc) using ensemble selection methods based on the Maximum Parsimony principle or Maximum Entropy principle.

Docking: rigid-body docking of protein complexes (or parts of the same protein) based on experimental NMR data.

More details are provided in the documentation (DOCS tabs) for each section or module.


1. R. Varadan, O. Walker, C. Pickart, D. Fushman, “Structural properties of polyubiquitin chains in solution,” J. Mol. Biol. (2002) 324, 637-647.

2. O. Walker, R. Varadan, D. Fushman,”Efficient and accurate determination of the overall rotational diffusion tensor of a molecule from 15N relaxation data using computer program ROTDIF,” J. Magn. Reson. (2004) 168, 336-345

3. D. Fushman, R. Varadan, M. Assfalg, O.Walker, “Determining domain orientation in macromolecules by using spin-relaxation and residual dipolar coupling measurements,” Progress in NMR Spectroscopy, (2004) 44, 189-214.

4. J. B. Hall, D. Fushman, “Variability of the 15N chemical shielding tensors in the B3 domain of protein G from 15N relaxation measurements at several fields. Implications for backbone order parameters,” J. Am. Chem. Soc. (2006) 128, 7855-7870.

5. Y. Ryabov, C. Geraghty, A.Varshney, D. Fushman, “An efficient computational method for predicting rotational diffusion tensors of globular proteins using an ellipsoid representation,” J. Am. Chem. Soc. (2006) 128, 15432-15444.

6. Y. Ryabov, D. Fushman, “Structural assembly of multidomain proteins and protein complexes guided by the overall rotational diffusion tensor,” J. Am. Chem. Soc. (2007) 129, 7894-7902.

7. K. Berlin, D. P. O’Leary, D. Fushman, “Improvement and Analysis of Computational Methods for Prediction of Residual Dipolar Couplings”, J. Magn. Reson (2009) 201, 25-33.

8. K. Berlin, D. P. O’Leary, D. Fushman, “Structural Assembly of Molecular Complexes Based on Residual Dipolar Couplings”, J. Am. Chem. Soc. (2010) 132, 8961-8972.

9. K. Berlin, D. P. O’Leary, D. Fushman, “Fast Approximations of the Rotational Diffusion Tensor and their Application to Structural Assembly of Molecular Complexes”, Proteins (2011) 79, 2268-2281.

10. M. D’Onofrio, E. Gianolio, A. Ceccon, F. Arena, S. Zanzoni, D. Fushman, S. Aime, H. Molinari, M. Assfalg, "High relaxivity supramolecular adducts between human liver fatty acid binding protein and amphiphilic Gd(III)-complexes: structural basis for the design of intracellular targeting MRI probes", Chemistry – A European Journal (2012) 18, 9919-9928.

11. K. Berlin, C. A. Castañeda, D. Schneidman-Duhovny, A. Sali, A. Nava-Tudela, and D. Fushman “Recovering a Representative Conformational Ensemble from Underdetermined Macromolecular Structural Data”, J Am Chem Soc (2013) 135, 16595–16609.

12. K. Berlin, A. Longhini, T. K. Dayie, D. Fushman, “Deriving Quantitative Dynamics Information for Proteins and RNAs using ROTDIF with a Graphical User Interface”, J Biomol NMR (2013) 57, 333-352.

13. A. Savelyev, E. Brookes, “GenApp: Extensible tool for rapid generation of web and native GUI applications”, Future Generation Computer Systems (2017). https://doi.org/10.1016/j.future.2017.09.069. https://genapp.rocks