Journal of Biomolecular NMR

, Volume 63, Issue 2, pp 141–150 | Cite as

CONNJUR R: an annotation strategy for fostering reproducibility in bio-NMR—protein spectral assignment

  • Matthew Fenwick
  • Jeffrey C. Hoch
  • Eldon Ulrich
  • Michael R. Gryk


Reproducibility is a cornerstone of the scientific method, essential for validation of results by independent laboratories and the sine qua non of scientific progress. A key step toward reproducibility of biomolecular NMR studies was the establishment of public data repositories (PDB and BMRB). Nevertheless, bio-NMR studies routinely fall short of the requirement for reproducibility that all the data needed to reproduce the results are published. A key limitation is that considerable metadata goes unpublished, notably manual interventions that are typically applied during the assignment of multidimensional NMR spectra. A general solution to this problem has been elusive, in part because of the wide range of approaches and software packages employed in the analysis of protein NMR spectra. Here we describe an approach for capturing missing metadata during the assignment of protein NMR spectra that can be generalized to arbitrary workflows, different software packages, other biomolecules, or other stages of data analysis in bio-NMR. We also present extensions to the NMR-STAR data dictionary that enable machine archival and retrieval of the “missing” metadata.


CONNJUR Data model Reproducibility Analysis NMR-STAR 



This research was funded by United States National Institutes of Health Grant GM-083072. The authors would like to thank Dr. Mark Maciejewski for kindly providing time-domain data of the Samp3 protein and Dr. Woonghee Lee for adding the reproducibility extensions to the NMRFam release of Sparky.

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10858_2015_9964_MOESM1_ESM.pdf (64 kb)
Supplementary material 1 (PDF 64 kb)


  1. Bahrami A et al (2009) Probabilistic interaction network of evidence algorithm and its application to complete labeling of peak lists from protein NMR spectroscopy. PLoS Comput Biol 5(3):e1000307MathSciNetCrossRefADSGoogle Scholar
  2. Bartels C et al (1995) The program XEASY for computer-supported NMR spectral analysis of biological macromolecules. J Biomol NMR 6(1):1–10CrossRefGoogle Scholar
  3. Bax A, Clore GM, Gronenborn AM (1990) 1H 1H correlation via isotropic mixing of 13C magnetization, a new three-dimensional approach for assigning 1H and 13C spectra of 13C-enriched proteins. J Magn Reson 88:425–431ADSGoogle Scholar
  4. Buckheit JB, Donoho DL (1995) Wavelab and reproducible research. Springer, New YorkCrossRefGoogle Scholar
  5. Collins FS, Tabek LA (2014) NIH plans to enhance reproducibility. Nature 505:612–613CrossRefGoogle Scholar
  6. Dall’Olio GM, Bertranpetit J, Laayouni H (2010) The annotation and the usage of scientific databases could be improved with public issue tracker software. Database 2010:baq035Google Scholar
  7. Eclipse IDE (2007) The Eclipse Foundation.
  8. Goddard TD, Kneller DG (2004) SPARKY 3. University of California, San Francisco, p 15Google Scholar
  9. Grzesiek S, Bax A (1992) An efficient experiment for sequential backbone assignment of medium-sized isotopically enriched proteins. J Magn Reson 99(1):201–207ADSGoogle Scholar
  10. Grzesiek S, Bax A (1993) Amino acid type determination in the sequential assignment procedure of uniformly 13C/15N-enriched proteins. J Biomol NMR 3(2):185–204CrossRefGoogle Scholar
  11. Grzesiek S, Anglister J, Bax A (1993) Correlation of backbone amide and aliphatic side-chain resonances in 13C 15N-enriched proteins by isotropic mixing of 13C magnetization. J Magn Reson Ser B 101(1):114–119CrossRefGoogle Scholar
  12. Guerry P, Herrmann T (2011) Advances in automated NMR protein structure determination. Q Rev Biophys 44(03):257–309CrossRefGoogle Scholar
  13. Güntert P (2004) Automated NMR structure calculation with CYANA. In: Downing AK (ed) Methods in Molecular Biology, vol. 278: Protein NMR techniques. Humana Press, Totowa, pp 353–378Google Scholar
  14. Güntert P (2009) Automated structure determination from NMR spectra. Eur Biophys J 38(2):129–143CrossRefGoogle Scholar
  15. Ioannidis JPA et al (2008) Repeatability of published microarray gene expression analyses. Nat Genet 41(2):149–155CrossRefGoogle Scholar
  16. Johnson BA (2004) Using NMRView to visualize and analyze the NMR spectra of macromolecules. In: Downing AK (ed) Methods in Molecular Biology, vol. 278: Protein NMR techniques. Humana Press, Totowa, pp 313–352Google Scholar
  17. Kay LE et al (1990) Three-dimensional triple-resonance NMR spectroscopy of isotopically enriched proteins. J Magn Reson 89(3):496–514ADSGoogle Scholar
  18. Keller RLJ (2004) Optimizing the process of nuclear magnetic resonance spectrum analysis and computer aided resonance assignment. Diss ETH No. 15947. Diss. Swiss Federal Institute of Technology, ZurichGoogle Scholar
  19. Landis SC et al (2012) A call for transparent reporting to optimize the predictive value of preclinical research. Nature 490(7419):187–191CrossRefADSGoogle Scholar
  20. Loeliger J, McCullough M (2012) Version control with Git: powerful tools and techniques for collaborative software development. O’Reilly Media Inc, SebastopolGoogle Scholar
  21. Marion D et al (1989) Three-dimensional heteronuclear NMR of nitrogen-15 labeled proteins. J Am Chem Soc 111(4):1515–1517MathSciNetCrossRefGoogle Scholar
  22. Montelione GT, Lyons BA, Emerson SD, Tashiro M (1992) An efficient triple resonance experiment using carbon-13 isotropic mixing for determining sequence-specific resonance assignments of isotopically enriched proteins. J Am Chem Soc 114(27):10974–10975CrossRefGoogle Scholar
  23. Open Source Initiative (2006) The MIT License.
  24. Peng RD (2011) Reproducible research in computational science. Science 334(6060):1226CrossRefADSGoogle Scholar
  25. Prinz F, Schlange T, Asadullah K (2011) Believe it or not: how much can we rely on published data on potential drug targets? Nat Rev Drug Discov 10(9):712CrossRefGoogle Scholar
  26. Rowland NMR Toolkit Script Generator. Web. September 18, 2014.
  27. Shen Y et al (2009) TALOS+: a hybrid method for predicting protein backbone torsion angles from NMR chemical shifts. J Biomol NMR 44(4):213–223CrossRefGoogle Scholar
  28. Stodden V, Miguez S (2014) Best practices for computational science: software infrastructure and environments for reproducible and extensible research. J Open Res Softw 2(1):1–6. doi: 10.5334/jors.ay
  29. Ulrich EL et al (2008) BioMagResBank. Nucleic Acids Res 36(suppl 1):D402–D408MathSciNetGoogle Scholar
  30. Vranken WF et al (2005) The CCPN data model for NMR spectroscopy: development of a software pipeline. Proteins 59(4):687–696CrossRefGoogle Scholar
  31. Williamson MP, Craven CJ (2009) Automated protein structure calculation from NMR data. J Biomol NMR 43(3):131–143CrossRefGoogle Scholar
  32. Zimmerman DE et al (1997) Automated analysis of protein NMR assignments using methods from artificial intelligence. J Mol Biol 269(4):592–610MathSciNetCrossRefGoogle Scholar
  33. Zuiderweg ERP, Fesik SW (1989) Heteronuclear three-dimensional NMR spectroscopy of the inflammatory protein C5a. Biochemistry 28(6):2387–2391CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Matthew Fenwick
    • 1
  • Jeffrey C. Hoch
    • 1
  • Eldon Ulrich
    • 2
  • Michael R. Gryk
    • 1
  1. 1.Department of Molecular Biology and BiophysicsUConn HealthFarmingtonUSA
  2. 2.Department of BiochemistryUniversity of Wisconsin-MadisonMadisonUSA

Personalised recommendations