Journal of Biomolecular NMR

, Volume 46, Issue 4, pp 257–270 | Cite as

The predictive accuracy of secondary chemical shifts is more affected by protein secondary structure than solvent environment

  • Marie-Laurence Tremblay
  • Aaron W. Banks
  • Jan K. RaineyEmail author


Biomolecular NMR spectroscopy frequently employs estimates of protein secondary structure using secondary chemical shift (Δδ) values, measured as the difference between experimental and random coil chemical shifts (RCCS). Most published random coil data have been determined in aqueous conditions, reasonable for non-membrane proteins, but potentially less relevant for membrane proteins. Two new RCCS sets are presented here, determined in dimethyl sulfoxide (DMSO) and chloroform:methanol:water (4:4:1 by volume) at 298 K. A web-based program, CS-CHEMeleon, has been implemented to determine the accuracy of secondary structure assessment by calculating and comparing Δδ values for various RCCS datasets. Using CS-CHEMeleon, Δδ predicted versus experimentally determined secondary structures were compared for large datasets of membrane and non-membrane proteins as a function of RCCS dataset, Δδ threshold, nucleus, localized parameter averaging and secondary structure type. Optimized Δδ thresholds are presented both for published and for the DMSO and chloroform:methanol:water derived RCCS tables. Despite obvious RCCS variations between datasets, prediction of secondary structure was consistently similar. Strikingly, predictive accuracy seems to be most dependent upon the type of secondary structure, with helices being the most accurately predicted by Δδ values using five different RCCS tables. We suggest caution when using Δδ-based restraints in structure calculations as the underlying dataset may be biased. Comparative assessment of multiple RCCS datasets should be performed, and resulting Δδ-based restraints weighted appropriately relative to other experimental restraints.


Random coil chemical shifts Nuclear magnetic resonance spectroscopy Dielectric constant Solvent effects Secondary chemical shifts Chemical shift index Protein secondary structure 



Thanks to Daniel Gaston for helpful discussions; to David Langelaan and Tyler Reddy for critical reading of the manuscript; to Bruce Stewart for technical support; Dr. David Waisman for CD spectropolarimeter access; Dr. Barbara Karten for access to a refrigerated centrifuge; Dr. Carmichael Wallace for access to a lyophilizer; and to Drs. Mike Lumsden, (NMR-3, Dalhousie University), Kathy Robertson (NMR-3), Ray Syvitski (NRC-IMB) and Ian Burton (NRC-IMB) for assistance during configuration of NMR experiments. This work was funded by a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant alongside partial stipend support for MLT from a Dalhousie Cancer Research Program grant to JKR. AWB received support as an NSERC Undergraduate Student Research Associate. Critical infrastructure was provided by a Faculty of Medicine Intramural Grant and startup funding from Dalhousie University. Operation of NMR-3 is funded by Dalhousie University and NSERC. The 700 MHz cryoprobe at the NRC-IMB was funded by an Atlantic Canada Opportunities Agency Grant to Dalhousie University.

Supplementary material

10858_2010_9400_MOESM1_ESM.pdf (849 kb)
Supplementary material 1 (PDF 849 kb)


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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Marie-Laurence Tremblay
    • 1
  • Aaron W. Banks
    • 1
  • Jan K. Rainey
    • 1
    • 2
    Email author
  1. 1.Department of Biochemistry & Molecular BiologyDalhousie UniversityHalifaxCanada
  2. 2.Department of ChemistryDalhousie UniversityHalifaxCanada

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