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

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.