Linear analysis of carbon-13 chemical shift differences and its application to the detection and correction of errors in referencing and spin system identifications

Abstract

Statistical analysis reveals that the set of differences between the secondary shifts of the α- and β-carbons for residues i of a protein (Δδ¹³C^α_i- Δδ¹³C^β_i) provides the means to detect and correct referencing errors for ¹H and ¹³C nuclei within a given dataset. In a correctly referenced protein dataset, linear regression plots of Δδ¹³C^α_i,Δδ¹³C^β_i, or Δδ¹H^α_i vs. (Δδ¹³C^α_i- Δδ¹³C^β_i) pass through the origin from two directions, the helix-to-coil and strand-to-coil directions. Thus, linear analysis of chemical shifts (LACS) can be used to detect referencing errors and to recalibrate the ¹H and ¹³C chemical shift scales if needed. The analysis requires only that the signals be identified with distinct residue types (intra-residue spin systems). LACS allows errors in calibration to be detected and corrected in advance of sequence-specific assignments and secondary structure determinations. Signals that do not fit the linear model (outliers) deserve scrutiny since they could represent errors in identifying signals with a particular residue, or interesting features such as a cis-peptide bond. LACS provides the basis for the automated detection of such features and for testing reassignment hypotheses. Early detection and correction of errors in referencing and spin system identifications can improve the speed and accuracy of chemical shift assignments and secondary structure determinations. We have used LACS to create a database of offset-corrected chemical shifts corresponding to nearly 1800 BMRB entries: 300 with and 1500 without corresponding three-dimensional (3D) structures. This database can serve as a resource for future analysis of the effects of amino acid sequence and protein secondary and tertiary structure on NMR chemical shifts.