NMR Backbone Assignment of Large Proteins by Using 13Cα‐Only Triple‐Resonance Experiments

Abstract

Nuclear magnetic resonance (NMR) is a powerful tool to interrogate protein structure and dynamics residue by residue. However, the prerequisite chemical-shift assignment remains a bottleneck for large proteins due to the fast relaxation and the frequency degeneracy of the (13) Cα nuclei. Herein, we present a covariance NMR strategy to assign the backbone chemical shifts by using only HN(CO)CA and HNCA spectra that has a high sensitivity even for large proteins. By using the peak linear correlation coefficient (LCC), which is a sensitive probe even for tiny chemical-shift displacements, we correctly identify the fidelity of approximately 92 % cross-peaks in the covariance spectrum, which is thus a significant improvement on the approach developed by Snyder and Brüschweiler (66 %) and the use of spectral derivatives (50 %). Thus, we calculate the 4D covariance spectrum from HN(CO)CA and HNCA experiments, in which cross-peaks with LCCs above a universal threshold are considered as true correlations. This 4D covariance spectrum enables the sequential assignment of a 42 kDa maltose binding protein (MBP), in which about 95 % residues are successfully assigned with a high accuracy of 98 %. Our LCC approach, therefore, paves the way for a residue-by-residue study of the backbone structure and dynamics of large proteins.