Premium
NMR Backbone Assignment of Large Proteins by Using 13 C α ‐Only Triple‐Resonance Experiments
Author(s) -
Wei Qingtao,
Chen Jiajing,
Mi Juan,
Zhang Jiahai,
Ruan Ke,
Wu Jihui
Publication year - 2016
Publication title -
chemistry – a european journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.687
H-Index - 242
eISSN - 1521-3765
pISSN - 0947-6539
DOI - 10.1002/chem.201601871
Subject(s) - covariance , chemical shift , chemistry , protein dynamics , residue (chemistry) , nuclear magnetic resonance spectroscopy , maltose binding protein , nuclear magnetic resonance , molecular dynamics , computational chemistry , physics , stereochemistry , mathematics , recombinant dna , biochemistry , statistics , fusion protein , gene
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 C α 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.