Minimizing Pervasive Artifacts in 4D Covariance Maps for Protein Side Chain NMR Assignments

Nuclear magnetic resonance (NMR) is a mainstay of biophysical studies that provides atomic level readouts to formulate molecular mechanisms. Side chains are particularly important to derive mechanisms involving proteins as they carry functional groups, but NMR studies of side chains are often limited by challenges in assigning their signals. Here, we designed a novel computational method that combines spectral derivatives and matrix square-rooting to produce reliable 4D covariance maps from routinely acquired 3D spectra and facilitates side chain resonance assignments. Thus, we generate two 4D maps from 3D-HcccoNH and 3D-HCcH-TOCSY spectra that each help overcome signal overlap or sensitivity losses. These 4D maps feature HC-HSQCs of individual side chains that can be paired to assigned backbone amide resonances of individual aliphatic signals, and both are obtained from a single modified covariance calculation. Further, we present 4D maps produced using conventional triple resonance experiments to easily assign asparagine side chain amide resonances. The 4D covariance maps encapsulate the lengthy manual pattern recognition used in traditional assignment methods and distill the information as correlations that can be easily visualized. We showcase the utility of the 4D covariance maps with a 10 kDa peptidyl carrier protein and a 52 kDa cyclization domain from a nonribosomal peptide synthetase.