A combined NMR and deep neural network approach for enhancing the spectral resolution of aromatic side chains in proteins.

Nuclear magnetic resonance (NMR) spectroscopy is an important technique for deriving the dynamics and interactions of macromolecules; however, characterizations of aromatic residues in proteins still pose a challenge. Here, we present a deep neural network (DNN), which transforms NMR spectra recorded on simple uniformly C-labeled samples to yield high-quality H-C correlation maps of aromatic side chains. Key to the success of the DNN is the design of NMR experiments that produce data with unique features to aid the DNN produce high-resolution spectra. The methodology was validated experimentally on protein samples ranging from 7 to 40 kDa in size, where it accurately reconstructed multidimensional aromatic H-C correlation maps, to facilitate H-C chemical shift assignments and to quantify kinetics. More generally, we believe that the strategy of designing new NMR experiments in combination with customized DNNs represents a substantial advance that will have a major impact on the study of molecules using NMR in the years to come.