Solution State Methyl NMR Spectroscopy of Large Non-Deuterated Proteins Enabled by Deep Neural Networks

Abstract

Methyl-TROSY nuclear magnetic resonance (NMR) spectroscopy is a powerful technique for characterising large biomolecules in solution. However, preparing samples for these experiments is arduous and entails deuteration, limiting its use. Here we demonstrate that NMR spectra recorded on protonated, uniformly 13C labelled, samples can be processed using deep neural networks to yield spectra that are of similar quality to typical deuterated methyl-TROSY spectra, potentially providing more information at a fraction of the cost. We validated the new methodology experimentally on three proteins with molecular weights in the range 42-360 kDa and further by analysing deep learning-processed NOESY spectra of Escherichia coli Malate Synthase G (81 kDa), where observed NOE cross-peaks were in good agreement with the available structure. The new method represents a substantial advance in the field of using deep learning to analyse complex magnetic resonance data and could have a major impact on the study of large biomolecules in the years to come.