Solution-state methyl NMR spectroscopy of large non-deuterated proteins enabled by deep neural networks
Year: 2024
Type: article
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 demanding and entails deuteration, limiting its use. Here we demonstrate that NMR spectra recorded on protonated, uniformly 13 C 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 information for proteins that cannot be produced in bacterial systems. We validate the methodology experimentally on three proteins with molecular weights in the range 42–360 kDa. We further demonstrate the applicability of our methodology to 3D NOESY spectra of Escherichia coli Malate Synthase G (81 kDa), where observed NOE cross-peaks are in good agreement with the available structure. The method represents an advance in the field of using deep learning to analyse complex magnetic resonance data and could have an impact on the study of large biomolecules in years to come.
Source: Nature Communications
Institutions: University College London, The Francis Crick Institute
Language: English
Subfield: Spectroscopy
Field: Chemistry
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $6,790
Funders: Wellcome Trust, Francis Crick Institute, UK Research and Innovation, Cancer Research UK, Research Councils UK, University College London, University of Oxford, National Institutes of Health, Medical Research Council, Engineering and Physical Sciences Research Council, Biotechnology and Biological Sciences Research Council, National Institute of General Medical Sciences
Awards: G1171806115, G2218843830, G2423823566, G3199518263, The UCL Integrated NMR Centre:Bridging fine-scale analysis of macromolecules and cell physiology with high-throughput population scale metabolomics, drug target validation and biomarker development ., Center for Biomolecular NMR Data Processing and Analysis, G3862669384, G5102230944, G5172917357, DeepNMR: Unleashing the full potential of NMR spectroscopy with artificial intelligence and deep learning, Characterising structure, interactions and dynamics of large molecular machines and intrinsically disordered proteins using novel carbon-detected NMR, G7055296076, A Coordinated Infrastructure for NMR for the Physical and Life Sciences: Upgrade to the Ultra-high Field 950 MHz Spectrometer at Oxford, G8019112989
OpenAlex ID: W4399604318