ArticleJournal of biomolecular NMR2022
Towards autonomous analysis of chemical exchange saturation transfer experiments using deep neural networks.
Article in Journal of biomolecular NMR, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Enhanced Quantitative Phosphocreatine MR Imaging of Skeletal Muscle Using a Global-Local Two-Branch Deep Learning Model.Magnetic resonance in medicine · 2026Article
- Many Ways Out: Beyond the Two-State Model of Protein Unfolding.ACS omega · 2026Review
- NMR methods for investigating functionally relevant biomolecular dynamics.Magnetic resonance letters · 2025Review
- Improving quantification accuracy of a nuclear Overhauser enhancement signal at -1.6 ppm at 4.7 T using a machine learning approach.Physics in medicine and biology · 2025Article
- Enhancing amide proton transfer imaging in ischemic stroke using a machine learning approach with partially synthetic data.NMR in biomedicine · 2025Article
- Protein NMR assignment by isotope pattern recognition.Science advances · 2024Article
- Solution-state methyl NMR spectroscopy of large non-deuterated proteins enabled by deep neural networks.Nature communications · 2024Article
- Studying micro to millisecond protein dynamics using simple amideJournal of biomolecular NMR · 2023Article
- Towards autonomous analysis of chemical exchange saturation transfer experiments using deep neural networks.Journal of biomolecular NMR · 2022Article
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Authors and funding
4 authors.
Funding
Abstract
Macromolecules often exchange between functional states on timescales that can be accessed with NMR spectroscopy and many NMR tools have been developed to characterise the kinetics and thermodynamics of the exchange processes, as well as the structure of the conformers that are involved. However, analysis of the NMR data that report on exchanging macromolecules often hinges on complex least-squares fitting procedures as well as human experience and intuition, which, in some cases, limits the widespread use of the methods. The applications of deep neural networks (DNNs) and artificial intelligence have increased significantly in the sciences, and recently, specifically, within the field of biomolecular NMR, where DNNs are now available for tasks such as the reconstruction of sparsely sampled spectra, peak picking, and virtual decoupling. Here we present a DNN for the analysis of chemical exchange saturation transfer (CEST) data reporting on two- or three-site chemical exchange involving sparse state lifetimes of between approximately 3-60 ms, the range most frequently observed via experiment. The work presented here focuses on the
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