Towards Ultimate NMR Resolution with Deep Learning
https://doi.org/10.5878/5xp8-g969
The dataset contains processed solution-state protein NMR spectra of MALT1 (45 kDa), Azurin (14 kDa), and Tau (disordered, 45.8 kDa), derived from experimentally recorded 2D and 3D data obtained in earlier studies and published previously:
(1) DOI: 10.1371/journal.pone.0146496; DOI: 10.1007/s12104-022-10105-3;
(2) DOI: 10.1110/ps.0225403;
(3) DOI: 10.1002/anie.202102758
All processed data are stored in NMRPipe format (.ft2 and .ft3 files) and were generated using standard NMR processing procedures. The data can be read and visualized using NMRPipe-compatible software, such as NMRPipe (https://www.ibbr.umd.edu/nmrpipe), the nmrglue Python package (https://github.com/jjhelmus/nmrglue), or other software supporting the NMRPipe format, including CCPN 3.0 (https://ccpn.ac.uk) and later versions. These processed spectra are used as input files for AI-based methods to improve NMR spectral resolution.
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All bearbetad data genererades i denna studie med hjälp av standardiserade NMR-bearbetningsprocedurer och lagras i NMRPipe-format (.ft2- och .ft3-filer). Datan är härledd från experimentellt inspelade 2D- och 3D-lösningstillstånds-protein-NMR-spektra för MALT1 (45 kDa), Azurin (14 kDa) och Tau (ostrukturerat, 45,8 kDa), som erhållits i tidigare studier och publicerats: tidigare:
(1) DOI: 10.1371/journal.pone.0146496; DOI: 10.1007/s12104-022-10105-3;
(2) DOI: 10.1110/ps.0225403;
(3) DOI: 10.1002/anie.202102758.
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Amir Jahangiri, Tatiana Agback, Ulrika Brath, Vladislav Orekhov, Towards Ultimate NMR Resolution with Deep Learning, arXiv preprint arXiv:2502.20793, 2025
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