Towards Ultimate NMR Resolution with Deep Learning
https://doi.org/10.5878/b8n4-x341
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/nmrpipeOpens in a new tab), the nmrglue Python package (https://github.com/jjhelmus/nmrglueOpens in a new tab), or other software supporting the NMRPipe format, including CCPN 3.0 (https://ccpn.ac.ukOpens in a new tab) and later versions. These processed spectra are used as input files for AI-based methods to improve NMR spectral resolution.
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Creator/Principal investigator(s):
- Tatiana Agback - Swedish University of Agricultural Sciences
- Vladislav Orekhov - University of Gothenburg - Institutionen för kemi och molekylärbiologi
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All processed data were generated in this study using standard NMR processing procedures and are stored in NMRPipe format (.ft2 and .ft3 files). The data are derived from experimentally recorded 2D and 3D solution-state protein NMR spectra of MALT1 (45 kDa), Azurin (14 kDa), and Tau (disordered, 45.8 kDa), obtained in previous 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
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Standard för svensk indelning av forskningsämnen 2025:
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Citation:
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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