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- W4387300963 abstract "Chemical shift transfer (CST) is a well-established technique in NMR spectroscopy that utilizes the chemical shift assignment of one protein (source) to identify chemical shifts of another (target). Given similarity between source and target systems (e.g., using homologs), CST allows the chemical shifts of the target system to be assigned using a limited amount of experimental data. In this study, we propose a deep-learning based workflow, ARTINA-CST, that automates this procedure, allowing CST to be carried out within minutes or hours of computational time and strictly without any human supervision. We characterize the efficacy of our method using three distinct synthetic and experimental datasets, demonstrating its effectiveness and robustness even when substantial differences exist between the source and target proteins. With its potential applications spanning a wide range of NMR projects, including drug discovery and protein interaction studies, ARTINA-CST is anticipated to be a valuable method that facilitates research in the field." @default.
- W4387300963 created "2023-10-04" @default.
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- W4387300963 date "2023-10-03" @default.
- W4387300963 modified "2023-10-07" @default.
- W4387300963 title "Chemical shift transfer: an effective strategy for protein NMR assignment with ARTINA" @default.
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- W4387300963 doi "https://doi.org/10.3389/fmolb.2023.1244029" @default.
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