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• W3003368716 abstract "Abstract Signal overlapping is a major bottleneck for protein NMR analysis. We propose a new method, stable-isotope-assisted parameter extraction (SiPex), to resolve overlapping signals by a combination of amino-acid selective isotope labeling (AASIL) and tensor decomposition. The basic idea of Sipex is that overlapping signals can be decomposed with the help of intensity patterns derived from quantitative fractional AASIL, which also provides amino-acid information. In SiPex, spectra for protein characterization, such as 15 N relaxation measurements, are assembled with those for amino-acid information to form a four-order tensor, where the intensity patterns from AASIL contribute to high decomposition performance even if the signals share similar chemical shift values or characterization profiles, such as relaxation curves. The loading vectors of each decomposed component, corresponding to an amide group, represent both the amino-acid and relaxation information. This information link provides an alternative protein analysis method that does not require “assignments” in a general sense; i.e., chemical shift determinations, since the amino-acid information for some of the residues allows unambiguous assignment according to the dual selective labeling. SiPex can also decompose signals in time-domain raw data without Fourier transform, even in non-uniformly sampled data without spectral reconstruction. These features of SiPex should expand biological NMR applications by overcoming their overlapping and assignment problems." @default.
• W3003368716 created "2020-02-07" @default.
• W3003368716 creator A5018412694 @default.
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• W3003368716 date "2020-01-30" @default.
• W3003368716 modified "2023-10-09" @default.
• W3003368716 title "Amino-acid selective isotope labeling enables simultaneous overlapping signal decomposition and information extraction from NMR spectra" @default.
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• W3003368716 doi "https://doi.org/10.1007/s10858-019-00295-9" @default.
• W3003368716 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7080692" @default.
• W3003368716 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32002710" @default.
• W3003368716 hasPublicationYear "2020" @default.
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