4.3 Article

Decoherence optimized tilted-angle cross polarization: A novel concept for sensitivity-enhanced solid-state NMR using ultra-fast magic angle spinning

期刊

JOURNAL OF MAGNETIC RESONANCE
卷 322, 期 -, 页码 -

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jmr.2020.106857

关键词

Solid state NMR; Ultra-fast MAS; CP; Biomolecular NMR; Multi-dimensional solid-state NMR; H-1-detected solid-state NMR

资金

  1. JST-Mirai Program (Japan) [JPMJMI17A2]
  2. JSPS KAKENHI grant (Japan) [JP15K21772]

向作者/读者索取更多资源

A novel approach called DOTA CP was proposed to improve cross-polarization efficiency in solid-state NMR, leading to enhanced sensitivity and efficient band-selective polarization transfer. Experimental results showed that DOTA CP outperformed traditional adiabatic CP in sensitivity for C-13-labeled proteins, demonstrating its effectiveness in various multidimensional applications of SSNMR.
Ultra-fast magic-angle spinning (UFMAS) at a MAS rate (omega(R)/2 pi) of 60 kHz or higher has dramatically improved the resolution and sensitivity of solid-state NMR (SSNMR). However, limited polarization transfer efficiency using cross-polarization (CP) between H-1 and rare spins such as C-13 still restricts the sensitivity and multi-dimensional applications of SSNMR using UFMAS. We propose a novel approach, which we call decoherence-optimized tilted-angle CP (DOTA CP), to improve CP efficiency with prolonged lifetime of 1H coherence in the spin-locked condition and efficient band-selective polarization transfer by incorporating off-resonance irradiation to H-1 spins. C-13 CP-MAS at omega(R)/2 pi of 70-90 kHz suggested that DOTA CP notably outperformed traditional adiabatic CP, a de-facto-standard CP scheme over the past decade, in sensitivity for the aliphatic-region spectra of C-13-labeled GB1 protein and N-formyl-MetLeu-Phe samples by up to 1.4- and 1.2-fold, respectively. H-1-detected 2D H-1/C-13 SSNMR for the GB1 sample indicated the effectiveness of this approach in various multidimensional applications. (C) 2020 Elsevier Inc. All rights reserved.

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