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AI-designed NMR spectroscopy RF pulses for fast acquisition at high and ultra-high magnetic fields

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  • V. S. Manu

    (University of Minnesota)

  • Cristina Olivieri

    (University of Minnesota
    University of Milan)

  • Gianluigi Veglia

    (University of Minnesota)

Abstract

Nuclear magnetic resonance (NMR) spectroscopy is a powerful high-resolution tool for characterizing biomacromolecular structure, dynamics, and interactions. However, the lengthy longitudinal relaxation of the nuclear spins significantly extends the total experimental time, especially at high and ultra-high magnetic field strengths. Although longitudinal relaxation-enhanced techniques have sped up data acquisition, their application has been limited by the chemical shift dispersion. Here we combined an evolutionary algorithm and artificial intelligence to design 1H and 15N radio frequency (RF) pulses with variable phase and amplitude that cover significantly broader bandwidths and allow for rapid data acquisition. We re-engineered the basic transverse relaxation optimized spectroscopy experiment and showed that the RF shapes enhance the spectral sensitivity of well-folded proteins up to 180 kDa molecular weight. These RF shapes can be tailored to re-design triple-resonance experiments for accelerating NMR spectroscopy of biomacromolecules at high fields.

Suggested Citation

  • V. S. Manu & Cristina Olivieri & Gianluigi Veglia, 2023. "AI-designed NMR spectroscopy RF pulses for fast acquisition at high and ultra-high magnetic fields," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39581-4
    DOI: 10.1038/s41467-023-39581-4
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    1. Daisuke Sakakibara & Atsuko Sasaki & Teppei Ikeya & Junpei Hamatsu & Tomomi Hanashima & Masaki Mishima & Masatoshi Yoshimasu & Nobuhiro Hayashi & Tsutomu Mikawa & Markus Wälchli & Brian O. Smith & Mas, 2009. "Protein structure determination in living cells by in-cell NMR spectroscopy," Nature, Nature, vol. 458(7234), pages 102-105, March.
    2. Yong Xu & Roberto Maya-Martinez & Nicolas Guthertz & George R. Heath & Iain W. Manfield & Alexander L. Breeze & Frank Sobott & Richard Foster & Sheena E. Radford, 2022. "Tuning the rate of aggregation of hIAPP into amyloid using small-molecule modulators of assembly," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
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