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AI-designed NMR spectroscopy RF pulses for fast acquisition at high and ultra-high magnetic fields.
Manu, V S; Olivieri, Cristina; Veglia, Gianluigi.
Afiliación
  • Manu VS; Department of Biochemistry, Molecular Biology & Biophysics and Department of Chemistry, University of Minnesota, Minneapolis, MN, 55455, USA.
  • Olivieri C; Department of Biochemistry, Molecular Biology & Biophysics and Department of Chemistry, University of Minnesota, Minneapolis, MN, 55455, USA.
  • Veglia G; Department of Chemistry, University of Milan, 20133, Milan, Italy.
Nat Commun ; 14(1): 4144, 2023 07 12.
Article en En | MEDLINE | ID: mdl-37438347
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.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Inteligencia Artificial Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Inteligencia Artificial Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido