Quantum chemistry-based NMR spin Hamiltonian parameters of GABA for quantitation in magnetic resonance spectroscopy

文献信息

发布日期 2007-04-17

DOI 10.1039/B700631D

影响因子 3.676

作者

A. R. Allouche, M. Aubert-Frécon, D. Graveron-Demilly

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摘要

Chemical shifts δ and spin–spin coupling constants J have been calculated using quantum chemistry approaches for the γ-amino butyric acid GABA which is a brain metabolite. Two theoretical methods HF and DFT/B3LYP, two basis sets 6-31G* and 6-311+G(2d,p) and two gauge-invariant methods CSGT and GIAO have been used. From δ and J values, NMR spectra have been obtained from the strongly coupled spin system Hamiltonian using the NMR-SCOPE package. Solvent effects have been considered within the polarisable continuum model. Comparisons between calculated and experimental NMR spectra at 300 MHz show that our best results correspond to the B3LYP/6-311+G(2d,p)-GIAO calculations. They are seen to be in good agreement with experiment. This demonstrates the usefulness of quantum chemistry methods for estimating NMR spin Hamiltonian parameters involved in specific algorithms used for quantitation of metabolites such as GABA.

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来源期刊

Physical Chemistry Chemical Physics

CiteScore: 5.5

自引率: 10.3%

年发文量: 3036

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.