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使用人工智能设计的用于氢、氮异核核磁共振光谱的宽带脉冲检测微生物提取物中的N标记代谢物。

Detection of N-labeled metabolites in microbial extracts using AI-designed broadband pulses for H, N heteronuclear NMR spectroscopy.

作者信息

V S Manu, Tonelli Marco, Bell Bailey, Sharma Alok K, Bugni Tim S, Veglia Gianluigi

机构信息

Department of Biochemistry, Molecular Biology & Biophysics, University of Minnesota, Minneapolis, Minnesota 55455, USA.

Department of Biochemistry, University of Wisconsin-Madison, 433 Babcock Drive, Madison, Wisconsin, 53706, USA.

出版信息

Analyst. 2025 Apr 22;150(9):1856-1861. doi: 10.1039/d5an00074b.

Abstract

Approximately 40% of bacterial and mammalian metabolites contain nitrogen-based chemical moieties such as amides, amines, and imines. The identification and quantification of these groups 2D H,N heteronuclear NMR spectroscopy have broadened the catalog of NMR-detected metabolites. However, these NMR experiments necessitate broadband radiofrequency (RF) pulses for inversion and refocusing operations to encompass the full range of N chemical shifts, a challenge that becomes increasingly apparent at high and ultra-high magnetic fields. Here, we show that a newly AI-designed broadband N universal 180° pulse for both inversion and refocusing incorporated in the 2D H, N heteronuclear single quantum coherence (2D H-N BB-HSQC) experiment significantly enhances spectral sensitivity. We demonstrate the advantage of the new technique by analyzing the crude extract of sp. WMMC264, a microbial strain that produces siderophores for iron absorption from the environment. The implementation of the AI-designed pulse in the 2D H-N BB-HSQC experiment will contribute to advancing the analysis of nitrogen-containing metabolites in biological fluids and cell extracts.

摘要

大约40%的细菌和哺乳动物代谢物含有基于氮的化学基团,如酰胺、胺和亚胺。这些基团的鉴定和定量 二维氢氮异核核磁共振光谱拓宽了核磁共振检测代谢物的目录。然而,这些核磁共振实验需要宽带射频(RF)脉冲进行反转和重聚焦操作,以涵盖氮化学位移的全范围,这一挑战在高磁场和超高磁场下变得越来越明显。在这里,我们表明,一种新的人工智能设计的宽带氮通用180°脉冲用于反转和重聚焦,并结合在二维氢氮异核单量子相干(2D H-N BB-HSQC)实验中,显著提高了光谱灵敏度。我们通过分析sp. WMMC264的粗提物来证明新技术的优势,sp. WMMC264是一种微生物菌株,能产生用于从环境中吸收铁的铁载体。在二维氢氮BB-HSQC实验中实施人工智能设计的脉冲将有助于推进生物流体和细胞提取物中含氮代谢物的分析。

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