Section of Bioanalytical Chemistry, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.
National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.
Magn Reson Chem. 2023 Dec;61(12):759-769. doi: 10.1002/mrc.5392. Epub 2023 Sep 4.
One-dimensional (1D) proton-nuclear magnetic resonance ( H-NMR) spectroscopy is an established technique for the deconvolution of complex biological sample types via the identification/quantification of small molecules. It is highly reproducible and could be easily automated for small to large-scale bioanalytical, epidemiological, and in general metabolomics studies. However, chemical shift variability is a serious issue that must still be solved in order to fully automate metabolite identification. Herein, we demonstrate a strategy to increase the confidence in assignments and effectively predict the chemical shifts of various NMR signals based upon the simplest form of statistical models (i.e., linear regression). To build these models, we were guided by chemical homology in serum/plasma metabolites classes (i.e., amino acids and carboxylic acids) and similarity between chemical groups such as methyl protons. Our models, built on 940 serum samples and validated in an independent cohort of 1,052 plasma-EDTA spectra, were able to successfully predict the H NMR chemical shifts of 15 metabolites within ~1.5 linewidths (Δv ) error range on average. This pilot study demonstrates the potential of developing an algorithm for the accurate assignment of H NMR chemical shifts based solely on chemically defined constraints.
一维（1D）质子核磁共振（1 H-NMR）光谱学是一种通过鉴定/定量小分子来解析复杂生物样本类型的成熟技术。它具有高度可重复性，并且可以轻松实现小型到大型生物分析、流行病学和一般代谢组学研究的自动化。然而，化学位移变异性仍然是一个亟待解决的严重问题，这对于完全实现代谢物鉴定的自动化至关重要。在这里，我们展示了一种策略，可以通过最简单的统计模型（即线性回归）来提高分配的置信度，并有效预测各种 NMR 信号的化学位移。为了构建这些模型，我们根据血清/血浆代谢物类别的化学同源性（即氨基酸和羧酸）以及甲基质子等化学基团之间的相似性进行指导。我们的模型基于 940 个血清样本构建，并在一个独立的 1052 个 EDTA 血浆光谱验证队列中进行了验证，平均能够成功预测 15 种代谢物的 1 H-NMR 化学位移，误差范围在 1.5 倍线宽（Δv）以内。这项初步研究表明，基于化学定义的约束条件，开发一种准确分配 1 H-NMR 化学位移的算法具有潜力。
