Panteleimon G. Takis, Varvara A. Aggelidou, Caroline J. Sands, Alexandra Louka
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引用次数: 0
摘要
一维质子核磁共振(1h - nmr)波谱是一种成熟的技术,通过小分子的鉴定/定量来反褶积复杂的生物样品类型。它具有很高的可重复性,可以很容易地自动化用于小型到大规模的生物分析,流行病学和一般代谢组学研究。然而,为了使代谢物鉴定完全自动化,化学位移可变性仍然是一个必须解决的严重问题。在此,我们展示了一种策略,以增加分配的置信度,并基于最简单的统计模型(即线性回归)有效地预测各种核磁共振信号的化学位移。为了建立这些模型,我们以血清/血浆代谢物类别(即氨基酸和羧酸)的化学同源性和化学基团(如甲基质子)之间的相似性为指导。我们的模型建立在940份血清样本上,并在1052份血浆edta光谱的独立队列中进行了验证,能够成功预测15种代谢物的1 H NMR化学位移,平均误差范围为1.5线宽(Δv1/2)。这项初步研究表明,开发一种仅基于化学定义约束的1 H NMR化学位移精确分配算法的潜力。
Mapping of 1H NMR chemical shifts relationship with chemical similarities for the acceleration of metabolic profiling: Application on blood products
One-dimensional (1D) proton-nuclear magnetic resonance (1H-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 1H NMR chemical shifts of 15 metabolites within ~1.5 linewidths (Δv1/2) error range on average. This pilot study demonstrates the potential of developing an algorithm for the accurate assignment of 1H NMR chemical shifts based solely on chemically defined constraints.
期刊介绍:
MRC is devoted to the rapid publication of papers which are concerned with the development of magnetic resonance techniques, or in which the application of such techniques plays a pivotal part. Contributions from scientists working in all areas of NMR, ESR and NQR are invited, and papers describing applications in all branches of chemistry, structural biology and materials chemistry are published.
The journal is of particular interest not only to scientists working in academic research, but also those working in commercial organisations who need to keep up-to-date with the latest practical applications of magnetic resonance techniques.