Mapping of 1H NMR chemical shifts relationship with chemical similarities for the acceleration of metabolic profiling: Application on blood products

IF 1.9 3区 化学 Q3 CHEMISTRY, MULTIDISCIPLINARY Magnetic Resonance in Chemistry Pub Date : 2023-09-04 DOI:10.1002/mrc.5392
Panteleimon G. Takis, Varvara A. Aggelidou, Caroline J. Sands, Alexandra Louka
{"title":"Mapping of 1H NMR chemical shifts relationship with chemical similarities for the acceleration of metabolic profiling: Application on blood products","authors":"Panteleimon G. Takis,&nbsp;Varvara A. Aggelidou,&nbsp;Caroline J. Sands,&nbsp;Alexandra Louka","doi":"10.1002/mrc.5392","DOIUrl":null,"url":null,"abstract":"<p>One-dimensional (1D) proton-nuclear magnetic resonance (<sup>1</sup>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 <sup>1</sup>H NMR chemical shifts of 15 metabolites within ~1.5 linewidths (Δ<i>v</i><sub>1/2</sub>) error range on average. This pilot study demonstrates the potential of developing an algorithm for the accurate assignment of <sup>1</sup>H NMR chemical shifts based solely on chemically defined constraints.</p>","PeriodicalId":18142,"journal":{"name":"Magnetic Resonance in Chemistry","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/epdf/10.1002/mrc.5392","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Magnetic Resonance in Chemistry","FirstCategoryId":"92","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/mrc.5392","RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

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.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
加速代谢谱的1h NMR化学位移关系与化学相似性的映射:在血液制品上的应用。
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
4.70
自引率
10.00%
发文量
99
审稿时长
1 months
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.
期刊最新文献
HRMAS NMR for Studying Solvent-Induced Mobility of Polymer Chains and Metallocene Migration Into Low-Density Polyethylene (LDPE). Structural Elucidation and Complete NMR Spectral Assignments of Monascus Monacolin Analogs. Issue Information Reversibly Compressible Cross-Linked Polystyrene Gels, Compatible With Toluene-d8 and Pyridine-d5, for Measurement of Residual Dipolar Couplings and Residual Chemical Shift Anisotropies. A New qNMR Compliant Savitzky-Golay Apodization Function for Resolution Enhancement.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1