• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

通过投影后校准对液相色谱保留时间进行通用且准确的预测。

Generic and accurate prediction of retention times in liquid chromatography by post-projection calibration.

作者信息

Zhang Yan, Liu Fei, Li Xiu Qin, Gao Yan, Li Kang Cong, Zhang Qing He

机构信息

Key Laboratory of Groundwater Conservation of MWR, China University of Geosciences, Beijing, 100083, People's Republic of China.

Division of Chemical Metrology and Analytical Science, National Institute of Metrology, Beijing, 100029, People's Republic of China.

出版信息

Commun Chem. 2024 Mar 8;7(1):54. doi: 10.1038/s42004-024-01135-0.

DOI:10.1038/s42004-024-01135-0
PMID:38459241
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10923921/
Abstract

Retention time predictions from molecule structures in liquid chromatography (LC) are increasingly used in MS-based targeted and untargeted analyses, providing supplementary evidence for molecule annotation and reducing experimental measurements. Nevertheless, different LC setups (e.g., differences in gradient, column, and/or mobile phase) give rise to many prediction models that can only accurately predict retention times for a specific chromatographic method (CM). Here, a generic and accurate method is present to predict retention times across different CMs, by introducing the concept of post-projection calibration. This concept builds on the direct projections of retention times between different CMs and uses 35 external calibrants to eliminate the impact of LC setups on projection accuracy. Results showed that post-projection calibration consistently achieved a median projection error below 3.2% of the elution time. The ranking results of putative candidates reached similar levels among different CMs. This work opens up broad possibilities for coordinating retention times between different laboratories and developing extensive retention databases.

摘要

液相色谱(LC)中基于分子结构的保留时间预测在基于质谱的靶向和非靶向分析中越来越多地被使用,为分子注释提供补充证据并减少实验测量。然而,不同的LC设置(例如,梯度、色谱柱和/或流动相的差异)产生了许多预测模型,这些模型只能准确预测特定色谱方法(CM)的保留时间。在此,通过引入投影后校准的概念,提出了一种通用且准确的方法来预测不同CM之间的保留时间。这一概念基于不同CM之间保留时间的直接投影,并使用35种外部校准物来消除LC设置对投影准确性的影响。结果表明,投影后校准始终实现洗脱时间的中值投影误差低于3.2%。在不同的CM之间,推定候选物的排名结果达到了相似的水平。这项工作为协调不同实验室之间的保留时间以及开发广泛的保留数据库开辟了广阔的可能性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f6b1/10923921/5cff7285623a/42004_2024_1135_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f6b1/10923921/e54daf63a1f7/42004_2024_1135_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f6b1/10923921/932360577734/42004_2024_1135_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f6b1/10923921/210a04c73c7f/42004_2024_1135_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f6b1/10923921/61c713be2591/42004_2024_1135_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f6b1/10923921/621493df9110/42004_2024_1135_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f6b1/10923921/3a52c3b351e2/42004_2024_1135_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f6b1/10923921/706b91ab01d9/42004_2024_1135_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f6b1/10923921/5cff7285623a/42004_2024_1135_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f6b1/10923921/e54daf63a1f7/42004_2024_1135_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f6b1/10923921/932360577734/42004_2024_1135_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f6b1/10923921/210a04c73c7f/42004_2024_1135_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f6b1/10923921/61c713be2591/42004_2024_1135_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f6b1/10923921/621493df9110/42004_2024_1135_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f6b1/10923921/3a52c3b351e2/42004_2024_1135_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f6b1/10923921/706b91ab01d9/42004_2024_1135_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f6b1/10923921/5cff7285623a/42004_2024_1135_Fig8_HTML.jpg

相似文献

1
Generic and accurate prediction of retention times in liquid chromatography by post-projection calibration.通过投影后校准对液相色谱保留时间进行通用且准确的预测。
Commun Chem. 2024 Mar 8;7(1):54. doi: 10.1038/s42004-024-01135-0.
2
Generalized Calibration Across Liquid Chromatography Setups for Generic Prediction of Small-Molecule Retention Times.通用液相色谱条件下的小分子保留时间通用预测的广义校准。
Anal Chem. 2020 May 5;92(9):6571-6578. doi: 10.1021/acs.analchem.0c00233. Epub 2020 Apr 17.
3
Perspective on the Future Approaches to Predict Retention in Liquid Chromatography.展望液相色谱保留预测的未来方法。
Anal Chem. 2021 Apr 13;93(14):5653-5664. doi: 10.1021/acs.analchem.0c05078. Epub 2021 Apr 2.
4
Retention projection enables accurate calculation of liquid chromatographic retention times across labs and methods.保留预测能够跨实验室和方法准确计算液相色谱保留时间。
J Chromatogr A. 2015 Sep 18;1412:43-51. doi: 10.1016/j.chroma.2015.07.108. Epub 2015 Aug 3.
5
Accurate prediction of retention in hydrophilic interaction chromatography by back calculation of high pressure liquid chromatography gradient profiles.通过高压液相色谱梯度曲线的反向计算准确预测亲水作用色谱中的保留情况。
J Chromatogr A. 2017 Oct 20;1520:75-82. doi: 10.1016/j.chroma.2017.08.050. Epub 2017 Aug 26.
6
Machine learning to predict retention time of small molecules in nano-HPLC.基于机器学习的小分子在纳升高效液相色谱中的保留时间预测。
Anal Bioanal Chem. 2020 Nov;412(28):7767-7776. doi: 10.1007/s00216-020-02905-0. Epub 2020 Aug 29.
7
A general strategy for performing temperature-programming in high performance liquid chromatography--prediction of segmented temperature gradients.一种在高效液相色谱中进行程序升温的通用策略——分段温度梯度的预测。
J Chromatogr A. 2011 Sep 28;1218(39):6898-906. doi: 10.1016/j.chroma.2011.08.022. Epub 2011 Aug 16.
8
Probabilistic metabolite annotation using retention time prediction and meta-learned projections.利用保留时间预测和元学习投影进行概率性代谢物注释。
J Cheminform. 2022 Jun 7;14(1):33. doi: 10.1186/s13321-022-00613-8.
9
Chromatography色谱法
10
Chromatographic models to predict the elution of ionizable analytes by organic modifier gradient in reversed phase liquid chromatography.用于预测反相液相色谱中有机改性剂梯度洗脱离解分析物的色谱模型。
J Chromatogr A. 2012 Jul 20;1247:71-80. doi: 10.1016/j.chroma.2012.05.070. Epub 2012 May 28.

引用本文的文献

1
Do experimental projection methods outcompete retention time prediction models in non-target screening? A case study on LC/HRMS interlaboratory comparison data.在非目标筛查中,实验投影方法是否比保留时间预测模型更具优势?基于液相色谱/高分辨质谱实验室间比对数据的案例研究。
Analyst. 2025 Jul 16. doi: 10.1039/d5an00323g.
2
Strategy to improve the confidence level of qualitative screening by high resolution mass spectrometry: A case study of mycotoxins in maize.提高高分辨率质谱法定性筛选置信水平的策略:以玉米中的霉菌毒素为例
Food Chem X. 2025 Apr 21;27:102467. doi: 10.1016/j.fochx.2025.102467. eCollection 2025 Apr.
3
Retention time dataset for heterogeneous molecules in reversed-phase liquid chromatography.

本文引用的文献

1
Retention time prediction for chromatographic enantioseparation by quantile geometry-enhanced graph neural network.运用分位数几何增强图神经网络预测色谱对映体拆分的保留时间。
Nat Commun. 2023 May 29;14(1):3095. doi: 10.1038/s41467-023-38853-3.
2
Cluster-based analysis of COVID-19 cases using self-organizing map neural network and K-means methods to improve medical decision-making.使用自组织映射神经网络和K均值方法对新冠肺炎病例进行基于聚类的分析,以改善医疗决策。
Inform Med Unlocked. 2022;32:101005. doi: 10.1016/j.imu.2022.101005. Epub 2022 Jul 5.
3
Probabilistic metabolite annotation using retention time prediction and meta-learned projections.
反相液相色谱中异质分子的保留时间数据集
Sci Data. 2024 Aug 29;11(1):946. doi: 10.1038/s41597-024-03780-5.
4
Critical review on in silico methods for structural annotation of chemicals detected with LC/HRMS non-targeted screening.关于液相色谱/高分辨质谱非靶向筛查检测到的化学物质结构注释的计算机模拟方法的批判性综述。
Anal Bioanal Chem. 2025 Jan;417(3):473-493. doi: 10.1007/s00216-024-05471-x. Epub 2024 Aug 14.
利用保留时间预测和元学习投影进行概率性代谢物注释。
J Cheminform. 2022 Jun 7;14(1):33. doi: 10.1186/s13321-022-00613-8.
4
Equivalent Carbon Number and Interclass Retention Time Conversion Enhance Lipid Identification in Untargeted Clinical Lipidomics.等效碳数和类间保留时间转换可增强非靶向临床脂质组学中的脂质鉴定。
Anal Chem. 2022 Mar 1;94(8):3476-3484. doi: 10.1021/acs.analchem.1c03770. Epub 2022 Feb 14.
5
MultiConditionRT: Predicting liquid chromatography retention time for emerging contaminants for a wide range of eluent compositions and stationary phases.MultiConditionRT:预测多种洗脱液组成和固定相条件下新兴污染物的液相色谱保留时间。
J Chromatogr A. 2022 Mar 15;1666:462867. doi: 10.1016/j.chroma.2022.462867. Epub 2022 Jan 31.
6
High-Performance Data Processing Workflow Incorporating Effect-Directed Analysis for Feature Prioritization in Suspect and Nontarget Screening.高性能数据处理工作流程,纳入效应导向分析,用于可疑物和非目标筛查中的特征优先级排序。
Environ Sci Technol. 2022 Feb 1;56(3):1639-1651. doi: 10.1021/acs.est.1c04168. Epub 2022 Jan 20.
7
TrendProbe: Time profile analysis of emerging contaminants by LC-HRMS non-target screening and deep learning convolutional neural network.趋势探针:通过 LC-HRMS 非靶向筛查和深度学习卷积神经网络分析新兴污染物的时间分布。
J Hazard Mater. 2022 Apr 15;428:128194. doi: 10.1016/j.jhazmat.2021.128194. Epub 2022 Jan 4.
8
Development and Application of Liquid Chromatographic Retention Time Indices in HRMS-Based Suspect and Nontarget Screening.基于高分辨质谱的候选物和非靶向筛查中液相色谱保留时间指数的开发与应用。
Anal Chem. 2021 Aug 24;93(33):11601-11611. doi: 10.1021/acs.analchem.1c02348. Epub 2021 Aug 12.
9
Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry.使用神经网络进行保留时间预测可增加交联质谱法中的鉴定数量。
Nat Commun. 2021 May 28;12(1):3237. doi: 10.1038/s41467-021-23441-0.
10
Ultra-high-performance liquid chromatography high-resolution mass spectrometry variants for metabolomics research.超高效液相色谱-高分辨质谱联用技术在代谢组学研究中的应用。
Nat Methods. 2021 Jul;18(7):733-746. doi: 10.1038/s41592-021-01116-4. Epub 2021 May 10.