• 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

利用光谱内插增强 MS/MS 文库以提高鉴定能力。

Augmentation of MS/MS Libraries with Spectral Interpolation for Improved Identification.

机构信息

Computing and Analytics Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States.

Signature Science and Technology Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States.

出版信息

J Chem Inf Model. 2022 Aug 22;62(16):3724-3733. doi: 10.1021/acs.jcim.2c00620. Epub 2022 Jul 29.

DOI:10.1021/acs.jcim.2c00620
PMID:35905451
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9400100/
Abstract

Tandem mass spectrometry (MS/MS) is a primary tool for the identification of small molecules and metabolites where resultant spectra are most commonly identified by matching them with spectra in MS/MS reference libraries. The high degree of variability in MS/MS spectrum acquisition techniques and parameters creates a significant challenge for building standardized reference libraries. Here we present a method to improve the usefulness of existing MS/MS libraries by augmenting available experimental spectra data sets with statistically interpolated spectra at unreported collision energies. We find that highly accurate spectral approximations can be interpolated from as few as three experimental spectra and that the interpolated spectra will be consistent with true spectra gathered from the same instrument as the experimental spectra. Supplementing existing spectral databases with interpolated spectra yields consistent improvements to identification accuracy on a range of instruments and precursor types. Applying this method yields significant improvements (∼10% more spectra correctly identified) on large data sets (2000-10 000 spectra), indicating this is a quick yet adept tool for improving spectral matching in situations where available reference libraries are not yet sufficient. We also find improvements of matching spectra across instrument types (between an Agilent Q-TOF and an Orbitrap Elite), at high collision energies (50-90 eV), and with smaller data sets available through MassBank.

摘要

串联质谱(MS/MS)是一种用于鉴定小分子和代谢物的主要工具,其产生的谱图通常通过与 MS/MS 参考库中的谱图相匹配来识别。MS/MS 谱图采集技术和参数的高度可变性给构建标准化参考库带来了重大挑战。在这里,我们提出了一种方法,通过在未报告的碰撞能下用统计插值谱图来扩充现有 MS/MS 库中的实验谱图数据集,从而提高现有 MS/MS 库的实用性。我们发现,只需三个实验谱图就可以对高度精确的光谱进行插值,并且插值光谱将与从与实验谱图相同仪器收集的真实光谱一致。在现有的光谱数据库中补充插值光谱,可以在一系列仪器和前体类型上提高鉴定准确性。在大数据集(2000-10000 个谱图)上应用该方法可以显著提高鉴定准确性(正确识别的谱图数量增加约 10%),这表明在现有参考库还不够充分的情况下,这是一种快速而有效的改进光谱匹配的工具。我们还发现,该方法可以提高跨仪器类型(安捷伦 Q-TOF 和 Orbitrap Elite 之间)、高碰撞能(50-90 eV)和通过 MassBank 获得的较小数据集的匹配谱图的准确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/446c/9400100/17797f30d367/ci2c00620_0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/446c/9400100/35dd5d5e8899/ci2c00620_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/446c/9400100/2c34f1b436ab/ci2c00620_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/446c/9400100/ee465bc91599/ci2c00620_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/446c/9400100/3d1c0644256f/ci2c00620_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/446c/9400100/70f961b9269f/ci2c00620_0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/446c/9400100/17797f30d367/ci2c00620_0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/446c/9400100/35dd5d5e8899/ci2c00620_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/446c/9400100/2c34f1b436ab/ci2c00620_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/446c/9400100/ee465bc91599/ci2c00620_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/446c/9400100/3d1c0644256f/ci2c00620_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/446c/9400100/70f961b9269f/ci2c00620_0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/446c/9400100/17797f30d367/ci2c00620_0006.jpg

相似文献

1
Augmentation of MS/MS Libraries with Spectral Interpolation for Improved Identification.利用光谱内插增强 MS/MS 文库以提高鉴定能力。
J Chem Inf Model. 2022 Aug 22;62(16):3724-3733. doi: 10.1021/acs.jcim.2c00620. Epub 2022 Jul 29.
2
Metabolomic spectral libraries for data-independent SWATH liquid chromatography mass spectrometry acquisition.用于数据非依赖型SWATH液相色谱质谱采集的代谢组学光谱库。
Anal Bioanal Chem. 2018 Mar;410(7):1873-1884. doi: 10.1007/s00216-018-0860-x. Epub 2018 Feb 6.
3
How Well Can We Predict Mass Spectra from Structures? Benchmarking Competitive Fragmentation Modeling for Metabolite Identification on Untrained Tandem Mass Spectra.从结构上预测质谱的能力如何?在未经训练的串联质谱上对代谢物鉴定进行竞争碎片建模的基准测试。
J Chem Inf Model. 2022 Sep 12;62(17):4049-4056. doi: 10.1021/acs.jcim.2c00936. Epub 2022 Aug 31.
4
Towards a full reference library of MS(n) spectra. II: A perspective from the library of pesticide spectra extracted from the literature/Internet.建立 MS(n) 谱全参考库。II:从文献/互联网中提取的农药谱库的角度来看。
Rapid Commun Mass Spectrom. 2011 Dec 30;25(24):3697-705. doi: 10.1002/rcm.5279.
5
Advancing the Prediction of MS/MS Spectra Using Machine Learning.利用机器学习推进串联质谱(MS/MS)谱图预测
J Am Soc Mass Spectrom. 2024 Oct 2;35(10):2256-2266. doi: 10.1021/jasms.4c00154. Epub 2024 Sep 11.
6
On the inter-instrument and the inter-laboratory transferability of a tandem mass spectral reference library. 3. Focus on ion trap and upfront CID.串联质谱参考文库的仪器间和实验室间可转移性。3. 聚焦于离子阱和 upfront CID。
J Mass Spectrom. 2012 Feb;47(2):263-70. doi: 10.1002/jms.2961.
7
MIDAS: a database-searching algorithm for metabolite identification in metabolomics.MIDAS:一种用于代谢组学中代谢物鉴定的数据库搜索算法。
Anal Chem. 2014 Oct 7;86(19):9496-503. doi: 10.1021/ac5014783. Epub 2014 Sep 11.
8
Evaluation of the sensitivity of the 'Wiley registry of tandem mass spectral data, MSforID' with MS/MS data of the 'NIST/NIH/EPA mass spectral library'.评价“威利串联质谱谱图库,MSforID”与“NIST/NIH/EPA 质谱库”的 MS/MS 数据的灵敏度。
J Mass Spectrom. 2013 Apr;48(4):487-96. doi: 10.1002/jms.3184.
9
Computational Expansion of High-Resolution-MS Spectral Libraries.高分辨率-MS 谱库的计算扩展。
Anal Chem. 2023 Nov 28;95(47):17284-17291. doi: 10.1021/acs.analchem.3c03343. Epub 2023 Nov 14.
10
msPurity: Automated Evaluation of Precursor Ion Purity for Mass Spectrometry-Based Fragmentation in Metabolomics.msPurity:基于质谱的代谢组学碎片化前体离子纯度的自动化评估。
Anal Chem. 2017 Feb 21;89(4):2432-2439. doi: 10.1021/acs.analchem.6b04358. Epub 2017 Feb 8.

引用本文的文献

1
Discovering organic reactions with a machine-learning-powered deciphering of tera-scale mass spectrometry data.通过机器学习驱动的太赫兹级质谱数据解析发现有机反应。
Nat Commun. 2025 Mar 16;16(1):2587. doi: 10.1038/s41467-025-56905-8.
2
Flash entropy search to query all mass spectral libraries in real time.实时查询所有质谱文库的 Flash 熵搜索。
Nat Methods. 2023 Oct;20(10):1475-1478. doi: 10.1038/s41592-023-02012-9. Epub 2023 Sep 21.

本文引用的文献

1
Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification.光谱熵在小分子化合物鉴定方面优于 MS/MS 点积相似度。
Nat Methods. 2021 Dec;18(12):1524-1531. doi: 10.1038/s41592-021-01331-z. Epub 2021 Dec 2.
2
MolDiscovery: learning mass spectrometry fragmentation of small molecules.MolDiscovery:小分子的质谱碎裂规律学习。
Nat Commun. 2021 Jun 17;12(1):3718. doi: 10.1038/s41467-021-23986-0.
3
Collision energies on QTof and Orbitrap instruments: How to make proteomics measurements comparable?
QTof 和 Orbitrap 仪器上的碰撞能:如何使蛋白质组学测量具有可比性?
J Mass Spectrom. 2021 Jan;56(1):e4693. doi: 10.1002/jms.4693. Epub 2020 Dec 5.
4
The key role of mass spectrometry in comprehensive research on new psychoactive substances.质谱在新精神活性物质综合研究中的关键作用。
J Mass Spectrom. 2021 Jul;56(7):e4673. doi: 10.1002/jms.4673. Epub 2020 Nov 5.
5
The current role of mass spectrometry in forensics and future prospects.质谱法在法医学中的当前作用和未来展望。
Anal Methods. 2020 Aug 28;12(32):3974-3997. doi: 10.1039/d0ay01113d. Epub 2020 Jul 28.
6
Machine Learning Applications for Mass Spectrometry-Based Metabolomics.基于质谱的代谢组学的机器学习应用
Metabolites. 2020 Jun 13;10(6):243. doi: 10.3390/metabo10060243.
7
Hitting the Jackpot - development of gas chromatography-mass spectrometry (GC-MS) and other rapid screening methods for the analysis of 18 fentanyl-derived synthetic opioids.中头彩——气相色谱-质谱联用(GC-MS)及其他用于分析18种芬太尼衍生合成阿片类药物的快速筛查方法的开发
Drug Test Anal. 2020 Jun;12(6):798-811. doi: 10.1002/dta.2771. Epub 2020 Feb 13.
8
DeepIso: A Deep Learning Model for Peptide Feature Detection from LC-MS map.DeepIso:一种从 LC-MS 图谱中检测肽特征的深度学习模型。
Sci Rep. 2019 Nov 20;9(1):17168. doi: 10.1038/s41598-019-52954-4.
9
Improving MetFrag with statistical learning of fragment annotations.利用片段注释的统计学习改进 MetFrag。
BMC Bioinformatics. 2019 Jul 5;20(1):376. doi: 10.1186/s12859-019-2954-7.
10
Rapid Prediction of Electron-Ionization Mass Spectrometry Using Neural Networks.使用神经网络对电子电离质谱进行快速预测。
ACS Cent Sci. 2019 Apr 24;5(4):700-708. doi: 10.1021/acscentsci.9b00085. Epub 2019 Mar 19.