Suppr超能文献

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.

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/9400100/35dd5d5e8899/ci2c00620_0001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验