Suppr超能文献

利用重水代谢标记进行蛋白质周转研究的保留时间对齐。

Retention Time Alignment for Protein Turnover Studies Using Heavy Water Metabolic Labeling.

机构信息

Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, 301 University Boulevard, Galveston, Texas77555, United States.

出版信息

J Proteome Res. 2023 Feb 3;22(2):410-419. doi: 10.1021/acs.jproteome.2c00592. Epub 2023 Jan 24.

Abstract

Retention time (RT) alignment has been important for robust protein identification and quantification in proteomics. In data-dependent acquisition mode, whereby the precursor ions are semistochastically chosen for fragmentation in MS/MS, the alignment is used in an approach termed matched between runs (MBR). MBR transfers peptides, which were fragmented and identified in one experiment, to a replicate experiment where they were not identified. Before the MBR transfer, the RTs of experiments are aligned to reduce the chance of erroneous transfers. Despite its widespread use in other areas of quantitative proteomics, RT alignment has not been applied in data analyses for protein turnover using an atom-based stable isotope-labeling agent such as metabolic labeling with deuterium oxide, DO. Deuterium incorporation changes isotope profiles of intact peptides in full scans and their fragment ions in tandem mass spectra. It reduces the peptide identification rates in current database search engines. Therefore, the MBR becomes more important. Here, we report on an approach to incorporate RT alignment with peptide quantification in studies of proteome turnover using heavy water metabolic labeling and LC-MS. The RT alignment uses correlation-optimized time warping. The alignment, followed by the MBR, improves labeling time point coverage, especially for long labeling durations.

摘要

保留时间(RT)对齐对于蛋白质组学中稳健的蛋白质鉴定和定量非常重要。在数据依赖型采集模式下,前体离子以半随机方式选择进行 MS/MS 碎片化,对齐用于一种称为运行间匹配(MBR)的方法。MBR 将在一个实验中碎片化和鉴定的肽转移到未鉴定的重复实验中。在进行 MBR 转移之前,需要对齐实验的 RT 以减少错误转移的机会。尽管 RT 对齐在其他定量蛋白质组学领域得到了广泛应用,但在使用基于原子的稳定同位素标记剂(如氘氧化水代谢标记)进行蛋白质周转的数据分析中尚未应用。氘掺入会改变完整肽在全扫描中的同位素分布及其串联质谱中的片段离子。它降低了当前数据库搜索引擎中的肽鉴定率。因此,MBR 变得更加重要。在这里,我们报告了一种在使用重水代谢标记和 LC-MS 进行蛋白质组周转研究中整合 RT 对齐和肽定量的方法。RT 对齐使用相关优化时间扭曲。对齐后进行 MBR,可以提高标记时间点的覆盖度,特别是对于较长的标记持续时间。

相似文献

1
Retention Time Alignment for Protein Turnover Studies Using Heavy Water Metabolic Labeling.
J Proteome Res. 2023 Feb 3;22(2):410-419. doi: 10.1021/acs.jproteome.2c00592. Epub 2023 Jan 24.
2
Using Heavy Mass Isotopomers for Protein Turnover in Heavy Water Metabolic Labeling.
J Proteome Res. 2021 Apr 2;20(4):2035-2041. doi: 10.1021/acs.jproteome.0c00873. Epub 2021 Mar 4.
5
Timepoint Selection Strategy for In Vivo Proteome Dynamics from Heavy Water Metabolic Labeling and LC-MS.
J Proteome Res. 2020 May 1;19(5):2105-2112. doi: 10.1021/acs.jproteome.0c00023. Epub 2020 Apr 2.
6
Protein turnover models for LC-MS data of heavy water metabolic labeling.
Brief Bioinform. 2022 Mar 10;23(2). doi: 10.1093/bib/bbab598.
7
Dynamic Proteomics: In Vivo Proteome-Wide Measurement of Protein Kinetics Using Metabolic Labeling.
Methods Enzymol. 2015;561:219-76. doi: 10.1016/bs.mie.2015.05.018. Epub 2015 Jul 17.
9
Metabolic deuterium oxide (DO) labeling in quantitative omics studies: A tutorial review.
Anal Chim Acta. 2023 Feb 15;1242:340722. doi: 10.1016/j.aca.2022.340722. Epub 2022 Dec 14.

引用本文的文献

1
Flexible Quality Control for Protein Turnover Rates Using d2ome.
Int J Mol Sci. 2023 Oct 25;24(21):15553. doi: 10.3390/ijms242115553.

本文引用的文献

2
Conditional Fragment Ion Probabilities Improve Database Searching for Nonmonoisotopic Precursors.
J Proteome Res. 2023 Feb 3;22(2):334-342. doi: 10.1021/acs.jproteome.2c00247. Epub 2022 Nov 22.
3
Reversed-Phase Liquid Chromatography of Peptides for Bottom-Up Proteomics: A Tutorial.
J Proteome Res. 2022 Dec 2;21(12):2846-2892. doi: 10.1021/acs.jproteome.2c00407. Epub 2022 Nov 10.
4
Harmonizing Labeling and Analytical Strategies to Obtain Protein Turnover Rates in Intact Adult Animals.
Mol Cell Proteomics. 2022 Jul;21(7):100252. doi: 10.1016/j.mcpro.2022.100252. Epub 2022 May 28.
5
An atlas of protein turnover rates in mouse tissues.
Nat Commun. 2021 Nov 26;12(1):6778. doi: 10.1038/s41467-021-26842-3.
7
IonQuant Enables Accurate and Sensitive Label-Free Quantification With FDR-Controlled Match-Between-Runs.
Mol Cell Proteomics. 2021;20:100077. doi: 10.1016/j.mcpro.2021.100077. Epub 2021 Apr 2.
8
Timepoint Selection Strategy for In Vivo Proteome Dynamics from Heavy Water Metabolic Labeling and LC-MS.
J Proteome Res. 2020 May 1;19(5):2105-2112. doi: 10.1021/acs.jproteome.0c00023. Epub 2020 Apr 2.
9
Proteome Dynamics from Heavy Water Metabolic Labeling and Peptide Tandem Mass Spectrometry.
Int J Mass Spectrom. 2019 Nov;445. doi: 10.1016/j.ijms.2019.116194. Epub 2019 Jul 27.
10
Evaluating False Transfer Rates from the Match-between-Runs Algorithm with a Two-Proteome Model.
J Proteome Res. 2019 Nov 1;18(11):4020-4026. doi: 10.1021/acs.jproteome.9b00492. Epub 2019 Oct 2.

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