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酸可裂解生物素-炔烃提高了生物正交非天然氨基酸标记分析中生物素标记肽直接检测的灵敏度。

Acid Cleavable Biotin-Alkyne Improves Sensitivity for Direct Detection of Biotin Labeled Peptides in Biorthogonal Noncanonical Amino Acid Tagging Analysis.

作者信息

McClatchy Daniel B, Garrett Patrick T, Jung Titus, Diedrich Jolene K, Yates John R

机构信息

Department of Molecular Medicine, The Scripps Research Institute, La Jolla, California 92037, United States.

出版信息

J Proteome Res. 2024 Dec 6;23(12):5487-5497. doi: 10.1021/acs.jproteome.4c00616. Epub 2024 Nov 23.

Abstract

Biorthogonal noncanonical amino acid tagging is a labeling strategy that covalently adds a biotin-alkyne (BA) to methionine analogs via a click reaction. When methionine analogs are incorporated into a proteome, enrichment of the BA-labeled proteins allows the detection of newly synthesized proteins (NSP) by mass spectrometry. We previously reported that our direct detection of biotin-containing tags strategy increased protein identifications by enriching for BA-peptides instead of BA-proteins. We compared the performance of cleavable BA (DADPS) and uncleavable BA in the identification and TMT quantification of the NSP. DADPS identified and quantified more than double the number of peptides than uncleavable BA. Interrogation of the data revealed that multiple factors are responsible for the superior performance of DADPS.

摘要

生物正交非天然氨基酸标记是一种通过点击反应将生物素-炔烃(BA)共价添加到甲硫氨酸类似物上的标记策略。当甲硫氨酸类似物掺入蛋白质组中时,对BA标记的蛋白质进行富集可通过质谱检测新合成的蛋白质(NSP)。我们之前报道过,我们的含生物素标签直接检测策略通过富集BA肽而非BA蛋白增加了蛋白质鉴定数量。我们比较了可裂解BA(DADPS)和不可裂解BA在NSP鉴定和TMT定量方面的性能。DADPS鉴定和定量的肽数量是不可裂解BA的两倍多。对数据的分析表明,多种因素导致了DADPS的卓越性能。

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本文引用的文献

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Prosit-TMT: Deep Learning Boosts Identification of TMT-Labeled Peptides.Prosit-TMT:深度学习助力 TMT 标记肽的鉴定。
Anal Chem. 2022 May 24;94(20):7181-7190. doi: 10.1021/acs.analchem.1c05435. Epub 2022 May 12.
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Detectability of Biotin Tags by LC-MS/MS.通过 LC-MS/MS 检测生物素标记物。
J Proteome Res. 2021 May 7;20(5):3002-3008. doi: 10.1021/acs.jproteome.0c01049. Epub 2021 Mar 29.
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Pyteomics 4.0: Five Years of Development of a Python Proteomics Framework.Pyteomics 4.0:五年 Python 蛋白质组学框架的发展。
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