A high-throughput approach reveals distinct peptide charging behaviors in electrospray ionization mass spectrometry.
A high-throughput approach reveals distinct peptide charging behaviors in electrospray ionization mass spectrometry.
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高通量方法揭示了电喷雾电离质谱中独特的肽充电行为。
DOI：
10.1101/2023.03.31.535171
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Regev,Oded
中科院分区：
文献类型：
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作者：
Xu,AllynM;Tang,LaurenC;Jovanovic,Marko;Regev,Oded
电喷雾离子化是质谱分析中用于分析物的强大且普遍的技术。分析物接收的电荷分布（电荷状态分布，CSD）是解释质谱的重要考虑因素。然而，由于对电离机制的不完全理解，影响CSD的分析物性质尚未完全理解。在这里，我们采用了一种基于机器学习的高通量方法，并分析了数十万种肽的CSD。有趣的是，一半的肽显示出与人们天真地期望的不同的电荷（碱性位点的数量）。我们发现，这些肽可以分为两个制度充电不足和过度充电，这两个制度显示出显着不同的充电特性。引人注目的是，在过度充电制度的肽显示最小的依赖于基本站点计数，更一般地说，这两个制度表现出不同的序列决定因素。这些发现强调了肽的丰富电离行为以及CSC增强肽识别的潜力。
Electrospray ionization is a powerful and prevalent technique used to ionize analytes in mass spectrometry. The distribution of charges that an analyte receives (charge state distribution, CSD) is an important consideration for interpreting mass spectra. However, due to an incomplete understanding of the ionization mechanism, the analyte properties that influence CSDs are not fully understood. Here, we employ a machine learning-based high-throughput approach and analyze CSDs of hundreds of thousands of peptides. Interestingly, half of the peptides exhibit charges that differ from what one would naively expect (number of basic sites). We find that these peptides can be classified into two regimes—undercharging and overcharging—and that these two regimes display markedly different charging characteristics. Strikingly, peptides in the overcharging regime show minimal dependence on basic site count, and more generally, the two regimes exhibit distinct sequence determinants. These findings highlight the rich ionization behavior of peptides and the potential of CSDs for enhancing peptide identification.