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Interpreting the Charge State Assignment in Electrospray Mass Spectra of Bioparticles

机译:解释生物粒子电喷雾质谱中的电荷状态分配

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In electrospray ionization mass spectra of heterogeneous protein complexes and other bioparticles, accurate mass determination is often hampered by the inaccuracy in determination of the charge states for individual signals. Here, we describe an algorithm that automatically minimizes the standard deviation in a series of related ion peaks with varying numbers of charges. The algorithm assumes that the mass is invariant and allows the determination of the correct charge state in a peak series. The analysis results in a periodic pattern, which can be interpreted as a harmonic oscillator, when the minimum standard deviation of a charge state series is found. We observed that a mass resolution of much less than 1000 in the acquired mass spectra is sufficient to achieve a correct charge state assignment. Moreover, the boundaries of mixed species can be identified by examining the loss of periodicity in the pattern of the analysis. We tested our algorithm successfully on novel spectra and on spectra reported in the literature with sample masses up to several million Dalton, e.g., viral particles, polyethylene glycol polymers, and polystyrene nanoparticles.
机译:在异质蛋白质复合物和其他生物颗粒的电喷雾电离质谱图中,准确的质量测定通常因确定单个信号的电荷状态不准确而受阻。在这里,我们描述了一种算法,该算法可自动在一系列带有变化电荷数的相关离子峰中最小化标准偏差。该算法假定质量是不变的,并允许确定峰序列中正确的电荷状态。当找到充电状态序列的最小标准偏差时,分析会得出一个周期性模式,该模式可以解释为谐波振荡器。我们观察到,所获取质谱图中的质量分辨率远远小于1000,足以实现正确的电荷状态分配。此外,可以通过检查分析模式中的周期性损失来确定混合物种的边界。我们在新颖的光谱和文献报道的光谱中成功地测试了我们的算法,样品质量高达几百万道尔顿,例如病毒颗粒,聚乙二醇聚合物和聚苯乙烯纳米颗粒。

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