Department of Physics, National Dong Hwa University, Shoufeng, Hualien, Taiwan 97401, ROC.
Anal Chem. 2011 Mar 15;83(6):1960-8. doi: 10.1021/ac102676z. Epub 2011 Feb 25.
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.
在异质蛋白质复合物和其他生物粒子的电喷雾电离质谱中，由于单个信号的电荷状态测定不准确，常常会妨碍准确质量的测定。这里，我们描述了一种算法，它可以自动最小化一系列具有不同电荷数的相关离子峰的标准偏差。该算法假设质量是不变的，并允许在峰系列中确定正确的电荷状态。当找到电荷状态系列的最小标准偏差时，分析结果会呈现周期性模式，可以将其解释为谐振子。我们观察到，在获得的质谱中，质量分辨率远远小于 1000 即可实现正确的电荷状态分配。此外，通过检查分析模式中周期性的丧失，可以识别混合物种的边界。我们成功地在新型光谱以及文献中报道的样品质量高达数百万道尔顿的光谱（例如病毒颗粒、聚乙二醇聚合物和聚苯乙烯纳米粒子）上测试了我们的算法。
