Department of Chemistry and Biochemistry, University of Arizona, Tucson, Arizona 85721.
Anal Chem. 2020 Mar 17;92(6):4395-4401. doi: 10.1021/acs.analchem.9b05272. Epub 2020 Feb 26.
The growing use of intact protein mass analysis, top-down proteomics, and native mass spectrometry have created a need for improved data analysis pipelines for deconvolution of electrospray (ESI) mass spectra containing multiple charge states and potentially without isotopic resolution. Although there are multiple deconvolution algorithms, there is no consensus for how to judge the quality of the deconvolution, and many scoring schemes are not published. Here, an intuitive universal score (UniScore) for ESI deconvolution is presented. The UniScore is the weighted average of deconvolution scores (DScores) for each peak multiplied by the of the fit to the data. Each DScore is composed of separate components to score (1) the uniqueness and fit of the deconvolution to the data, (2) the consistency of the peak shape across different charge states, (3) the smoothness of the charge state distribution, and (4) symmetry and separation of the peak. Example scores are provided for a range of experimental and simulated data. By providing a means of judging the quality of the overall deconvolution as well as individual mass peaks, the UniScore scheme provides a foundation for standardizing ESI data analysis of larger molecules and enabling the use of ESI deconvolution in automated data analysis pipelines.
完整蛋白质量分析、自上而下蛋白质组学和天然质谱的应用日益广泛,因此需要改进数据分析流程,以解析包含多个电荷态且可能没有同位素分辨率的电喷雾(ESI)质谱。尽管有多种解卷积算法,但对于如何判断解卷积质量尚无共识,而且许多评分方案并未公布。这里提出了一种直观的 ESI 解卷积通用评分(UniScore)。UniScore 是每个峰的解卷积评分(DScore)与数据拟合度的乘积的加权平均值。每个 DScore 由单独的分量组成,用于(1)对解卷积与数据的独特性和拟合度进行评分,(2)对不同电荷态下的峰形一致性进行评分,(3)对电荷态分布的平滑度进行评分,以及(4)对峰的对称性和分离度进行评分。为一系列实验和模拟数据提供了示例评分。通过提供一种判断整体解卷积质量以及单个质量峰质量的方法,UniScore 方案为标准化较大分子的 ESI 数据分析以及在自动化数据分析管道中使用 ESI 解卷积奠定了基础。