A Universal Score for Deconvolution of Intact Protein and Native Electrospray Mass Spectra.,Analytical Chemistry

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A Universal Score for Deconvolution of Intact Protein and Native Electrospray Mass Spectra.
Analytical Chemistry ( IF 6.7 ) Pub Date : 2020-02-18 , DOI: 10.1021/acs.analchem.9b05272
Michael T Marty ₁

Affiliation

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 R2 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.

中文翻译：

完整蛋白和天然电喷雾质谱反卷积的通用评分。

完整蛋白质质量分析，自上而下的蛋白质组学和天然质谱的使用日益增长，因此需要改进的数据分析管道，以对包含多个电荷状态且可能没有同位素拆分的电喷雾（ESI）质谱进行反卷积。尽管有多种反卷积算法，但如何判断反卷积的质量尚无共识，许多计分方案也未发表。这里，展示了用于ESI反卷积的直观通用分数（UniScore）。UniScore是每个峰的反卷积分数（DScores）的加权平均值乘以拟合数据的R2。每个DScore由单独的组件组成，以对（1）反卷积与数据的唯一性和拟合度进行评分，（2）不同电荷状态下峰形的一致性，（3）电荷状态分布的平滑度，以及（4）峰的对称和分离。提供了一系列实验和模拟数据的示例分数。通过提供一种判断总体反卷积以及各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数据分析管道中使用ESI反卷积提供了基础。

更新日期：2020-02-26

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