Mass spectrometry-based proteomics technologies are prime methods for the high-throughput identification of proteins in complex biological samples. Nevertheless, there are still technical limitations that hinder the ability of mass spectrometry to identify low abundance proteins in complex samples. Characterizing such proteins is essential to provide a comprehensive understanding of the biological processes taking place in cells and tissues. Still today, most mass spectrometry-based proteomics approaches use a data-dependent acquisition strategy, which favors the collection of mass spectra from proteins of higher abundance. Since the computational identification of proteins from proteomics data is typically performed after mass spectrometry analysis, large numbers of mass spectra are typically redundantly acquired from the same abundant proteins, and little to no mass spectra are acquired for proteins of lower abundance. We therefore propose a novel supervised learning algorithm, MealTime-MS, that identifies proteins in real-time as mass spectrometry data are acquired and prevents further data collection from confidently identified proteins to ultimately free mass spectrometry resources to improve the identification sensitivity of low abundance proteins. We use real-time simulations of a previously performed mass spectrometry analysis of a HEK293 cell lysate to show that our approach can identify 92.1% of the proteins detected in the experiment using 66.2% of the MS2 spectra. We also demonstrate that our approach outperforms a previously proposed method, is sufficiently fast for real-time mass spectrometry analysis, and is flexible. Finally, MealTime-MS' efficient usage of mass spectrometry resources will provide a more comprehensive characterization of proteomes in complex samples.

中文翻译：

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MealTime-MS：机器学习指导的实时质谱分析，用于蛋白质鉴定和有效的动态排除。

基于质谱的蛋白质组学技术是高通量鉴定复杂生物样品中蛋白质的主要方法。尽管如此，仍然存在技术上的限制，阻碍了质谱法鉴定复杂样品中低丰度蛋白质的能力。表征此类蛋白质对于全面了解细胞和组织中发生的生物学过程至关重要。时至今日，大多数基于质谱的蛋白质组学方法仍使用依赖数据的采集策略，这有利于从丰度更高的蛋白质中收集质谱。由于通常是在质谱分析之后根据蛋白质组学数据对蛋白质进行计算鉴定，因此通常会从相同的丰富蛋白质中多余地获取大量质谱，丰度较低的蛋白质几乎没有质谱。因此，我们提出了一种新颖的监督学习算法MealTime-MS，该算法可在获取质谱数据时实时识别蛋白质，并防止从自信地识别的蛋白质中进一步收集数据，从而最终释放质谱资源，从而提高了低丰度蛋白质的识别灵敏度。我们使用先前对HEK293细胞裂解物进行质谱分析的实时模拟结果显示，我们的方法可以使用66.2％的MS2光谱识别实验中检测到的92.1％的蛋白质。我们还证明了我们的方法优于先前提出的方法，对于实时质谱分析足够快，并且具有灵活性。最后，MealTime-MS'