Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, United States.
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, United States.
J Proteome Res. 2021 Jan 1;20(1):498-505. doi: 10.1021/acs.jproteome.0c00544. Epub 2020 Dec 17.
Deisotoping, or the process of removing peaks in a mass spectrum resulting from the incorporation of naturally occurring heavy isotopes, has long been used to reduce complexity and improve the effectiveness of spectral annotation methods in proteomics. We have previously described MSFragger, an ultrafast search engine for proteomics, that did not utilize deisotoping in processing input spectra. Here, we present a new, high-speed parallelized deisotoping algorithm, based on elements of several existing methods, that we have incorporated into the MSFragger search engine. Applying deisotoping with MSFragger reveals substantial improvements to database search speed and performance, particularly for complex methods like open or nonspecific searches. Finally, we evaluate our deisotoping method on data from several instrument types and vendors, revealing a wide range in performance and offering an updated perspective on deisotoping in the modern proteomics environment.
去同位素峰处理,即在质谱中消除由于天然存在的重同位素而导致的峰,长期以来一直被用于降低蛋白质组学中谱图注释方法的复杂性并提高其效率。我们之前描述过 MSFragger,这是一种用于蛋白质组学的超快搜索引擎,它在处理输入谱图时没有利用去同位素峰处理。在这里,我们提出了一种新的高速并行去同位素峰处理算法,该算法基于几种现有方法的元素,并将其纳入 MSFragger 搜索引擎中。在 MSFragger 中应用去同位素峰处理可显著提高数据库搜索速度和性能,特别是对于开放或非特异性搜索等复杂方法。最后,我们在来自几种仪器类型和供应商的数据上评估了我们的去同位素峰处理方法,揭示了性能的广泛差异,并提供了现代蛋白质组学环境中去同位素峰处理的更新视角。