Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen, Denmark.
Center for Experimental Bioinformatics, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, DK-5230 Odense, Denmark.
J Proteome Res. 2021 Apr 2;20(4):2042-2055. doi: 10.1021/acs.jproteome.0c00892. Epub 2021 Feb 4.
Small ubiquitin-like modifiers (SUMO) and ubiquitin are frequent post-translational modifications of proteins that play pivotal roles in all cellular processes. We previously reported mass spectrometry-based proteomics methods that enable profiling of lysines modified by endogenous SUMO or ubiquitin in an unbiased manner, without the need for genetic engineering. Here we investigated the applicability of precursor mass filtering enabled by MaxQuant.Live to our SUMO and ubiquitin proteomics workflows, which efficiently avoided sequencing of precursors too small to be modified but otherwise indistinguishable by mass-to-charge ratio. Using precursor mass filtering, we achieved a much higher selectivity of modified peptides, ultimately resulting in up to 30% more SUMO and ubiquitin sites identified from replicate samples. Real-time exclusion of unmodified peptides by MQL resulted in 90% SUMO-modified precursor selectivity from a 25% pure sample, demonstrating great applicability for digging deeper into ubiquitin-like modificomes. We adapted the precursor mass filtering strategy to the new Exploris 480 mass spectrometer, achieving comparable gains in SUMO precursor selectivity and identification rates. Collectively, precursor mass filtering via MQL significantly increased identification rates of SUMO- and ubiquitin-modified peptides from the exact same samples, without the requirement for prior knowledge or spectral libraries.
小泛素样修饰物（SUMO）和泛素是蛋白质的常见翻译后修饰物，在所有细胞过程中发挥关键作用。我们之前报道了基于质谱的蛋白质组学方法，这些方法能够以非偏见的方式对内源性 SUMO 或泛素修饰的赖氨酸进行分析，而无需进行基因工程。在这里，我们研究了 MaxQuant.Live 提供的前体质量过滤在我们的 SUMO 和泛素蛋白质组学工作流程中的适用性，该方法有效地避免了测序那些太小而无法被修饰但在质荷比上无法区分的前体。使用前体质量过滤，我们实现了更高的修饰肽选择性，最终从重复样本中鉴定出多达 30%的 SUMO 和泛素位点。通过 MQL 实时排除未修饰的肽，从 25%纯度的样品中获得了 90%的 SUMO 修饰前体的选择性，这表明其非常适用于深入挖掘泛素样修饰组。我们将前体质量过滤策略适应于新型的 Exploris 480 质谱仪，在 SUMO 前体选择性和鉴定率方面取得了相当的提高。总的来说，通过 MQL 进行前体质量过滤，从完全相同的样品中大大提高了 SUMO 和泛素修饰肽的鉴定率，而无需事先了解或使用光谱库。
