Department of Genome Sciences, University of Washington, Seattle, Washington 98195 United States.
Science for Life Laboratory, KTH - Royal Institute of Technology, Box 1031, 17121 Solna, Sweden.
J Proteome Res. 2022 Apr 1;21(4):891-898. doi: 10.1021/acs.jproteome.1c00894. Epub 2022 Feb 27.
Bottom-up proteomics provides peptide measurements and has been invaluable for moving proteomics into large-scale analyses. Commonly, a single quantitative value is reported for each protein-coding gene by aggregating peptide quantities into protein groups following protein inference or parsimony. However, given the complexity of both RNA splicing and post-translational protein modification, it is overly simplistic to assume that all peptides that map to a singular protein-coding gene will demonstrate the same quantitative response. By assuming that all peptides from a protein-coding sequence are representative of the same protein, we may miss the discovery of important biological differences. To capture the contributions of existing proteoforms, we need to reconsider the practice of aggregating protein values to a single quantity per protein-coding gene.
自下而上的蛋白质组学提供了肽测量方法,对于将蛋白质组学推向大规模分析是非常宝贵的。通常,通过在蛋白质推断或简约后将肽定量聚集成蛋白质组,为每个蛋白质编码基因报告一个单一的定量值。然而,鉴于 RNA 剪接和翻译后蛋白质修饰的复杂性,假设映射到单个蛋白质编码基因的所有肽都将表现出相同的定量响应是过于简单化的。通过假设蛋白质编码序列的所有肽都代表相同的蛋白质,我们可能会错过发现重要生物学差异的机会。为了捕获现有蛋白质变体的贡献,我们需要重新考虑将蛋白质值聚合到每个蛋白质编码基因的单个数量的做法。