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Systematic detection of functional proteoform groups from bottom-up proteomic datasets
bioRxiv - Systems Biology Pub Date : 2020-12-22 , DOI: 10.1101/2020.12.22.423928
Isabell Bludau , Max Frank , Christian Dörig , Yujia Cai , Moritz Heusel , George Rosenberger , Paola Picotti , Ben C. Collins , Hannes Röst , Ruedi Aebersold

The cellular proteome, the ensemble of proteins derived from a genome, catalyzes and controls thousands of biochemical functions that are the basis of living cells. Whereas the protein coding regions of the genome of the human and many other species are well known, the complexity and composition of proteomes largely remains to be explored. This task is challenging because mechanisms including alternative splicing and post-translational modifications generally give rise to multiple distinct, but related proteins - proteoforms - per coding gene that expand the functional capacity of a cell. Bottom-up proteomics is a mass spectrometric method that infers the identity and quantity of proteins from the measurement of peptides derived from these proteins by proteolytic digestion. Whereas bottom-up proteomics has become the method of choice for the detection of translation products from essentially any gene, the inherent missing link between measured peptides and their parental proteins has so far precluded the systematic assessment of proteoforms and thus limited the resolution of proteome maps. Here we present a novel, data-driven strategy to assign peptides to unique functional proteoform groups based on peptide correlation patterns across large bottom-up proteomic datasets. Our strategy does not fully characterize specific proteoforms, as is achievable in top-down approaches. Rather, it clusters peptides into functional proteoform groups that are directly linked to the biological context of the study. This allows the detection of tens to hundreds of proteoform groups in an untargeted fashion from bottom-up proteomics experiments. We applied the strategy to two types of bottom-up proteomic datasets. The first is a protein complex co-fractionation dataset where native complexes across two different cell cycle stages were resolved and analyzed. Here, our approach enabled the systematic detection and evaluation of assembly specific proteoforms at an unprecedented scale. The second is a protein abundance vs. sample data matrix typical for bottom-up cohort studies consisting of tissue samples from the mouse BXD genetic reference panel. In either data type the method detected state-specific proteoform groups that could be linked to distinct molecular mechanisms including proteolytic cleavage, alternative splicing and phosphorylation. We envision that the presented approach lays the foundation for a systematic assessment of proteoforms and their functional implications directly from bottom-up proteomic datasets.

中文翻译:

从下至上的蛋白质组学数据集系统检测功能性蛋白质组

细胞蛋白质组是一种来自基因组的蛋白质,它催化并控制着成千上万的生物化学功能,这些功能是活细胞的基础。尽管人类和许多其他物种的基因组的蛋白质编码区是众所周知的,但是蛋白质组的复杂性和组成在很大程度上仍待探索。这项任务具有挑战性,因为包括替代剪接和翻译后修饰在内的机制通常会在每个编码基因中产生多种不同但相关的蛋白质-蛋白质型-从而扩大细胞的功能能力。自下而上的蛋白质组学是一种质谱方法,可通过蛋白水解消化测定衍生自这些蛋白质的肽,从而推断蛋白质的身份和数量。自下而上的蛋白质组学已成为从基本上任何基因中检测翻译产物的首选方法,而迄今为止,所测肽段及其亲本蛋白之间固有的缺失联系已阻止了对蛋白形式的系统评估,因此限制了蛋白质组图谱的解析度。在这里,我们提出了一种新颖的,数据驱动的策略,可根据跨大型自下而上的蛋白质组学数据集的肽相关模式,将肽分配给独特的功能性蛋白组。我们的策略不能像自上而下的方法那样完全表征特定的蛋白形式。而是将肽簇聚成功能蛋白形式的基团,这些基团直接与研究的生物学背景相关。这允许从下而上的蛋白质组学实验以无针对性的方式检测数十到数百个蛋白质组。我们将该策略应用于两种自下而上的蛋白质组数据集。第一个是蛋白质复合物共馏分数据集,其中解析和分析了跨越两个不同细胞周期阶段的天然复合物。在这里,我们的方法能够以前所未有的规模对组装特定蛋白形式进行系统检测和评估。第二个是蛋白质丰度与样本数据矩阵之间的关系,通常用于自下而上的队列研究中,由来自小鼠BXD遗传参考小组的组织样本组成。在这两种数据类型中,该方法均检测到状态特定的蛋白形式基团,这些基团可能与不同的分子机制相关,包括蛋白水解切割,选择性剪接和磷酸化。
更新日期:2020-12-22
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