To a large extent functional diversity in cells is achieved by the expansion of molecular complexity beyond that of the coding genome. Various processes create multiple distinct but related proteins per coding gene – so-called proteoforms – that expand the functional capacity of a cell. Evaluating proteoforms from classical bottom-up proteomics datasets, where peptides instead of intact proteoforms are measured, has remained difficult. Here we present COPF, a tool for COrrelation-based functional ProteoForm assessment in bottom-up proteomics data. It leverages the concept of peptide correlation analysis to systematically assign peptides to co-varying proteoform groups. We show applications of COPF to protein complex co-fractionation data as well as to more typical protein abundance vs. sample data matrices, demonstrating the systematic detection of assembly- and tissue-specific proteoform groups, respectively, in either dataset. 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.

在很大程度上，细胞的功能多样性是通过将分子复杂性扩展到编码基因组之外来实现的。每个编码基因的各种过程会产生多种不同但相关的蛋白质（即所谓的蛋白质形式），从而扩展细胞的功能能力。从经典的自下而上的蛋白质组数据集中评估蛋白质形式仍然很困难，在这些数据集中测量的是肽而不是完整的蛋白质形式。在这里，我们介绍 COPF，一种用于自下而上蛋白质组数据中基于关联的功能 ProteoForm 评估的工具。它利用肽相关分析的概念将肽系统地分配给共变的蛋白质组。我们展示了 COPF 在蛋白质复合物共分离数据以及更典型的蛋白质丰度与样本数据矩阵中的应用，证明了在任一数据集中分别对组装和组织特异性蛋白质组进行系统检测。我们设想所提出的方法为直接从自下而上的蛋白质组数据集系统评估蛋白质型及其功能意义奠定了基础。