Medical Bioinformatics, Medical Faculty, Ruhr University Bochum, Bochum, Germany.
Medizinisches Proteom-Center, Medical Faculty, Ruhr University Bochum, Bochum, Germany.
Methods Mol Biol. 2025;2859:53-64. doi: 10.1007/978-1-0716-4152-1_4.
Protein inference is an often neglected though crucial step in most proteomic experiments. In the bottom-up proteomic approach, the actual molecules of interest, the proteins, are digested into peptides before measurement on a mass spectrometer. This approach introduces a loss of information: The actual proteins must be inferred based on the identified peptides. While this might seem trivial, there are certain problems, one of the biggest being the presence of peptides that are shared among proteins. These amino acid sequences can, based on the database used for identification, belong to more than one protein. If such peptides are identified in a sample, it cannot be said which proteins actually were in the sample, but only an estimate on the most probable proteins or protein groups can be given based on a predefined inference strategy.Here we describe the effect of the chosen database for peptide identification on the number of shared peptides. Afterward, the mainly used protein inference methods will be sketched, and the necessity of stringent false discovery rate on peptide and protein level is discussed. Finally, we explain how the tool "PIA or protein inference algorithms" can be used together with the workflow environment KNIME and OpenMS to perform protein inference in a common proteomic experiment.
蛋白质推断是大多数蛋白质组学实验中经常被忽视但至关重要的步骤。在自上而下的蛋白质组学方法中，实际感兴趣的分子，即蛋白质，在质谱仪上进行测量之前被消化成肽。这种方法会导致信息丢失：必须根据鉴定的肽推断实际的蛋白质。虽然这似乎微不足道，但存在某些问题，其中最大的问题之一是存在在蛋白质之间共享的肽。根据用于鉴定的数据库，这些氨基酸序列可以属于多个蛋白质。如果在样品中鉴定出这样的肽，则不能说实际上有哪些蛋白质存在于样品中，而只能根据预设的推断策略给出最可能的蛋白质或蛋白质组的估计。在这里，我们描述了选择用于肽鉴定的数据库对共享肽数量的影响。之后，将简述主要使用的蛋白质推断方法，并讨论肽和蛋白质水平上严格的错误发现率的必要性。最后，我们解释了如何使用“PIA 或蛋白质推断算法”工具与 KNIME 和 OpenMS 工作流环境一起在常见的蛋白质组学实验中执行蛋白质推断。
