Biomedical Center, Protein Analysis Unit, Faculty of Medicine, Ludwig-Maximilians-Universität München, Munich, Germany.
Graduate School for Quantitative Biosciences (QBM), Ludwig-Maximilians-Universität Munich, Munich, Germany.
Adv Exp Med Biol. 2021;1336:105-128. doi: 10.1007/978-3-030-77252-9_6.
Discovering protein complexes in vivo is of vital importance to understand the evolution and function of biological systems. Proteomics analysis has evolved as a state-of-the-art technique in elucidating the above information. A combination of liquid chromatography (LC) and liquid chromatography coupled to shotgun mass spectrometry (LC-MS) provides the most exhaustive information in this regard. However, a significant amount of computational effort is required for the meaningful interpretation of the generated datasets. In this chapter we describe in detail the state-of-the-art pipeline to discover soluble protein complexes and provide practical advice focusing on typical situations a biologist faces while analyzing such proteomics datasets. Furthermore, we briefly describe two commonly used software packages to solve the described problem: Weka for training protein-protein interactions (PPIs) using machine learning (ML) and Cytoscape for clustering the interaction network.
在体内发现蛋白质复合物对于理解生物系统的进化和功能至关重要。蛋白质组学分析已经发展成为阐明上述信息的一种最先进的技术。液相色谱(LC)和液相色谱与串联质谱(LC-MS)的组合在这方面提供了最详尽的信息。然而,对于生成数据集的有意义的解释需要大量的计算工作。在本章中,我们详细描述了用于发现可溶性蛋白质复合物的最新技术,并提供了一些实用的建议,重点介绍了生物学家在分析此类蛋白质组学数据集时面临的典型情况。此外,我们还简要描述了两个常用于解决该问题的软件包:Weka 用于使用机器学习(ML)训练蛋白质-蛋白质相互作用(PPIs),以及 Cytoscape 用于对相互作用网络进行聚类。