Wu Shaowen, Zhang Sheng, Liu Chun-Ming, Fernie Alisdair R, Yan Shijuan
State Key Laboratory of Swine and Poultry Breeding Industry, Guangdong Key Laboratory of Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, China.
Proteomics and Metabolomics Facility, Institute of Biotechnology, Cornell University, Ithaca, New York, USA.
Mol Cell Proteomics. 2025 Jan;24(1):100887. doi: 10.1016/j.mcpro.2024.100887. Epub 2024 Nov 27.
The foundation of all biological processes is the network of diverse and dynamic protein interactions with other molecules in cells known as the interactome. Understanding the interactome is crucial for elucidating molecular mechanisms but has been a longstanding challenge. Recent developments in mass spectrometry (MS)-based techniques, including affinity purification, proximity labeling, cross-linking, and co-fractionation mass spectrometry (MS), have significantly enhanced our abilities to study the interactome. They do so by identifying and quantifying protein interactions yielding profound insights into protein organizations and functions. This review summarizes recent advances in MS-based interactomics, focusing on the development of techniques that capture protein-protein, protein-metabolite, and protein-nucleic acid interactions. Additionally, we discuss how integrated MS-based approaches have been applied to diverse biological samples, focusing on significant discoveries that have leveraged our understanding of cellular functions. Finally, we highlight state-of-the-art bioinformatic approaches for predictions of interactome and complex modeling, as well as strategies for combining experimental interactome data with computation methods, thereby enhancing the ability of MS-based techniques to identify protein interactomes. Indeed, advances in MS technologies and their integrations with computational biology provide new directions and avenues for interactome research, leveraging new insights into mechanisms that govern the molecular architecture of living cells and, thereby, our comprehension of biological processes.
所有生物过程的基础是细胞内各种动态蛋白质与其他分子相互作用的网络，即互作组。理解互作组对于阐明分子机制至关重要，但一直是一项长期挑战。基于质谱（MS）的技术，包括亲和纯化、邻近标记、交联和共分离质谱，最近的发展显著增强了我们研究互作组的能力。这些技术通过识别和量化蛋白质相互作用，对蛋白质组织和功能产生了深刻的见解。本综述总结了基于质谱的互作组学的最新进展，重点关注捕获蛋白质-蛋白质、蛋白质-代谢物和蛋白质-核酸相互作用的技术发展。此外，我们讨论了基于质谱的综合方法如何应用于各种生物样品，重点关注利用我们对细胞功能理解的重大发现。最后，我们强调了用于预测互作组和复杂建模的最新生物信息学方法，以及将实验互作组数据与计算方法相结合的策略，从而提高基于质谱的技术识别蛋白质互作组的能力。事实上，质谱技术的进步及其与计算生物学的整合为互作组研究提供了新的方向和途径，利用了对控制活细胞分子结构的机制的新见解，从而增进了我们对生物过程的理解。
