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共馏分质谱分析技术的发展趋势:一种全新的蛋白质互作网络发现的金标准。

Trends in co-fractionation mass spectrometry: A new gold-standard in global protein interaction network discovery.

机构信息

Division of Oncology, Division of Oncological Sciences, Knight Cancer Institute, Oregon Health and Science University (OHSU), Portland, OR, USA.

Division of Oncology, Division of Oncological Sciences, Knight Cancer Institute, Oregon Health and Science University (OHSU), Portland, OR, USA.

出版信息

Curr Opin Struct Biol. 2024 Oct;88:102880. doi: 10.1016/j.sbi.2024.102880. Epub 2024 Jul 11.

Abstract

Co-fractionation mass spectrometry (CF-MS) uses biochemical fractionation to isolate and characterize macromolecular complexes from cellular lysates without the need for affinity tagging or capture. In recent years, this has emerged as a powerful technique for elucidating global protein-protein interaction networks in a wide variety of biospecimens. This review highlights the latest advancements in CF-MS experimental workflows including machine learning-guided analyses, for uncovering dynamic and high-resolution protein interaction landscapes with enhanced sensitivity, accuracy and throughput, enabling better biophysical characterization of endogenous protein complexes. By addressing challenges and emergent opportunities in the field, this review underscores the transformative potential of CF-MS in advancing our understanding of functional protein interaction networks in health and disease.

摘要

共分离质谱(CF-MS)利用生化分级分离技术,从细胞裂解物中分离和鉴定大分子复合物,而无需亲和标记或捕获。近年来,它已成为一种强大的技术,可以在各种生物样本中阐明全局蛋白质-蛋白质相互作用网络。本文重点介绍了 CF-MS 实验工作流程的最新进展,包括机器学习指导的分析,用于揭示具有增强灵敏度、准确性和通量的动态和高分辨率蛋白质相互作用图谱,从而更好地对内源性蛋白质复合物进行生物物理特性分析。通过解决该领域的挑战和新兴机遇,本文强调了 CF-MS 在推进我们对健康和疾病中功能性蛋白质相互作用网络的理解方面的变革潜力。

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