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：
共分馏质谱（CF-MS）使用生化分馏从细胞裂解物中分离和表征大分子复合物，而无需亲和标记或捕获。近年来,这已成为阐明各种生物样本中整体蛋白质-蛋白质相互作用网络的强大技术。这篇综述重点介绍了CF-MS实验工作流程的最新进展，包括机器学习指导分析，用于发现具有增强灵敏度的动态和高分辨率蛋白质相互作用景观，精度和吞吐量，能够更好地表征内源性蛋白质复合物。通过应对该领域的挑战和紧急机遇，这篇综述强调了CF-MS在促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转化潜力。
