蛋白质组
相互作用体
计算生物学
质谱法
蛋白质组学
模块化(生物学)
化学
基因亚型
表型
蛋白质基因组学
生物
色谱法
生物化学
遗传学
基因组
基因组学
基因
作者
Fabian Frommelt,Andrea Fossati,Federico Uliana,Fabian Wendt,Peng Xue,Moritz Heusel,Bernd Wollscheid,Ruedi Aebersold,Rodolfo Ciuffa,Matthias Gstaiger
标识
DOI:10.1101/2023.03.22.533843
摘要
Abstract Most, if not all, proteins are organized in macromolecular assemblies, which represent key functional units regulating and catalyzing the majority of cellular processes in health and disease. Ever-advancing analytical capabilities promise to pinpoint lesions in proteome modularity driving disease phenotypes. Affinity purification of the protein of interest combined with LC-MS/MS (AP-MS) represents the method of choice to identify interacting proteins. The composition of complex isoforms concurrently present in the AP sample can however not be resolved from a single AP-MS experiment but requires computational inference from multiple time-and resource-intensive reciprocal AP-MS experiments. In this study we introduce Deep Interactome Profiling by Mass Spectrometry (DIP-MS) which combines affinity enrichment with BN-PAGE separation, DIA mass spectrometry and deep-learning-based signal processing to resolve complex isoforms sharing the same bait protein in a single experiment. We applied DIP-MS to probe the organisation of the human prefoldin (PFD) family of complexes, resolving distinct PFD holo- and sub-complex variants, complex-complex interactions and complex isoforms with new subunits that were experimentally validated. Our results demonstrate that DIP-MS can reveal proteome modularity at unprecedented depth and resolution and thus represents a critical steppingstone to relate a proteome state to phenotype in both healthy and diseased conditions.
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