The article introduces a novel method called Deep Interactome Profiling by Mass Spectrometry (DIP-MS), which combines affinity purification (AP) with blue-native PAGE separation and data-independent acquisition (DIA) mass spectrometry to resolve complex isoforms sharing the same bait protein in a single experiment. DIP-MS is designed to overcome the limitations of traditional AP-MS, which can identify both direct and indirect interactors but often fails to distinguish them from different complexes present in the sample. The method is applied to study the human prefoldin family of complexes, revealing distinct prefoldin holo- and subcomplex variants, complex-complex interactions, and complex isoforms with new subunits. The authors developed PPIprophet, a deep-learning-based protein-complex deconvolution system, to process DIP-MS data and extract information such as protein-protein interactions (PPIs), complex stoichiometry, and prey-prey interactions. Benchmarking against other techniques shows that DIP-MS provides broader dynamic range, higher resolution, and more extensive and denser networks compared to SEC-MS and reciprocal AP-MS. The study demonstrates that DIP-MS can reveal proteome modularity at unprecedented depth and resolution, advancing our understanding of modular protein organization and its role in cellular proteostasis.The article introduces a novel method called Deep Interactome Profiling by Mass Spectrometry (DIP-MS), which combines affinity purification (AP) with blue-native PAGE separation and data-independent acquisition (DIA) mass spectrometry to resolve complex isoforms sharing the same bait protein in a single experiment. DIP-MS is designed to overcome the limitations of traditional AP-MS, which can identify both direct and indirect interactors but often fails to distinguish them from different complexes present in the sample. The method is applied to study the human prefoldin family of complexes, revealing distinct prefoldin holo- and subcomplex variants, complex-complex interactions, and complex isoforms with new subunits. The authors developed PPIprophet, a deep-learning-based protein-complex deconvolution system, to process DIP-MS data and extract information such as protein-protein interactions (PPIs), complex stoichiometry, and prey-prey interactions. Benchmarking against other techniques shows that DIP-MS provides broader dynamic range, higher resolution, and more extensive and denser networks compared to SEC-MS and reciprocal AP-MS. The study demonstrates that DIP-MS can reveal proteome modularity at unprecedented depth and resolution, advancing our understanding of modular protein organization and its role in cellular proteostasis.