DIP-MS is an ultra-deep interaction proteomics method for deconvoluting protein complexes. It combines affinity purification with blue-native-PAGE separation, data-independent acquisition with mass spectrometry, and deep-learning-based signal processing to resolve complex isoforms sharing the same bait protein in a single experiment. The method was applied to study the human prefoldin family of complexes, revealing distinct holo- and subcomplex variants, complex–complex interactions, and new subunits. DIP-MS provides unprecedented depth and resolution in proteome modularity analysis. Understanding protein spatial organization into functional modules is a key goal in molecular systems biology. Protein complexes, defined as stable assemblies, are key regulators of cellular functions. They are contextual and can adapt to cellular type or state by changing subunit composition, stoichiometry, localization, and abundance. AP–MS has been the method of choice for analyzing protein complexes, but it may not identify proteins from different complexes. Therefore, multiple reciprocal AP–MS experiments are needed to deconvolve MS data into distinct molecular entities. Alternative methods such as SEC–MS and BNP-MS have been introduced to fractionate native complexes by hydrodynamic radius and size, respectively. However, these methods are limited by sensitivity, sample loading capacity, and resolution of SEC columns. DIP-MS provides three critical improvements: (1) a miniaturized sample preparation procedure requiring ten times less material than traditional chromatography-based separation; (2) a fast data-independent acquisition with mass spectrometry (DIA-MS) scheme with increased throughput; and (3) a deep-learning framework trained on over 1.5 million binary interactions from 32 cofractionation datasets, enabling prediction of PPIs, identification of multiple instances of protein complexes, and robust deconvolution of complex profiling data into functional modules. DIP-MS was applied to analyze the interactome of human prefoldin proteins. Prefoldins play a central role in cellular proteostasis via stabilizing nascent proteins in interplay with other chaperones. They are best known as part of the evolutionarily conserved heterohexameric canonical prefoldin (PFD) complex. In addition to the prototypical PFD complex, complexes containing prefoldin subunits have been implicated in various cellular processes. DIP-MS identified 319 PFD–PFDL-specific interactors and revealed a previously unknown PFD homolog and deconvolved the PFD and PFDL-complex landscape into multiple complex instances. DIP-MS was benchmarked against AP–MS and SEC–MS workflows. It identified more interaction partners than both methods, recovering roughly 30% of the interactors in public databases. DIP-MS generated more extensive and denser networks and recapitulated a larger portion of the ground truth. It also resolved more complexes than SEC–MS and showed structural similarity to SEC–MS butDIP-MS is an ultra-deep interaction proteomics method for deconvoluting protein complexes. It combines affinity purification with blue-native-PAGE separation, data-independent acquisition with mass spectrometry, and deep-learning-based signal processing to resolve complex isoforms sharing the same bait protein in a single experiment. The method was applied to study the human prefoldin family of complexes, revealing distinct holo- and subcomplex variants, complex–complex interactions, and new subunits. DIP-MS provides unprecedented depth and resolution in proteome modularity analysis. Understanding protein spatial organization into functional modules is a key goal in molecular systems biology. Protein complexes, defined as stable assemblies, are key regulators of cellular functions. They are contextual and can adapt to cellular type or state by changing subunit composition, stoichiometry, localization, and abundance. AP–MS has been the method of choice for analyzing protein complexes, but it may not identify proteins from different complexes. Therefore, multiple reciprocal AP–MS experiments are needed to deconvolve MS data into distinct molecular entities. Alternative methods such as SEC–MS and BNP-MS have been introduced to fractionate native complexes by hydrodynamic radius and size, respectively. However, these methods are limited by sensitivity, sample loading capacity, and resolution of SEC columns. DIP-MS provides three critical improvements: (1) a miniaturized sample preparation procedure requiring ten times less material than traditional chromatography-based separation; (2) a fast data-independent acquisition with mass spectrometry (DIA-MS) scheme with increased throughput; and (3) a deep-learning framework trained on over 1.5 million binary interactions from 32 cofractionation datasets, enabling prediction of PPIs, identification of multiple instances of protein complexes, and robust deconvolution of complex profiling data into functional modules. DIP-MS was applied to analyze the interactome of human prefoldin proteins. Prefoldins play a central role in cellular proteostasis via stabilizing nascent proteins in interplay with other chaperones. They are best known as part of the evolutionarily conserved heterohexameric canonical prefoldin (PFD) complex. In addition to the prototypical PFD complex, complexes containing prefoldin subunits have been implicated in various cellular processes. DIP-MS identified 319 PFD–PFDL-specific interactors and revealed a previously unknown PFD homolog and deconvolved the PFD and PFDL-complex landscape into multiple complex instances. DIP-MS was benchmarked against AP–MS and SEC–MS workflows. It identified more interaction partners than both methods, recovering roughly 30% of the interactors in public databases. DIP-MS generated more extensive and denser networks and recapitulated a larger portion of the ground truth. It also resolved more complexes than SEC–MS and showed structural similarity to SEC–MS but