The study presents BioPlex 2.0, a comprehensive protein interaction network derived from affinity purification-mass spectrometry (AP-MS) that includes interactions from over 25% of human protein-coding genes. This network contains 56,553 interactions involving 10,961 proteins, representing the largest such network to date. BioPlex 2.0 provides insights into protein communities, disease-related networks, and functional modules, with over 29,000 previously unknown co-associations. It identifies 1320 protein communities, many of which are enriched with genes essential for cellular fitness. Additionally, 442 communities are associated with over 2000 disease annotations, placing numerous candidate disease genes into a cellular framework. BioPlex 2.0 surpasses previous interaction networks in depth and breadth, offering a valuable resource for exploring proteome organization and protein function. The network was generated using a robust AP-MS pipeline, including the CompPASS-Plus algorithm for identifying high-confidence interacting proteins. BioPlex 2.0 also enables the identification of domain-domain associations, subcellular localization, and functional modules. It reveals that many protein families, such as kinases, are highly interactive. The study further explores the relationship between protein complexes and disease, mapping disease-related genes onto BioPlex 2.0 communities. It identifies clusters of proteins associated with various diseases, including neoplasms, hypertensive disease, and congenital disorders. BioPlex 2.0 also provides insights into the functional roles of poorly characterized proteins, such as GATSL3 (now CASTOR1), which functions as a cellular arginine sensor. The network is validated through multiple experiments, including immunoprecipitation-western analysis and immunofluorescence. BioPlex 2.0 is available for public use, offering a valuable resource for systems-level studies of protein interactions and for elucidating the biology of incompletely characterized proteins. The study highlights the importance of understanding protein interaction networks for elucidating cellular function and disease mechanisms.The study presents BioPlex 2.0, a comprehensive protein interaction network derived from affinity purification-mass spectrometry (AP-MS) that includes interactions from over 25% of human protein-coding genes. This network contains 56,553 interactions involving 10,961 proteins, representing the largest such network to date. BioPlex 2.0 provides insights into protein communities, disease-related networks, and functional modules, with over 29,000 previously unknown co-associations. It identifies 1320 protein communities, many of which are enriched with genes essential for cellular fitness. Additionally, 442 communities are associated with over 2000 disease annotations, placing numerous candidate disease genes into a cellular framework. BioPlex 2.0 surpasses previous interaction networks in depth and breadth, offering a valuable resource for exploring proteome organization and protein function. The network was generated using a robust AP-MS pipeline, including the CompPASS-Plus algorithm for identifying high-confidence interacting proteins. BioPlex 2.0 also enables the identification of domain-domain associations, subcellular localization, and functional modules. It reveals that many protein families, such as kinases, are highly interactive. The study further explores the relationship between protein complexes and disease, mapping disease-related genes onto BioPlex 2.0 communities. It identifies clusters of proteins associated with various diseases, including neoplasms, hypertensive disease, and congenital disorders. BioPlex 2.0 also provides insights into the functional roles of poorly characterized proteins, such as GATSL3 (now CASTOR1), which functions as a cellular arginine sensor. The network is validated through multiple experiments, including immunoprecipitation-western analysis and immunofluorescence. BioPlex 2.0 is available for public use, offering a valuable resource for systems-level studies of protein interactions and for elucidating the biology of incompletely characterized proteins. The study highlights the importance of understanding protein interaction networks for elucidating cellular function and disease mechanisms.