Integrating high-resolution proximity labelling with orthogonal interactome benchmarks: Insights from Kinetoplastid...

Proximity labelling has progressed from a specialised protein interaction methodology into a pivotal component of modern spatial proteomics. This review presents a comprehensive overview of the technological development of proximity labelling methodologies for mapping proximal protein associations, encompassing first-generation BioID platforms to advanced systems including TurboID, XL-BioID, APEX2 and UltraID. Special emphasis is placed on advances in labelling kinetics, enzyme miniaturisation, conditional activation, and spatial precision. The convergence of proximity labelling with orthogonal tag-free strategies, such as size-exclusion chromatography-mass spectrometry and cross-linking mass spectrometry, is further examined alongside emerging CRISPR-Cas9-based endogenous tagging and artificial intelligence-driven structural prediction frameworks. These complementary strategies are discussed in the context of their potential contribution to reshaping next-generation interactomics. With kinetoplastids as an illustrative model system, reported proximity labelling investigations in Leishmania, Trypanosoma brucei, and Trypanosoma cruzi are systematically consolidated. Critical challenges include parasite-specific biotin metabolism, oxidative stress linked with peroxidase-based labelling, limitations in endogenous tagging, and the extensive dark proteome impeding functional annotation. Next-generation technologies and artificial intelligence-driven interactomics are further explored within kinetoplastid biology, while emphasising their broader potential to combine high-resolution proximity labelling, orthogonal tag-free benchmarking, genome engineering, and artificial intelligence-driven structural analysis across various biological systems, thereby advancing comprehensive interactome mapping and functional proteome annotation. SIGNIFICANCE: Interactomics is a key pillar of systems biology, offering the indispensable functional framework that transforms protein catalogues into dynamic and predictive models of biological systems. To the best of our knowledge, this review represents one of the most comprehensive syntheses integrating all reported proximity labelling implementations in Leishmania, Trypanosoma brucei, and Trypanosoma cruzi up to 2026. Its importance lies in bringing together the development of proximity labelling into a multi-layered methodological framework that now includes robust reference datasets, such as the recently reported large-scale physical interactomes of L. donovani (16,095 interactions) and T. cruzi, generated employing SEC-MS and XL-MS methodologies. Through addressing the "dark proteome," this review positions proximity labelling within a broader multi-modal interactomics framework integrating SEC-MS, XL-MS, and AI-assisted structural modelling, while offering a structured roadmap built around next-generation technologies, including PerTurboID for perturbations, iAPEX for redox challenges, and CRISPR-Cas for physiological tagging. Collectively, these advances may facilitate the development of increasingly predictive and spatially resolved interactome models across complex biological systems.