Integrating high-resolution proximity labelling with orthogonal interactome benchmarks: Insights from Kinetoplastid systems
JOURNAL OF PROTEOMICS, cilt.332, ss.105739, 2027 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Derleme
- Cilt numarası: 332
- Basım Tarihi: 2027
- Doi Numarası: 10.1016/j.jprot.2026.105739
- Dergi Adı: JOURNAL OF PROTEOMICS
- Derginin Tarandığı İndeksler: Academic Search Ultimate (EBSCO), Engineering Source (EBSCO), Scopus, Science Citation Index Expanded (SCI-EXPANDED), BIOSIS, Chemical Abstracts Core, EMBASE, MEDLINE
- Sayfa Sayıları: ss.105739
- Ankara Üniversitesi Adresli: Evet
Özet
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.