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Updated: Feb 2, 2026

In Vivo Proximity Biotinylation for Protein Interaction Studies in Paramecium tetraurelia
Published on: September 12, 2025
Getting to know the neighborhood: using proximity-dependent biotinylation to characterize protein complexes and map
Anne-Claude Gingras1, Kento T Abe1, Brian Raught2
1Lunenfeld-Tanenbaum Research Institute, Sinai Health System, and Department of Molecular Genetics, University of Toronto, 600 University Ave, Rm 992, Toronto, ON, M5G1X5, Canada.
Abstract:
The use of proximity-dependent biotinylation approaches combined with mass spectrometry (e.g. BioID and APEX) has revolutionized the study of protein-protein interactions and organellar proteomics. These powerful techniques are based on the fusion of an enzyme (e.g. a biotin ligase or peroxidase) to a 'bait' protein of interest, which is then expressed in a relevant biological setting. Addition of enzyme substrate enables covalent biotin labeling of proteins in the vicinity of the bait in vivo. These approaches thus allow for the capture and identification of 'neighborhood' proteins in the context of a living cell, and provide data that are complementary to more established techniques such as fractionation or affinity purification. As compared to standard affinity-based purification approaches, proximity-dependent biotinylation (PDB) can help to: first, identify interactions with and amongst membrane proteins, and other polypeptide classes that are less amenable to study by standard pulldown techniques; second, enrich for transient and/or low affinity interactions that are not readily captured using affinity purification approaches; third, avoid post-lysis artefacts associated with standard biochemical purification experiments and; fourth, provide deep insight into the organization of membrane-less organelles and other subcellular structures that cannot be easily isolated or purified. Given the increasing use of these techniques to answer a variety of different types of biological questions, it is important to understand how best to design PDB-MS experiments, what type of data they generate, and how to analyze and interpret the results.
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