Computational design and evaluation of optimal bait sets for scalable proximity proteomics.

ABSTRACT: The spatial organization of proteins within eukaryotic cells underlies essential biological processes and can be mapped by identifying nearby proteins using proximity-dependent biotinylation approaches such as BioID. When applied systematically to hundreds of bait proteins, BioID has localized thousands of endogenous proteins in human cells, generating a comprehensive view of subcellular organization. However, the need for large bait sets limits the scalability of BioID for context-dependent spatial profiling across different cell types, states, or perturbations. To address this, we develop a benchmarking framework with multiple complementary metrics to assess how well a given bait subset recapitulates the structure and coverage of a reference BioID dataset. We also introduce GENBAIT, a ge

SUBMITTER: Kasmaeifar V 

PROVIDER: S-EPMC12546598 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature