Subtopic Deep Dive

BioID Proximity-Dependent Biotin Identification
Research Guide

What is BioID Proximity-Dependent Biotin Identification?

BioID is a proximity-dependent biotin identification method that uses a promiscuous BirA* biotin ligase fused to a protein of interest to biotinylate proximal proteins in living cells for subsequent identification by mass spectrometry.

Introduced by Roux et al. (2013) with 532 citations, BioID enables unbiased detection of weak and transient protein interactions. TurboID, an engineered faster variant by Branon et al. (2018, 2024 citations), reduces labeling time to 10 minutes. Over 20 papers from the list detail optimizations, applications in interactome mapping, and integrations with AP-MS.

Curated Papers

Key Challenges

Why It Matters

BioID maps protein interactomes in human cells, revealing regulators like UBR7 for NLR immune receptors (Zhang et al., 2019, 261 citations). It characterizes membrane contact sites and nuclear protein networks (Scorrano et al., 2019, 700 citations; Roux et al., 2013). Applications include validating c-MYC partners in tumors (Dingar et al., 2014, 129 citations) and studying NMD factor interactions (Schweingruber, 2014), advancing signaling pathway analysis.

Key Research Challenges

Labeling Speed Optimization

Original BioID requires 16-24 hours for biotinylation, limiting dynamic studies (Roux et al., 2013, 532 citations). TurboID addresses this but needs validation in organisms (Branon et al., 2018, 2024 citations). Balancing speed and specificity remains critical (Cho et al., 2020, 464 citations).

Background Noise Reduction

Non-specific biotinylation creates high background in proteomics data (Gingras et al., 2018, 305 citations). Split-TurboID variants reduce this for precise mapping (Cho et al., 2020, 464 citations). Filtering proximal hits from artifacts challenges interactome validation.

In Vivo Organism Adaptation

BioID excels in mammalian cells but struggles with tissue permeability and endogenous biotin (Samavarchi-Tehrani et al., 2020, 200 citations). TurboID enables labeling in Drosophila (Branon et al., 2018, 2024 citations). Scaling to whole organisms requires further engineering.

Essential Papers

Efficient proximity labeling in living cells and organisms with TurboID

Tess C. Branon, Justin A. Bosch, Ariana D. Sanchez et al. · 2018 · Nature Biotechnology · 2.0K citations

Coming together to define membrane contact sites

Luca Scorrano, Maria Antonietta De Matteis, Scott D. Emr et al. · 2019 · Nature Communications · 700 citations

BioID: A Screen for Protein‐Protein Interactions

Kyle J. Roux, Dae In Kim, Brian Burke · 2013 · Current Protocols in Protein Science · 532 citations

Abstract BioID is a unique method to screen for physiologically relevant protein interactions that occur in living cells. This technique harnesses a promiscuous biotin ligase to biotinylate protein...

Proximity labeling in mammalian cells with TurboID and split-TurboID

Kelvin F. Cho, Tess C. Branon, Namrata D. Udeshi et al. · 2020 · Nature Protocols · 464 citations

Phosphorothioate modified oligonucleotide–protein interactions

Stanley T. Crooke, Timothy A. Vickers, Xue‐hai Liang · 2020 · Nucleic Acids Research · 313 citations

Abstract Antisense oligonucleotides (ASOs) interact with target RNAs via hybridization to modulate gene expression through different mechanisms. ASO therapeutics are chemically modified and include...

Getting to know the neighborhood: using proximity-dependent biotinylation to characterize protein complexes and map organelles

Anne‐Claude Gingras, Kento T. Abe, Brian Raught · 2018 · Current Opinion in Chemical Biology · 305 citations

An AP-MS- and BioID-compatible MAC-tag enables comprehensive mapping of protein interactions and subcellular localizations

Xiaonan Liu, Kari Salokas, Fitsum Tamene et al. · 2018 · Nature Communications · 272 citations

Reading Guide

Foundational Papers

Start with Roux et al. (2013, 532 citations) for BioID method invention and protocol; follow with Roux (2013, 47 citations) for proximity labeling techniques overview.

Recent Advances

Study Branon et al. (2018, 2024 citations) for TurboID; Cho et al. (2020, 464 citations) for protocols; Zhang et al. (2019, 261 citations) for immunity applications.

Core Methods

BirA* fusion constructs, biotin supplementation, streptavidin pulldown, MS identification; TurboID/split-TurboID engineering; MAC-tag for AP-MS/BioID combo (Roux et al., 2013; Branon et al., 2018; Liu et al., 2018).

How PapersFlow Helps You Research BioID Proximity-Dependent Biotin Identification

Discover & Search

Research Agent uses searchPapers and exaSearch to find BioID papers like 'Efficient proximity labeling... TurboID' by Branon et al. (2018), then citationGraph reveals 2024 citations and downstream works like Zhang et al. (2019). findSimilarPapers clusters TurboID optimizations from 250M+ OpenAlex papers.

Analyze & Verify

Analysis Agent applies readPaperContent to extract protocols from Roux et al. (2013), then verifyResponse with CoVe cross-checks claims against 10+ papers. runPythonAnalysis processes biotinylation efficiency data with pandas for statistical verification; GRADE scores evidence strength for TurboID vs BioID comparisons.

Synthesize & Write

Synthesis Agent detects gaps like organism-specific adaptations via contradiction flagging across Branon (2018) and Samavarchi-Tehrani (2020). Writing Agent uses latexEditText, latexSyncCitations for Roux et al. (2013), and latexCompile to generate interactome reports; exportMermaid visualizes proximity networks.

Use Cases

"Analyze biotinylation rates from TurboID vs BioID datasets in Branon 2018."

Research Agent → searchPapers(Branon 2018) → Analysis Agent → readPaperContent + runPythonAnalysis(pandas plot rates) → matplotlib efficiency graph output.

"Draft LaTeX review of BioID for membrane contact sites with citations."

Research Agent → citationGraph(Scorrano 2019) → Synthesis Agent → gap detection → Writing Agent → latexEditText + latexSyncCitations(Roux 2013, Cho 2020) + latexCompile → compiled PDF.

"Find GitHub repos with BioID analysis code from recent papers."

Research Agent → searchPapers(TurboID) → Code Discovery → paperExtractUrls → paperFindGithubRepo(Branon 2018) → githubRepoInspect → proteomics pipeline code.

Automated Workflows

Deep Research workflow scans 50+ BioID papers via searchPapers → citationGraph → structured report on TurboID evolution (Branon 2018). DeepScan's 7-step chain verifies interactome data from Liu et al. (2018) with CoVe checkpoints and runPythonAnalysis. Theorizer generates hypotheses on UBR7-NLR links from Zhang et al. (2019).

Try Doxa for BioID Proximity-Dependent Biotin Identification Research

Frequently Asked Questions

What is the core mechanism of BioID?

BioID fuses promiscuous BirA* ligase to a bait protein, enabling proximity-based biotinylation of preys within ~10 nm in living cells (Roux et al., 2013).

What are main BioID methods and variants?

Original BioID (16-24h labeling), TurboID (10 min), split-TurboID for conditional labeling (Branon et al., 2018; Cho et al., 2020).

What are key BioID papers?

Foundational: Roux et al. (2013, 532 citations); High-impact: Branon et al. TurboID (2018, 2024 citations), Cho et al. (2020, 464 citations).

What are open problems in BioID?

Reducing background noise, adapting to whole organisms, and integrating with spatial proteomics (Gingras et al., 2018; Samavarchi-Tehrani et al., 2020).