Labeling Cell Surface Receptors with Ligand.BirA* Bispecifics
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide BirA*, a mutant form of the biotinylating enzyme BirA, can nonspecifically biotinylate ε-amino groups on lysines of proteins. Based on the promiscuous labeling nature of BirA*, plasmids expressing fusion constructs of BirA* to a given ligand have been used to transfect eukaryotic cells, leading to the biotinylation of intracellular proteins interacting or in close proximity to such Ligand.BirA* constructs. Mass spectrometry performed on the recovered biotinylated partners allows one to map intracellular protein interactors, a technique known as BioID. In contrast, the expression and purification of recombinant Ligand.BirA* constructs could serve as a powerful tool for labeling and detecting cell surface receptors. Here, we report the design and expression of recombinant Affibody.BirA* constructs, Z EGFR:1907 .BirA* and Z HER2:243 .BirA*, as protein bispecifics able to biotinylate their respective receptors EGFR and HER2 on the surface of MDA-MB-231 (EGFR +, EpCaM +, and CD44 + ) and SK-OV-3 (HER2 ++, EGFR +, EpCaM +, and CD44 + ) cancer cells. These Affibody.BirA* constructs retain both their BirA* enzymatic activity as well as their receptor-binding function. Importantly, MDA-MB-231 and SK-OV-3 cells biotinylated with Affibody.BirA* constructs did label their receptors EGFR and HER2 but did not biotinylate irrelevant antigens such as EpCaM or CD44 present on the surface of both cell lines. Ligand.BirA* bispecifics may represent a promising class of agents to identify unknown receptors on cell surfaces.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".