Antibody-Based Fusion Proteins Allow Ca<sup>2+</sup> Rewiring to Most Extracellular Ligands
Bibliographic record
Abstract
The Ca 2+ signaling toolkit is the set of proteins used by living systems to generate and respond to Ca 2+ signals. The selective expression of these proteins in particular tissues, cell types and subcellular locations allows the Ca 2+ signal to regulate a diverse set of cellular processes. Through synthetic biology, the Ca 2+ signaling toolkit can be expanded beyond the natural repertoire to potentially allow a non-natural ligand to control downstream cellular processes. To realize this potential, we exploited the ability of an antibody to bind its antigen exclusively in combination with the ability of the cytoplasmic domain of vascular endothelial growth factor receptor 2 (VEGFR2) to generate a Ca 2+ signal upon oligomerization. Using protein fusions between antibody variants ( i.e., nanobody, single-chain antibody and the monoclonal antibody) and the VEGFR2 cytoplasmic domain, Ca 2+ signals were generated in response to extracellular stimulation with green fluorescent protein, mCherry, tumor necrosis factor alpha and soluble CD14. The Ca 2+ signal generation by the stimulus did not require a stringent transition from monomer to oligomer state, but instead only required an increase in the oligomeric state. The Ca 2+ signal generated by these classes of antibody-based fusion proteins can be rewired with a Ca 2+ indicator or with an engineered Ca 2+ activated RhoA to allow for antigen screening or migration to most extracellular ligands, respectively.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".