Use of NanoBiT and NanoBRET to monitor fluorescent VEGF‐A binding kinetics to VEGFR2/NRP1 heteromeric complexes in living cells
Bibliographic record
Abstract
Background and Purpose VEGF‐A is a key mediator of angiogenesis, primarily signalling via VEGF receptor 2 (VEGFR2). Endothelial cells also express the co‐receptor neuropilin‐1 (NRP1) that potentiates VEGF‐A/VEGFR2 signalling. VEGFR2 and NRP1 had distinct real‐time ligand binding kinetics when monitored using BRET. We previously characterised fluorescent VEGF‐A isoforms tagged at a single site with tetramethylrhodamine (TMR). Here, we explored differences between VEGF‐A isoforms in living cells that co‐expressed both receptors. Experimental Approach Receptor localisation was monitored in HEK293T cells expressing both VEGFR2 and NRP1 using membrane‐impermeant HaloTag and SnapTag technologies. To isolate ligand binding pharmacology at a defined VEGFR2/NRP1 complex, we developed an assay using NanoBiT complementation technology whereby heteromerisation is required for luminescence emissions. Binding affinities and kinetics of VEGFR2‐selective VEGF 165 b‐TMR and non‐selective VEGF 165 a‐TMR were monitored using BRET from this defined complex. Key Results Cell surface VEGFR2 and NRP1 were co‐localised and formed a constitutive heteromeric complex. Despite being selective for VEGFR2, VEGF 165 b‐TMR had a distinct kinetic ligand binding profile at the complex that largely remained elevated in cells over 90 min. VEGF 165 a‐TMR bound to the VEGFR2/NRP1 complex with kinetics comparable to those of VEGFR2 alone. Using a binding‐dead mutant of NRP1 did not affect the binding kinetics or affinity of VEGF 165 a‐TMR. Conclusion and Implications This NanoBiT approach enabled real‐time ligand binding to be quantified in living cells at 37°C from a specified complex between a receptor TK and its co‐receptor for the first time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".