Superagonistic Activation of ErbB-1 by EGF-related Growth Factors with Enhanced Association and Dissociation Rate Constants
Bibliographic record
Abstract
Epidermal growth factor (EGF) and transforming growth factor-α (TGFα) are mitogenic hormones that exert their activity primarily by binding to the EGF receptor, also known as ErbB-1. We have recently characterized a set of EGF/TGFα chimeric molecules with similar high affinity for ErbB-1 as EGF and TGFα and shown that three of these chimeras induce mitogenic cell stimulation at already a 10-fold lower concentration than their wild-type counterparts (Lenferink, A. E., Kramer, R. H., van Vugt, M. J., Königswieser, M., DiFiore, P. P., van Zoelen, E. J., and van de Poll, M. L. (1997) Biochem. J. 327, 859–865). In the present study we show that these so-called superagonistic chimeras do not differ from EGF and TGFα in their ability to induce ErbB-1 tyrosine phosphorylation but are considerably more potent in activation of mitogen-activated protein kinase phosphorylation. Direct cell binding studies and analysis of ligand-receptor interaction by surface plasmon resonance measurements revealed that both the association rate constant ( k on ) and the dissociation rate constant ( k off ) of these superagonists is 3–5-fold higher in comparison with the wild-type ligands and nonsuperagonistic chimeras. These data indicate that the dynamic on and off rate constants for receptor binding may be more specific parameters for determining the mitogenic activity of peptide hormones than their constants for equilibrium receptor binding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".