Identification of New Peptide Ligands for Epidermal Growth Factor Receptor Using Phage Display and Computationally Modeling their Mode of Binding
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Peptide phage display, a powerful method for ligand identification, was used to identify new peptide ligands for epidermal growth factor receptor. A-431 cells expressing epidermal growth factor receptor were used as the matrix in a cell-based subtractive biopanning approach using a 7-mer peptide displaying phage library. Two novel peptide ligands were identified and tested for their affinities and functional effects on epidermal growth factor receptor. The identified peptides were able to inhibit the epidermal growth factor-induced phosphorylation of epidermal growth factor receptor in a concentration-dependent manner. The results of affinity binding experiments showed that the natural ligand, that is epidermal growth factor, was able to inhibit competitively the binding of peptide-bearing phage to epidermal growth factor receptor expressing A-431 cells. Molecular modeling studies were used to calculate the free energies for the binding of peptides to the receptor-binding site as well as proposing the interaction modes for this binding. The calculated values for the binding energies were found to be similar to our experimental data and those of previously reported studies.
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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 it