Identification of New Peptide Ligands for Epidermal Growth Factor Receptor Using Phage Display and Computationally Modeling their Mode of Binding
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.
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) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".