The Bioactivity and Receptor Affinity of Recombinant Tagged EGF Designed for Tissue Engineering Applications Is Defined by the Nature and Position of the Tags
Bibliographic record
Abstract
For tissue engineering applications, growth factor immobilization on cell culture scaffolds bears the potential to stimulate cell proliferation while minimizing costs associated to soluble growth factor supply. In order to evaluate the potential of a de novo-designed heterodimerization peptide pair, namely the E and K coils, for epidermal growth factor (EGF) grafting on various scaffolds, production of coil-tagged EGF chimeras using a mammalian cell expression system as well as their purification have been performed. The influence of the type of coil (E or K) upon EGF bioactivity, assessed in an in vitro cell assay, was compared to that of the fragment crystallizable (Fc) domain of immunoglobulin G by monitoring phosphorylation of EGF receptor (EGFR) upon chimeric EGF exposure. Our results demonstrate that the type and the location of the tag have a strong impact on growth factor bioactivity (EC50 ranging from 5.5 to 63 nM). Additional surface plasmon resonance-based biosensor experiments were conducted to test the ability of captured chimeric EGF to bind to their receptor ectodomain in vitro. These experiments indicated that the oriented coiled-coil-mediated immobilization of EGF was significantly more efficient than a random approach as coil-tagged EGF displayed enhanced affinities for artificially dimerized EGFR ectodomain when compared to Fc-tagged EGF (apparent KD of 5 pM vs. 16 nM). Altogether, our results highly suggest that coil-tagged chimeras represent an attractive avenue for the oriented immobilization of growth factors for tissue engineering applications and that HEK293 cells offer a robust platform for their expression in a bioactive form.
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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 | 0.000 | 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".