A ligand‐pseudoreceptor system based on <i>de novo</i> designed peptides for the generation of adenoviral vectors with altered tropism
Bibliographic record
Abstract
BACKGROUND: Delivery of transgenes into specific tissues by adenovirus vectors (AdVs) relies on ablations of their natural tropism and on introduction of a new tropism. If the interaction with its natural receptor is ablated, a new packaging cell line is required to produce the AdV. In the present study, we have used two de novo designed peptides (E-Coil and K-Coil) that interact with each other with high affinity to establish a new receptor-ligand system for the propagation of retargeted AdVs. METHODS: We produced a cell line (293E) expressing on its surface a pseudoreceptor containing the E-Coil. An AdV (AdFK4m/GFP) lacking the interaction with the primary receptor for adenovirus (CAR) and containing the K-Coil inserted at the fiber C-terminus was constructed and tested using two strategies: (1) an RGD motif (Arg-Gly-Asp) was inserted into the HI-loop of the fiber; (2) AdFK4m/GFP was conjugated to a bispecific adaptor for the epidermal growth factor receptor (EGFR). RESULTS: AdFK4m/GFP infected 293E cells more efficiently than cells lacking the pseudoreceptor. The transduction was due to the K-Coil/E-Coil specific interaction since it was competed by addition of soluble K-Coil, but not soluble fiber. We demonstrated that the modified AdV was retargeted toward alpha v integrin by inclusion of the RGD motif, or toward EGFR using the bispecific adaptor. CONCLUSIONS: We have established a new system to produce AdVs ablated of natural tropism. This system should permit the retargeting of AdVs by inserting new ligands within the fiber or through the interaction with bispecific adaptors.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".