Signal sequence as a determinant in expressing disulfide-stabilized single chain antibody variable fragments (sc-dsFv) against human VEGF
Bibliographic record
Abstract
Phage-displayed single chain variable fragment (scFv) libraries have been powerful tools in antibody engineering. But the scFv structures are frequently unstable due to the dissociation of the dimeric interface between the two variable domains. One solution is the sc-dsFv construct, where the single chain variable domain fragment is stabilized with an additional interface disulfide bond, leading to stable and homogeneous dimeric interface for the sc-dsFv structure. However, the phagemid system that is capable of effective expression for both sc-dsFv-pIII fusion proteins on phage surface and secreted non-fusion sc-dsFv in bacterial culture medium has not been demonstrated. In this work, a biological combinatorial approach was applied to optimize the signal sequence N-terminal to the sc-dsFv-pIII fusion protein encoded in a phagemid. The optimized sc-dsFv phage display systems were compatible with both the phage-based directed evolution procedure and the high throughput screening of the soluble sc-dsFv. The utility of the phagemid systems was demonstrated in generating anti-VEGF sc-dsFv with VEGF-binding affinity one order of magnitude higher than the corresponding scFv, due only to the interface disulfide bond in the sc-dsFv. Moreover, the protein stability of the sc-dsFv construct was unmatched by the corresponding scFv. These advantages of the sc-dsFv were gained through the interface disulfide bond of the sc-dsFv and the novel signal sequence in the phagemid.
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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) | 0.000 | 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 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".