A novel synthetic trivalent single chain variable fragment (tri-scFv) construction platform based on the SpyTag/SpyCatcher protein ligase system
Bibliographic record
Abstract
Advances in antibody engineering provide strategies to construct recombinant antibody-like molecules with modified pharmacokinetic properties. Multermerization is one strategy that has been used to produce antibody-like molecules with two or more antigen binding sites. Multimerization enhances the functional affinity (avidity) and can be used to optimize size and pharmacokinetic properties. Most multimerization strategies involve genetically fusing or non-covalently linking antibody fragments using oligomerization domains. Recent studies have defined guidelines for producing antibody-like molecules with optimal tumor targeting properties, which require intermediates size (70–120 kDa) and bi- or tri-valency. We described a highly modular antibody-engineering platform for rapidly constructing synthetic, trivalent single chain variable fragments (Tri-scFv) using the SpyCatcher/SpyTag protein ligase system. We used this platform to construct an anti-human epidermal growth factor receptor 3 (HER3) Tri-scFv. We generated the anti-HER3 Tri-scFv by genetically fusing a SpyCatcher to the C-terminus of an anti-HER3 scFv and ligating it to a synthetic Tri-SpyTag peptide. The anti-HER3 Tri-scFv bound recombinant HER3 with an apparent K D of 2.67 nM, which is approximately 12 times lower than the K D of monomeric anti-HER3 scFv (31.2 nM). Anti-HER3 Tri-scFv also bound endogenous cell surface expressed HER3 stronger than the monomer anti-HER3 scFv. We used the SpyTag/SpyCatcher protein ligase system to ligate anti-HER3 scFv fused to a SpyCatcher at its C-termini to a Tri-SpyTag to construct Tr-scFv. This system allowed the construction of a Tri-scFv with all the scFv antigen-binding sites pointed outwards. The anti-HER3 Tri-scFv bound recombinant and endogenously expressed HER3 with higher functional affinity (avidity) than the monomeric anti-HER3 scFv. The Tri-scFv had the size, valency, and functional affinity that are desired for therapeutic and imaging applications. Use of the SpyTag/SpyCatcher protein ligase system allows Tri-scFvs to be rapidly constructed in a simple, modular manner, which can be easily applied to scFvs or other antibody fragments targeting other antigens.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".