Synthetic Modular Antibody Construction by Using the SpyTag/SpyCatcher Protein‐Ligase System
Why this work is in the frame
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Bibliographic record
Abstract
Efforts to engineer recombinant antibodies for specific diagnostic and therapy applications are time consuming and expensive, as each new recombinant antibody needs to be optimized for expression, stability, bio-distribution, and pharmacokinetics. We have developed a new way to construct recombinant antibody-like "devices" by using a bottom-up approach to build them from well-behaved discrete recombinant antibody domains or "parts". Studies on antibody structure and function have identified antibody constant and variable domains with specific functions that can be expressed in isolation. We used the SpyTag/SpyCatcher protein ligase to join these parts together, thereby creating devices with desired properties based on summed properties of parts and in configurations that cannot be obtained by using genetic engineering. This strategy will create optimized recombinant antibody devices at reduced costs and with shortened development times.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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 it