Generation and Selection of Synthetic Human Antibody Libraries via Phage Display
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Synthetic antibody libraries enable the development of antibodies that can recognize virtually any antigen, with affinity and specificity profiles that are superior to those of natural antibodies. By using highly stable and optimized frameworks, synthetic antibody libraries can be rapidly generated by precisely designing synthetic DNA, allowing absolute control over the position and chemical diversity introduced while expanding the sequence space for antigen recognition. Here, we describe a detailed protocol for the generation of highly diverse synthetic antibody phage display libraries based on a single framework, with diversity genetically incorporated by using finely designed mutagenic oligonucleotides. This general method enables the facile construction of large antibody libraries with precisely tunable features, resulting in the rapid development of recombinant antibodies for virtually any antigen.
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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 | 0.000 | 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 it