Recombinant renewable polyclonal antibodies
Bibliographic record
Abstract
Only a small fraction of the antibodies in a traditional polyclonal antibody mixture recognize the target of interest, frequently resulting in undesirable polyreactivity. Here, we show that high-quality recombinant polyclonals, in which hundreds of different antibodies are all directed toward a target of interest, can be easily generated in vitro by combining phage and yeast display. We show that, unlike traditional polyclonals, which are limited resources, recombinant polyclonal antibodies can be amplified over one hundred million-fold without losing representation or functionality. Our protocol was tested on 9 different targets to demonstrate how the strategy allows the selective amplification of antibodies directed toward desirable target specific epitopes, such as those found in one protein but not a closely related one, and the elimination of antibodies recognizing common epitopes, without significant loss of diversity. These recombinant renewable polyclonal antibodies are usable in different assays, and can be generated in high throughput. This approach could potentially be used to develop highly specific recombinant renewable antibodies against all human gene products.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".