Recombinant Genetic Libraries and Human Monoclonal Antibodies
Bibliographic record
Abstract
In order to comprehensively manipulate the human proteome we require a vast repertoire of pharmacological reagents. To address these needs we have developed repertoires of synthetic antibodies by phage display, where diversified oligonucleotides are used to modify the complementarity-determining regions (CDRs) of a human antigen-binding fragment (Fab) scaffold. As diversity is produced outside the confines of the mammalian immune system, synthetic antibody libraries allow us to bypass several limitations of hybridoma technology while improving the experimental parameters under which pharmacological reagents are produced. Here we describe the methodologies used to produce synthetic antibody libraries from a single human framework with diversity restricted to four CDRs. These synthetic repertoires can be extremely functional as they produce highly selective, high affinity Fabs to the majority of soluble human antigens. Finally we describe selection methodologies that allow us to overcome immuno-dominance in our selections to target a variety of epitopes per antigen. Together these methodologies allow us to produce human monoclonal antibodies to manipulate the human proteome.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".