Optimizing Production of Antigens and Fabs in the Context of Generating Recombinant Antibodies to Human Proteins
Bibliographic record
Abstract
We developed and optimized a high-throughput project workflow to generate renewable recombinant antibodies to human proteins involved in epigenetic signalling. Three different strategies to produce phage display compatible protein antigens in bacterial systems were compared, and we found that in vivo biotinylation through the use of an Avi tag was the most productive method. Phage display selections were performed on 265 in vivo biotinylated antigen domains. High-affinity Fabs (<20nM) were obtained for 196. We constructed and optimized a new expression vector to produce in vivo biotinylated Fabs in E. coli. This increased average yields up to 10-fold, with an average yield of 4 mg/L. For 118 antigens, we identified Fabs that could immunoprecipitate their full-length endogenous targets from mammalian cell lysates. One Fab for each antigen was converted to a recombinant IgG and produced in mammalian cells, with an average yield of 15 mg/L. In summary, we have optimized each step of the pipeline to produce recombinant antibodies, significantly increasing both efficiency and yield, and also showed that these Fabs and IgGs can be generally useful for chromatin immunoprecipitation (ChIP) protocols.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".