Selection of recombinant anti‐<scp>SH</scp>3 domain antibodies by high‐throughput phage display
Bibliographic record
Abstract
Antibodies are indispensable tools in biochemical research and play an expanding role as therapeutics. While hybridoma technology is the dominant method for antibody production, phage display is an emerging technology. Here, we developed and employed a high-throughput pipeline that enables selection of antibodies against hundreds of antigens in parallel. Binding selections using a phage-displayed synthetic antigen-binding fragment (Fab) library against 110 human SH3 domains yielded hundreds of Fabs targeting 58 antigens. Affinity assays demonstrated that representative Fabs bind tightly and specifically to their targets. Furthermore, we developed an efficient affinity maturation strategy adaptable to high-throughput, which increased affinity dramatically but did not compromise specificity. Finally, we tested Fabs in common cell biology applications and confirmed recognition of the full-length antigen in immunoprecipitation, immunoblotting and immunofluorescence assays. In summary, we have established a rapid and robust high-throughput methodology that can be applied to generate highly functional and renewable antibodies targeting protein domains on a proteome-wide scale.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".