A phage‐displayed single‐chain Fab library optimized for rapid production of single‐chain <scp>IgGs</scp>
Bibliographic record
Abstract
Phage-displayed synthetic antibody (Ab) repertoires have become a major source of affinity reagents for basic and clinical research. Specific Abs identified from such libraries are often screened as fragments antigen binding (Fabs) produced in bacteria, and those with desired biochemical characteristics are reformatted for production as full-length immunoglobulin G (IgG) in mammalian cells. The conversion of Fabs to IgGs is a cumbersome and often rate-limiting step in the development of Abs. Moreover, biochemical properties required for lead IgG development are not always shared by the Fabs, and these issues are not uncovered until a significant effort has been spent on Abs that ultimately will not be useful. Thus, there is a need for simple and rapid techniques to convert phage-displayed Fabs to IgGs at an early stage of the Ab screening process. We report the generation of a highly diverse phage-displayed synthetic single-chain Fab (scFab) library, in which the light and heavy chains were tethered with an optimized linker. Following selection, pools of scFabs were converted to single-chain IgGs (scIgGs) en masse, enabling facile screening of hundreds of phage-derived scIgGs. We show that this approach can be used to rapidly screen for and select scIgGs that target cell-surface receptors, and scIgGs behave the same as conventional IgGs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".