Human Combinatorial Fab Library Yielding Specific and Functional Antibodies against the Human Fibroblast Growth Factor Receptor 3
Bibliographic record
Abstract
The human combinatorial antibody library Fab 1 (HuCAL-Fab 1) was generated by transferring the heavy and light chain variable regions from the previously constructed single-chain Fv library (Knappik, A., Ge, L., Honegger, A., Pack, P., Fischer, M., Wellnhofer, G., Hoess, A., Wölle, J., Plückthun, A., and Virnekäs, B. (2000) J. Mol. Biol. 296, 57-86), diversified in both complementarity-determining regions 3 into a novel Fab display vector, yielding 2.1 x 10(10) different antibody fragments. The modularity has been retained in the Fab display and screening plasmids, ensuring rapid conversion into various antibody formats as well as antibody optimization using prebuilt maturation cassettes. HuCAL-Fab 1 was challenged against the human fibroblast growth factor receptor 3, a potential therapeutic antibody target, against which, to the best of our knowledge, no functional antibodies could be generated so far. A unique screening mode was designed utilizing recombinant functional proteins and cell lines differentially expressing fibroblast growth factor receptor isoforms diversified in expression and receptor dependence. Specific Fab fragments with subnanomolar affinities were isolated by selection without any maturation steps as determined by fluorescence flow cytometry. Some of the selected Fab fragments completely inhibit target-mediated cell proliferation, rendering them the first monoclonal antibodies against fibroblast growth factor receptors having significant function blocking activity. This study validates HuCAL-Fab 1 as a valuable source for the generation of target-specific antibodies for therapeutic applications.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".