Structure-Directed and Tailored Diversity Synthetic Antibody Libraries Yield Novel Anti-EGFR Antagonists
Bibliographic record
Abstract
We tested whether grafting an interaction domain into the hypervariable loop of a combinatorial antibody library could promote targeting to a specific epitope. Formation of the epidermal growth factor receptor (EGFR) signaling heterodimer involves extensive contacts mediated by a "dimerization loop." We grafted the dimerization loop into the third hypervariable loop of a synthetic antigen-binding fragment (Fab) library and diversified other loops using a tailored diversity strategy. This structure-directed Fab library and a naı̈ve synthetic Fab library were used to select Fabs against EGFR. Both libraries yielded high affinity Fabs that bound to overlapping epitopes on cell-surface EGFR, inhibited receptor activation, and targeted epitopes distinct from those of cetuximab and panitumumab. Epitope mapping experiments revealed complex sites of interaction, comprised of domains I and II but not exclusively localized to the receptor dimerization loop. These results validate the grafting approach for designing Fab libraries and also underscore the versatility of naı̈ve synthetic libraries.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".