Structural Survey of Antigen Recognition by Synthetic Human Antibodies
Bibliographic record
Abstract
Synthetic antibody libraries have been used extensively to isolate and optimize antibodies. To generate these libraries, the immunological diversity and the antibody framework(s) that supports it outside of the binding regions are carefully designed/chosen to ensure favorable functional and biophysical properties. In particular, minimalist, single-framework synthetic libraries pioneered by our group have yielded a vast trove of antibodies to a broad array of antigens. Here, we review their systematic and iterative development to provide insights into the design principles that make them a powerful tool for drug discovery. In addition, the ongoing accumulation of crystal structures of antigen-binding fragment (Fab)-antigen complexes generated with synthetic antibodies enables a deepening understanding of the structural determinants of antigen recognition and usage of immunoglobulin sequence diversity, which can assist in developing new strategies for antibody and library optimization. Toward this, we also survey here the structural landscape of a comprehensive and unbiased set of 50 distinct complexes derived from these libraries and compare it to a similar set of natural antibodies with the goal of better understanding how each achieves molecular recognition and whether opportunities exist for iterative improvement of synthetic libraries. From this survey, we conclude that despite the minimalist strategies used for design of these synthetic antibody libraries, the overall structural interaction landscapes are highly similar to natural repertoires. We also found, however, some key differences that can help guide the iterative design of new synthetic libraries via the introduction of positionally tailored diversity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".