Structural Survey of Antigen Recognition by Synthetic Human Antibodies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it