Development of a potent high-affinity human therapeutic antibody via novel application of recombination signal sequence–based affinity maturation
Bibliographic record
Abstract
Therapeutic antibody development requires discovery of an antibody molecule with desired specificities and drug-like properties. For toxicological studies, a therapeutic antibody must bind the ortholog antigen with a similar affinity to the human target to enable relevant dosing regimens, and antibodies falling short of this affinity design goal may not progress as therapeutic leads. Herein, we report the novel use of mammalian recombination signal sequence (RSS)-directed recombination for complementarity-determining region-targeted protein engineering combined with mammalian display to close the species affinity gap of human interleukin (IL)-13 antibody 731. This fully human antibody has not progressed as a therapeutic in part because of a 400-fold species affinity gap. Using this nonhypothesis-driven affinity maturation method, we generated multiple antibody variants with improved IL-13 affinity, including the highest affinity antibody reported to date (34 fM). Resolution of a cocrystal structure of the optimized antibody with the cynomolgus monkey (or nonhuman primate) IL-13 protein revealed that the RSS-derived mutations introduced multiple successive amino-acid substitutions resulting in a de novo formation of a π-π stacking-based protein-protein interaction between the affinity-matured antibody heavy chain and helix C on IL-13, as well as an introduction of an interface-distant residue, which enhanced the light chain-binding affinity to target. These mutations synergized binding of heavy and light chains to the target protein, resulting in a remarkably tight interaction, and providing a proof of concept for a new method of protein engineering, based on synergizing a mammalian display platform with novel RSS-mediated library generation.
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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 | 0.001 |
| 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".