<i>In vitro</i> Evolution of Antibody Affinity via Insertional Mutagenesis Scanning of an Entire Antibody Variable Region
Bibliographic record
Abstract
Abstract We report the first systematic combinatorial exploration of affinity enhancement of antibodies by insertions and deletions (InDels). Transposon-based introduction of InDels via the method TRIAD was used to generate large libraries with random in-frame InDels across the entire scFv gene that were further recombined and screened by ribosome display. Knowledge of potential insertion points from TRIAD libraries formed the basis of exploration of length and sequence diversity of novel insertions by insertional-scanning mutagenesis (ISM). An overall 256-fold affinity improvement of an anti-IL-13 antibody BAK1 as a result of InDel mutagenesis and combination with known point mutations validates this approach and suggests that the results of this InDel approach and conventional exploration of point mutations can synergize to generate antibodies with higher affinity. Significance Insertion/deletion (InDel) mutations play key roles in genome and protein evolution. Despite their prominence in evolutionary history, the potential of InDels for changing function in protein engineering by directed evolution remains unexplored. Instead point mutagenesis is widely used. Here we create antibody libraries containing InDels and demonstrate that affinity maturation can be achieved in this way, establishing an alternative to the point mutation strategies employed in all previous in vitro selections. These InDels mirror the observation of considerable length variation in loops of natural antibodies originating from the same germline genes and be combined with point mutations, making both natural sources of functional innovation available for artificial evolution in the test tube.
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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 | 0.001 |
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".