Optimization of synthetic human <scp>V<sub>H</sub></scp> affinity and solubility through in vitro affinity maturation and minimal camelization
Bibliographic record
Abstract
Abstract An attractive feature of human V H s over camelid V H Hs as immunotherapeutics is their perceived lower risk of immunogenicity. While human V H s can readily be obtained from synthetic phage display libraries, they often suffer from low affinity and poor solubility compared to V H Hs derived from immune libraries. Using SARS‐CoV‐2 spike protein as a model antigen, we screened a synthetic human V H phage display library and identified a diverse set of antigen‐specific V H s. However, the V H s exhibited low affinity, and many had low solubility; that is, they were prone to aggregation. To explore the feasibility of improving the affinity, we subjected a representative V H to in vitro affinity maturation. We created a yeast surface display library of V H variants employing a site‐saturated mutagenesis approach targeting complementarity‐determining regions and selected against the target antigen. Next‐generation sequencing of the selected variants, combined with structural modeling, identified a set of V H s as potentially improved candidates. Characterization of these candidates revealed several V H s with improved affinities of up to 100‐fold ( K D s as low as 3 nM) and potent neutralization capabilities; however, they still showed significant aggregation. By introducing as few as two camelid residues into the framework region 2 of a high‐affinity V H (a process referred to as camelization), we were able to completely solubilize the V H without compromising its affinity and other important attributes, including thermostability and protein A binding. This study demonstrates the feasibility of generating high‐affinity, ‐solubility, and ‐stability human V H s from synthetic libraries through a combination of in vitro affinity maturation and minimal camelization.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".