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 VHs over camelid VHHs as immunotherapeutics is their perceived lower risk of immunogenicity. While human VHs can readily be obtained from synthetic phage display libraries, they often suffer from low affinity and poor solubility compared to VHHs derived from immune libraries. Using SARS‐CoV‐2 spike protein as a model antigen, we screened a synthetic human VH phage display library and identified a diverse set of antigen‐specific VHs. However, the VHs 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 VH to in vitro affinity maturation. We created a yeast surface display library of VH 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 VHs as potentially improved candidates. Characterization of these candidates revealed several VHs with improved affinities of up to 100‐fold (KDs 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 VH (a process referred to as camelization), we were able to completely solubilize the VH 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 VHs 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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".