Selection of Non-aggregating VH Binders from Synthetic VH Phage-Display Libraries
Bibliographic record
Abstract
The particular interest in VH antibody fragments stems from the fact that they can rival their "naturally occurring" single-domain antibody (sdAb) counterparts (camelid VHHs and shark VNARs) with regard to such desirable characteristics as stability, solubility, expression, and ability to penetrate cryptic epitopes and outperform them in terms of less immunogenicity, a much valued property in human immunotherapy applications. However, human VHs are typically prone to aggregation. Various approaches for developing non-aggregating human VHs with binding specificities have relied on a combination of recombinant DNA technology and phage-display technology. VH gene libraries are constructed synthetically by randomizing the CDRs of a single VH scaffold fused to a gene encoding a phage coat protein. Recombinant phage expressing the resulting VH libraries in fusion with the pIII protein is propagated in Escherichia coli. Monoclonal phage displaying VHs with specificities for target antigens are isolated from the libraries by a process called panning. The exertion of stability pressure in addition to binding pressure during panning ensures that the isolated VH binders are also non-aggregating. The genes encoding the desired VHs selected from the libraries are packaged within the phage particles, linking genotype and phenotype, hence making possible the identification of the selected VHs through identifying its physically linked genotype. Here, we describe the application of recombinant DNA and phage-display technologies for the construction of a phage-displayed human VH library, the panning of the library against a protein, and the expression, purification, and characterization of non-aggregating VHs isolated by panning.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".