Optimal Design Features of Camelized Human Single-domain Antibody Libraries
Bibliographic record
Abstract
We have constructed a human V(H) library based on a camelized V(H) sequence. The library was constructed with complete randomization of 19 of the 23 CDR3 residues and was panned against two monoclonal antibody targets to generate V(H) sequences for determination of the antigen contact residue positions. Furthermore, the feasibility and desirability of introducing a disulfide bridge between CDR1 and CDR3 was investigated. Sequences derived from the library showed a bias toward the use of C-terminal CDR3 residues as antigen contact residues. Mass spectrometric analyses indicated that CDR1-CDR3 disulfide formation was universal. However, surface plasmon resonance and NMR data showed that the CDR3 constraint imposed by the disulfide bridge was not always desirable. Very high yields of soluble protein products and lack of protein aggregation, as demonstrated by the quality of the (1)H-(15)N HSQC spectra, indicated that the V(H) sequence for library construction was a good choice. These results should be useful in the design of V(H) libraries with optimal features.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".