Improving solubility and refolding efficiency of human VHs by a novel mutational approach
Bibliographic record
Abstract
The antibody V(H) domains of camelids tend to be soluble and to resist aggregation, in contrast to human V(H) domains. For immunotherapy, attempts have therefore been made to improve the properties of human V(H)s by camelization of a small set of framework residues. Here, we have identified through sequence comparison of well-folded llama V(H) domains an alternative set of residues (not typically camelid) for mutation. Thus, the solubility and thermal refolding efficiency of a typical human V(H), derived from the human antibody BT32/A6, were improved by introduction of two mutations in framework region (FR) 1 and 4 to generate BT32/A6.L1. Three more mutations in FR3 of BT32/A6.L1 further improved the thermal refolding efficiency while retaining solubility and cooperative melting profiles. To demonstrate practical utility, BT32/A6.L1 was used to construct a phage display library from which were isolated human V(H)s with good antigen binding activity and solubility. The engineered human V(H) domains described here may be useful for immunotherapy, due to their expected low immunogenicity, and in applications involving transient high temperatures, due to their efficient refolding after thermal denaturation.
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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) | 0.000 | 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) | 0.000 | 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 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".