Disulfide linkage engineering for improving biophysical properties of human VH domains
Bibliographic record
Abstract
To enhance their therapeutic potential, human antibody heavy chain variable domains (V(H)s) would benefit from increased thermostability. The highly conserved disulfide linkage that connects Cys23 and Cys104 residues in the core of V(H) domains is crucial to their stability and function. It has previously been shown that the introduction of a second disulfide linkage can increase the thermostability of camelid heavy-chain antibody variable domains (V(H)Hs). Using four model domains we demonstrate that this strategy is also applicable to human V(H) domains. The introduced disulfide linkage, formed between Cys54 and Cys78 residues, increased the thermostability of V(H)s by 14-18°C. In addition, using a novel hexa-histidine capture technology, circular dichroism, turbidity, size exclusion chromatography and multiangle light scattering measurements, we demonstrate reduced V(H) aggregation in domains with the Cys54-Cys78 disulfide linkage. However, we also found that the engineered disulfide linkage caused conformational changes, as indicated by reduced binding of the V(H)s to protein A. This indicates that it may be prudent to use the synthetic V(H) libraries harboring the engineered disulfide linkage before screening for affinity reagents. Such strategies may increase the number of thermostable binders.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".