Site-Directed Mutagenesis for Improving Biophysical Properties of VH Domains
Bibliographic record
Abstract
Recombinant antibody fragments are significant therapeutic and diagnostic reagents. As such, their efficacy depends heavily on their affinities and biophysical properties. Thus, mutagenesis approaches have been extensively applied to recombinant antibodies to improve their affinity, stability, and solubility. Among the existing recombinant antibody variants, human V(H) domains stand out as the ones with the general need of solubility engineering at some point during their development; this solubility engineering step transforms V(H)s into nonaggregating, functional entities, rendering them useful as therapeutic and diagnostic reagents. Here, we present one of several approaches that have been employed to develop nonaggregating human V(H) domains. We apply an in vitro site-directed mutagenesis approach to an aggregating human V(H) domain by means of a splice overlap extension technique. The resultant mutant V(H)s are nonaggregating in contrast to the parent wild type V(H) and less prone to aggregation following thermal unfolding.
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 | 0.000 |
| 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 | 0.001 |
| 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".