Antibody light chain variable domains and their biophysically improved versions for human immunotherapy
Bibliographic record
Abstract
We set out to gain deeper insight into the potential of antibody light chain variable domains (VLs) as immunotherapeutics. To this end, we generated a naïve human VL phage display library and, by using a method previously shown to select for non-aggregating antibody heavy chain variable domains (VHs), we isolated a diversity of VL domains by panning the library against B cell super-antigen protein L. Eight domains representing different germline origins were shown to be non-aggregating at concentrations as high as 450 µM, indicating VL repertoires are a rich source of non-aggregating domains. In addition, the VLs demonstrated high expression yields in E. coli, protein L binding and high reversibility of thermal unfolding. A side-by-side comparison with a set of non-aggregating human VHs revealed that the VLs had similar overall profiles with respect to melting temperature (T(m)), reversibility of thermal unfolding and resistance to gastrointestinal proteases. Successful engineering of a non-canonical disulfide linkage in the core of VLs did not compromise the non-aggregation state or protein L binding properties. Furthermore, the introduced disulfide bond significantly increased their T(m)s, by 5.5-17.5 ° C, and pepsin resistance, although it somewhat reduced expression yields and subtly changed the structure of VLs. Human VLs and engineered versions may make suitable therapeutics due to their desirable biophysical features. The disulfide linkage-engineered VLs may be the preferred therapeutic format because of their higher stability, especially for oral therapy applications that necessitate high resistance to the stomach's acidic pH and pepsin.
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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".