Identification of cross-reactive single-domain antibodies against serum albumin using next-generation DNA sequencing
Bibliographic record
Abstract
Antibodies that cross-react with multiple isoforms or homologue of a given protein are often desirable, especially in therapeutic applications. Here, we report the identification of several unique, clonally unrelated, single-domain antibodies (sdAbs) that bind to multiple serum albumin orthologues (human, rhesus, rat and mouse) with low-to-medium nanomolar affinity from a llama immunized only with human serum albumin. Using single-round panning of a phage-displayed sdAb library against serum albumins and next-generation DNA sequencing, we were able to predict patterns of sdAb reactivity to the albumins of different species with ∼90% accuracy. We expect this strategy to be generally applicable for identifying cross-reactive sdAbs, particularly when these exist at low frequency and/or are poorly enriched by panning. Moreover, the sdAbs identified here are of potential interest for serum half-life extension of biologics.
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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 | 0.001 | 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) | 0.000 | 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".