Identification of cross-reactive single-domain antibodies against serum albumin using next-generation DNA sequencing
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 it