An engineered redox‐switchable streptavidin mutein enables the high‐affinity capture and efficient elution of biotinylated ligands
Bibliographic record
Abstract
Although the high affinity and specificity of the interaction between streptavidin and biotin are widely utilized to isolate biotinylated ligands, their elution over practical timescales requires the use of strongly denaturing conditions that are incompatible with many target biomolecules. To overcome this limitation, we previously engineered a disulfide bond into a critical loop of streptavidin to create the redox-sensitive M88 mutein, which releases biotinylated ligands ~19,000-fold faster in the reduced state when compared with the oxidized state. To optimize the speed and efficiency of ligand recovery, we describe how multiple mechanisms like disulfide bond reduction, increasing temperature, elevating pH, and adding organic cosolvents can dramatically increase the rates of dissociation for different types of biotinylated ligands bound to M88 or wild-type streptavidin coupled to magnetic beads. By combining these mechanisms, we demonstrate how a range of biotinylated biomolecules (peptide, oligonucleotide, and protein) can be efficiently recovered under mild conditions that preserve biological activity. M88 magnetic beads thus provide many advantages over currently available methods using either wild-type streptavidin or lower affinity biotin-binding proteins for applications requiring both high affinity capture and efficient release of target biomolecules under non-denaturing conditions.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".