Development and Characterization of a Series of Soluble Tetrameric and Monomeric Streptavidin Muteins with Differential Biotin Binding Affinities
Bibliographic record
Abstract
The strong biotin-streptavidin interaction limits the application of streptavidin as a reversible affinity matrix for purification of biotinylated biomolecules. To address this concern, a series of single, double, and triple streptavidin muteins with different affinities to biotin were designed. The strategy involves mutating one to three strategically positioned residues (Ser-45, Thr-90, and Asp-128) that interact with biotin and other framework structure-maintaining residues of streptavidin. The muteins were produced in soluble forms via secretion from Bacillus subtilis. The impact of individual residues on the overall structure of streptavidin is reflected by the formation of monomeric streptavidin to different extents. Of the three targeted residues, Asp-128 has the most dramatic effect (Asp-128 > Thr-90 > Ser-45). Conversion of all three targeted residues to alanine results in a soluble biotin binding mutein that exists 100% in the monomeric state. Both wild-type and mutated (monomeric and tetrameric) streptavidin proteins were purified, and their kinetic parameters (on- and off-rates) were determined using a BIAcore biosensor with biotin-conjugated bovine serum albumin immobilized to the sensor chip. This series of muteins shows a wide spectrum of affinity toward biotin (K(d) from 10(-6) to 10(-11) m). Some of them have the potential to serve as reversible biotin binding agents.
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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 | 0.001 |
| 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) | 0.000 | 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".