Engineering Streptavidin and a Streptavidin-Binding Peptide with Infinite Binding Affinity and Reversible Binding Capability: Purification of a Tagged Recombinant Protein to High Purity via Affinity-Driven Thiol Coupling
Bibliographic record
Abstract
To extend and improve the utility of the streptavidin-binding peptide tag (SBP-tag) in applications ranging from affinity purification to the reversible immobilization of recombinant proteins, a cysteine residue was introduced to the streptavidin mutein SAVSBPM18 and the SBP-tag to generate SAVSBPM32 and SBP(A18C), respectively. This pair of derivatives is capable of forming a disulfide bond through the newly introduced cysteine residues. SAVSBPM32 binds SBP-tag and biotin with binding affinities (Kd ~ 10-8M) that are similar to SAVSBPM18. Although SBP(A18C) binds to SAVSBPM32 more weakly than SBP-tag, the binding affinity is sufficient to bring the two binding partners together efficiently before they are locked together via disulfide bond formation-a phenomenon we have named affinity-driven thiol coupling. Under the condition with SBP(A18C) tags in excess, two SBP(A18C) tags can be captured by a tetrameric SAVSBPM32. The stoichiometry of the disulfide-bonded SAVSBPM32-SBP(A18C) complex was determined using a novel two-dimensional electrophoresis method which has general applications for analyzing the composition of disulfide-bonded protein complexes. To illustrate the application of this reversible immobilization technology, optimized conditions were established to use the SAVSBPM32-affinity matrix for the purification of a SBP(A18C)-tagged reporter protein to high purity. Furthermore, we show that the SAVSBPM32-affinity matrix can also be applied to purify a biotinylated protein and a reporter protein tagged with the unmodified SBP-tag. The dual (covalent and non-covalent) binding modes possible in this system offer great flexibility to many different applications which need reversible immobilization capability.
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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 | 0.000 |
| 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 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".