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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".