Affinity‐enhanced protein partitioning in decyl β‐<scp>D</scp>‐glucopyranoside two‐phase aqueous micellar systems
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Bibliographic record
Abstract
Liquid-liquid extraction in two-phase aqueous complex-fluid systems has been proposed as a scalable, versatile, and cost-effective purification method for the downstream processing of biotechnological products. In the case of two-phase aqueous micellar systems, careful choices of the phase-forming surfactants or surfactant mixtures allow these systems to separate biomolecules based on size, hydrophobicity, charge, or specific affinity. In this article, we investigate the affinity-enhanced partitioning of a model affinity-tagged protei--green fluorescent protein fused to a family 9 carbohydrate-binding module (CBM9-GFP)--in a two-phase aqueous micellar system generated from the nonionic surfactant n-decyl beta-D-glucopyranoside (C10G1), which acts simultaneously as the phase-former and the affinity ligand. In this simple system, CBM9-GFP was extracted preferentially into the micelle-rich phase, despite the opposing tendency of the steric, excluded-volume interactions operating between the protein and the micelles. We obtained more than a sixfold increase (from 0.47 to 3.1) in the protein partition coefficient (Kp), as compared to a control case where the affinity interactions were "turned off" by the addition of a competitive inhibitor (glucose). It was demonstrated conclusively that the observed increase in Kp can be attributed to the specific affinity between the CBM9 domain and the affinity surfactant C10G1, suggesting that the method can be generally applied to any CBM9-tagged protein. To rationalize the observed phenomenon of affinity-enhanced partitioning in two-phase aqueous micellar systems, we formulated a theoretical framework to model the protein partition coefficient. The modeling approach accounts for both the excluded-volume interactions and the affinity interactions between the protein and the surfactants, and considers the contributions from the monomeric and the micellar surfactants separately. The model was shown to be consistent with the experimental data, as well as with our current understanding of the CBM9 domain.
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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 | 0.000 | 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 | 0.001 |
| 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 it