Affinity‐enhanced protein partitioning in decyl β‐<scp>D</scp>‐glucopyranoside two‐phase aqueous micellar systems
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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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 | 0.000 | 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".