Highly Ordered Protein Nanorings Designed by Accurate Control of Glutathione S-Transferase Self-Assembly
Bibliographic record
Abstract
Protein self-assembly into exquisite, complex, yet highly ordered architectures represents the supreme wisdom of nature. However, precise manipulation of protein self-assembly behavior in vitro is a great challenge. Here we report that by taking advantage of the cooperation of metal-ion-chelating interactions and nonspecific protein-protein interactions, we achieved accurate control of the orientation of proteins and their self-assembly into protein nanorings. As a building block, we utilized the C2-symmetric protein sjGST-2His, a variant of glutathione S-transferase from Schistosoma japonicum having two properly oriented His metal-chelating sites on the surface. Through synergic metal-coordination and non-covalent interactions, sjGST-2His self-assembled in a fixed bending manner to form highly ordered protein nanorings. The diameters of the nanorings can be regulated by tuning the strength of the non-covalent interaction network between sjGST-2His interfaces through variation of the ionic strength of the solution. This work provides a de novo design strategy that can be applied in the construction of novel protein superstructures.
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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) | 0.000 | 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 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".