Selective Crystallization of Proteins Using Engineered Nanonucleants
Bibliographic record
Abstract
This study reports for the first time a detailed experimental investigation of protein crystallization in engineered nanoconfined spaces with both controlled pore diameters and narrow pore size distributions. We propose a systematic approach for controlling the nucleation and crystallization of biological macromolecules based on a relationship between the protein radius of gyration ( R g ) and specific pore diameter. A series of nanonucleants with ordered mesopores having narrow pore size distributions were prepared. The templates were tested for proteins ranging in molecular weight from 14 to 450 kDa. Well-formed protein crystals were obtained on only one of the five presented nanonucleants for all protein cases tested, highlighting the unique template selectivity exhibited by these nucleants. In addition, concanavalin A and catalase were both crystallized at ∼2 times lower supersaturation levels than previously reported by any known method. Our observations fully support theoretical studies that predict the enhanced thermodynamic stability of proteins in nanoconfined cavities, including specifically the importance of nucleant pore diameter with respect to protein radius of gyration. The nucleants described here could have major industrial applications for downstream separation and purification of biopharmaceuticals, as well as improved opportunities for the crystallization of complex proteins for structural determination.
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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".