Analysis and Statistics of Crystallisation Success Increase by Composition Modification of Protein and Precipitant Mixing Ratio
Bibliographic record
Abstract
The nucleation zone has to be reached for any crystal to grow, and the search for crystallization conditions of new proteins is a trial and error process. Here a convenient screening strategy is studied in detail that varies the volume ratio of protein sample to the reservoir solution in the drop to initiate crystallization that is named “composition modification”. It is applied after the first screen and has been studied with twelve proteins. Statistical analysis shows a significant improvement in screening using this strategy. The average improvement of “hits” at different temperatures is between 32 and 42%, for examples, 41.8% ± 14.0% and 35.7% ± 12.4% (± standard deviation) at 288 K and 300 K, respectively. Remarkably, some new crystals were found by composition modification which increased the probability of reaching the nucleation zone to initiate crystallization. This was confirmed by a phase diagram study. It is also demonstrated that composition modification can further increase crystallisation success significantly (1.3 times) after the improvement of “hits” by temperature screening. The trajectories of different composition modifications during vapour diffusion were plotted, further demonstrating that protein crystallizability can be increased by hitting more parts of the nucleation zone. It was also found to facilitate the finding of initial crystals for proteins of low solubility. These proteins gradually become more concentrated during the vapour diffusion process starting from a larger protein solution ratio in the initial mixture. Keywords: Composition modification, nucleation zone, protein crystallizability, protein with low solubility, statistics, PDB, NMR data, trajectories, Sigma Chemicals, NAD kinase, hits increase, chymotrypsinogen, crystallizability, supersaturationComposition modification, nucleation zone, protein crystallizability, protein with low solubility, statistics, PDB, NMR data, trajectories, Sigma Chemicals, NAD kinase, hits increase, chymotrypsinogen, crystallizability, supersaturation
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".