A New Model for Simulating 3-D Crystal Growth and Its Application to the Study of Antifreeze Proteins
Bibliographic record
Abstract
A novel computational technique for modeling crystal formation has been developed that combines three-dimensional (3-D) molecular representation and detailed energetics calculations of molecular mechanics techniques with the less-sophisticated probabilistic approach used by statistical techniques to study systems containing millions of molecules undergoing billions of interactions. Because our model incorporates both the structure of and the interaction energies between participating molecules, it enables the 3-D shape and surface properties of these molecules to directly affect crystal formation. This increase in model complexity has been achieved while simultaneously increasing the number of molecules in simulations by several orders of magnitude over previous statistical models. We have applied this technique to study the inhibitory effects of antifreeze proteins (AFPs) on ice-crystal formation. Modeling involving both fish and insect AFPs has produced results consistent with experimental observations, including the replication of ice-etching patterns, ice-growth inhibition, and specific AFP-induced ice morphologies. Our work suggests that the degree of AFP activity results more from AFP ice-binding orientation than from AFP ice-binding strength. This technique could readily be adapted to study other crystal and crystal inhibitor systems, or to study other noncrystal systems that exhibit regularity in the structuring of their component molecules, such as those associated with the new nanotechnologies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".