Using geometric criteria to study helix-like structures produced in molecular dynamics simulations of single amylose chains in water
Bibliographic record
Abstract
Amylose is a linear polymer chain of α-d-glucose units connected through α(1 → 4) glycosidic bonds. Experimental studies show that in non-polar solvents, single amylose chains form helical structures containing precise H-bond patterns. However, both experimental and computational studies indicate that these perfectly H-bonded helices are not stable in pure water. Nevertheless, amylose chains are observed to form helix-like structures in molecular dynamics (MD) simulations that exhibit imperfect H-bond patterns. In this paper, we study the structure of amylose chains in water using MD simulations to identify and characterize these "imperfect" helical structures. To this end we devise geometry-based criteria to define imperfect helical structures in amylose chains. Using this approach, the propensity of amylose chains to form these structures is quantified as a function of chain length and solvent temperature. This analysis also uncovers both short and long time helix-breaking mechanisms such as band-flips and kinks in the chain. This geometric approach to defining imperfect helices thus allows us to give new insight into the secondary structure of single amylose chains in spite of imperfect H-bond patterns.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".