Bibliographic record
Abstract
In this work we focus on anti-parallel β-sheets to study hydration of side chains and polar groups of the backbone using all-atom molecular dynamics simulations. We show that: (i) water distribution around the backbone does not depend significantly on amino acid sequence, (ii) more water molecules are found around oxygen than nitrogen atoms of the backbone, and (iii) water molecules around nitrogen are highly localized in the planed formed by peptide backbones. To study hydration around side chains we note that anti-parallel β-sheets exhibit two types of cross-strand pairing: Hydrogen-Bond (HB) and Non-Hydrogen-Bond (NHB) pairing. We show that distributions of water around alanine, leucine, and valine side chains are very different at HB compared to NHB faces. For alanine pairs, the space between side chains has a higher concentration of water if residues are located in the NHB face of the β-sheet as opposed to the HB face. For leucine residues, the HB face is found to be dry while the space between side chains at the NHB face alternates between being occupied and non-occupied by water. Surprisingly, for valine residues the NHB face is dry, whereas the HB face is occupied by water. We postulate that these differences in water distribution are related to context dependent propensities observed for β-sheets.
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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 | 0.001 |
| 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".