Simulated force-induced unfolding of α-helices: dependence of stretching stability on primary sequence
Bibliographic record
Abstract
Some of the principles that determine a protein's native fold can be probed with techniques for single-molecule manipulation. Yet, understanding the effects of an external force at atomic level still requires computer simulations. Here, we employ a novel protocol for steered molecular dynamics that allows for internal energy redistribution (and possibly, re-equilibration) while the molecule is subject to a mechanical perturbation. The approach is used to study how the stretching of alpha-helices is qualitatively affected by variations in primary sequence. Despite the simplifications introduces, our results indicate a trend whereby different amino acids can increase the resistance to mechanical unfolding depending on side chain polarity and the dynamics of side-chain internal torsions. Whereas the cooperative transition from alpha-helix to 310-helix and to a rod-like conformer prevails when stretching many sequences, we also find that the onset of the unfolding can be delayed by a range of alternative pathways which include events of helical refolding or long-lived intermediates with partial helical content.
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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 | 0.000 | 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) | 0.001 | 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".