Exploring the energy landscape of proteins: A characterization of the activation-relaxation technique
Bibliographic record
Abstract
Finding the global energy minimum region of a polypeptide chain, independently of the starting conformation and in a reasonable computational time, is of fundamental interest. As the energy landscape of proteins is very rugged, sampling is hindered by the vast number of minima existing on this multidimensional landscape. In this study, we use activation-relaxation technique (ART) to explore the energy landscape of a series of peptide models with 14, 26, and 28 amino acids. Peptides are modeled by a reduced off-lattice representation and a simplified OPEP-like (optimized potential for efficient peptide-structure prediction) energy model. ART defines moves directly in the energy landscape and can generate with equal efficiency events with root-mean-square deviation as small as 0.1 or as large as 4 Å. Our results show that (i) ART trajectories are reversible and provide real activated paths; (ii) ART simulations converge to the same low-energy minimum region, for a wide range of starting configurations; (iii) ART method can sample the phase space effectively, going through many hyper-basins, and can generate significant moves in a single event. Possible applications of ART method to biomolecules are discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".