Efficient generation of low-energy folded states of a model protein. II. Automated histogram filtering
Bibliographic record
Abstract
A number of short, Monte Carlo simulated annealing runs are performed on a highly frustrated 69-mer off-lattice model protein, consisting of a chain of 69 beads that are either hydrophobic, hydrophilic, or neutral in nature, and which demonstrably folds into a six-stranded β-barrel structure. We employ an iterative, consensus-based scheme to cluster the 725 nonbonded distances between the hydrophobic beads using, in tandem, Ward’s method for hierarchical clustering and k-means partitional clustering. We also independently analyze the same data using computer-automated histogram filtering, a technology designed to cluster high-dimensional data, without the tedium and subjectivity required by our iterative implementation of the two classical clustering methods. The memberships of low-energy clusters obtained from both classical clustering and automated histogram filtering approaches are remarkably similar. Nonbonded distance constraints are derived from these clusters and from small sets of the original unclustered conformations obtained by simulated annealing. Employing a distance geometry approach, we efficiently generate novel, low-energy conformations from each set of distance constraints, including the apparent native structure, up to 40 times faster than by doing additional simulated annealing runs. Over 33 000 unique locally optimized conformations are generated in total, substantially augmenting the number of low-energy states located by the original simulated annealing runs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".