Search for folding nuclei in native protein structures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
UNLABELLED: The problem of finding folding nuclei (a set of native contacts that play an important role in folding) along with identifying folding pathways (a time-ordered sequence of folding events) of proteins is one of the most important problems in protein chemistry. Here we propose a novel and simple approach to address this problem as follows: given the topology of the native state, identify native contacts that form folding nuclei based on a graph-theoretical approach that considers effective contact order (effective loop closure) as its objective function. MOTIVATION: A number of computational methods for the prediction of folding nuclei already exists in the literature, but most of them rely on restrictive assumptions about the nature of nuclei or the process of folding. Our motivation is to develop a simple, efficient and robust algorithm to find an ensemble of pathways with the lowest effective contact order and to identify contacts that are crucial for folding. RESULTS: Our approach is different from the previously used methods in that it uses efficient graph algorithms and does not formulate restrictive assumptions about folding nuclei. Our predictions provide more details concerning the protein folding pathway than most other methods in the literature. We demonstrate the success of our approach by predicting folding nuclei for a dataset of proteins for which experimental kinetic data is available. We show that our method compares favourably with other methods in the literature and that its results agree with experimental results. AVAILABILITY: The executable for the proposed algorithm is available at http://www.cs.ubc.ca/~/foldingnuclei.html
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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 | 0.001 | 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 it