A Data‐Driven Accelerated Sampling Method for Searching Functional States of Proteins
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Protein exhibits distinct characteristics in different functional states. The lack of structural information for proteins hinders the understanding of their function. Here, a data‐driven accelerated (DA2) sampling method is proposed, which is capable of searching new functional states of protein from a known structure with high efficiency. The key function of DA2 sampling is to drive the conformational change of protein along its intrinsic motion without introducing biased potential/force, where principle component analysis is applied on‐the‐fly to reduce the highly redundant information generated by molecular dynamics simulations. In this work, the capacity and accuracy of DA2 sampling are validated by using alanine dipeptide. This protocol is then applied to search for the closed state of N‐terminal calmodulin (nCaM) from the open one. The identified structure resembles the crystal structure of nCaM in its closed state, with a root‐mean‐square deviation between the two of only 1.8 Å. Interestingly, independent DA2 samplings disclose different open‐to‐closed transition pathways for nCaM, which is likely to have implications for its biological functions. Therefore, DA2 sampling is expected to play important roles in exploring functional states of a broad spectrum of proteins at atomic level that are not easily determined experimentally.
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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 | 0.000 | 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 it