A Data‐Driven Accelerated Sampling Method for Searching Functional States of Proteins
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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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".