Bibliographic record
Abstract
Linear motifs (LMs) are protein-protein interaction sites, typically consisting of ~4-20 amino acid residues that are often found in disordered proteins or regions, and function largely independent from other parts of the proteins they are found in. These short sequence patterns are involved in a wide spectrum of biological functions including cell cycle control, transcriptional regulation, enzymatic catalysis, cell signaling, protein trafficking, etc. Even though LMs may adopt defined structures in complexes with targets, which can be determined by conventional methods, their uncomplexed states can be highly dynamic and difficult to characterize. This hinders our understanding of the structure-function relationship of LMs. Here, the uncomplexed states of 6 different LMs are investigated using atomistic molecular dynamics (MD) simulations. The total simulation time was about 63 μs. The results show that LMs can have distinct conformational propensities, which often resemble their complexed state. As a result, the free state structure and dynamics of LMs may hold important clues regarding binding mechanisms, affinities and specificities. The findings should be helpful in advancing our understanding of the mechanisms whereby disordered amino acid sequences bind targets, modeling disordered proteins/regions, and computational prediction of binding affinities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".