Characterization of the Free State Ensemble of the CoRNR Box Motif by Molecular Dynamics Simulations
Bibliographic record
Abstract
Intrinsically disordered proteins (IDPs) and regions are highly prevalent in eukaryotic proteomes, and like folded proteins, they perform essential biological functions. Interaction sites in folded proteins are generally formed by tertiary structures, whereas IDPs use short segments called linear motifs (LMs). Despite their short length and lack of stable structure, LMs may have considerable structural propensities, which often resemble bound-state conformations with targets. Structural data is crucial for understanding the molecular basis of protein interactions and development of targeted pharmaceuticals, but IDPs present considerable challenges to experimental techniques. As a result, IDPs are largely underrepresented in the Protein Data Bank. In the face of experimental challenges, molecular dynamics (MD) simulations have proven to be a useful tool for structural characterization of IDPs. Here, the free state ensemble of the nuclear receptor corepressor 1 (NCOR1) CoRNR box 3 motif, which is important for binding to nuclear receptors to control gene expression, is studied using MD simulations of a total of 8 μs. Transitions between disordered and α-helical conformations resembling a bound-state structure were observed throughout the trajectory, indicating that the motif may have a natural conformational bias toward bound-state structures. The data shows that the disordered and folded populations are separated by a low energy (4-6 kJ/mol) barrier, and the presence of off-pathway intermediates, leading to a C-terminally folded species that cannot efficiently transition into a completely folded conformation. Structural transitions and folding pathways within the free state ensemble were well-described by principal component analysis (PCA) of the peptide backbone dihedral angles, with the analysis providing insight for increasing structural homogeneity of the ensemble.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".