Binding of Disordered Peptides to Kelch: Insights from Enhanced Sampling Simulations
Bibliographic record
Abstract
Keap1 protein plays an essential role in regulating cellular oxidative stress response and is a crucial binding hub for multiple proteins, several of which are intrinsically disordered proteins (IDP). Among Kelch's IDP binding partners, NRF2 and PTMA are the two most interesting cases. They share a highly similar binding motif; however, NRF2 binds to Kelch with a binding affinity of approximately 100-fold higher than that of PTMA. In this study, we perform an exhaustive sampling composed of 6 μs well-tempered metadynamics and 2 μs unbiased molecular dynamics (MD) simulations aiming at characterizing the binding mechanisms and structural properties of these two peptides. Our results agree with previous experimental observations that PTMA is remarkably more disordered than NRF2 in both the free and bound states. This explains PTMA's lower binding affinity. Our extensive sampling also provides valuable insights into the vast conformational ensembles of both NRF2 and PTMA, supports the hypothesis of coupled folding-binding, and confirms the essential role of linear motifs in IDP binding.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".