Energetics of Protein Thermodynamic Cooperativity: Contributions of Local and Nonlocal Interactions
Bibliographic record
Abstract
The respective roles of local and nonlocal interactions in the thermodynamic cooperativity of proteins are investigated using continuum (off-lattice) native-centric Gō-like models with a coarse-grained C$_α$ chain representation. We study a series of models in which the (local) bond- and torsion-angle terms have different strengths relative to the (nonlocal) pairwise contact energy terms. Conformational distributions in these models are sampled by Langevin dynamics. Thermodynamic cooperativity is characterized by the experimental criteria requiring the van't Hoff to calorimetric enthalpy ratio $ΔH_{\rm vH}/ΔH_{\rm cal}\approx 1$ (the calorimetric criterion), as well as a two-state-like variation of the average radius of gyration upon denaturation. We find that both local and nonlocal interactions are critical for thermodynamic cooperativity. Chain models with either much weakened local conformational propensities or much weakened favorable nonlocal interactions are significantly less cooperative than chain models with both strong local propensities and strong favorable nonlocal interactions. These findings are compared with results from a recently proposed lattice model with a local-nonlocal coupling mechanism; their relationship with experimental measurements of protein cooperativity and chain compactness is discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".