The energy landscapes of metamorphic proteins
Bibliographic record
Abstract
Most proteins fold into a unique three-dimensional structure called the native state. Recently some examples have been found of so-called metamorphic proteins that undergo reversible large-scale structural transformations between different native states. In this thesis, we develop simulation methods and models to study the thermodynamics of these transformations, both at the coarse-grained and all-atom levels. Because our understanding of the physics fold switching is incomplete, our models utilize in part so-called structure-based or Gō-like potentials, which provide energetic bias towards one, or more, native states. We employ these computational methods to two different fold switch systems: the bacterial protein RfaH and the engineered fold switch system GA/GB. Our models are developed and tested on experimental data for these systems. We study both equilibrium properties, such as stability properties and the characteristics of their energy landscapes, and kinetic properties, such as the mechanism that trigger fold switching and molecular details of the fold switch process. We also study, for the GA/GB system, what role macromolecular crowding effects play for controlling which of the native states is most stable.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".