Mutational investigation of protein folding transition states by Φ-value analysis and beyond: lessons from SH3 domain foldingThis paper is one of a selection of papers published in this special issue entitled “Canadian Society of Biochemistry, Molecular & Cellular Biology 52nd Annual Meeting — Protein Folding: Principles and Diseases” and has undergone the Journal's usual peer review process.
Bibliographic record
Abstract
Understanding how proteins adopt their unique native structures requires a complete structural characterization of the rate-limiting transition state(s) along the folding pathway. By definition, transition states are not significantly populated and are only accessible via folding kinetics studies. In this respect, interpreting the kinetic effects of amino acid substitutions (especially to Ala) via Phi-value analysis is the most common method to probe the structure of these transient, yet important states. A critical review of the key assumptions required for rigorous interpretation of Phi values reveals that a multiple substitution strategy in which a position of interest is mutated to a variety of amino acids, and not exclusively to Ala, provides the best means to characterize folding transition states. This approach has proven useful in revealing non-native interactions and (or) conformations in folding transition states. Moreover, by simultaneously examining the folding kinetics of multiple substitutions made at a single surface-exposed position using the Brønsted analysis the backbone conformation in a folding transition state can be investigated. For folding equilibria with exchange rates on the order of milliseconds, the kinetic parameters for Phi-value analysis can be obtained from NMR relaxation dispersion experiments, under fully native conditions, along with a wealth of high-resolution structural information about the states in exchange (native, denatured, and intermediate states that populate the pathway). This additional structural information, which is not readily obtained through stopped-flow based methods, can significantly facilitate the interpretation of Phi values because it often reports on the validity of the assumptions required for a rigorous interpretation of Phi values.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".