Site-specific contributions to the pH dependence of protein stability
Bibliographic record
Abstract
Understanding protein stability is a significant challenge requiring characterization of interactions within both folded and unfolded states. Of these, electrostatic interactions influence ionization equilibria of acidic and basic groups and diversify their pK(a) values. The pH dependence of the thermodynamic stability (Delta G(FU)) of a protein arises as a consequence of differential pK(a) values between folded and unfolded states. Previous attempts to calculate pH-dependent contributions to stability have been limited by the lack of experimental unfolded state pK(a) values. Using recently developed NMR spectroscopic methods, we have determined residue-specific pK(a) values for a thermodynamically unstable Src homology 3 domain in both states, enabling the calculation of the pH dependence of stability based on simple analytical expressions. The calculated pH stability profile obtained agrees very well with experiment, unlike profiles derived from two current models of electrostatic interactions within unfolded states. Most importantly, per-residue contributions to the pH dependence of Delta G(FU) derived from the data provide insights into specific electrostatic interactions in both the folded and unfolded states and their roles in protein stability. These interactions include a hydrogen bond between the Asp-8 side-chain and the Lys-21 backbone amide group in the folded state, which represents a highly conserved interaction in Src homology 3 domains.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".