Evaluation of Methods for the Calculation of the p<i>K</i><sub>a</sub> of Cysteine Residues in Proteins
Why this work is in the frame
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Bibliographic record
Abstract
Methods for the calculation of the pKa ionizable amino acids are valuable tools for understanding pH-dependent properties of proteins. Cysteine is unique among the amino acids because of the chemical reactivity of its thiol group (S-H), which plays an instrumental role in several biochemical and regulatory functions. The acidity of noncatalytic cysteine residues is a factor in their susceptibility to chemical modification. Despite the plethora of existing pKa computing methods, no definitive protocol exists for accurately calculating the pKa's of cysteine residues in proteins. A cysteine pKa test set was developed, which is comprised of 18 cysteine residues in 12 proteins where the pKa's have been determined experimentally and an experimental structure is available. The pKa's of these residues were calculated using three methods that use an implicit solvent model (H++, MCCE, and PROPKA) and an all-atom replica-exchange thermodynamic integration approach with the CHARMM36 and AMBER ff99SB-ILDNP force fields. The models that use implicit solvation methods were generally unreliable in predicting cysteine residue pKa's, with RMSDs between 3.41 and 4.72 pKa units. On average, the explicit solvent methods performed better than the implicit solvent methods. RMSD values of 2.40 and 3.20 were obtained for simulations with the CHARMM36 and AMBER ff99SB-ILDNP force fields, respectively. Further development of these methods is necessary because the performance of the best method is similar to that of the null-model (RMSD = 2.74) and these differences in RMSD are of limited statistical significance given the small size of our test set.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.003 |
| 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) | 0.000 | 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 it