The Hydration Structure of Methylthiolate from QM/MM Molecular Dynamics
Bibliographic record
Abstract
Thiols are widely present in biological systems, most notably as the side chain of cysteine amino acids in proteins. Thiols can be deprotonated to form a thiolate, which affords a diverse range of enzymatic activity and modes for chemical modification of proteins. Parameters for modeling thiolates using molecular mechanical force fields have not yet been validated, in part due to the lack of structural data on thiolate solvation. Here, the CHARMM36 and Amber models for thiolates in aqueous solutions are assessed using free energy perturbation and QM/MM MD simulations. The hydration structure of methylthiolate was calculated from 1 ns of QM/MM MD (PBE0/def2-TZVP//TIP3P), which show that the water–S- distances are approximately 2 Å. The CHARMM thiolate parameters predict a thiolate S radius close to the QM/MM value and predict a hydration Gibbs energy of -331 kJ/mol, close to the experimental value of -318 kJ/mol. The cysteine thiolate model in the Amber force field underestimates the thiolate radius by 0.2 Å and overestimates the thiolate hydration energy by 119 kJ/mol because it uses the same Lennard-Jones parameters for thiolates as for thiols. A recent Drude polarizable model for methylthiolate with optimized thiolate parameters also performs well. SAPT2+ analysis indicates exchange repulsion is larger for the methylthiolate, consistent with it having a more diffuse electron density distribution in comparison to the parent thiol. These data demonstrate that it is important to define distinct non-bonded parameters for the protonated/deprotonated states of amino acid side chains in molecular mechanical force fields.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".