Sulfur‐Aromatic Interactions: Modeling Cysteine and Methionine Binding to Tyrosinate and Histidinium Ions to Assess Their Influence on Protein Electron Transfer
Bibliographic record
Abstract
Abstract Cys and Met interactions with aromatic residues stabilize proteins and also may alter their redox properties. We establish here how ionization of the aromatic groups may influence such interactions. Specifically, ab initio quantum mechanical calculations at the MP2(full)/6‐311++G(d,p) level of theory are performed on the gas‐phase complexes of hydrogen sulfide, methanethiol (MeSH), and dimethyl sulfide (Me2S), with the imidazolium and phenolate ions and their 4‐methylated forms. The S‐ligands bind the aromatic ions more tightly than the neutral species, preferentially edge‐on to imidazolium and en‐face to phenolate. Charge transfer occurs within the complexes, which will impact the redox properties of the interacting moieties. The CHARMM36 force field, calibrated using potential energy curves generated at the same level of theory, yields affinities (kcal mol−1) in water of −4.3 and −3.1 for MeSH‐ and Me2S‐imidazolium, and −2.9 and −2.1 for the phenolate complexes. En‐face binding is preferred in water, with an equilibrium S‐ring‐centroid separation of ∼4 Å, which increases to > 5 Å in Me2S‐phenolate. Their high gas‐phase and aqueous stability suggests that S‐aromatic‐ion complexes are an important determinant of protein behavior. Since the uncalibrated CHARMM36 force field predicts very weak S‐aromatic‐ion binding in water (−0.3 to −0.6 kcal mol−1) and in the gas phase, the optimized parameters should be used to obtain a reliable description of these interactions in proteins.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".