Modeling Protein S–Aromatic Motifs Reveals Their Structural and Redox Flexibility
Bibliographic record
Abstract
S–aromatic motifs are important noncovalent forces for protein stability and function but remain poorly understood. Hence, we performed quantum calculations at the MP2(full)/6-311++G(d,p) level on complexes between Cys (H2S, MeSH) and Met (Me2S) models with models of Phe (benzene, toluene), Trp (indole, 3-methylindole), Tyr (phenol, 4-methylphenol), and His (imidazole, 4-methylimidazole). The most stable gas-phase conformers exhibit binding energies of −2 to −6 kcal/mol, and the S atom lies perpendicular to the ring plane. This reveals preferential interaction with the ring π-system, except in the imidazoles where S binds edge-on to an N atom. Complexation tunes the gas-phase vertical ionization potentials of the ligands over as much as 1 eV, and strong σ- or π-type H-bonding supports charge transfer to the H-bond donor, rendering it more oxidizable. When the S atom acts as an H-bond acceptor (N/O–Har···S), calibration of the CHARMM36 force field (by optimizing pair-specific Lennard-Jones parameters) is required. Implementing the optimized parameters in molecular dynamics simulations in bulk water, we find stable S–aromatic complexes with binding free energies of −0.6 to −1.1 kcal/mol at ligand separations up to 8 Å. The aqueous S–aromatics exhibit flexible binding conformations, but edge-on conformers are less stable in water. Reflecting this, only 0.3 to 10% of the S–indole, S–phenol, and S–imidazole structures are stabilized by N/O–Har···S or S–H···Oar/Nar σ-type H-bonding. The wide range of energies and geometries found for S–aromatic interactions and their tunable redox properties expose the versatility and variability of the S–aromatic motif in proteins and allow us to predict a number of their reported properties.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".