Self-Assembly of Cysteine Dimers at the Gold Surface: A Computational Study of Competing Interactions
Bibliographic record
Abstract
The only proteinogenic acid with a mercapto group, cysteine is the main participant in the binding of proteins and peptides to the surfaces of noble metals. A chiral molecule, it becomes a major player in surface patterning for chiral amplification, biosensing, and chiral catalysis. Here, we examine the interplay of molecule–surface and molecule–molecule interactions in the self-assembly process of monomers, dimers, and trimers of l -cysteine on a (1 × 2)-reconstructed Au(110) surface, and the implications for chiral recognition. Multiple adsorbed configurations of l -cysteine and l -cysteinate in neutral and zwitterionic forms were generated using molecular dynamics simulations, serving as starting points for further density functional theory (DFT)-based optimizations. We found that binding for both monomers and dimers was stronger at kink sites formed on the surface during the chemisorption process, and was unlikely to occur along the highly coordinated trough sites. In this, DFT calculations disagreed with MD simulations using centrosymmetric potentials, which tended to maximize coordination of the adsorbate groups, and ignore differences in reactivity of the various Au sites, unless specifically included in the force field. Kink-site bound homochiral l -cysteine dimers were particularly stable relative to both heterochiral and trimer structures, while molecules more weakly bound at more stable surface locations did not exhibit chiral recognition. If barriers to the diffusion of Au atoms along the surface can be overcome, the four-atom vacancy structures proposed by Kuhnle et al. ( Nature 2002, 415, 891) provide reactive kink sites, ideally spaced for binding homochiral cysteinate dimers, with highly stable COOH-based hydrogen bonding.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".