Structural Investigation of DHICA Eumelanin Using Density Functional Theory and Classical Molecular Dynamics Simulations
Bibliographic record
Abstract
Eumelanin is an important pigment, for example, in skin, hair, eyes, and the inner ear. It is a highly heterogeneous polymer with 5,6-dihydroxyindole-2-carboxylic acid (DHICA) and 5,6-dihydroxyindole (DHI) building blocks, of which DHICA is reported as the more abundant in natural eumelanin. The DHICA-eumelanin protomolecule consists of three building blocks, indole-2-carboxylic acid-5,6-quinone (ICAQ), DHICA and pyrrole-2,3,5-tricarboxylic acid (PTCA). Here, we focus on the self-assembly of DHICA-eumelanin using multi-microsecond molecular dynamics (MD) simulations at various concentrations in aqueous solutions. The molecule was first parameterized using density functional theory (DFT) calculations. Three types of systems were studied: (1) uncharged DHICA-eumelanin, (2) charged DHICA-eumelanin corresponding to physiological pH, and (3) a binary mixture of both of the above protomolecules. In the case of uncharged DHICA-eumelanin, spontaneous aggregation occurred and water molecules were present inside the aggregates. In the systems corresponding to physiological pH, all the carboxyl groups are negatively charged and the DHICA-eumelanin model has a net charge of -4. The effect of K+ ions as counterions was investigated. The results show high probability of binding to the deprotonated oxygens of the carboxylate anions in the PTCA moiety. Furthermore, the K+ counterions increased the solubility of DHICA-eumelanin in its charged form. A possible explanation is that the charged protomolecules favor binding to the K+ ions rather than aggregating and binding to other protomolecules. The binary mixtures show aggregation of uncharged DHICA-eumelanins; unlike the charged systems with no aggregation, a few charged DHICA-eumelanins are present on the surface of the uncharged aggregation, binding to the K+ ions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 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".