Free energy and stacking of eumelanin nanoaggregates
Bibliographic record
Abstract
Abstract Eumelanin, a member of the melanin family, is a black-brown insoluble pigment. It possesses a broad range of properties such as antioxidation, free radical scavenging, photo-protection, and charge carrier transportation. Surprisingly, the exact molecular structure of eumelanin remains undefined. It is, however, generally considered to consist of two main building blocks, 5,6-dihydroxyindole (DHI) and 5,6-dihydroxyindole carboxylic acid (DHICA). We focus on DHI and report, for the first time, a computational investigation of structural properties of DHI eumelanin aggregates in aqueous solutions. First, multi-microsecond molecular dynamics (MD) simulations at different concentrations were performed to investigate aggregation and ordering of tetrameric DHI-eumelanin protomolecules. This was followed by umbrella sampling (US) and density functional theory (DFT) calculations to study the physical mechanisms of stacking. Aggregation occurs through formation of nanoscale stacks and was observed in all systems. Further analyses showed that aggregation and coarsening of the domains is due to decrease in hydrogen bonds between the eumelanins and water; while domains exist, there is no long-range order. The results show non-covalent stacks with and interlayer distance between eumelanin protomolecules being less than 3.5 Å. This is in good agreement with transmission electron microscopy data. Both free energy calculations and DFT revealed strong stacking interactions. The electrostatic potential map provides an explanation and a rationale for the slightly sheared relative orientations and, consequently, for the curved shapes of the nanoscale domains.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".