Interaction of an ionic complementary peptide with a hydrophobic graphite surface
Bibliographic record
Abstract
Protein adsorption on a surface plays an important role in biomaterial science and medicine. It is strongly related to the interaction between the protein residues and the surface. Here we report all-atom molecular dynamics simulations of the adsorption of an ionic complementary peptide, EAK16-II, to the hydrophobic highly ordered pyrolytic graphite surface. We find that, the hydrophobic interaction is the main force to govern the adsorption, and the peptide interchain electrostatic interaction affects the adsorption rate. Under neutral pH condition, the interchain electrostatic attraction facilitates the adsorption, whereas under acidic and basic conditions, because of the protonation and deprotonation of glutamic acid and lysine residues, respectively, the resulting electrostatic repulsion slows down the adsorption. We also found that under basic condition, during the adsorption peptide Chain II will be up against a choice to adsorb to the surface through the hydrophobic interaction or to form a temporary hydrophobic core with the deposited peptide Chain I. These results provide a basis for understanding some of the fundamental interactions governing peptide adsorption on the surface, which can shed new light on novel applications, such as the design of implant devices and drug delivery materials.
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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 | 0.000 | 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) | 0.002 | 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".