Interplay between adsorbed peptide structure, trapped water, and surface hydrophobicity
Bibliographic record
Abstract
Atomistic molecular dynamics simulations were used to study the influence of interfacial water on the orientation and conformation of a facewise amphipathic α-helical peptide adsorbed to hydrophilic and hydrophobic substrates. Water behavior beneath the peptide adsorbed to a hydrophilic surface was observed to vary with the height of the peptide above the surface. In general, the orientation of water close to the peptide (with the oxygen atom pointing up toward the peptide) was complementary to that observed near the hydrophilic surface in the absence of peptide. That is, no change in orientation of water trapped between the peptide and a hydrophilic surface is required as the peptide approaches the surface. The adsorption of the peptide to the hydrophilic surface was observed to be mediated by a layer of ordered water. Water was found to be largely excluded on adsorption to the hydrophobic surface. However, the small amount of water present was observed to be highly ordered. At the closest point of contact to the hydrophobic surface, the peptide was observed to make direct contact. These findings shed light on the fundamental driving forces of peptide adsorption to hydrophobic and hydrophilic surfaces in aqueous environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".