CGMD simulation of interaction between hemagglutinin fusion peptides and lipid bilayer membranes
Bibliographic record
Abstract
Microscopic level interaction between fusion-peptides and lipid bilayer membranes plays a crucial role in membrane fusion, a key step of viral infection. In this paper, we use coarse-grained molecular dynamics (CGMD) simulations to study the interaction between hemagglutinin fusion-peptides and phospholipid bilayer membranes. With CGMD, we are able to simulate the interaction of fusion peptides with a relatively large piece of membrane for a sufficiently long time period, which is necessary for a detailed understanding of the fusion process. A conformation of the peptide with a kink at the level of phosphate group is obtained, consistent with NMR and EPR studies. Our results show that the N-terminal segment of the peptide inserts more deeply into the membrane bilayer compared to the C-terminal segment, as observed in previous experiments. Our simulations also show that the presence of fusion peptides inside the membrane may cause bilayer thinning and lipid molecule disorder. Finally, our results reveal that peptides tend to aggregate, indicating cluster formation as seen in many experiments.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".