Molecular Dynamics of Fibrinogen Adsorption onto Graphene, but Not onto Poly(ethylene glycol) Surface, Increases Exposure of Recognition Sites That Trigger Immune Response
Bibliographic record
Abstract
Changes in the conformation of blood proteins due to their binding to nonbiological surfaces is the initial step in the chain of immunological reactions to foreign bodies. Despite the large number of experimental studies that have been performed on fibrinogen adsorption to nonbiological surfaces, a clear picture describing this complex process has eluded researchers to date. Developing a better understanding of the behavior of bioactive fibrinogen motifs upon their interaction with surfaces may facilitate the design of advanced materials with improved biocompatibility. This is especially important within the context of medical implants. Here we present results of explicit-solvent, all-atom MD simulations of the adsorption of the fibrinogen D-domain onto a graphene surface and a poly(ethylene glycol) (PEG) surface. Our results are consistent with experimental observations that interactions with PEG do not induce significant conformational changes on immune-reactive sites present in the D-domain of fibrinogen. In contrast, our results indicate that significant conformational changes induced by adsorption to graphene surfaces may occur under conditions that promote a high density of blood proteins on the surface. The structural rearrangements observed on graphene directly affect the secondary structure content of the D-domain, with consequent exposure of the recognition sites P1 (γ190-202) and P2 (γ377-395) and the subsite P2-C (γ383-395) involved in immune response. Analysis of the structural parameters of the MD conformers was shown to accurately assess the biocompatibility of the modeled surfaces.
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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 | 0.001 |
| 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".