Morphological control of receptor-mediated endocytosis
Bibliographic record
Abstract
Abstract Receptor-mediated endocytosis is the primary process for nanoparticle uptake in cells and one of the main entry mechanisms for viral infection. The cell membrane adheres to the particle (nanoparticle or virus) and then wraps it to form a vesicle delivered to the cytosol. Previous findings identified a minimum radius for a spherical particle below which endocytosis cannot occur. This is due to the insufficient driving force, from receptor-ligand affinity, to overcome the energy barrier created by membrane bending. In this paper, we extend this result to the case of clathrin-mediated endocytosis, which is the most common pathway for virus entry. Moreover, we investigate the effect of ligand inhibitors on the particle surface, motivated by viral antibodies, peptides or phage capsids nanoparticles. We determine the necessary conditions for endocytosis by considering the additional energy barrier due to the membrane bending to wrap such inhibiting protrusions. We find that the density and size of inhibitors determine the size range of internalized particles, and endocytosis is completely blocked above critical thresholds. The assembly of a clathrin coat with a spontaneous curvature increases the energy barrier and sets a maximum particle size (in agreement with experimental observations on smooth particles). Our investigation suggests that morphological considerations can inform the optimal design of neutralizing viral antibodies and new strategies for targeted nanomedicine.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".