Multifaceted membrane binding head of the SARS-CoV-2 spike protein
Bibliographic record
Abstract
The SARS-CoV-2 spike protein presents a surface with enormous membrane binding potential to host tissues and organelles of infected cells. Its exposed trimeric head binds not only the angiotensin-converting enzyme 2 (ACE2), but also host phospholipids which are missing from all existing structures. Hence, the membrane interaction surfaces that mediate viral fusion, entry, assembly and egress remain unclear. Here the spike:membrane docking sites are identified based on membrane optimal docking area (MODA) analysis of 3D structures of spike proteins in closed and open conformations at endocytic and neutral pH levels as well as ligand complexes. This reveals multiple membrane binding sites in the closed spike head that together prefer convex membranes and are modulated by pH, fatty acids and post-translational modifications including glycosylation. The exposure of the various membrane interaction sites adjusts upon domain repositioning within the trimer, allowing formation of intermediate bilayer complexes that lead to the prefusion state while also enabling ACE2 receptor recognition. In contrast, all antibodies that target the spike head would block the membrane docking process that precedes ACE2 recognition. Together this illuminates the engagements of the spike protein with plasma, endocytic, ER or exocytic vesicle membranes that help to drive the cycle of viral infection, and offers novel sites for intervention.
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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".