Facilitating and restraining virus infection using cell-attachable soluble viral receptors
Bibliographic record
Abstract
SARS-CoV-2 uses the receptor binding domain (RBD) of its spike protein to recognize and infect host cells by binding to the cell surface receptor angiotensin converting enzyme 2 (ACE2). The ACE2 receptor is composed of peptidase domain (PD), collectrin-like domain, transmembrane domain, and short cytoplasmic domain, and may exist as a dimer on cell surface. The RBD binding site is located atop of the ACE2 PD, but the involvement of other domains in virus infection is uncertain. We found that the ACE2 PD alone, whether anchored to cell membrane via a glycosylphosphatidylinositol anchor or attached to another surface protein, is fully functional as a receptor for spike-mediated cell fusion and virus infection. However, for ACE2 to function as the viral receptor, the RBD binding site must be positioned in close proximity to the cell membrane. Elevating the surface height of ACE2 using long and rigid protein spacers reduces or eliminates cell fusion and virus infection. Moreover, we found that the RBD-targeting neutralizing antibodies, nanobodies, and de novo designed miniprotein binders, when present on cell surface, also act as viral receptors, facilitating cell fusion and virus infection. Our data demonstrate that RBD binding and close membrane proximity are essential properties for a receptor to effectively mediate SARS-CoV-2 infection. Importantly, we show that soluble RBD-binders can be engineered to make cells either susceptible or resistant to virus infection, which has significant implications for antiviral therapy and various virus-mediated applications.
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 | 0.000 |
| 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) | 0.001 | 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".