Identification of RSV Fusion Protein Interaction Domains on the Virus Receptor, Nucleolin
Bibliographic record
Abstract
Nucleolin is an essential cellular receptor to human respiratory syncytial virus (RSV). Pharmacological targeting of the nucleolin RNA binding domain RBD1,2 can inhibit RSV infections in vitro and in vivo; however, the site(s) on RBD1,2 which interact with RSV are not known. We undertook a series of experiments designed to: document RSV-nucleolin co-localization on the surface of polarized MDCK cells using immunogold electron microscopy, to identify domains on nucleolin that physically interact with RSV using biochemical methods and determine their biological effects on RSV infection in vitro, and to carry out structural analysis toward informing future RSV drug development. Results of immunogold transmission and scanning electron microscopy showed RSV-nucleolin co-localization on the cell surface, as would be expected for a viral receptor. RSV, through its fusion protein (RSV-F), physically interacts with RBD1,2 and these interactions can be competitively inhibited by treatment with Palivizumab or recombinant RBD1,2. Treatment with synthetic peptides derived from two 12-mer domains of RBD1,2 inhibited RSV infection in vitro, with structural analysis suggesting these domains are potentially feasible for targeting in drug development. In conclusion, the identification and characterization of domains of nucleolin that interact with RSV provide the essential groundwork toward informing design of novel nucleolin-targeting compounds in RSV drug development.
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 | 0.000 |
| 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".