Mapping respiratory syncytial virus fusion protein interactions with the receptor IGF1R and the impact of alanine-scanning mutagenesis on viral infection
Bibliographic record
Abstract
Respiratory syncytial virus (RSV) has two main surface glycoproteins, the attachment glycoprotein (G) and the fusion (F) protein, which together mediate viral entry. Attachment is mediated by the RSV-G protein, while the RSV-F protein makes specific contact with the cellular insulin-like growth factor 1 receptor (IGF1R). This interaction leads to IGF1R activation and initiates a signalling cascade that calls the co-receptor, nucleolin, from the nucleus to the cell surface, where it can trigger viral fusion. We performed molecular docking analysis, which provided a potential set of 35 residues in IGF1R that may be important for interactions with RSV-F. We used alanine-scanning mutagenesis to generate IGF1R mutants and assessed their abundance and maturation, as well as the effect of mutation on RSV infection. We identified several mutations that appear to inhibit IGF1R maturation; but surprisingly, these mutations had no significant effect on RSV infection. This suggests that maturation of IGF1R may not be required for RSV infection. Additionally, we identified one residue, S788, that, when mutated, significantly reduced RSV infection. Further analysis revealed that this mutation disrupted a hydrogen bonding network that may be important for both IGF1R maturation and RSV infection.
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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) | 0.001 | 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".