A single amino acid mutation alters multiple neutralization epitopes in the respiratory syncytial virus fusion glycoprotein
Bibliographic record
Abstract
Respiratory syncytial virus (RSV) is the leading cause of infant hospitalization. All current RSV therapeutics, including antibody prophylaxis and adult vaccination, target the RSV fusion glycoprotein (RSV-F). The seven neutralization sites on RSV-F are highly conserved and infrequently mutate. Here, we show that a single amino acid mutation at position 305 in RSV-F significantly alters antigenic recognition of RSV-F binding sites and reduces the susceptibility of RSV to neutralizing antibodies. In an in vitro evolution assay, we show that RSV-F L305I occurs in a majority of RSV quasi-species. Computational modeling predicted that the L305I mutation altered the epitope landscape of RSV-F, resulting in changes to neutralizing antibody sensitivity and affinity towards the RSV-F glycoprotein. Screening of published RSV-F sequences revealed that position 305 in RSV-F was conserved with a leucine and isoleucine in RSV-A and RSV-B subtypes respectively. Our study suggests that select amino acids in RSV-F may act as 'conformational switches' for RSV to evade host serum antibodies. This work has important implications in understanding RSV evolution and resistance as it suggests that mutational resistance to neutralizing antibodies can occur at sites distal to antigenic epitopes, significantly altering antibody sensitivity to viral infection. These unique antigenic landscape changes should be considered in the context of vaccine and therapeutic development in order to better understand viral mechanisms of evasion and resistance.
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".