Evidence map›Paper›PMID 42461048›Full record

ReviewJournal of virology2026

From sites to structure to serology: a roadmap for structure-aware molecular evolution of antigenically evolving viruses.

Sanni Översti, Spyros Lytras, Shusuke Kawakubo, Jumpei Ito, Mahan Ghafari

Abstract readReview
In one paragraph

Review in Journal of virology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Sanni ÖverstiDepartment of Biology, University of Oxford, Oxford, United Kingdom.ORCID 0000-0001-7118-1281
Spyros LytrasAntigen Evolution & Design Lab, Department of Structural Biology and Chemistry, Institut Pasteur, Paris, France.ORCID 0000-0003-4202-6682
Shusuke KawakuboDivision of Systems Virology, Department of Microbiology and Immunology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.ORCID 0000-0002-5330-5532
Jumpei ItoLaboratory of Virus Informatics, Department of Biological Informatics, Bioinformatics Center, Research Institute for Microbial Diseases, The University of Osaka, Suita, Japan.ORCID 0000-0003-0440-8321
Mahan GhafariDepartment of Biology, University of Oxford, Oxford, United Kingdom.ORCID 0000-0002-4123-3287

Funding

Wellcome TrustWellcome Trust Early Career 309205/Z/24/Z
6 · The paper itself

Abstract

The genomic deluge has pushed viral molecular evolution into a site-resolved era. For antigenically evolving viruses such as influenza and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), dense genomic sampling now supports mutation-annotated phylogenies and per-site estimates of mutation and substitution processes. These data highlight strong effects of sequence context, genomic region, RNA structure, and protein-level constraints that are blurred by classic uniform substitution models. In parallel, accurate structure prediction and emerging structure-aware phylogenetic and machine-learning approaches provide practical ways to map mutations onto three-dimensional constraints, identify structurally plausible escape routes, and interpret evolutionary rate variation through solvent exposure, packing, stability, glycosylation, receptor-binding interfaces, and epitope geometry. Finally, antigenic cartography translates some forms of genetic change into an epidemiologically meaningful phenotype-antigenic distance-while predictive modeling increasingly enables sequence-to-antigenicity inference for variants that have not yet been tested experimentally. Here, we outline a practical framework linking sites, structure, and serology for viruses in which antigenic evolution is a major component of immune escape and lineage turnover; highlight why genetic and antigenic "clocks" can diverge; and discuss how integrating genomic surveillance data, phylogenetics, structural analysis, and predictive modeling could support more prospective variant assessment and improved vaccine and therapeutic design.

Indexed as

Antigens, ViralEvolution, MolecularSARS-CoV-2EpitopesGenome, ViralHumansMachine LearningMutationPhylogenyAntigens, ViralEpitopesantigenic cartographyantigenic evolutionmachine learningmolecular evolutionprotein structure-function

Identifiers

PMID42461048
PMCPMC13483445

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.