ArticleNAR genomics and bioinformatics2026
Inferring context-specific site variation with evotuned protein language models.
Article in NAR genomics and bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- From sites to structure to serology: a roadmap for structure-aware molecular evolution of antigenically evolving viruses.Journal of virology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multiple sequence alignments (MSAs) have been traditionally used for making inferences about site-specific diversity in proteins. Recent advancements in the field of artificial intelligence have highlighted the potential of protein language models (pLMs) to capture similar protein properties. Unlike MSAs, pLMs can make inferences from single sequences, without the need for a set of aligned sequences. In this study, we introduce a variation of the Context-Dependent Entropy metric, based on pLM embeddings instead of MSA input, to assess protein site conservation and variability. We test this metric using versions of two popular pLMs (ESM-2 and protT5) fine-tuned on the diversity of different Influenza A virus subtype hemagglutinin proteins. Our study demonstrates how our pLM entropy metric can capture which sites are more likely to change in a specific sequence context and how fine-tuning pLMs on a set of evolutionarily related proteins (evotuning) can improve the models' understanding of the group's diversity.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.