ArticleNature methods2026
Quantifying uncertainty in protein representations across models and tasks.
Article in Nature methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- EmmaEmb: A quantitative framework for analyzing embedding spaces in molecular biology.Patterns (New York, N.Y.) · 2026Article
- De novo generation and computational screening of dual-targeting short peptide inhibitors against PBP2b and PBP2x in drug-resistant Streptococcus Pneumoniae.Molecular diversity · 2026Article
- AbTune: layer-wise selective fine-tuning of protein language models for antibodies.Briefings in bioinformatics · 2026Article
- Beyond sequence similarity: toward function-based screening of nucleic acid synthesis.Frontiers in bioengineering and biotechnology · 2026Article
- ImmUQBench: a benchmark on uncertainty quantification of protein immunogenicity prediction.Oxford open immunology · 2026Article
- Calibrated Variant Effect Prediction at the Residue Level Using Conditional Score Distributions.bioRxiv : the preprint server for biology · 2025Article
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Authors and funding
2 authors.
Funding
Abstract
Biomolecular embeddings serve as efficient representations of sequence and structure, enabling tasks such as similarity searches, structure and function prediction and estimation of biophysical properties. However, relying on embeddings without assessing their ability to accurately represent biomolecules is a critical flaw-akin to using a scalpel in surgery without verifying its sharpness. Here we propose a means to evaluate the capacity of protein language models to encode biologically meaningful information. For each protein, representation uncertainty is scored as the fraction of non-biological 'synthetic' sequences among its nearest neighbors in latent space. Our analysis reveals that low-quality embeddings often fail to capture meaningful biology, displaying vector properties indistinguishable from those of randomly generated sequences. Our model-agnostic scoring framework is, to our knowledge, the first to quantify protein sequence embedding reliability. It enables embedding screening prior to downstream applications and inferences, significantly improving their reliability. We propose that embedding evaluation should be undertaken for other uses of language models in science as well.
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Registered trials
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