Assessing uncertainty of sequence representations generated by protein language models
Assessing uncertainty of sequence representations generated by protein language models
Language model-inferred embeddings are replacing structure-derived descriptions of proteins, genes and genomes. We propose a model-agnostic measure to quantify reliability of these new representations.
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Fig. 1: RNS-based assessments of embeddings identify poorly represented proteins across different data sets.
References
- Vaswani, A. et al. Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017). This work introduces the attention mechanism in transformer architecture.
- Weissenow, K. & Rost, B. Are protein language models the new universal key? Curr. Opin. Struct. Biol. 91, 102997 (2025). This review article discusses the transition from evolutionary information to machine-learned embeddings for protein prediction.
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- Dallago, C. et al. Learned embeddings from deep learning to visualize and predict protein sets. Curr. Protoc. 1, e113 (2021). This article introduces ‘Bioembeddings’, a publicly available library of pLM pipelines.
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- Saul, B. N. & Christian, D. W. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48, 443–453 (1970). The earliest work we have identified that illustrates use of random sequences to evaluate significance of protein sequence similarities.
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This is a summary of: Prabakaran, R. & Yana Bromberg, Y. Quantifying uncertainty in protein representations across models and tasks. Nat. Methods https://doi.org/10.1038/s41592-026-03028-7 (2026).
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Assessing uncertainty of sequence representations generated by protein language models. Nat Methods (2026). https://doi.org/10.1038/s41592-026-03027-8
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- Published: 01 April 2026
- Version of record: 01 April 2026
- DOI: https://doi.org/10.1038/s41592-026-03027-8
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