ArticlePatterns (New York, N.Y.)2025
Tokenized and continuous embedding compressions of protein sequence and structure.
Article in Patterns (New York, N.Y.), 2025. 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.
- Contrastive learning unites sequence and structure in a global representation of protein space.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Detecting and quantifying overparametrization in RNA language models with REDIAL.bioRxiv : the preprint server for biology · 2026Article
- Learning physical interactions to compose biological large language models.Communications chemistry · 2026Review
- SLAE: Strictly Local All-atom Environment for Protein Representation.bioRxiv : the preprint server for biology · 2025Article
- An interpretable alphabet for local protein structure search based on amino acid neighborhoods.Bioinformatics (Oxford, England) · 2025Article
- Language models reveal a complex sequence basis for adaptive convergent evolution of protein functions.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
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Authors and funding
8 authors.
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Abstract
Existing protein machine learning representations typically model either the sequence or structure distribution, with the other modality implicit. Here, we characterize an embedding of the joint distribution of protein sequence and structure by compressing the latent space of the protein folding model ESMFold. This provides mechanistic interpretability insights, as well as a flexible compressed representation. We term these CHEAP (compressed hourglass embedding adaptations of proteins) embeddings. In continuous compression schemes, the ESMFold latent space can be reduced by factors of 128
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