ArticleNature methods2025
Prediction of protein subcellular localization in single cells.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Artificial intelligence in biologic drug discovery: A review of methodological evolution and therapeutic applications.Acta pharmaceutica Sinica. B · 2026Review
- A comprehensive benchmark of sequence-based subcellular localization predictors for human proteins.Nature methods · 2026Article
- Genome-Wide Identification and Characterization of the MYB Transcription Factor Family inGenes · 2026Article
- Generative machine learning unlocks the first proteome-wide image of human cells.bioRxiv : the preprint server for biology · 2026Article
- Advances and opportunities for computational interrogation of plant proteins.The Plant journal : for cell and molecular biology · 2026Review
- Partially shared multi-modal embedding learns holistic representation of cell state.Nature computational science · 2026Article
- A predictive framework for stop-loss variants with C-terminal extensions.Nucleic acids research · 2026Article
- Protein codes and mobility together shape cellular function and disease.Trends in biochemical sciences · 2026Review
- PLiCat: decoding protein-lipid interactions by large language model.Briefings in bioinformatics · 2025Article
- Kinase signaling cascades: an updated mechanistic landscape.Chemical science · 2025Review
- Spatial protein redistribution: wandering but not lost.Cellular and molecular life sciences : CMLS · 2025Review
- Prediction of protein subcellular localization in single cells.Nature methods · 2025Article
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5 authors.
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Abstract
The subcellular localization of a protein is important for its function, and its mislocalization is linked to numerous diseases. Existing datasets capture limited pairs of proteins and cell lines, and existing protein localization prediction models either miss cell-type specificity or cannot generalize to unseen proteins. Here we present a method for Prediction of Unseen Proteins' Subcellular localization (PUPS). PUPS combines a protein language model and an image inpainting model to utilize both protein sequence and cellular images. We demonstrate that the protein sequence input enables generalization to unseen proteins, and the cellular image input captures single-cell variability, enabling cell-type-specific predictions. Experimental validation shows that PUPS can predict protein localization in newly performed experiments outside the Human Protein Atlas used for training. Collectively, PUPS provides a framework for predicting differential protein localization across cell lines and single cells within a cell line, including changes in protein localization driven by mutations.
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