ArticleNature communications2024
Protein Condensate Atlas from predictive models of heteromolecular condensate composition.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- A practical guide to investigating biomolecular condensates: a comment from the plant community.Science China. Life sciences · 2026Review
- Rethinking bioinformatics in liquid-liquid phase separation: data resources, predictive models, and an event-centric perspective.Briefings in bioinformatics · 2026Review
- Dynamic yet well-defined organization of the FUS RGG3 dense phase.Communications chemistry · 2026Article
- Expression of nano-engineered RNA organelles in bacteria.Nature communications · 2026Article
- Biomolecular condensates sustain pH gradients at equilibrium through charge neutralization.Nature chemistry · 2026Article
- Proximal proteomics reveals a landscape of human nuclear condensates.Nature cell biology · 2025Article
- Beyond peptide targeting sequences: machine learning of cellular condensate localization.Cell research · 2025Article
- Empirical Assessment of Sequence-Based Predictions of Intrinsically Disordered Regions Involved in Phase Separation.Biomolecules · 2025Article
- Characterizing protein sequence determinants of nuclear condensates by high-throughput pooled imaging with CondenSeq.Nature methods · 2025Article
- Article
- Leveraging protein structural information to improve variant effect prediction.Current opinion in structural biology · 2025Review
- Protein codes promote selective subcellular compartmentalization.Science (New York, N.Y.) · 2025Article
- How to build the virtual cell with artificial intelligence: Priorities and opportunities.Cell · 2024Article
- How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities.ArXiv · 2024Article
- Protein Condensate Atlas from predictive models of heteromolecular condensate composition.Nature communications · 2024Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biomolecular condensates help cells organise their content in space and time. Cells harbour a variety of condensate types with diverse composition and many are likely yet to be discovered. Here, we develop a methodology to predict the composition of biomolecular condensates. We first analyse available proteomics data of cellular condensates and find that the biophysical features that determine protein localisation into condensates differ from known drivers of homotypic phase separation processes, with charge mediated protein-RNA and hydrophobicity mediated protein-protein interactions playing a key role in the former process. We then develop a machine learning model that links protein sequence to its propensity to localise into heteromolecular condensates. We apply the model across the proteome and find many of the top-ranked targets outside the original training data to localise into condensates as confirmed by orthogonal immunohistochemical staining imaging. Finally, we segment the condensation-prone proteome into condensate types based on an overlap with biomolecular interaction profiles to generate a Protein Condensate Atlas. Several condensate clusters within the Atlas closely match the composition of experimentally characterised condensates or regions within them, suggesting that the Atlas can be valuable for identifying additional components within known condensate systems and discovering previously uncharacterised condensates.
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