ArticleProceedings of the National Academy of Sciences of the United States of America2024
AlphaFold2-based prediction of the co-condensation propensity of proteins.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 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
- An ALS-associated mutation in the C-terminal α-helix of TDP-43 uncouples condensate formation and amyloid assembly.Protein science : a publication of the Protein Society · 2026Article
- Expression of nano-engineered RNA organelles in bacteria.Nature communications · 2026Article
- Protein folding: From physics-chemical rules and cellular machineries of protein quality control to AI solutions.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
The process of protein phase separation into liquid condensates has been implicated in the formation of membraneless organelles (MLOs), which selectively concentrate biomolecules to perform essential cellular functions. Although the importance of this process in health and disease is increasingly recognized, the experimental identification of proteins forming MLOs remains a complex challenge. In this study, we addressed this problem by harnessing the power of AlphaFold2 to perform computational predictions of the conformational properties of proteins from their amino acid sequences. We thus developed the CoDropleT (co-condensation into droplet transformer) method of predicting the propensity of co-condensation of protein pairs. The method was trained by combining experimental datasets of co-condensing proteins from the CD-CODE database with curated negative datasets of non-co-condensing proteins. To illustrate the performance of the method, we applied it to estimate the propensity of proteins to co-condense into MLOs. Our results suggest that CoDropleT could facilitate functional and therapeutic studies on protein condensation by predicting the composition of protein condensates.
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