ArticleBriefings in bioinformatics2021
Computational resources for identifying and describing proteins driving liquid-liquid phase separation.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed, 68 citations in OpenAlex.
- Biomolecular coacervation-mediated materials: Phase states, phase transitions, and biomedical applications.Bioactive materials · 2027Review
- Clathrin adaptors drive phase separation in endocytosis and trafficking.Communications biology · 2026Article
- Rethinking bioinformatics in liquid-liquid phase separation: data resources, predictive models, and an event-centric perspective.Briefings in bioinformatics · 2026Review
- Liquid-Liquid Phase Separation in Major Hallmarks of Cancer.Cell proliferation · 2026Review
- A Systematic Evaluation of Protein Phase Separation Predictors across Diverse Protein Landscapes.Computational and structural biotechnology journal · 2026Article
- Diffusion-based size determination of solute particles: a method adapted for postsynaptic proteins.FEBS open bio · 2026Article
- Proteome-wide computational analyses reveal links between protein condensate formation and RNA biology.Science advances · 2025Article
- Missense mutations in intrinsically disordered protein regions link pathogenicity and phase separation.The Journal of biological chemistry · 2025Article
- Phase-separating fusion proteins drive cancer by upsetting transcription regulation.Genome biology · 2025Article
- Article
- catGRANULE 2.0: accurate predictions of liquid-liquid phase separating proteins at single amino acid resolution.Genome biology · 2025Article
- Liquid-Liquid Phase Separation Associated with Intrinsically Disordered Proteins: Experimental and Computational Tools.Current protein & peptide science · 2025Review
- A variational graph-partitioning approach to modeling protein liquid-liquid phase separation.Cell reports. Physical science · 2024Article
- Leveraging a large language model to predict protein phase transition: A physical, multiscale, and interpretable approach.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Decoding the genomic landscape of chromatin-associated biomolecular condensates.Nature communications · 2024Article
- Article
- Evidence for widespread cytoplasmic structuring into mesoscale condensates.Nature cell biology · 2024Article
- Defining the condensate landscape of fusion oncoproteins.Nature communications · 2023Article
- ParSe 2.0: A web tool to identify drivers of protein phase separation at the proteome level.Protein science : a publication of the Protein Society · 2023Article
- A New Phase of Networking: The Molecular Composition and Regulatory Dynamics of Mammalian Stress Granules.Chemical reviews · 2023Review
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Authors and funding
3 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One of the most intriguing fields emerging in current molecular biology is the study of membraneless organelles formed via liquid-liquid phase separation (LLPS). These organelles perform crucial functions in cell regulation and signalling, and recent years have also brought about the understanding of the molecular mechanism of their formation. The LLPS field is continuously developing and optimizing dedicated in vitro and in vivo methods to identify and characterize these non-stoichiometric molecular condensates and the proteins able to drive or contribute to LLPS. Building on these observations, several computational tools and resources have emerged in parallel to serve as platforms for the collection, annotation and prediction of membraneless organelle-linked proteins. In this survey, we showcase recent advancements in LLPS bioinformatics, focusing on (i) available databases and ontologies that are necessary to describe the studied phenomena and the experimental results in an unambiguous way and (ii) prediction methods to assess the potential LLPS involvement of proteins. Through hands-on application of these resources on example proteins and representative datasets, we give a practical guide to show how they can be used in conjunction to provide in silico information on LLPS.
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