ArticleNucleic acids research2020
PhaSePro: the database of proteins driving liquid-liquid phase separation.
Article in Nucleic acids research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 130 papers, 1 of them a synthesis that pooled it.
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Who cites it
130 citing papers in PubMed, 1 synthesis or guideline pooled it, 213 citations in OpenAlex.
- A comparative meta-analysis of membraneless organelle-associated proteins with age related proteome of C. elegans.Cell stress & chaperones · 2022Pooled it
- Engineering short-sequence elements for condensate-like assemblies by de novo design.Synthetic and systems biotechnology · 2027Article
- Ptbp1 Condensates With Phase Separation-Like Features in Cardiomyocyte Nuclei Regulate Splicing of Proliferative Genes in Neonatal Cardiomyocytes.Development, growth & differentiation · 2026Article
- Opposing roles of serine and charge in IDR condensate miscibility.Nature chemical biology · 2026Article
- Taxonomy-aware, disorder-matched benchmarking of phase-separating protein predictors.Genome biology · 2026Article
- Mapping interactions between disordered regions reveals promiscuity in biomolecular condensate formation.Nature communications · 2026Article
- Liquid-liquid phase separation and the formation of amyloid fibrils from DcpS scavenger enzymes.Scientific reports · 2026Article
- Rethinking bioinformatics in liquid-liquid phase separation: data resources, predictive models, and an event-centric perspective.Briefings in bioinformatics · 2026Review
- MuFGPS: enhancing liquid-liquid phase separation protein prediction through multi-level features and ensemble learning.Briefings in bioinformatics · 2026Article
- FuzDrop: sequence-based prediction of the propensity of proteins for liquid-liquid phase separation and aggregation.Nature protocols · 2026Review
- Pathogenic variations illuminate functional constraints in intrinsically disordered proteins.iScience · 2026Article
- Modern resources for intrinsic disorder predictions: protein language models, deep learning, meta-servers, and databases.Cellular and molecular life sciences : CMLS · 2026Review
- PhaSepDB 3.0: a comprehensive knowledgebase of phase separation-related proteins from AI-assisted curation.Nucleic acids research · 2026Article
- Phase separation in plants: mechanisms, functions, and a simplified workflow for investigation.Frontiers in plant science · 2026Review
- A Systematic Evaluation of Protein Phase Separation Predictors across Diverse Protein Landscapes.Computational and structural biotechnology journal · 2026Article
- Deep learning model of post-translational modification regulating liquid-liquid phase separation.Communications chemistry · 2025Article
- Proteome-wide computational analyses reveal links between protein condensate formation and RNA biology.Science advances · 2025Article
- Heterochromatome wide analyses reveal MBD2 as a phase separation scaffold for heterochromatin compartmentalization and composition.Nucleic acids research · 2025Article
- Understanding Biases in Liquid-Liquid Phase Separation: Investigating Amino Acid Enrichments in Phase-Separating Proteins toward Peptide Design.Biomacromolecules · 2025Article
- Biomolecular condensates: molecular structure, biological functions, diseases, and therapeutic targets.Molecular biomedicine · 2025Review
70 more citing papers are in PubMed but not listed here.
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Authors and funding
14 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Membraneless organelles (MOs) are dynamic liquid condensates that host a variety of specific cellular processes, such as ribosome biogenesis or RNA degradation. MOs form through liquid-liquid phase separation (LLPS), a process that relies on multivalent weak interactions of the constituent proteins and other macromolecules. Since the first discoveries of certain proteins being able to drive LLPS, it emerged as a general mechanism for the effective organization of cellular space that is exploited in all kingdoms of life. While numerous experimental studies report novel cases, the computational identification of LLPS drivers is lagging behind, and many open questions remain about the sequence determinants, composition, regulation and biological relevance of the resulting condensates. Our limited ability to overcome these issues is largely due to the lack of a dedicated LLPS database. Therefore, here we introduce PhaSePro (https://phasepro.elte.hu), an openly accessible, comprehensive, manually curated database of experimentally validated LLPS driver proteins/protein regions. It not only provides a wealth of information on such systems, but improves the standardization of data by introducing novel LLPS-specific controlled vocabularies. PhaSePro can be accessed through an appealing, user-friendly interface and thus has definite potential to become the central resource in this dynamically developing field.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.