ArticleNature communications2024
Precise prediction of phase-separation key residues by machine learning.
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 56 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
56 citing papers in PubMed, 70 citations in OpenAlex.
- A practical guide to investigating biomolecular condensates: a comment from the plant community.Science China. Life sciences · 2026Review
- Liquid-Liquid Phase Separation-Enhanced Multienzyme Catalysis: Mechanisms and Applications.ChemSusChem · 2026Review
- Directed evolution of functional intrinsically disordered proteins.Nature chemical biology · 2026Article
- High-throughput identification of endogenous biomolecular condensates and phase-separating proteins.Nature protocols · 2026Review
- A machine learning framework for predicting and modulating condition-dependent protein phase separation.Nature communications · 2026Article
- Transposon targeting non-coding RNA transcription targets G/C-rich tracts and is facilitated by an intrinsically disordered protein in Tetrahymena.Science China. Life sciences · 2026Article
- Taxonomy-aware, disorder-matched benchmarking of phase-separating protein predictors.Genome biology · 2026Article
- Dysregulation of liquid-liquid phase separation: from molecular mechanisms to targeted therapies in cardiovascular diseases.Journal of nanobiotechnology · 2026Review
- amyloid-predict and LLPS-predict: Predicting phase separation propensities in the intrinsically disordered proteome.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Epigenetically controlled endothelial promyelocytic leukemia drives liver inflammation and fibrosis.The Journal of clinical investigation · 2026Article
- Bridging technical innovation and computational advances in studies of RNA-protein assemblies.Nature reviews. Genetics · 2026Review
- USP7 at PML Nuclear Bodies: A Protein Interaction Network Perspective.International journal of molecular sciences · 2026Article
- Rethinking bioinformatics in liquid-liquid phase separation: data resources, predictive models, and an event-centric perspective.Briefings in bioinformatics · 2026Review
- Bipartite DNA binding domain of transcription factor BCL11B binds clustered short DNA sequence motifs.bioRxiv : the preprint server for biology · 2026Article
- Small molecules modulate the biomolecular condensates and liquid‒liquid phase separation.Acta pharmaceutica Sinica. B · 2026Review
- EBF2 condensates enable adipose thermogenesis through ZFP423 sequestration and epigenetic remodeling.Nature communications · 2026Article
- Prediction of plant phase-separating proteins using positive-unlabeled learning.Genome biology · 2026Article
- Machine Learning-Guided Engineering of Protein Phase Separation Properties in Immune Regulation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Investigating Phase Separation in Genome Folding via Multiscale Computational Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Phase separation of PGL-3 driven by structured domains that oligomerize and interact with RGG motifs.EMBO reports · 2026Article
Corrections and comments
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Authors and funding
24 authors at 3 institutions in 1 country.
Funding
Abstract
Understanding intracellular phase separation is crucial for deciphering transcriptional control, cell fate transitions, and disease mechanisms. However, the key residues, which impact phase separation the most for protein phase separation function have remained elusive. We develop PSPHunter, which can precisely predict these key residues based on machine learning scheme. In vivo and in vitro validations demonstrate that truncating just 6 key residues in GATA3 disrupts phase separation, enhancing tumor cell migration and inhibiting growth. Glycine and its motifs are enriched in spacer and key residues, as revealed by our comprehensive analysis. PSPHunter identifies nearly 80% of disease-associated phase-separating proteins, with frequent mutated pathological residues like glycine and proline often residing in these key residues. PSPHunter thus emerges as a crucial tool to uncover key residues, facilitating insights into phase separation mechanisms governing transcriptional control, cell fate transitions, and disease development.
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Registered trials
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.