ArticleBMC bioinformatics2022
Prediction of liquid-liquid phase separating proteins using machine learning.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 118 papers.
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Who cites it
118 citing papers in PubMed, 199 citations in OpenAlex.
- Engineering short-sequence elements for condensate-like assemblies by de novo design.Synthetic and systems biotechnology · 2027Article
- Unlocking the bacterial condensome: from "dark matter" to design.Molecular biology of the cell · 2026Article
- Characterization of Liquid-Liquid Phase Separation of Companion of Cellulose Synthases under Stress Mimicking Conditions.bioRxiv : the preprint server for biology · 2026Article
- Assessment of Aggregation- and Condensation-Prone Regions of Proteins Involved in Neurodegenerative, Neurological and Mental-State Diseases.Biomolecules · 2026Article
- FUS controls muscle differentiation through phase separation-mediated recruitment of the transcription factors MEF2 and ETV5.The EMBO journal · 2026Article
- Exploring the BICC1 Interactome in HEK293T Cells: Insights Into RNA-Mediated Protein Networks and Biomolecular Condensates.FASEB bioAdvances · 2026Article
- A machine learning framework for predicting and modulating condition-dependent protein phase separation.Nature communications · 2026Article
- CELF2 promotes 4R Tau splicing via nuclear clustering and drives cognitive dysfunction in tauopathy models.Nature communications · 2026Article
- Phosphorylation tunes strain-specific protein condensation during rotavirus replication organelle assembly.The EMBO journal · 2026Article
- Taxonomy-aware, disorder-matched benchmarking of phase-separating protein predictors.Genome biology · 2026Article
- 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
- Prediction of plant phase-separating proteins using positive-unlabeled learning.Genome biology · 2026Article
- Ser423-phosphorylated MECP2 (MECP2-pS423) accumulates in human brain, cerebrospinal fluid and serum in sporadic Alzheimer's disease.Acta neuropathologica · 2026Article
- Assessing the performance of multimodal large language models in experimental information extraction from liquid-liquid phase separation literature.Scientific reports · 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
- Cell flocculation and phase-separation support macro-scale tissue slab construction in a scaffold-free manner.Materials today. Bio · 2026Article
- Global maps of transcription factor properties reveal threshold-based formation of DNA-bound and mobile clusters.Science advances · 2026Article
- CELF2 Promotes Tau Exon 10 Inclusion via Hinge Domain-Mediated Nuclear Condensation, Driving Cognitive Dysfunction in Tauopathy Models.Research square · 2026Article
58 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundThe liquid-liquid phase separation (LLPS) of biomolecules in cell underpins the formation of membraneless organelles, which are the condensates of protein, nucleic acid, or both, and play critical roles in cellular function. Dysregulation of LLPS is implicated in a number of diseases. Although the LLPS of biomolecules has been investigated intensively in recent years, the knowledge of the prevalence and distribution of phase separation proteins (PSPs) is still lag behind. Development of computational methods to predict PSPs is therefore of great importance for comprehensive understanding of the biological function of LLPS.
resultsBased on the PSPs collected in LLPSDB, we developed a sequence-based prediction tool for LLPS proteins (PSPredictor), which is an attempt at general purpose of PSP prediction that does not depend on specific protein types. Our method combines the componential and sequential information during the protein embedding stage, and, adopts the machine learning algorithm for final predicting. The proposed method achieves a tenfold cross-validation accuracy of 94.71%, and outperforms previously reported PSPs prediction tools. For further applications, we built a user-friendly PSPredictor web server ( http://www.pkumdl.cn/PSPredictor ), which is accessible for prediction of potential PSPs.
conclusionsPSPredictor could identifie novel scaffold proteins for stress granules and predict PSPs candidates in the human genome for further study. For further applications, we built a user-friendly PSPredictor web server ( http://www.pkumdl.cn/PSPredictor ), which provides valuable information for potential PSPs recognition.
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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.