ArticleProceedings of the National Academy of Sciences of the United States of America2022
Screening membraneless organelle participants with machine-learning models that integrate multimodal features.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 87 papers.
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Who cites it
87 citing papers in PubMed, 136 citations in OpenAlex.
- The thermodynamic state map of biomolecular condensates.Communications biology · 2026Review
- Assessment of Aggregation- and Condensation-Prone Regions of Proteins Involved in Neurodegenerative, Neurological and Mental-State Diseases.Biomolecules · 2026Article
- Beyond the structure-function paradigm: A comprehensive review of intrinsically disordered proteins.Biochemistry and biophysics reports · 2026Review
- High-throughput identification of endogenous biomolecular condensates and phase-separating proteins.Nature protocols · 2026Review
- Aberrant Phase Separation of Endothelial MAML1 Causes Congenital Heart Disease by Suppressing Notch Activity.Circulation · 2026Article
- A practical guide to investigating biomolecular condensates: a comment from the plant community.Science China. Life sciences · 2026Review
- Opposing roles of serine and charge in IDR condensate miscibility.Nature chemical biology · 2026Article
- A machine learning framework for predicting and modulating condition-dependent protein phase separation.Nature communications · 2026Article
- Kinase condensates enrich ATP and trigger autophosphorylation.Cell reports · 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
- Article
- MuFGPS: enhancing liquid-liquid phase separation protein prediction through multi-level features and ensemble learning.Briefings in bioinformatics · 2026Article
- Rethinking bioinformatics in liquid-liquid phase separation: data resources, predictive models, and an event-centric perspective.Briefings in bioinformatics · 2026Review
- FuzDrop: sequence-based prediction of the propensity of proteins for liquid-liquid phase separation and aggregation.Nature protocols · 2026Review
- Mediator Tail Subunits Hierarchically Couple Transcriptional Condensates to Gene Activation and Genome Organization.bioRxiv : the preprint server for biology · 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
- Navigating condensate micropolarity to enhance small-molecule drug targeting.Nature chemical biology · 2026Article
27 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein self-assembly is one of the formation mechanisms of biomolecular condensates. However, most phase-separating systems (PS) demand multiple partners in biological conditions. In this study, we divided PS proteins into two groups according to the mechanism by which they undergo PS: PS-Self proteins can self-assemble spontaneously to form droplets, while PS-Part proteins interact with partners to undergo PS. Analysis of the amino acid composition revealed differences in the sequence pattern between the two protein groups. Existing PS predictors, when evaluated on two test protein sets, preferentially predicted self-assembling proteins. Thus, a comprehensive predictor is required. Herein, we propose that properties other than sequence composition can provide crucial information in screening PS proteins. By incorporating phosphorylation frequencies and immunofluorescence image-based droplet-forming propensity with other PS-related features, we built two independent machine-learning models to separately predict the two protein categories. Results of independent testing suggested the superiority of integrating multimodal features. We performed experimental verification on the top-scored proteins DHX9,
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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.