ArticleBriefings in bioinformatics2025
Prediction of liquid-liquid phase separation proteins based on protein language model.
Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Taxonomy-aware, disorder-matched benchmarking of phase-separating protein predictors.Genome biology · 2026Article
- Rethinking bioinformatics in liquid-liquid phase separation: data resources, predictive models, and an event-centric perspective.Briefings in bioinformatics · 2026Review
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Authors and funding
5 authors.
Funding
Abstract
Liquid-liquid phase separation (LLPS) enables biomolecules to form membraneless condensates critical for cellular functions like RNA metabolism and protein synthesis. Identifying LLPS-associated proteins is essential for understanding their roles in cellular organization and disease. Current prediction methods are hindered by complex and inefficient feature extraction processes. Here, we present a novel framework combining a protein language model ProtT5 with a KmerConv module for local sequence pattern detection and a multi-head attention mechanism for global sequence information extraction. This integrated approach achieves high predictive performance across multiple diverse datasets and generalizes effectively across different species. Our method provides a robust and efficient tool for systematic LLPS protein identification, advancing research into biomolecular aggregation and its implications for health and disease.
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Registered trials
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