ArticleBriefings in bioinformatics2025
MambaPhase: deep learning for liquid-liquid phase separation protein classification.
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 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Advances in engineering and applications of synthetic phase-separated membraneless organelles in biotechnology.Synthetic and systems biotechnology · 2026Review
- Taxonomy-aware, disorder-matched benchmarking of phase-separating protein predictors.Genome biology · 2026Article
- 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
- Phase Separation-Regulated Fungal Growth, Sexual Development, Adaptation and Synthetic Biology Applications.Journal of fungi (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Liquid-liquid phase separation plays a critical role in cellular processes, including protein aggregation and RNA metabolism, by forming membraneless subcellular structures. Accurate identification of phase-separated proteins is essential for understanding and controlling these processes. Traditional identification methods are effective but often costly and time-consuming. The recent machine learning methods have reduced these costs, but most models are restricted to classifying scaffold and client proteins with limited experimental conditions. To address this limitation, we developed a Mamba-based encoder using contrastive learning that incorporates separation probability, protein type, and experimental conditions. Our model achieved 95.2% accuracy in predicting phase-separated proteins and an ROCAUC score of 0.87 in classifying scaffold and client proteins. Further validation in the DgHBP-2 drug delivery system demonstrated its potential for condition modulation in drug development. This study provides an effective framework for the accurate identification and control of phase separation, facilitating advancements in biomedical research and therapeutic applications.
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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.