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PhaseNet: A computational framework for identifying phase-separating proteins based on protein language model.

Xuxin He1, Jiahui Guan2, Peilin Xie1

  • 1Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172, Shenzhen, China.

International Journal of Biological Macromolecules
|November 15, 2025
PubMed
Summary
This summary is machine-generated.

PhaseNet accurately identifies and classifies phase-separating proteins, crucial for understanding cellular processes and disease. This computational tool enhances the discovery of proteins involved in liquid-liquid phase separation (LLPS).

Keywords:
Deep learningEnsemble learningPhase-separating proteinsProtein language model

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Area of Science:

  • Biochemistry and Molecular Biology
  • Computational Biology
  • Genomics and Proteomics

Background:

  • Liquid-liquid phase separation (LLPS) drives the formation of biomolecular condensates essential for cellular functions.
  • Dysregulation of LLPS is implicated in various human diseases, highlighting the need for precise protein identification.
  • Identifying phase-separating proteins is critical for understanding condensate mechanisms and disease pathogenesis.

Purpose of the Study:

  • To develop PhaseNet, a dual-task computational framework for distinguishing phase-separating proteins (PSPs) from non-PSPs.
  • To classify PSPs into self-assembling (PS-Self) and partner-dependent (PS-Part) categories.
  • To provide a robust and interpretable tool for systematic discovery and annotation of PSPs.

Main Methods:

  • PhaseNet integrates features from protein language models (ESM), sequence encodings (ZSCALE, BLOSUM), and a CNN-BiGRU with multi-head attention.
  • Heterogeneous features are fused via an attention-guided strategy and optimized with HSIC regularization for enhanced discrimination.
  • A secondary task utilizes Lasso-based feature selection on ESM embeddings and a stacking ensemble of five classifiers (Random Forest, Extra Trees, GBDT, XGBoost, MLP).

Main Results:

  • PhaseNet demonstrates superior performance in both general identification and fine-grained classification of PSPs compared to existing predictors.
  • The framework effectively distinguishes LLPS proteins from non-LLPS proteins.
  • PhaseNet accurately classifies LLPS proteins into PS-Self and PS-Part categories.

Conclusions:

  • PhaseNet offers a significant advancement in the computational prediction of phase-separating proteins.
  • The modular and interpretable design facilitates systematic discovery and annotation of proteins involved in LLPS.
  • This tool aids in elucidating the molecular mechanisms of biomolecular condensates and their role in health and disease.