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An artificial intelligence-based approach for identifying the proteins regulating liquid-liquid phase separation.

Zahoor Ahmed1,2, Kiran Shahzadi3, Rui Li1

  • 1The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731 Sichuan, China.

Briefings in Bioinformatics
|July 9, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an AI model to identify proteins regulating liquid-liquid phase separation (LLPS), crucial for cell functions. The model accurately predicts these key proteins, advancing condensate biology and synthetic systems design.

Keywords:
ESM2_t36LLPSmultilayer perceptronregulator proteins in LLPS

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Area of Science:

  • Biochemistry and Molecular Biology
  • Computational Biology
  • Biophysics

Background:

  • Liquid-liquid phase separation (LLPS) forms membrane-less organelles, vital for cellular processes like RNA metabolism and signal transduction.
  • Regulator proteins are essential for controlling LLPS dynamics and cellular responses.
  • Targeting LLPS regulators has potential applications in biomaterials, drug delivery, and synthetic biology.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI)-based approach for identifying proteins that regulate LLPS.
  • To explore the biophysical properties of LLPS regulator proteins using AI interpretation methods.

Main Methods:

  • Construction of a dataset comprising 913 positive and 6584 negative protein sequences for LLPS regulators.
  • Extraction of semantic information from protein sequences using the ESM2_t36 pretrained protein language model.
  • Training a multilayer perceptron classifier and interpreting results using SHapley Additive exPlanations (SHAP).

Main Results:

  • The AI model achieved 0.78 accuracy in identifying LLPS regulator proteins on a test dataset.
  • The model outperformed traditional sequence-based methods and other pretrained embedding techniques.
  • SHAP interpretation identified charged and disordered residues as key biophysical patterns in regulator proteins.

Conclusions:

  • Deep contextual protein representations and neural network classifiers can accurately identify LLPS regulator proteins.
  • This AI tool facilitates a deeper understanding of condensate biology.
  • The findings enable the design of novel synthetic phase-separating systems.