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Author Spotlight: Evaluating Biophysical Assays for Characterizing PROTACS Ternary Complexes
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Interpretable PROTAC Degradation Prediction With Structure-Informed Deep Ternary Attention Framework.

Zhenglu Chen1, Chunbin Gu2, Shuoyan Tan3

  • 1School of Pharmacy, Lanzhou University, Lanzhou, 730000, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|September 30, 2025
PubMed
Summary

This study introduces PROTAC-STAN, a deep learning framework for predicting Proteolysis Targeting Chimera (PROTAC) degradation. It enhances accuracy and interpretability by integrating molecular structure and attention mechanisms, accelerating drug discovery.

Keywords:
deep learninginterpretabilitymolecular dynamics simulationproteolysis targeting chimera

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Area of Science:

  • Biochemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • Proteolysis Targeting Chimeras (PROTACs) offer a novel approach to degrade disease-causing proteins, including those previously considered 'undruggable'.
  • Current PROTAC development heavily relies on extensive wet-lab experiments, which are costly and time-consuming.
  • Existing deep learning models for PROTAC degradation prediction often neglect crucial hierarchical molecular representations and protein structural data, limiting their predictive power and interpretability.

Purpose of the Study:

  • To develop an interpretable deep learning framework, PROTAC-STAN, for accurate prediction of PROTAC-induced protein degradation.
  • To address the limitations of existing methods by incorporating hierarchical molecular features and protein structural information.
  • To provide insights into the molecular interactions governing PROTAC efficacy through an attention-based mechanism.

Main Methods:

  • Developed PROTAC-STAN, a structure-informed deep ternary attention network (STAN).
  • Represented PROTAC molecules using atom, molecule, and property hierarchies.
  • Integrated protein structural data for Proteins-Of-Interest (POIs) and E3 ligases using a protein language model.
  • Utilized a novel ternary attention network to model interactions among PROTAC components and target proteins at the substructure level.

Main Results:

  • PROTAC-STAN achieved over 10% improvement in multiple performance metrics compared to state-of-the-art baseline methods.
  • The framework provides significant interpretability, visualizing interactions at atomic and residue levels.
  • Case studies and exploratory evaluations confirmed the practical applicability and robustness of PROTAC-STAN.

Conclusions:

  • PROTAC-STAN offers a powerful and interpretable deep learning approach for predicting PROTAC degradation.
  • The model's ability to integrate structural information and provide mechanistic insights accelerates PROTAC research and development.
  • PROTAC-STAN is poised to become a foundational tool for advancing the field of targeted protein degradation.