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Targeted protein degradation (TPD) uses molecules to degrade proteins. This study benchmarks docking tools for modeling proteolysis-targeting chimeras (PROTACs), finding rDock effective and highlighting the need for receptor flexibility in PROTAC computational modeling.

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Area of Science:

  • Computational chemistry and drug discovery
  • Structural biology and molecular modeling
  • Pharmacology and chemical biology

Background:

  • Targeted protein degradation (TPD) is a novel therapeutic strategy.
  • Proteolysis-targeting chimeras (PROTACs) are a key TPD modality.
  • Computational modeling of PROTACs presents significant challenges due to their complexity.

Purpose of the Study:

  • To evaluate physics-based and machine learning (ML) docking tools for modeling PROTAC-E3L ternary complexes.
  • To establish performance benchmarks for PROTAC docking strategies.
  • To provide guidelines for computational modeling in PROTAC discovery.

Main Methods:

  • Analysis of 43 experimentally resolved POI-PROTAC-E3L structures.
  • Benchmarking GLIDE, MOE, rDock, DiffDock, and GeoDirDock.
  • Incorporation of van der Waals scaling, hydrogen bond constraints, NMA for flexibility, and AlphaFold2 structures.

Main Results:

  • rDock with high sampling outperformed other physics-based docking tools.
  • ML-based tools showed competitive RMSD but limited generalizability and required postprocessing.
  • Normal mode analysis (NMA) significantly improved docking accuracy by incorporating receptor flexibility.

Conclusions:

  • rDock is a strong candidate for PROTAC docking, especially with high sampling.
  • ML docking tools require careful validation and refinement for PROTAC applications.
  • Integrating receptor flexibility via NMA is crucial for accurate PROTAC ternary complex modeling.