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Revisiting Protein-Protein Docking: A Systematic Evaluation Framework.

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|September 18, 2025
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A new framework benchmarks protein-protein docking methods. AlphaFold3 excels in predicting complex structures, outperforming traditional tools, but deep learning models struggle with out-of-distribution generalization.

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Area of Science:

  • Computational biology
  • Structural biology
  • Bioinformatics

Background:

  • Protein-protein interactions are crucial for biological processes.
  • Accurate prediction of protein complex structures is vital for understanding mechanisms and drug design.
  • Protein-protein docking methods require rigorous evaluation.

Purpose of the Study:

  • To establish a comprehensive benchmarking framework for evaluating protein-protein docking methods.
  • To compare traditional and deep learning (DL)-based docking approaches.
  • To assess the out-of-distribution (OOD) generalization capabilities of DL models.

Main Methods:

  • Developed a benchmarking framework using DockingBenchmark 5.5, AACBench, and PPCBench datasets.
  • Evaluated 11 docking methods, including traditional (HDOCK, PatchDock, PIPER, ZDOCK) and DL-based (AlphaFold3, AlphaFold-Multimer, etc.).
  • Assessed performance on flexible docking, antibody-antigen complex docking, and OOD generalization.

Main Results:

  • AlphaFold3 showed a superior top-5 success rate (77.98%) in docking against apo structures.
  • HDOCK achieved a high success rate (85.24%) against holo structures but lower against apo.
  • AlphaFold3 was most accurate for antibody-antigen docking (31.78% success rate) and outperformed AlphaFold-Multimer.
  • All DL models showed reduced performance on the OOD PPCBench dataset.

Conclusions:

  • The proposed framework enables systematic evaluation of diverse protein-protein docking methods.
  • AlphaFold3 demonstrates strong performance, particularly against apo structures and in antibody-antigen modeling.
  • Current DL-based docking methods face challenges in OOD generalization, highlighting areas for future improvement.