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Integrating AlphaFold and pyDock for Protein–Protein Complex Modeling

This repository accompanies the chapter “Modeling Protein–Protein Complexes by Combining pyDock and AlphaFold” published in Methods in Molecular Biology (2026), and provides a practical, reproducible implementation of the workflow described by Rodríguez-Lumbreras et al. .

The main goal is to demonstrate how artificial intelligence–based modeling (AlphaFold2-Multimer and AlphaFold3) can be combined with energy-based scoring from pyDock to improve the accuracy of protein–protein complex predictions, particularly for challenging cases such as:

• antibody–antigen complexes
• multiprotein assemblies
• weak or transient interactions
• highly flexible proteins

The repository is organized into three folders, each corresponding to a major stage of the workflow: model generation with AlphaFold, energy scoring with pyDock, and final integration of both approaches.

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📁 Repository Structure

├── 3.1_Generating_3D_Models_for_Protein_Protein_Complexes_with_AlphaFold/
│     Scripts and examples for generating multiple conformations
│     using ColabFold AlphaFold2-Multimer, including the relaxation
│     of all recycled intermediate models generated by AF2.
│
├── 3.3.2_Computing_pyDock_Scores_for_a_Set_of_Complexes/
│     Templates (.ini ) generation, bindEy execution, chain reconstruction
│     with SCWRL, and parsed pyDock energy tables.
│
├── 3.4_Combined_Model_Confidence_and_pyDock_Score/
│     Scripts for integrating AlphaFold confidence metrics (AF-MC) with 
│     pyDock energy scores, including parsed energy tables and extracted 
│     AF2/AF3 metadata. This section computes z-scores for both scoring 
│     functions, generates combined AF–pyDock rankings, and outputs the 
│     final prioritized model list.

Each section contains ready-to-use scripts, test cases, and short usage notes.

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🧬 3.1 Generating Diverse Complex Models with AlphaFold

This folder contains:

• Workflows for AlphaFold2-Multimer (versions v1, v2, v3) with:

  • increased recycles
  • dropout during inference
  • saving all intermediate recycles
  • multiple seeds

• ColabFold and LocalColabFold pipelines for rapid predictions without large databases.

• AlphaFold3 examples (server-based and local execution).

• FASTA templates for heterodimers and homooligomers.

The aim is to generate >100 structural models per complex, which is essential for the subsequent scoring stage.

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⚡ 3.3.2 pyDock Energy Scoring

This folder includes:

• Automatic generation of all required *.ini files.

• Parallel execution of bindEy via Greasy.

• Optional side-chain reconstruction using SCWRL3/4.

• Example *.ene energy tables including:

  • Electrostatics (ELE)
  • Desolvation (DESOLV)
  • Van der Waals (VDW)
  • pyDock total energy (0.1·VDW)
  • pyDock total energy (1.0·VDW)

The VHH–RNase A (PDB 4POU) complex is provided as an illustrative example.

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🔗 3.4. Integrating AlphaFold Confidence and pyDock Energies

combining AlphaFold model confidence (AF-MC = 0.8·ipTM + 0.2·pTM) with pyDock energies using z-score normalization.

Included:

• Extraction of AF-MC from AF2 log.txt or AF3 summary_confidence.json.

• Computation of:

  Z = (X − μ) / σ
  

• Calculation of:

  • Z_AF-MC
  • Z_pyDock-1VDW
  • Z_combined = Z_AF-MC – Z_pyDock-1VDW

• Final ranking and filtering of top predictions.

When AF-MC < 0.8, the pipeline automatically falls back to classical pyDock docking, following the decision tree shown in Fig. 1 of the chapter.

In this repository, only the components highlighted in the red box of the figure are implemented, namely:

• Generation of AlphaFold2-Multimer models using ColabFold (optional use AlphaFold3 server)
• Extraction of ipTM and pTM
• Computation of Model Confidence (AF-MC)
• Calculation of pyDock energy scoring for AF2-generated complexes

The remaining module—the docking stage starting from monomeric or unbound structures—is not included here. If docking poses are needed, they can be generated via the pyDockWEB server:

👉 https://life.bsc.es/pid/pydockweb

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📘 Case Studies Included

1. VHH–RNase A (4POU) → AF2 rank 1 fails; pyDock identifies an acceptable model.

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🛠 Requirements

• Python ≥ 3.8
• pyDock ≥ 3.0
• SCWRL3 or SCWRL4
• Greasy (for task parallelization)
• AlphaFold2-Multimer / ColabFold / AlphaFold3 (depending on workflow)

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🚀 Quick Installation

git clone https://github.com/PyDock/AF_pyDock/
cd AF_pyDock

Each internal folder includes its own usage notes and example scripts.

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📄 Citation

If you use this repository, please cite:

Rodríguez-Lumbreras LA, Monteagudo V, Fernández-Recio J. Modeling Protein–Protein Complexes by Combining pyDock and AlphaFold. Methods in Molecular Biology (2026).

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🤝 Contributing

Contributions, suggestions, and pull requests are welcome.

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📧 Contact

For questions related to the protocol or pyDock software:

Juan Fernández-Recio Group