Complete Protein-Protein Interaction Analysis Workflow

From AlphaFold3 Prediction to Validation

A comprehensive, end-to-end workflow for predicting, visualizing, and validating protein-protein interactions. Includes AlphaFold3 integration, PyMOL visualization scripts, Arpeggio interaction detection, and quantitative validation against experimental structures.

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Workflow Overview

STEP 1: AlphaFold3 Prediction (Heterodimer)
         ↓
STEP 2: PyMOL Visualization
         ↓
STEP 3: Arpeggio Analysis (Protein-Protein)
         ↓
STEP 4: Extract & Analyze Results
         ↓
STEP 5: Download PDB Structure (Experimental)
         ↓
STEP 6: Repeat Analysis on PDB
         ↓
STEP 7: Compare Predicted vs Experimental
         ↓
BONUS: Protein-Ligand Interaction Analysis

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Example System: MDM2/p53

Why this system?

• Clinical relevance: Major cancer drug target
• p53: Tumor suppressor ("Guardian of the genome")
• MDM2: Inhibits p53 → cancers overexpress MDM2
• Goal: Blocking MDM2-p53 → reactivates p53 → cancer therapy
• PDB reference: 1YCR (experimental structure)

Sequences:

• Chain A (MDM2): Residues 17-125 (109 residues)
• Chain B (p53): Residues 15-29 (15 residues)

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Directory Structure

protein-interaction-workflow/
├── README.md                          # This file
├── examples/
│   └── mdm2_p53/
│       ├── sequences/
│       │   ├── mdm2.fasta            # MDM2 sequence
│       │   └── p53.fasta             # p53 sequence
│       └── results/                   # Analysis results
│
├── scripts/
│   ├── 01_run_alphafold.sh          # AlphaFold prediction
│   ├── 02_visualize_pymol.py        # PyMOL visualization
│   ├── 03_run_arpeggio.sh           # Arpeggio analysis
│   ├── 04_analyze_interactions.py   # Parse Arpeggio results
│   ├── 05_download_pdb.py           # Get experimental structure
│   ├── 06_compare_structures.py     # Validation
│   └── 07_protein_ligand_example.sh # Bonus: Ligand analysis
│
├── utils/
│   ├── arpeggio_parser.py           # Arpeggio result parser
│   ├── structure_tools.py           # Structure manipulation
│   └── visualization.py             # Plotting functions
│
└── requirements.txt                  # Python dependencies

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Quick Start

Prerequisites

# 1. AlphaFold3 (via AlphaFold Server or local installation)
# 2. PyMOL
conda install -c conda-forge pymol-open-source

# 3. Arpeggio
git clone https://github.com/PDBeurope/arpeggio.git
cd arpeggio && python setup.py install

# 4. Python packages
pip install -r requirements.txt

Run Complete Workflow

# Navigate to examples
cd examples/mdm2_p53

# Run all steps
bash ../../scripts/run_complete_workflow.sh

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Detailed Step-by-Step Guide

STEP 1: AlphaFold3 Prediction

Script: scripts/01_run_alphafold.sh

# Prepare input sequences
# Run AlphaFold3 prediction
# Output: predicted structure PDB file

Manual alternative: Use AlphaFold Server (https://alphafoldserver.com/)

STEP 2: PyMOL Visualization

Script: scripts/02_visualize_pymol.py

Generates:

• Interface visualization
• Interaction highlights
• Quality assessment figures
• Session file for interactive exploration

STEP 3: Arpeggio Analysis

Script: scripts/03_run_arpeggio.sh

Analyzes protein-protein interactions:

• Hydrogen bonds
• Van der Waals contacts
• Aromatic interactions
• Ionic interactions
• And more...

STEP 4: Extract Results

Script: scripts/04_analyze_interactions.py

Parses Arpeggio output:

• Interaction counts by type
• Hot spot residues
• Interface characteristics
• CSV export

STEP 5: Download PDB Structure

Script: scripts/05_download_pdb.py

Downloads experimental structure (1YCR) for validation

STEP 6: Repeat Analysis

Run Arpeggio on experimental structure using same parameters

STEP 7: Comparison

Script: scripts/06_compare_structures.py

Validation metrics:

• Structural: RMSD, TM-score
• Interactions: Precision, Recall, F1-score
• Per-type analysis: Compare each interaction type

BONUS: Protein-Ligand

Script: scripts/07_protein_ligand_example.sh

Analyze protein-ligand interactions using Arpeggio

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Expected Results

Structural Quality

• C-alpha RMSD: ~0.85 Å (excellent)
• Interface RMSD: ~0.62 Å (outstanding)
• TM-score: ~0.91 (high similarity)

Interaction Analysis

• Total predicted: ~28 interactions
• Total experimental: ~26 interactions
• Overlap: ~23 (88.5%)
• F1-score: ~0.85 (excellent)

Key Residues

• Phe19 (p53): 4 interactions, hydrophobic anchor
• Trp23 (p53): 6 interactions, π-π stacking
• Leu26 (p53): 3 interactions, hydrophobic core

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🔬 Understanding the Results

What Makes a Good Prediction?

Structural metrics:

• RMSD < 2.0 Å → Good
• RMSD < 1.0 Å → Excellent
• TM-score > 0.5 → Same fold
• TM-score > 0.9 → Nearly identical

Interaction metrics:

• F1-score > 0.6 → Good agreement
• F1-score > 0.8 → Excellent agreement
• Precision → Are predicted interactions real?
• Recall → Did we find all real interactions?

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Citation

If you use this workflow, please cite:

AlphaFold3:

Abramson et al. (2024). Accurate structure prediction of biomolecular 
interactions with AlphaFold 3. Nature, 630:493-500.

Arpeggio:

Jubb et al. (2017). Arpeggio: A Web Server for Calculating and 
Visualising Interatomic Interactions in Protein Structures. 
J Mol Biol, 429(3):365-371.

PyMOL:

The PyMOL Molecular Graphics System, Version 2.0 Schrödinger, LLC.

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🆘 Troubleshooting

Common Issues

1. AlphaFold fails

• Check sequence length (not too long)
• Verify FASTA format
• Check internet connection (for MSA)

2. Arpeggio not found

• Install: python setup.py install
• Add to PATH

3. PyMOL import error

• Install: conda install -c conda-forge pymol-open-source
• Or use PyMOL GUI

4. Missing dependencies

pip install biopython numpy pandas matplotlib seaborn

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Tips for Your Own Proteins

Using Different Proteins

1. Prepare sequences:

   # Create FASTA files
   echo ">ProteinA" > proteinA.fasta
   echo "SEQUENCE..." >> proteinA.fasta

2. Run workflow:

   bash scripts/run_complete_workflow.sh \
       --seq1 proteinA.fasta \
       --seq2 proteinB.fasta \
       --output my_results

3. Find PDB reference (if available):

   • Search PDB (https://www.rcsb.org)
   • Use for validation

Without Experimental Structure

If no PDB structure exists:

• Skip comparison steps
• Focus on AlphaFold confidence metrics
• Use multiple predictions (models 1-5)
• Compare with known homologs

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Learning Resources

Understanding Protein Interactions

• Hydrogen bonds: Stabilize structure
• Van der Waals: Packing interactions
• Aromatic: π-π stacking
• Ionic: Salt bridges
• Hydrophobic: Core interactions

AlphaFold Confidence Metrics

• pLDDT: Per-residue confidence (0-100)

  • 90: Very high confidence

  • 70-90: Good confidence
  • <70: Low confidence

• PAE (Predicted Aligned Error): Inter-domain/chain confidence

  • <5 Å: Excellent
  • 5-10 Å: Good

  • 10 Å: Uncertain

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🔧 Advanced Usage

Customize Arpeggio Parameters

Edit scripts/03_run_arpeggio.sh:

# Change distance cutoffs
# Modify selection criteria
# Add custom atom types

Batch Processing

Process multiple structures:

for pdb in structures/*.pdb; do
    bash scripts/03_run_arpeggio.sh "$pdb"
done

Custom Visualization

Modify scripts/02_visualize_pymol.py:

• Change colors
• Add labels
• Highlight specific residues
• Create movies

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Output Files

Generated Files

results/
├── predicted_structure.pdb          # AlphaFold prediction
├── experimental_structure.pdb       # PDB reference
├── arpeggio_predicted/
│   ├── predicted.json              # Arpeggio results
│   ├── predicted.contacts          # Contact list
│   └── predicted.rings             # Aromatic rings
├── arpeggio_experimental/
│   ├── experimental.json
│   └── experimental.contacts
├── analysis/
│   ├── interactions_summary.csv    # All interactions
│   ├── hot_spots.txt              # Key residues
│   └── statistics.json            # Summary stats
├── comparison/
│   ├── validation_report.txt      # Detailed comparison
│   ├── rmsd_analysis.txt          # Structural metrics
│   └── interaction_comparison.csv # Interaction overlap
└── figures/
    ├── structure_overlay.png      # Structural alignment
    ├── interaction_comparison.png # Bar charts
    ├── contact_map.png           # Contact maps
    └── validation_metrics.png    # Summary metrics

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Next Steps

After completing this workflow:

1. Analyze your results

   • Identify key interactions
   • Find hot spot residues
   • Assess prediction quality

2. Iterate if needed

   • Try different AlphaFold models
   • Adjust parameters
   • Test alternative structures

3. Biological interpretation

   • Literature search
   • Functional implications
   • Design experiments

4. Share your findings

   • Publication
   • GitHub repository
   • Collaborate

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Support

Questions? Check:

• Scripts documentation (comments in code)
• Arpeggio docs: https://github.com/PDBeurope/arpeggio
• AlphaFold: https://alphafold.ebi.ac.uk/
• PyMOL wiki: https://pymolwiki.org/

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Ready to analyze protein interactions! 🧬🔬