Antibody-Antigen Docking Analysis

This tutorial demonstrates a complete AI-powered computational immunotherapy workflow using Chiral Potter's integrated platform. We'll predict antibody structures with Boltz-2, model complexes with DiffDock-PP, and analyze binding with comprehensive reporting and Mol* visualization.

Tutorial Overview

Learning Objectives

By completing this tutorial, you will:

  • Predict antibody structures from sequence using Boltz-2 model
  • Master protein-protein docking with DiffDock-PP
  • Analyze binding interfaces with comprehensive docking reports and CAPRI metrics
  • Create publication-quality visualizations with integrated Mol* viewer

Biological Context: Antibody-Antigen Recognition

We'll model the 4G6K complex interaction:

  • System: Antibody-antigen binding complex from PDB 4G6K
  • Application: Understanding molecular recognition mechanisms
  • Research Goal: Analyze binding interfaces and interaction patterns
  • Method: Complete AI-powered structure-to-complex workflow

Complete Workflow

Structure Prediction

Boltz-2 AI-powered antibody structure prediction

Protein-Protein Docking

DiffDock-PP complex formation modeling

Docking Report

Comprehensive binding analysis with CAPRI metrics

Mol* Visualization

Interactive 3D exploration and publication graphics

Stage 1: Structure Prediction with Boltz-2

Setting Up the Prediction

Create New Project and Workflow

  • Project name: "4G6K-Complex-Analysis"
  • Workflow name: "Antibody-antigen docking"

Prepare Sequence Input Upload the antibody sequences from the 4G6K complex:

Heavy chain (4G6K_H):

>A|protein
QVQLQESGPGLVKPSQTLSLTCSFSGFSLSTSGMGVGWIRQPSGKGLEWLAHIWWDGDESYN
PSLKSRLTISKDTSKNQVSLKITSVTAADTAVYFCARNRYDPPWFVDWGQGTLVTVSSASTK
GPSVFPLAPSSKSTSGGTAALGCLVKDYFPEPVTVSWNSGALTSGVHTFPAVLQSSGLYSLS
SVVTVPSSSLGTQTYICNVNHKPSNTKVDKRVEP

Light chain (4G6K_L):

>B|protein
DIQMTQSTSSLSASVGDRVTITCRASQDISNYLSWYQQKPGKAVKLLIYYTSKLHSGVPSRF
SGSGSGTDYTLTISSLQQEDFATYFCLQGKMLPWTFGQGTKLEIKRTVAAPSVFIFPPSDEQ
LKSGTASVVCLLNNFYPREAKVQWKVDNALQSGNSQESVTEQDSKDSTYSLSSTLTLSKADY
EKHKVYACEVTHQGLSSPVTKSFNRG

Configure Boltz-2 Parameters

  • Output Format: PDB
  • Recycling Steps: 3
  • Sampling Steps: 200
  • Diffusion Samples: 1
  • Step Scale: 1.638
  • Devices: 1
  • Accelerator: GPU

Understanding Boltz-2 Configuration

Input Options

Sequence Input

  • FASTA format for antibody chains
  • Multi-chain prediction for heavy and light chains
  • MSA automatically generated

Quality Settings

Confidence Levels (0-1 scale)

  • pLDDT > 0.9: Very high confidence
  • pLDDT 0.7-0.9: Confident regions
  • pLDDT 0.5-0.7: Low confidence
  • pLDDT < 0.5: Very low confidence

Boltz-2 Confidence Score

Calculated as: 0.8 × overall pLDDT + 0.2 × interface pTM

  • Combines local structure confidence with interface quality
  • Range: 0 (low) to 1 (high confidence)

Antibody Features

Antibody-Specific Considerations

  • CDR Loops: Complementarity-determining regions
  • Framework Regions: Structural scaffold
  • Heavy/Light Pairing: Chain association
  • Antigen Binding: Paratope-epitope interaction

Boltz-2 Antibody Capabilities

  • Trained on extensive antibody structures
  • Handles CDR loop flexibility
  • Predicts heavy-light chain orientation
  • Models antigen binding interfaces

Stage 2: AI-Powered Protein-Protein Docking

Configuring DiffDock-PP

Add DiffDock-PP node to your workflow:

  • num_samples: 40,
  • run_name: "4G6K"

For antibody-antigen docking, provide the antibody as the ligand and antigen as the receptor for optimal results with DiffDock-PP's training data.

Docking Execution

The platform will:

Protein Encoding

  • ESM-2 generates embeddings for both proteins
  • Captures evolutionary and structural information
  • Takes ~2 minutes for protein preparation

Pose Generation

  • Diffusion model samples 60 binding orientations
  • Explores rotational and translational space
  • Focuses on biologically relevant interfaces

Quality Assessment

  • Confidence scoring for each pose

DiffDock-PP Output Files

  • Pickle files: Raw predictions and RMSD data
  • PDB poses: Poses by ranking automatically exported for visualization
  • Summary.txt: Job metadata and run information

Stage 3: Docking Report Generation

Understanding DiffDock-PP Output

The docking generates 40 poses with confidence scores:

Confidence Score Assessment

  • High Confidence: score > 0
  • Moderate Confidence: -1.5 < score ≤ 0
  • Low Confidence: score ≤ -1.5

Additional Metrics (if reference available)

When a reference structure is provided, the system calculates Complex RMSD, Ligand RMSD, and Interface RMSD for detailed accuracy assessment.

The Docking Report processes DiffDock-PP output files to generate analysis:

Report Generation

  • HTML dashboard: Interactive confidence analysis and pose ranking
  • Quality assessment: High/Moderate/Low confidence classification
  • Visualization: Confidence score plots and pose comparisons

Interactive Report Features

Dashboard Navigation

  • Summary page: Key metrics and best poses at a glance
  • Detailed analysis: Deep dive into individual poses
  • Comparison tools: Side-by-side pose evaluation
  • Export options: Download figures and data tables

Key Visualizations

  1. Score distributions: Statistical analysis of all poses
  2. Interface heatmaps: Interaction frequency across poses
  3. 3D overlay: Structural diversity visualization
  4. Interaction networks: Detailed molecular contacts

Stage 4: Mol* Visualization and Export

Integrated Mol* Viewer

The platform provides seamless embedded visualization:

Visualization Features

  1. Multi-pose comparison: Overlay and compare top solutions
  2. Interface analysis: Detailed interaction visualization
  3. Publication graphics: High-resolution image export

Conclusion

This tutorial demonstrates the power of fully AI-powered workflows for antibody-antigen analysis. By combining Boltz-2's structure prediction with DiffDock-PP's docking and comprehensive reporting tools, researchers can go from sequence to detailed binding analysis without requiring experimental structures.

Key Takeaways

  1. End-to-end AI enables structure-based drug design from sequence alone
  2. Comprehensive analysis through automated reporting and visualization
  3. Quality assessment using CAPRI metrics and multi-pose analysis

Resources and References

Key Publications

  1. Ille, A. M. et al. (2025). Human protein interactome structure prediction at scale with Boltz-2. bioRxiv. https://doi.org/10.1101/2025.07.03.663068
  2. Ketata, M. A. et al. (2023). DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models. arXiv:2304.03889.
  3. Sehnal, D. et al. (2021). Mol* Viewer: modern web app for 3D visualization and analysis of large biomolecular structures. Nucleic Acids Research, 49(W1), W431-W437.
  4. GitHub repositories:

This tutorial demonstrates the complete AI-powered workflow for antibody-antigen docking analysis using Boltz-2, DiffDock-PP, and comprehensive reporting tools.