Protein Docking Configurator

from scripts.main import DockingConfigurator

config = DockingConfigurator()

# Calculate box from receptor and active site
config.from_active_site("protein.pdb", ["A:120", "A:145", "A:189"])
config.write_vina_config("config.txt", exhaustiveness=32)

# Calculate box from receptor and reference ligand
config.from_reference_ligand("protein.pdb", "ligand.pdb", padding=5.0)
config.write_autodock4_gpf("protein.gpf", spacing=0.375)

Parameter Description

Command Line Parameters

Output

• AutoDock Vina: Generates config.txt configuration file
• AutoDock4: Generates .gpf (Grid Parameter File) and corresponding macromolecule files

Dependencies

• Python 3.8+
• numpy

Examples

# Example 1: Using active site residues
python scripts/main.py --receptor 1abc_receptor.pdb --active-site-residues "A:45,A:92,A:156" --software vina --output vina_config.txt

# Example 2: Using reference ligand with custom Box size
python scripts/main.py --receptor kinase.pdb --reference-ligand ATP.pdb --software vina --size-x 25 --size-y 25 --size-z 25

# Example 3: AutoDock4 configuration
python scripts/main.py --receptor protein.pdb --active-site-residues "A:100" --software autodock4 --spacing 0.375 --output protein.gpf

Notes

1. Input PDB files should have water molecules and heteroatoms removed (unless needed)
2. It is recommended to protonate and calculate charges for the receptor (using AutoDock Tools, etc.)
3. Grid Box size should be sufficient to cover ligand conformational space, typically 20-30Å
4. Active site residues should include catalytic residues and key binding residues

Risk Assessment

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• Input file paths validated (no ../ traversal)
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no stack traces exposed)
• Dependencies audited

Prerequisites

No additional Python packages required.

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Performance optimization
  • Additional feature support