protein-struct-viz

Generate PyMOL scripts for highlighting specific protein residues in molecular structures.

Overview

This skill creates PyMOL command scripts to visualize protein structures with specific residues highlighted using various representation styles (sticks, spheres, surface, etc.).

Usage

The skill generates .pml script files that can be executed directly in PyMOL to:

• Load PDB structures
• Apply custom color schemes
• Highlight specific residues with different representation styles
• Create publication-ready visualization settings

Input Parameters

Residue Specification Format

• Format: chain:resnum:resname or resnum (for single chain)
• Examples: A:145:ASP, B:23:LYS, 156
• Wildcards: A:* (all residues in chain A)

Example

python scripts/main.py --pdb 1mbn --residues "A:64:HIS,A:93:VAL,A:97:LEU" --style sticks --color_scheme rainbow --output myoglobin_active_site.pml

This will generate a PyMOL script highlighting the specified residues in myoglobin's active site.

Output

Generated .pml script includes:

1. Structure loading commands
2. Background and lighting settings
3. Global representation settings
4. Specific residue highlighting
5. View optimization commands
6. Optional: ray tracing for high-quality images

References

See references/ directory for:

• PyMOL command reference
• Color palette templates
• Example scripts for common visualization tasks

Technical Difficulty

Medium - requires understanding of PyMOL scripting syntax and protein structure concepts.

Dependencies

• PyMOL (installed separately)
• Python 3.7+
• No Python package dependencies (generates plain text scripts)

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
• API requests use HTTPS only
• Input validated against allowed patterns
• API timeout and retry mechanisms implemented
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no internal paths exposed)
• Dependencies audited
• No exposure of internal service architecture

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