AutoMD-Viz - Publication-Quality Visualization for Molecular Dynamics

Version: 1.0.0

Author: Xuan Guo (xguo608@connect.hkust-gz.edu.cn)

License: MIT

Repository: https://github.com/Billwanttobetop/automd-viz

📖 Overview

AutoMD-Viz is a standalone visualization toolkit for generating publication-quality figures from molecular dynamics simulation data. It supports multiple visualization types and journal-specific styles (Nature, Science, Cell).

Key Features:

• 🎨 Molecular structure visualization (PyMOL)
• 📊 Data plotting (Matplotlib/Seaborn)
• 🎬 Trajectory visualization (PCA/t-SNE/UMAP)
• 📦 Automated report generation
• 🎯 Journal-specific styles (Nature/Science/Cell)
• 🔧 High-resolution output (300-600 DPI, SVG/PDF/EPS)

🚀 Quick Start

Installation

# Via ClawHub
clawhub install automd-viz

# Or manual installation
git clone https://github.com/Billwanttobetop/automd-viz.git
cd automd-viz
chmod +x automd-viz.sh

Basic Usage

# Generate protein structure figure
./automd-viz.sh --type structure --structure protein.pdb --style nature

# Plot RMSD/RMSF data
./automd-viz.sh --type data --input rmsd.xvg --style science

# Trajectory visualization (PCA)
./automd-viz.sh --type trajectory --structure protein.pdb --trajectory md.xtc

# Generate complete report
./automd-viz.sh --type report --structure protein.pdb --trajectory md.xtc --style nature

📋 Visualization Types

1. Structure Visualization (--type structure)

Generate high-quality molecular structure figures using PyMOL.

Options:

• --structure - Input structure (PDB/GRO)
• --style - Journal style
• --representation - Display style
• --color - Coloring scheme
• --resolution <300|600> - Output DPI

Example:

./automd-viz.sh --type structure \
  --structure protein.pdb \
  --style nature \
  --representation cartoon \
  --color spectrum \
  --resolution 600

Output:

• structure_nature.png (high-resolution raster)
• structure_nature.pse (PyMOL session)

2. Data Plotting (--type data)

Plot time-series data (RMSD, RMSF, energy, etc.) with journal-quality formatting.

Options:

• --input - Input data file (XVG format)
• --style - Journal style
• --xlabel - X-axis label
• --ylabel - Y-axis label
• --title - Plot title

Example:

./automd-viz.sh --type data \
  --input rmsd.xvg \
  --style science \
  --xlabel "Time (ns)" \
  --ylabel "RMSD (nm)"

Output:

• data_plot.pdf (vector graphics)
• data_plot.png (raster graphics)

3. Trajectory Visualization (--type trajectory)

Visualize trajectory in reduced dimensionality space (PCA/t-SNE/UMAP).

Options:

• --structure - Reference structure
• --trajectory - Trajectory file (XTC/TRR)
• --method - Dimensionality reduction method
• --style - Journal style

Example:

./automd-viz.sh --type trajectory \
  --structure protein.pdb \
  --trajectory md.xtc \
  --method pca \
  --style nature

Output:

• trajectory_pca_2d.pdf (2D projection)
• trajectory_pca_3d.pdf (3D projection)
• free_energy_landscape.pdf (FEL)

4. Automated Report (--type report)

Generate a complete set of publication-ready figures.

Options:

• --structure - Reference structure
• --trajectory - Trajectory file
• --input - Analysis results directory
• --style - Journal style

Example:

./automd-viz.sh --type report \
  --structure protein.pdb \
  --trajectory md.xtc \
  --input analysis-results/ \
  --style nature

Output:

• figures/ directory with all figures
• VISUALIZATION_REPORT.md (summary)

🎨 Journal Styles

Nature Style

• Font: Arial
• Font size: 7-9 pt
• Line width: 0.5-1.0 pt
• Color: Colorblind-friendly palette
• Format: PDF/EPS (vector)

Science Style

• Font: Helvetica
• Font size: 8-10 pt
• Line width: 0.75-1.25 pt
• Color: High-contrast palette
• Format: PDF/EPS (vector)

Cell Style

• Font: Arial
• Font size: 8-12 pt
• Line width: 1.0-1.5 pt
• Color: Vibrant palette
• Format: PDF/EPS (vector)

🔧 Dependencies

Required:

• Python 3.7+
• NumPy
• Matplotlib
• Seaborn

Optional (for advanced features):

• PyMOL (structure visualization)
• scikit-learn (PCA/t-SNE)
• umap-learn (UMAP)
• MDAnalysis (trajectory processing)

Auto-install:

pip install numpy matplotlib seaborn scikit-learn umap-learn MDAnalysis

📚 Integration with AutoMD-GROMACS

AutoMD-Viz is designed to work seamlessly with AutoMD-GROMACS analysis results.

After running analysis:

# Run analysis
advanced-analysis -s md.tpr -f md.xtc

# Visualize results
automd-viz --type report --input advanced-analysis/ --style nature

Supported analysis outputs:

• RMSD/RMSF/Rg (from analysis.sh)
• PCA/Clustering (from advanced-analysis.sh)
• Binding analysis (from binding-analysis.sh)
• Trajectory analysis (from trajectory-analysis.sh)
• Property analysis (from property-analysis.sh)

🐛 Troubleshooting

See publication-viz-errors.md for common issues and solutions.

Quick fixes:

• PyMOL not found → Install PyMOL or use --no-structure
• Font issues → Install required fonts or use --font-fallback
• Memory errors → Reduce trajectory frames with --stride

📖 Examples

See examples/ directory for complete workflows:

• example_protein/ - Protein structure visualization
• example_ligand/ - Protein-ligand complex
• example_membrane/ - Membrane protein system
• example_trajectory/ - Trajectory analysis

🤝 Contributing

Contributions welcome! Please submit issues and pull requests on GitHub.

📄 License

MIT License - see LICENSE file for details.

📧 Contact

• Author: Xuan Guo
• Email: xguo608@connect.hkust-gz.edu.cn
• GitHub: @Billwanttobetop