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AutoMD-Viz

Automation

Generate publication-quality molecular dynamics visualizations including structures, data plots, trajectory projections, and full reports with journal-specif...

Install

openclaw skills install automd-viz

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 <file> - Input structure (PDB/GRO)
  • --style <nature|science|cell> - Journal style
  • --representation <cartoon|surface|sticks> - Display style
  • --color <spectrum|chain|secondary> - 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 <file> - Input data file (XVG format)
  • --style <nature|science|cell> - Journal style
  • --xlabel <text> - X-axis label
  • --ylabel <text> - Y-axis label
  • --title <text> - 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 <file> - Reference structure
  • --trajectory <file> - Trajectory file (XTC/TRR)
  • --method <pca|tsne|umap> - Dimensionality reduction method
  • --style <nature|science|cell> - 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 <file> - Reference structure
  • --trajectory <file> - Trajectory file
  • --input <dir> - Analysis results directory
  • --style <nature|science|cell> - 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

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