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

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📋 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.

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📄 License

MIT License - see LICENSE file for details.

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📧 Contact

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