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

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📖 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)

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🚀 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 - 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)

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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)

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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)

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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)

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🎨 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)

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🔧 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

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📚 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)

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🐛 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

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📖 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

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🤝 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