MDSASA-Bolt ⚡️

MDSASA-Bolt is a high-performance Python library for computing solvent accessible surface area (SASA) of molecular dynamics trajectories. It's a drop-in replacement for mdakit-sasa that leverages RustSASA under the hood to deliver dramatically improved performance while maintaining full compatibility with MDAnalysis workflows.

Features

• ⚡️ Ludicrous Speed: 20x faster than mdakit-sasa.
• 🔄 Drop-in Replacement: Compatible with existing mdakit-sasa workflows.
• 🧬 MDAnalysis Integration: Seamlessly works with MDAnalysis Universe and AtomGroup objects.
• 🦀 Powered by RustSASA: Leverages Rust's performance and safety.
• 🧪 Validated: Tested against Freesasa/mdakit_sasa.
• 📊 Trajectory Analysis: Built for analyzing entire MD trajectories efficiently
• 🐍 Pure Python API: Familiar interface for Python developers

Quick Start

Installation

pip install mdsasa-bolt

Basic Usage

import MDAnalysis as mda
from mdsasa_bolt import SASAAnalysis

# Load your trajectory
u = mda.Universe("topology.pdb", "trajectory.dcd")

# Create SASA analysis
sasa_analysis = SASAAnalysis(u, select="protein")

# Run the analysis
sasa_analysis.run()

# Access results
print(f"Mean total SASA: {sasa_analysis.results.mean_total_area:.2f} Ų")
print(f"SASA per frame: {sasa_analysis.results.total_area}")
print(f"SASA per residue: {sasa_analysis.results.residue_area}")

Advanced Usage

import MDAnalysis as mda
from mdsasa_bolt import SASAAnalysis

# Load trajectory
u = mda.Universe("system.gro", "trajectory.xtc")

# Analyze specific selection with custom frame range
sasa_analysis = SASAAnalysis(
    u,
    select="resname LYS or resname ARG",  # Only basic residues
)

# Run analysis
sasa_analysis.run(
    start=100,                     # Start from frame 100
    stop=1000,                     # End at frame 1000
    step=10,                       # Analyze every 10th frame
    probe_radius=1.4,              # Custom probe radius Default:1.4
    n_points=960                   # Custom number of points Default: 960
)

# Results are available as numpy arrays
total_sasa_per_frame = sasa_analysis.results.total_area
residue_sasa_matrix = sasa_analysis.results.residue_area  # Shape: (n_frames, n_residues)
mean_total_sasa = sasa_analysis.results.mean_total_area

Performance Benchmarks 🚀

Benchmarks were performed using molecular dynamics data for 4IAQ from the GPCRMD database. Hypefine (w/ runs =3) was used to measure the time taken. Results:

Test system: MDAnalysisTests trajectory data

Validation 📊

MDSASA-Bolt has been thoroughly validated against reference implementations to ensure accuracy:

MDSASA-Bolt acheives a pearson correlation > 0.99 and an RMSE of 209.14 when compared against mdakit_sasa.

API Reference

SASAAnalysis

The main analysis class that integrates with MDAnalysis.

Parameters

• universe_or_atomgroup (Universe or AtomGroup): MDAnalysis Universe or AtomGroup to analyze
• select (str, optional): Selection string for atoms (default: "all")
• start (int, optional): First frame to analyze
• stop (int, optional): Last frame to analyze
• step (int, optional): Step size between frames

Results

After calling run(), results are available in the results attribute:

• total_area (numpy.ndarray): Total SASA for each frame
• residue_area (numpy.ndarray): SASA per residue for each frame (shape: n_frames × n_residues)
• mean_total_area (float): Mean total SASA across all frames

Contributing

Contributions are welcome! Please feel free to submit pull requests and open issues. As this is an actively developed library, we encourage sharing your thoughts, ideas, suggestions, and feedback.

⚠️ A Note on Compatibility with mdakit_sasa

Inferring the element of an atom can be quite complicated. mdsasa-bolt does it's best to match the freesasa element inference algorithm, but it may not always be accurate, and may throw an error in some cases where Freesasa will work. Because of this we recommend that you use input files with explicit element information whenever possible.

License

This project is licensed under the GNU General Public License v2.0 - see the LICENSE file for details.

Acknowledgments

• Built on top of RustSASA library
• Integrates seamlessly with MDAnalysis
• Inspired by the mdakit-sasa project