molecular-simulations 0.4.1

A small package for building molecular systems using the AMBER \ force field and deploying OpenMM simulations on HPC clusters using Parsl.

molecular-simulations

A comprehensive Python toolkit for building, running, and analyzing molecular dynamics simulations using the AMBER force field ecosystem and OpenMM.

📖 Full Documentation

Features

🔨 System Building

• Explicit solvent systems with OPC water model
• Implicit solvent support for faster calculations
• Small molecule parameterization via GAFF2

⚡ Simulation Engine

• OpenMM integration (v8.0+) with GPU acceleration
• Advanced simulations Constant-pH and Empirical Valence Bond
• HPC deployment via Parsl for PBS schedulers
• MM-PBSA calculations for binding free energy estimation in parallel
• Flexible configuration for various cluster environments

📊 Analysis Tools

• Automatic clustering with KMeans++ and dimensionality reduction (PCA)
• Protein-protein interaction analysis using covariance matrix approach
• Interaction energy fingerprinting (electrostatic + Lennard-Jones)
• Linear interaction energy calculations (static and dynamic)
• Interface scoring with ipTM, ipSAE, pDockQ, and pDockQ2
• SASA calculations (absolute and relative) via MDAnalysis
• Residue energy footprinting for binding site characterization

Installation

Install from PyPI:

pip install molecular-simulations

For small molecule support (requires RDKit and OpenBabel):

pip install molecular-simulations[ligand]

For development:

pip install molecular-simulations[dev]

Quick Start

Building a Solvated System

from molecular_simulations.build import ExplicitSolvent
from pathlib import Path

# Build an explicitly solvated system
pdb_file = Path('/path/to/protein.pdb')
output_dir = Path('/path/to/outputs')

builder = ExplicitSolvent(output_dir, pdb_file)
builder.build()

# Outputs: topology (.prmtop) and coordinates (.inpcrd)

Running a Simulation

from molecular_simulations.simulate import Simulator

# Initialize and run simulation
sim = Simulator(
    path=builder.out.parent, # Directory containing simulation inputs
)
sim.run()

Analyzing Trajectories

Interaction Energy Fingerprinting

from molecular_simulations.analysis import Fingerprinter

fp = Fingerprinter(
    topology='/path/to/system.prmtop',
    trajectory='/path/to/trajectory.dcd',
    target_selection='segid A',
    binder_selection='segid B'
)
fp.run()
fp.save()  # Saves to fingerprint.npz

Automatic Clustering

from molecular_simulations.analysis import AutoKMeans

clusterer = AutoKMeans(
    data_directory='/path/to/features/', # should be populated with .npy files
    max_clusters=10,
    reduction_algorithm='PCA',
    reduction_kws={'n_components': 2}
)
clusterer.run()
clusterer.save_labels()   # Saves cluster assignments
clusterer.save_centers()  # Saves cluster centroids

MM-PBSA implementation

from molecular_simulations.simulate.mmpbsa import MMPBSA

mmpbsa = MMPBSA(
    top='/path/to/file.prmtop',
    dcd='/path/to/traj.dcd',
    selections=[':1-100', ':101-200'], # cpptraj-style selections
    n_cpus=1,                          # CPU-based parallelism supported
    amberhome='/path/to/dir',          # should be above /bin/cpptraj
    parallel_mode='frame'              # frame or serial
)

Protein-Protein Interaction Analysis

from molecular_simulations.analysis import PPInteractions

ppi = PPInteractions(
    top='/path/to/topology.prmtop',
    traj='/path/to/trajectory.dcd',
    out='/path/to/outputs/',
    sel1='chainID A',
    sel2='chainID B'
)
ppi.run()  # Analyzes H-bonds, salt bridges, hydrophobic contacts

Interface Scoring (ipSAE/pDockQ)

from molecular_simulations.analysis import ipSAE

scorer = ipSAE(
    structure_file='/path/to/complex.pdb',
    pae_file='/path/to/pae.json',  # AlphaFold PAE matrix
    plddt_file='/path/to/plddt.json'
)
scores = scorer.run()
# Returns: ipTM, ipSAE, pDockQ, pDockQ2 scores

SASA Calculations

from molecular_simulations.analysis import SASA, RelativeSASA

# Absolute SASA
sasa = SASA(universe, selection='protein')
sasa.run()
results = sasa.measure_sasa()

# Relative SASA (normalized by max accessible area)
rsasa = RelativeSASA(universe, selection='protein')
rsasa.run()

Supported Force Fields

|==============AMBER==============|

Component	Force Field	Notes
Proteins	ff19SB	Fixed-charge, recommended
Proteins	ff15ipq	Polarizable
DNA	OL21	Latest AMBER DNA parameters
RNA	OL3	Standard RNA parameters
Small molecules	GAFF2	General AMBER Force Field 2
Water (explicit)	OPC	4-point model
Water (polarizable)	SPC/Eb	For ff15ipq systems

|=============CHARMM=============| See OpenMM documentation on providing parameter sets. CHARMM does not translate very well to the pre-build XML files for unusual lipids, small molecules, etc.

Requirements

• Python ≥ 3.10
• OpenMM ≥ 8.0
• MDAnalysis ≥ 2.7
• Parsl ≥ 2024.1.29
• NumPy, SciPy, scikit-learn, Polars
• ambertools
• Optional: RDKit, OpenBabel (for ligand parameterization)

Known Issues

• OpenMM versions 8.0-8.1 may exhibit slower integration times for larger systems due to a known bug
• Advanced features are tested but not all edge cases have been encountered, please open an Issue if you find one

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Citation

If you use this software in your research, please cite:

@software{molecular_simulations,
  author = {Sinclair, Matt},
  title = {molecular-simulations: A Python toolkit for MD simulation and analysis},
  url = {https://github.com/msinclair-py/molecular-simulations},
  year = {2025}
}

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• OpenMM for molecular dynamics engine
• MDAnalysis for trajectory analysis
• Parsl for parallel workflow execution
• AMBER force field developers