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Automated CG-to-AA workflow for protein condensate simulations

Project description

CondenSimAdapter

CondenSimAdapter is an automated workflow for protein condensate simulations, covering the main stages from coarse-grained (CG) to all-atom (AA).

Installation

Quick Start

# 1. Create a conda environment
conda create -n conden python=3.11 -y
conda activate conden

# 2. Install from PyPI
pip install CondenSimAdapter

# 3. Verify installation
adapter --version

Installation Options

Core Package Only

pip install CondenSimAdapter

With Simulation Dependencies (recommended)

pip install "CondenSimAdapter[sim]"

This includes OpenMM, MDAnalysis, mdtraj, and other simulation tools.

With Machine Learning Dependencies (for neural network backmapping)

pip install "CondenSimAdapter[ml]"

This includes PyTorch, DGL, and e3nn for CG-to-AA backmapping.

Full Installation (everything)

pip install "CondenSimAdapter[all]"

Optional: COCOMO Multidomain Protein Support

pip install git+https://github.com/feiglab/mdsim.git
pip install numpy==1.26.4 mdtraj==1.11.0

Note: mdsim claims it needs higher dependency versions, but downgrading numpy and mdtraj to the versions above does not affect COCOMO simulations.

Development Installation

If you want to modify the source code:

git clone https://github.com/hanlab-computChem/CondenSimAdapter.git
cd CondenSimAdapter
pip install -e ".[dev]"

Testing

# Run tests
pytest tests/

# Run with coverage
pytest tests/ --cov=CondenSimAdapter

Usage

Command Line Interface

# Show help
adapter --help

# Run backmapping
adapter backmap -c cg_structure.pdb -o aa_structure.pdb

# Check model status
adapter models status

Python API

from CondenSimAdapter import backmap

# Backmap CG structure to AA
backmap.convert("cg_input.pdb", "aa_output.pdb")

Requirements

  • Python >= 3.10, < 3.12 (3.11 recommended)
  • CUDA >= 12.1 (for ML features)
  • GROMACS >= 2023 (install separately)

Links

License

GPL-3.0

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