CondenSimAdapter 1.0.0b2

Automated CG-to-AA workflow for protein condensate simulations

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

Standard Installation (recommended)

pip install CondenSimAdapter

Includes all core functionality: OpenMM, MDAnalysis, mdtraj, and other simulation tools.

With Machine Learning (optional)

For neural network backmapping (requires CUDA-matched PyTorch):

pip install "CondenSimAdapter[ml]"

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

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

• PyPI: https://pypi.org/project/CondenSimAdapter/
• Documentation: https://github.com/hanlab-computChem/CondenSimAdapter#readme
• Issues: https://github.com/hanlab-computChem/CondenSimAdapter/issues

License

GPL-3.0