CondenSimAdapter 1.0.3

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

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 (Recommended)

Step 1: Create conda environment and install heavy dependencies

# Create environment with Python 3.11
conda create -n condensim python=3.11 -y
conda activate condensim

# Install OpenMM (from conda-forge)
conda install -c conda-forge openmm=8.2.0

# Install PyTorch (adjust CUDA version as needed, here using CUDA 12.1)
conda install pytorch=2.4.1 pytorch-cuda=12.1 -c pytorch -c nvidia

# Install DGL for CUDA 12.1
conda install -c dglteam/label/cu121 dgl=1.1.3

For different CUDA versions, adjust the PyTorch and DGL installation:

• CUDA 11.8: pytorch-cuda=11.8 and conda install -c dglteam/label/cu118 dgl=1.1.3
• CPU only: conda install pytorch=2.4.1 cpuonly -c pytorch and conda install -c dglteam dgl=1.1.3

Step 2: Install CondenSimAdapter from PyPI

pip install CondenSimAdapter

This installs the core package with ML backmapping support (requires Step 1 conda dependencies to be pre-installed).

Step 3: Verify installation

adapter --version

Alternative: Pure pip Installation (Not Recommended)

If you cannot use conda, you can install ML dependencies via pip:

pip install CondenSimAdapter[ml]

⚠️ Warning: Installing PyTorch and DGL via pip may fail or cause CUDA compatibility issues. Use conda installation (above) for best results.

Minimal Installation (No ML Backmapping)

If you don't need backmapping functionality:

pip install CondenSimAdapter[minimal]

Development Installation

# 1. Follow Step 1 above to install conda dependencies

# 2. Clone and install in editable mode
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 cg2all backmapping (adjust conda packages for your CUDA version)
• 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