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emDNA GPU - Complete Package Structure

Project Overview

This is a complete Python refactor of the emDNA C++ software with GPU acceleration support. The package provides energy minimization for DNA and DNA-protein complexes using modern GPU computing.

Directory Structure

emdna-gpu/
├── README.md
├── LICENSE
├── setup.py
├── requirements.txt
├── pyproject.toml
├── environment.yml
├── .gitignore
├── docs/
│   ├── index.md
│   ├── installation.md
│   ├── quickstart.md
│   ├── api_reference.md
│   └── examples.md
├── emdna-gpu/
│   ├── __init__.py
│   ├── core.py                  # Main DNA structure classes
│   ├── minimizer.py            # Energy minimization engine
│   ├── constraints.py          # Constraint management
│   ├── analysis.py             # Sequence analysis tools
│   ├── gpu_utils.py            # GPU device management
│   ├── io.py                   # File I/O operations
│   ├── visualization.py        # 3D visualization tools
│   └── parameters/
│       ├── __init__.py
│       ├── dna_params.py       # DNA parameter sets
│       └── protein_params.py   # Protein binding parameters
├── tests/
│   ├── test_core.py
│   ├── test_minimizer.py
├── examples/
│   ├── benchmark_gpu.py

Key Features of the Refactor

1. GPU Acceleration

  • Full GPU support using PyTorch and CuPy
  • Automatic device detection (CUDA, MPS, CPU)
  • GPU memory management
  • Parallel computation of energy terms

2. Modern Python Architecture

  • Object-oriented design with dataclasses
  • Type hints for better IDE support
  • Modular structure with separate concerns
  • Comprehensive error handling and logging

3. Enhanced Functionality

  • Multiple optimization algorithms (Adam, SGD, L-BFGS)
  • Sequence-dependent parameters
  • Protein binding sites with energy contributions
  • Circular DNA support
  • Constraint management system

4. Performance Optimizations

  • Vectorized operations using PyTorch tensors
  • Batch processing for large structures
  • Memory-efficient data structures
  • Automatic differentiation for gradients

5. File I/O and Visualization

  • HDF5 format for efficient storage
  • PDB export for compatibility
  • Integration with molecular visualization tools
  • Built-in plotting capabilities

6. Testing and Documentation

  • Comprehensive test suite
  • Example scripts and notebooks
  • API documentation
  • Performance benchmarks

Installation Instructions

  1. Prerequisites:

    • Python 3.8+
    • CUDA 11.0+ (for GPU support)
    • Git

  2. Clone and Install:

    git clone https://github.com/yourusername/emdna-gpu.git
cd emdna-gpu
pip install -e .

  3. Verify Installation:

    import emdna_gpu
print(emdna_gpu.__version__)