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synth-dynamics: Time-Resolved Ensemble Generator\n\n[![codecov](https://codecov.io/gh/elkins-lab/synth-dynamics/branch/main/graph/badge.svg)](https://codecov.io/gh/elkins-lab/synth-dynamics)\n[![PyPI version](https://img.shields.io/pypi/v/synth-dynamics.svg)](https://pypi.org/project/synth-dynamics/)\n[![Python](https://img.shields.io/pypi/pyversions/synth-dynamics.svg)](https://pypi.org/project/synth-dynamics/)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n[![Tests](https://github.com/elkins-lab/synth-dynamics/actions/workflows/test.yml/badge.svg)](https://github.com/elkins-lab/synth-dynamics/actions/workflows/test.yml)\n[![Lint](https://github.com/elkins-lab/synth-dynamics/actions/workflows/lint.yml/badge.svg)](https://github.com/elkins-lab/synth-dynamics/actions/workflows/lint.yml)\n[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)\n[![Checked with mypy](https://img.shields.io/badge/type%20checked-mypy-blue)](https://mypy-lang.org/)\n\n\n`synth-dynamics` is a fast, lightweight molecular dynamics engine designed to generate meaningful conformational ensembles of proteins. Unlike full-atom simulations (like GROMACS or Amber), `synth-dynamics` uses a **Coarse-Grained Anisotropic Network Model (ANM)** and **Langevin dynamics** to capture the essential global motions of proteins with minimal computational overhead.\n\nThis tool is designed to bridge the gap between static structures and time-averaged experimental observables, such as NMR relaxation parameters, SAXS Kratky plots, or FRET efficiency distributions.\n\n---\n\n### 🧪 For Structural Biologists\n*   **Ensemble Generation:** Rapidly sample the conformational landscape of a protein to simulate disordered states or flexible loops.\n*   **Integration:** Works seamlessly with `MDAnalysis` and `biotite` for trajectory processing.\n\n### 🤖 For Machine Learning Researchers\n*   **Dynamic Training Data:** Generate synthetic molecular trajectories to train time-series models (LSTMs, Transformers) or dynamic GNNs.\n*   **High Performance:** Optimized for throughput, allowing the generation of millisecond-scale ensembles in seconds.\n\n---\n\n## Key Features\n\n- **Coarse-Grained Simulation**: Models proteins using C-alpha atoms and harmonic \"spring\" networks.\n- **Fast Langevin Engine**: Propagates coordinates using a stable, overdamped Langevin integrator.\n- **Experimental Integration**: Perfect for generating the structural ensembles needed for `synth-nmr` or `synth-saxs`.\n- **Easy to Use**: Simple API for loading PDBs, configuring forcefields, and running simulations.\n- **Extensively Tested**: 100% test coverage ensuring reliability and correctness.\n\n## 📚 Tutorials\n\nExperience **synth-dynamics** directly in your browser:\n\n- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/elkins-lab/synth-dynamics/blob/main/examples/interactive_tutorials/enm_dynamics.ipynb) **Elastic Network Models \u0026 Langevin Dynamics** — Learn how to predict protein flexibility and simulate thermal fluctuations.\n\n## Installation\n\n`synth-dynamics` requires Python 3.10+ and the following dependencies:\n\n```bash\npip install numpy MDAnalysis scipy\n```\n\nTo install the documentation theme:\n```bash\npip install sphinx_rtd_theme\n```\n\n## Quick Start\n\nRunning a simulation is straightforward:\n\n```python\nfrom synth_dynamics import System, ANMForceField, LangevinIntegrator, Simulation\n\n# 1. Load the system (automatically filters for C-alpha atoms)\nsystem = System(\"protein.pdb\")\n\n# 2. Define the Anisotropic Network Model forcefield\n# Cutoff (15A) and spring constant determine the flexibility\nff = ANMForceField(system.equilibrium_coords, cutoff=15.0, spring_constant=1.0)\n\n# 3. Initialize the Langevin integrator (dt in ps, T in Kelvin)\nintegrator = LangevinIntegrator(dt=0.1, temperature=300.0, friction=1.0)\n\n# 4. Run and save the trajectory\nsim = Simulation(system, ff, integrator)\nsim.run(n_steps=1000, output_path=\"trajectory.dcd\", stride=10)\n```\n\n## Documentation\n\nFull API documentation and usage guides are available in the `docs/` directory. You can build the HTML documentation locally:\n\n```bash\ncd docs\nsphinx-build -b html . _build/html\n```\n\n## Testing\n\nTo run the test suite and verify coverage:\n\n```bash\nPYTHONPATH=. pytest --cov=synth_dynamics tests/\n```\n\n## License\n\nThis project is licensed under the MIT License - see the LICENSE file for details (if applicable).\n\n## Related Projects\n\nThis library is part of the **synth-pdb ecosystem** — use it to generate trajectories for [synth-nmr](https://github.com/elkins-lab/synth-nmr) or [synth-saxs](https://github.com/elkins-lab/synth-saxs) ensemble averaging:\n\n- [synth-pdb](https://github.com/elkins-lab/synth-pdb) — Core protein structure generator\n- [synth-nmr](https://github.com/elkins-lab/synth-nmr) — NMR observables simulator\n- [synth-saxs](https://github.com/elkins-lab/synth-saxs) — SAXS profile simulator\n- [synth-cryo-em](https://github.com/elkins-lab/synth-cryo-em) — Cryo-EM map simulator\n- [diff-biophys](https://github.com/elkins-lab/diff-biophys) — Differentiable JAX biophysics kernels\n\n## Contributing\n\nContributions are welcome! Please open an issue or pull request on [GitHub](https://github.com/elkins-lab/synth-dynamics). Run `pre-commit run --all-files` before submitting.\n\n## Citation\n\n```bibtex\n@software{synth_dynamics,\n  author  = {Elkins, George},\n  title   = {synth-dynamics: Coarse-grained protein dynamics for ensemble generation},\n  year    = {2026},\n  url     = {https://github.com/elkins-lab/synth-dynamics},\n  version = {0.1.3}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Felkins-lab%2Fsynth-dynamics","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Felkins-lab%2Fsynth-dynamics","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Felkins-lab%2Fsynth-dynamics/lists"}