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.
This 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.
- Ensemble Generation: Rapidly sample the conformational landscape of a protein to simulate disordered states or flexible loops.
- Integration: Works seamlessly with
MDAnalysisandbiotitefor trajectory processing.
- Dynamic Training Data: Generate synthetic molecular trajectories to train time-series models (LSTMs, Transformers) or dynamic GNNs.
- High Performance: Optimized for throughput, allowing the generation of millisecond-scale ensembles in seconds.
- Coarse-Grained Simulation: Models proteins using C-alpha atoms and harmonic "spring" networks.
- Fast Langevin Engine: Propagates coordinates using a stable, overdamped Langevin integrator.
- Experimental Integration: Perfect for generating the structural ensembles needed for
synth-nmrorsynth-saxs. - Easy to Use: Simple API for loading PDBs, configuring forcefields, and running simulations.
- Extensively Tested: 100% test coverage ensuring reliability and correctness.
Experience synth-dynamics directly in your browser:
Elastic Network Models & Langevin Dynamics — Learn how to predict protein flexibility and simulate thermal fluctuations.
synth-dynamics requires Python 3.10+ and the following dependencies:
pip install numpy MDAnalysis scipyTo install the documentation theme:
pip install sphinx_rtd_themeRunning a simulation is straightforward:
from synth_dynamics import System, ANMForceField, LangevinIntegrator, Simulation
# 1. Load the system (automatically filters for C-alpha atoms)
system = System("protein.pdb")
# 2. Define the Anisotropic Network Model forcefield
# Cutoff (15A) and spring constant determine the flexibility
ff = ANMForceField(system.equilibrium_coords, cutoff=15.0, spring_constant=1.0)
# 3. Initialize the Langevin integrator (dt in ps, T in Kelvin)
integrator = LangevinIntegrator(dt=0.1, temperature=300.0, friction=1.0)
# 4. Run and save the trajectory
sim = Simulation(system, ff, integrator)
sim.run(n_steps=1000, output_path="trajectory.dcd", stride=10)Full API documentation and usage guides are available in the docs/ directory. You can build the HTML documentation locally:
cd docs
sphinx-build -b html . _build/htmlTo run the test suite and verify coverage:
PYTHONPATH=. pytest --cov=synth_dynamics tests/This project is licensed under the MIT License - see the LICENSE file for details (if applicable).
This library is part of the synth-pdb ecosystem — use it to generate trajectories for synth-nmr or synth-saxs ensemble averaging:
- synth-pdb — Core protein structure generator
- synth-nmr — NMR observables simulator
- synth-saxs — SAXS profile simulator
- synth-cryo-em — Cryo-EM map simulator
- diff-biophys — Differentiable JAX biophysics kernels
Contributions are welcome! Please open an issue or pull request on GitHub. Run pre-commit run --all-files before submitting.
@software{synth_dynamics,
author = {Elkins, George},
title = {synth-dynamics: Coarse-grained protein dynamics for ensemble generation},
year = {2026},
url = {https://github.com/elkins/synth-dynamics},
version = {0.1.1}
}