This package aids in the analysis of dissipation-corrected targeted molecular dynamics (dcTMD) simulations. The method enforces rare unbinding events of ligands from proteins via a constraint pulling bias. Subsequently, free energy profiles and friction factors are estimated along the unbinding coordinate. For a methodological overview, see our article.
S. Wolf, and G. Stock,
Targeted molecular dynamics calculations of free energy profiles using a nonequilibrium friction correction., J. Chem. Theory Comput. 2018 14 (12), 6175-6182,
doi: 10.1021/acs.jctc.8b00835
This package will be published soon:
V. Tänzel, M. Jäger, D. Nagel, and S. Wolf,
Dissipation Corrected Targeted Molecular Dynamics,
in preparation 2023
We kindly ask you to cite these articles in case you use this software package for published works.
- Intuitive usage via module and CI
- Sklearn-style API for fast integration into your Python workflow
- Supports Python 3.8-3.12
- Multitude of publications with dcTMD
- Estimation of free energy profiles and friction factors along the unbinding coordinate of ligands as described by Wolf and Stock 2018.
- Analysis of separate unbinding pathways as described by Wolf et al. 2022.
The package will be available on PiPY and conda. Until then, install it via:
python3 -m pip install git+ssh://[email protected]/moldyn/dcTMD.gitCheck out the documentation for an overview over all modules as well as the tutorials.
- New Features:
- Gaussian error estimation
- 2d distribution WorkSet plots
- Estimator plots: free energy, friction & both
- Normality plot
- Confidence intervals
- Exponential estimator class
- Discuss gaussian kernel borders