Analyse targeted molecular dynamics data with dcTMD
Project description
Features • Installation • Tutorials • Docs
dcTMD
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.,
Journal of chemical theory and computation (2018)
This package will be published soon:
V. Tänzel, and M. Jäger, 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.
Features
- Intuitive usage via module and CI
- Sklearn-style API for fast integration into your Python workflow
- Supports Python 3.8-3.10
- Multitude of publications with dcTMD
Implemented Key Functionalities
- 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.
Installation
The package will be available on PiPY and conda. Until then, install it via:
python3 -m pip install git+ssh://git@github.com/moldyn/dcTMD.git
Usage
Check out the documentation for an overview over all modules as well as the tutorials.
Roadmap
- 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
Project details
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