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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., 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.

Features

  • 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

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