dcTMD 0.3.0

Analyse targeted molecular dynamics data with dcTMD

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., 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 3.8-3.10
• 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://[e-mail]/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