Skip to content

Getting Started

Introduction

This python package aids with the analysis of targeted molecular dynamics (TMD) trajectories according to dissipation-corrected TMD method. TMD simulations enforce rare unbinding events of ligands from proteins via a constraint pulling bias. With dcTMD, free energy profiles and friction factors are estimated along the unbinding coordinate. For a methodological overview, see our article

Disclaimer

Warning This package is still in beta stage. Please open an issue if you encounter any bug/error.

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 (2022)

We kindly ask you to cite these articles in case you use this software package for published works.

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

Sections:

  • Theoretical Background:: Here, you will learn the basic theory behind dcTMD. Including Jarzinskys equality, the derivation of the free energy and friction estimate as well as the main assumptions made on the way.

  • Create pulling trajectories with Gromacs:: Here, you will learn how you can set up constraint targeted MD simulations using the pull code implemented in Gromacs.

  • dcTMD Analysis: In section dcTMD via Work and dcTMD via Force you will learn how to analyse the constraint pulling trajectories with dcTMD as described in Theory.

  • Command Line Interface: In this section, we will provide a short guide to the command line interface of dcTMD, which provides some common analysis and visualization functionality.