Project ID: plumID:21.004
Name: Machine Learning and Enhanced Sampling Simulations for Computing the Potential of Mean Force and Standard Binding Free Energy
Archive: https://gitlab.iit.it/Dorothea.Gobbo/pathnest/-/raw/master/pathNEST.zip
Category: bio
Keywords: machine learning, well-tempered metadynamics, path collective variable, potential of mean force, standard binding free energy calculations, host-guest, protein-ligand unbinding
PLUMED version: 2.5
Contributor: Dorothea Gobbo
Submitted on: 14 Jan 2021
Last revised: 04 Aug 2021
Publication: M. Bertazzo, D. Gobbo, S. Decherchi, A. Cavalli, Machine Learning and Enhanced Sampling Simulations for Computing the Potential of Mean Force and Standard Binding Free Energy. Journal of Chemical Theory and Computation. 17, 5287–5300 (2021)
PLUMED input files
| File | Compatible with |
|---|---|
| GSK/3/plumed_path.dat |   |
| HST-GST/CB8-G6/plumed_path.dat | |
Last tested: 22 Apr 2024, 20:52:25
Project description and instructions
For all systems, gro and top are provided. In the folders HST-GST/CB8-G6 and GSK/3, the plumed_path.dat files are provided in order to run Well-Tempered MetaD with PCVs along the dissociation paths that have been optimized in terms of spacing between consecutive frames. The input files were used with GROMACS 2016.5 and PLUMED 2.5.
Submission history
[v1] 14 Jan 2021: original submission
[v2] 04 Aug 2021: updated doi
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