Project ID: plumID:20.012
Name: Combining Machine Learning and Enhanced Sampling Techniques for Efficient and Accurate Calculation of Absolute Binding Free Energies
Archive: https://github.com/Gervasiolab/Gervasio-Protein-Dynamics/raw/master/Fun-metaD_Fun-SWISH/PLUMED-NEST.zip
Category: bio
Keywords: metadynamics, well-tempered ensemble, ligand binding, binding affinity calculations, novel COLVAR, funnel restraints, Hamiltonian replica-exchange, PathCV, COMetPath, SWISH
PLUMED version: 2.4.2
Contributor: Francesco Gervasio
Submitted on: 29 Apr 2020
Last revised: 23 May 2020
Publication: R. Evans et al., Combining Machine Learning and Enhanced Sampling Techniques for Efficient and Accurate Calculation of Absolute Binding Free Energies, Journal of Chemical Theory and Computation (2020)

PLUMED input files

File Compatible with
COMet_Path/plumed.dat tested on v2.6 tested on master with LOAD
fun_SWISH/plumed.dat tested on v2.6 tested on master with LOAD
fun_metaD/plumed.dat tested on v2.6 tested on master with LOAD

Last tested: 03 Jun 2020, 17:10:07

Project description and instructions
These are the input files used to fully converge the free energy landscapes of the human soluble epoxide hydrolase (sEH, PDB code 5AKK) using different methodologies, Funnel-shaped restraint metadynamics (fun-metaD), funnel-shaped restraint with SWISH (fun-SWISH) and COMetPath. The input files were used with GROMACS 2018.3 and PLUMED 2.4.2 using the AMBER99SB-disp force field. They include a novel COLVAR(ProjectionOnAxis.cpp) which is included in all directories.

Submission history
[v1] 29 Apr 2020: original submission
[v2] 23 May 2020: updated doi

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plumeDnest:20.012