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MM fitting code for the paper "Systematic improvement of empirical energy functions in the era of machine learning" https://onlinelibrary.wiley.com/doi/full/10.1002/jcc.27367

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FF-Energy

A package for calculating the energy of a system of particles using a force field.

Design

  sequenceDiagram
    participant PDB
    participant CHARMM
    participant Molpro
    participant Orbkit
    participant Energies
    participant FitFF
    
      PDB->>Molpro: a
      Molpro->>Orbkit: wavefunctions
      PDB->>CHARMM: psf/pdb/par
      CHARMM->>Energies: MM energies
      Molpro->>Energies: QM energies
      Orbkit->>Energies: Coulomb
      Energies->>FitFF: Regression
      
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Installation

Requirements

Installation using conda is recommended. The following packages are required:

conda env create --file environment.yml

conda install -c conda-forge povray

Simple (Python Only)

pip install -e .

PyCHARMM (Energy Evaluations and MD in CHARMM)

The python interface for the Havard Molecular Mechanics (CHARMM) program is required for the CHARMM energy calculations. The PyCHARMM package is not available on PyPI and must be installed manually.

Instructions, from Dr. Kai Toepfer, are included in the pycharmm_runner directory.

Usage

Using the FFE tools

Make CHARMM* jobs to calculate energies:

python ff_energy/ffe/cli.py  -m water_cluster -t pbe0dz -e tip3 -chmj

[*uses the CHARMM executable to calculate energies]

Make Molpro jobs to calculate energies:

python ff_energy/ffe/cli.py -m water_tests -t pbe0dz -e tip3 -mj -s

Make Orbkit jobs to calculate the Coulomb integrals:

 python ff_energy/ffe/cli.py -m ions -t pbe0dz -e tip3 -cj -s -msp  /home/boittier/pcnccr

ESP View:

python ff_energy/ffe/cli.py -m water_tests -t pbe0dz -e tip3 -esp

Gather data:

python ff_energy/ffe/cli.py -d -m water_cluster -t pbe0dz -e tip3

Sampling

MD (CHARMM)

Choose residues based on name, extract residue and nearest neighbours from the trajectory:

python MD_sampling.py -v -dl /home/boittier/pcbach/charmmions/step5_1.dcd /home/boittier/pcbach/charmmions/step5_2.dcd /home/boittier/pcbach/charmmions/step5_3.dcd /home/boittier/pcbach/charmmions/step5_4.dcd -p /home/boittier/pcbach/charmmions/step3_pbcsetup.psf -r POT -n 35

PyDCM

Scripts available, CLI in development.

Contributing

Eric D. Boittier

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MM fitting code for the paper "Systematic improvement of empirical energy functions in the era of machine learning" https://onlinelibrary.wiley.com/doi/full/10.1002/jcc.27367

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