espfit

Infrastruture to train espaloma with experimental observables

Installation

mamba create -n espfit python=3.11
mamba install -c conda-forge espaloma=0.3.2
#uninstall openff-toolkit and install a customized version to support dgl graphs created using openff-toolkit=0.10.6
conda uninstall --force openff-toolkit
pip install git+https://github.com/kntkb/openff-toolkit.git@7e9d0225782ef723083407a1cbf1f4f70631f934
#install openeye-toolkit
mamba install openeye-toolkits -c openeye
#uninstall openmmforcefields if < 0.12.0
conda uninstall --force openmmforcefields
#use pip instead of mamba to avoid dependency issues with ambertools and python
pip install git+https://github.com/openmm/openmmforcefields@0.12.0
#install openmmtools
mamba install openmmtools
#install barnaba
mamba install barnaba

Quick Usage

from espfit.utils.graphs import CustomGraphDataset  
path = 'espfit/data/qcdata/openff-toolkit-0.10.6/dgl2/protein-torsion-sm/'
ds = CustomGraphDataset.load(path)
ds.reshape_conformation_size(n_confs=50, include_min_energy_conf=True)
ds.compute_relative_energy()
# Create esplama model
from espfit.app.train import EspalomaModel
filename = 'espfit/data/config/config.toml'
# Override training settings in config.toml
kwargs = {'output_directory_path': 'checkpoints', 'epochs': 100}
model = EspalomaModel.from_toml(filename, **kwargs)
model.dataset_train = ds
# Set sampler settings
model.train_sampler(sampler_patience=800, neff_threshold=0.2, sampler_weight=1)

Standalone Usage

Change logging

from espfit.utils import logging
logging.get_logging_level()
#>'INFO'
logging.set_logging_level('DEBUG')

Train espaloma

# Load dgl graph data
from espfit.utils.graphs import CustomGraphDataset  
path = 'espfit/data/qcdata/openff-toolkit-0.10.6/dgl2/protein-torsion-sm/'
ds = CustomGraphDataset.load(path)
ds.reshape_conformation_size(n_confs=50)
ds.compute_relative_energy()
# Create esplama model
from espfit.app.train import EspalomaModel
filename = 'espfit/data/config/config.toml'
model = EspalomaModel.from_toml(filename)
model.dataset_train = ds
# Change default training settings
model.epochs = 100
model.output_directory_path = 'path/to/output'
# Train (default output directory is current path)
model.train()

Standard MD (default: espaloma-0.3.2 force field for solute molecules)

# Create a new system and run simulation
from espfit.app.sampler import SetupSampler
c = SetupSampler()
filename = 'espfit/data/target/testsystems/nucleoside/pdbfixer_min.pdb'
c.create_system(biopolymer_file=filename)
c.minimize()
# Change default settings
c.nsteps = 1000
c.run()
# Export to XML
c.export_xml(exportSystem=True, exportState=True, exportIntegrator=True, output_directory_path='path/to/output')

Re-start MD from exisiting XML

from espfit.app.sampler import SetupSampler
c = SetupSampler.from_xml(input_directory_path='path/to/input')
c.nsteps = 1000
c.run()

Compute RNA J-couplings from MD trajectory

from experiment import RNASystem
rna = RNASystem()
rna.load_traj(input_directory_path='path/to/input')
couplings = rna.compute_jcouplings(couplings=['H1H2', 'H2H3', 'H3H4'], residues=['A_1_0'])

Prerequisite

• Modified version of openff-toolkit 0.11.5 is required to train espaloma using dgl datasets (Zenodo), which were used to train espaloma-0.3.
• OpenEye toolkit is required to load PBD files into OpenFF Molecule objects. Academic license can be obtained here.

Copyright

Copyright (c) 2023, Ken Takaba

Acknowledgements

Project based on the Computational Molecular Science Python Cookiecutter version 1.1.