ParallelTrajPBSACube, ConformerGatheringData, ParallelConfTrajsToLigTraj,
ParallelConcatenateTrajMMPBSACube)
from MDOrion.TrjAnalysis.cubes_clusterAnalysis import (
ParallelClusterOETrajCube, ParallelMakeClusterTrajOEMols,
ParallelMDTrajAnalysisClusterReport, ParallelClusterPopAnalysis,
ParallelTrajAnalysisReportDataset, MDFloeReportCube)
from MDOrion.System.cubes import CollectionSetting
job = WorkFloe('Analyze Protein-Ligand MD', title='Analyze Protein-Ligand MD')
job.description = open(
path.join(path.dirname(__file__), 'AnalyzePLMD_desc.rst'), 'r').read()
job.classification = [['Specialized MD']]
job.uuid = "7438db4d-30b1-478c-afc0-e921f0336c78"
job.tags = [tag for lists in job.classification for tag in lists]
# Ligand setting
iMDInput = DatasetReaderCube("MDInputReader", title="MD Input Reader")
iMDInput.promote_parameter("data_in",
promoted_name="in",
title="MD Input Dataset",
description="MD Input Dataset")
# This Cube is necessary for the correct work of collection and shard
coll_open = CollectionSetting("OpenCollection", title="Open Collection")
coll_open.set_parameters(open=True)
trajCube = ParallelTrajToOEMolCube("TrajToOEMolCube",
print('host:', socket.gethostname())
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s' + socket.gethostname() +
'%(levelname)-8s %(message)s',
datefmt='%Y-%m-%d %H:%M:%S')
from floe.api import WorkFloe
from cuberecord.cubes import DatasetReaderCube, DatasetWriterCube
from torsion.cubes import ParallelGenerateFragments
# Declare Floe, add metadata for UI
job = WorkFloe('Generate Torsional Fragments')
job.description = """
Generate Torsional Fragments
"""
job.classification = [["Torsion"]]
# Declare cubes
ifs = DatasetReaderCube('ifs')
fraggenCube = ParallelGenerateFragments('fraggenCube')
fraggen_failure = DatasetWriterCube('fraggen_failure')
ofs = DatasetWriterCube('ofs')
cubes = [ifs, fraggenCube, ofs, fraggen_failure]
# Promote parameters
ifs.promote_parameter('data_in', promoted_name='data_in')
ofs.promote_parameter('data_out',
promoted_name='ofs',
description='Floe output',
default='output',
title='Successes')
Parameters:
-----------
complex (file): OEB file of the prepared system
Optional:
--------
picosec (float): Number of picoseconds to warm up the complex
temperature (decimal): target final temperature in K
pressure (decimal): target final pressure in atm
Outputs:
--------
ofs: Outputs the constant temperature and pressure system
"""
job.classification = [['NPT']]
job.uuid = "4ea1a8ab-5e8b-41c9-9317-3e36b72491fe"
job.tags = [tag for lists in job.classification for tag in lists]
ifs = DatasetReaderCube("SystemReader", title="System Reader")
ifs.promote_parameter("data_in", promoted_name="system", title='System Input File',
description="System input file")
npt = ParallelMDNptCube('npt', title='NPT Simulation')
npt.promote_parameter('time', promoted_name='nanoseconds', default=0.01,
description='Length of MD run in nanoseconds')
npt.promote_parameter('temperature', promoted_name='temperature', default=300.0,
description='Selected temperature in K')
npt.promote_parameter('pressure', promoted_name='pressure', default=1.0,
description='Selected pressure in atm')
npt.promote_parameter('md_engine', promoted_name='md_engine', default='OpenMM',
Ex: python floes/openmm_prepMDminimize.py --complex complex.oeb --ofs-data_out min.oeb --steps 1000`
Parameters:
-----------
complex (file): OEB file of the prepared protein:ligand complex
Optional:
--------
steps (int): Number of MD steps to minimize the system. If 0 until convergence will be reached
Outputs:
--------
ofs: Outputs the minimized system
"""
job.classification = [['Simulation']]
job.tags = [tag for lists in job.classification for tag in lists]
ifs = OEMolIStreamCube("complex", title="Complex Reader")
ifs.promote_parameter("data_in",
promoted_name="complex",
title='Complex Input File',
description="protein:ligand complex input file")
minComplex = OpenMMminimizeCube('minComplex')
minComplex.promote_parameter('steps', promoted_name='steps')
ofs = OEMolOStreamCube('ofs', title='OFS-Success')
ofs.set_parameters(backend='s3')
fail = OEMolOStreamCube('fail', title='OFS-Failure')
fail.set_parameters(backend='s3')
# current license or subscription to the applicable OpenEye offering.
# THE SAMPLE CODE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
# EXPRESS OR IMPLIED. OPENEYE DISCLAIMS ALL WARRANTIES, INCLUDING, BUT
# NOT LIMITED TO, WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
# PARTICULAR PURPOSE AND NONINFRINGEMENT. In no event shall OpenEye be
# liable for any damages or liability in connection with the Sample Code
# or its use.
from floe.api import WorkFloe
from orionplatform.cubes import DatasetReaderCube, DatasetWriterCube
from am1bcc_charge.am1bcc_charge import AM1BCCCharge
# Declare and document floe
job = WorkFloe("am1bcc_charge", title="am1bcc charge")
job.description = ("AM1BCC Charge")
job.classification = [["Charge"]]
job.tags = ["Charge"]
# Declare Cubes
input_cube = DatasetReaderCube("input_cube")
charge_cube = MyCube("charge_cube")
output_cube = DatasetWriterCube("output_cube")
# Add cubes to floe
job.add_cube(input_cube)
job.add_cube(charge_cube)
job.add_cube(output_cube)
# Promote parameters
input_cube.promote_parameter("data_in",
promoted_name="in",
**Use at your own risk!**
## About this software
See [http://getyank.org](http://getyank.org) for more information on YANK and the alchemical free energy calculations it supports.
YANK is free (libre) open source software licensed under the [MIT License](https://github.com/choderalab/yank/blob/master/LICENSE).
All source code is available at: [http://github.com/choderalab/yank](http://github.com/choderalab/yank)
YANK is produced by the Chodera lab: [http://choderalab.org](http://choderalab.org).
Please help us make it better by contributing code or funds.
"""
job.classification =[["YANK", "Binding free energies", "OpenMM", "choderalab"]]
job.tags = [tag for lists in job.classification for tag in lists]
ifs = OEMolIStreamCube("ifs")
ifs.promote_parameter("data_in", promoted_name="molecules", description="Input molecules")
yank_cube = YankBindingCube('yank_binding', title = 'Yank for binding free energies')
for parameter_name in ['receptor', 'solvent', 'temperature', 'pressure', 'nsteps_per_iteration', 'simulation_time', 'timestep', 'minimize', 'verbose']:
promoted_name = parameter_name
description = yank_cube.parameters()[parameter_name].description
yank_cube.promote_parameter(parameter_name, promoted_name=promoted_name, description=description)
success_ofs = OEMolOStreamCube("success_ofs")
success_ofs.promote_parameter("data_out", promoted_name="success", description="Output molecules")
failure_ofs = OEMolOStreamCube("failure_ofs")
from cubes.input_cubes import IndexInputCube, OEMolTriggeredIStreamCube
from cubes.compute_cubes import (AccuMolList, ParallelFastFPRanking,
IndexGenerator, PrepareRanking,
ParallelFastFPInsertKA, AccumulateRankings,
AnalyseRankings)
from cubes.output_cubes import TextRankingOutputCube, PlotResults, ResultsOutputCube
from floe.api import WorkFloe, CubeGroup
from floe.api import OEMolOStreamCube
from floe.api import OEMolIStreamCube
# Declare Floe, add metadata for UI
job = WorkFloe('VS Floe')
job.description = """
Read an index text file and write the indices in an other file
"""
job.classification = [["Virtual Screening", "Create Ranking"]]
# Declare Cubes
act_reader = OEMolIStreamCube('act_reader')
act_reader.promote_parameter('data_in', promoted_name='act_db')
#index_reader = IndexInputCube('index_reader')
#index_reader.promote_parameter('data_in', promoted_name='index_log')
index_generator = IndexGenerator('index generator')
accu_act = AccuMolList('accumulate actives')
#calc_fp = CalculateFPCube('calculate fingerprints')
prep_sim_calc = PrepareRanking('prepare similarity calculation')
prep_sim_calc.promote_parameter('method', promoted_name='method')
calc_sim = ParallelFastFPRanking('calculate similarity value')
calc_sim.promote_parameter('url', promoted_name='url')
Parameters:
-----------
complex (file): OEB file of the prepared protein:ligand complex
Optional:
--------
picosec (float): Number of picoseconds to warm up the complex
temperature (decimal): target final temperature after warming
Outputs:
--------
ofs: Outputs the constant temperature and volume system
"""
job.classification = [['NVT']]
job.tags = [tag for lists in job.classification for tag in lists]
ifs = OEMolIStreamCube("complex", title="Complex Reader")
ifs.promote_parameter("data_in", promoted_name="complex", title='Complex Input File',
description="protein:ligand complex input file")
nvt = OpenMMnvtCube('nvt')
nvt.promote_parameter('time', promoted_name='picosec', default=10.0)
nvt.promote_parameter('temperature', promoted_name='temperature', default=300.0,
description='Selected temperature in K')
# Restraints
nvt.promote_parameter('restraints', promoted_name='restraints', default='noh (ligand or protein)')
nvt.promote_parameter('restraintWt', promoted_name='restraintWt', default=2.0)
# Trajectory and logging info frequency intervals
nvt.promote_parameter('trajectory_interval', promoted_name='trajectory_interval', default=100,
from os import path
from floe.api import WorkFloe
from MDOrion.MDEngines.Gromacs.cubes import (InputGromacs,
GromacsProxyCube,
GromacsRunCube,
WriterRecordCube)
from orionplatform.cubes import DatasetWriterCube
job = WorkFloe('PlainGromacs', title='Plain Gromacs')
job.description = open(path.join(path.dirname(__file__), 'PlainGromacs_desc.rst'), 'r').read()
job.classification = [['General MD']]
job.uuid = "f092b164-7400-403d-8861-b25ff741cab5"
job.tags = [tag for lists in job.classification for tag in lists]
ifs = InputGromacs("Input File", title="Input file")
ifs.promote_parameter('tpr', promoted_name='tpr', default=None)
ifs.promote_parameter("prefix_name", promoted_name="Flask prefix", default="Flask")
ifs.promote_parameter("data_in", promoted_name='in')
proxy = GromacsProxyCube("GromacsProxy", title="Gromacs Proxy Cube")
gmx = GromacsRunCube("GromacsRun", title="Gromacs Run")
gmx.promote_parameter("verbose", promoted_name="verbose", default=False)
ofs = WriterRecordCube("OutputRecords", title="Output Records")
fail = DatasetWriterCube('fail', title='Failures')
#!/usr/bin/env python
from cubes.input_cubes import Test
from cubes.compute_cubes import ParallelFastROCSRanking, AccumulateRankings
from cubes.output_cubes import TextRankingOutputCube
from floe.api import WorkFloe, CubeGroup
from floe.api import OEMolIStreamCube
from floe.api import OEMolOStreamCube, FileOutputCube
# Declare Floe, add metadata for UI
job = WorkFloe('FastROCS Test Floe')
job.classification=[['Test']]
job.tags=[['yippee ki yay mf']]
job.title='test FastROCS Server'
job.description = """
Read a molecule query and return the FastROCS Server Results
"""
input_cube = Test('input')
request_cube = ParallelFastROCSRanking('request_cube')
request_cube.promote_parameter('url', promoted_name='url')
accu_cube = AccumulateRankings('accu')
accu_cube.promote_parameter('url', promoted_name='url')
output_cube = TextRankingOutputCube('results_output')
output_cube.promote_parameter('name', promoted_name='name')
job.add_cubes(input_cube, request_cube, accu_cube, output_cube)
input_cube.success.connect(request_cube.data_input)
from __future__ import unicode_literals
"""
Copyright (C) 2016 OpenEye Scientific Software
"""
from floe.api import WorkFloe, FileOutputCube
from PlatformTestCubes.cubes import BenchmarkCube
job = WorkFloe("OpenMM Benchmarking")
job.description = """
Performs Benchmarking upon all available Platforms
Based on OpenMM SimTK Benchmarking script
"""
job.classification = [
["OpenMM", "Platforms", "Benchmarking"]
]
job.tags = [tag for lists in job.classification for tag in lists]
benchmark_cube = BenchmarkCube("benchmark_cube")
ofs = FileOutputCube("ofs")
ofs.set_parameters(name="Orion_OpenMM_Benchmarks.txt")
job.add_cubes(benchmark_cube, ofs)
benchmark_cube.success.connect(ofs.intake)
if __name__ == "__main__":
job.run()
Ex: python floes/openmm_MDprep.py --ligands ligands.oeb --protein protein.oeb --ofs-data_out prep.oeb
Parameters:
-----------
ligands (file): oeb file of ligand posed in the protein active site.
protein (file): oeb file of the protein structure, assumed to be pre-prepared
Optionals:
-----------
Outputs:
--------
ofs: Outputs a ready system to MD production run
"""
job.classification = [['BindingFreeEnergy', 'Yank']]
job.tags = [tag for lists in job.classification for tag in lists]
# Ligand setting
iligs = LigandReader("LigandReader", title="Ligand Reader")
iligs.promote_parameter("data_in",
promoted_name="ligands",
title="Ligand Input File",
description="Ligand file name")
chargelig = LigChargeCube("LigCharge")
chargelig.promote_parameter(
'max_conformers',
promoted_name='max_conformers',
description="Set the max number of conformers per ligand",
default=800)
# NOT LIMITED TO, WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
# PARTICULAR PURPOSE AND NONINFRINGEMENT. In no event shall OpenEye be
# liable for any damages or liability in connection with the Sample Code
# or its use.
from floe.api import WorkFloe
from cuberecord import (DatasetReaderCube,
DatasetWriterCube)
from {{cookiecutter.module_name}} import MyCube
# Declare and document floe
my_floe = WorkFloe('my_floe', title="My Floe")
my_floe.description = "Outputs the input records unchanged unless the parameter is set to false, in which case nothing " \
"is outputted"
my_floe.classification = [["Examples"]]
my_floe.tags = ["Examples", "I didn't edit the tags"]
# Declare Cubes
input_cube = DatasetReaderCube('input_cube')
switch_cube = MyCube('switch_cube')
output_cube = DatasetWriterCube('output_cube')
# Add cubes to floe
my_floe.add_cube(input_cube)
my_floe.add_cube(switch_cube)
my_floe.add_cube(output_cube)
# Promote parameters
input_cube.promote_parameter('data_in', promoted_name='in', title='Input data set of records')
switch_cube.promote_parameter('switch', promoted_name='switch', title="Switch controlling Output")
from floe.api import WorkFloe
from MDOrion.TrjAnalysis.cubes_clusterAnalysis import ExtractMDDataCube
from orionplatform.cubes import DatasetReaderCube, DatasetWriterCube
from os import path
job = WorkFloe("Extract Short Trajectory MD Results for Download",
title="Extract Short Trajectory MD Results for Download")
job.description = open(path.join(path.dirname(__file__), 'MDData_desc.rst'),
'r').read()
job.classification = [['MD Data']]
job.uuid = "6665ca20-6014-4f3b-8d02-4b5d15b75ee3"
job.tags = [tag for lists in job.classification for tag in lists]
ifs = DatasetReaderCube("SystemReader", title="System Reader")
ifs.promote_parameter(
"data_in",
promoted_name="system",
title='STMDA Input File',
description=
"The Dataset produced by the Short Trajectory MD with Analysis floe")
data = ExtractMDDataCube("MDData", title="Extract MD Data")
data.promote_parameter('out_file_name',
promoted_name='out_file_name',
'%(levelname)-8s %(message)s',
datefmt='%Y-%m-%d %H:%M:%S')
from floe.api import WorkFloe
from cuberecord import DatasetReaderCube, DatasetWriterCube
from torsion.cubes import GenerateFragments, \
GenerateStartingConfs, \
GenerateTorsionalConfs, \
ParallelPsi4EnergyCalculation, \
ProfileAssembler
# Declare Floe, add metadata for UI
job = WorkFloe('Torsional Strain Energy')
job.description = """
Calculate torsional strain energy.
"""
job.classification = [["Torsional Strain"]]
# Declare cubes
ifs = DatasetReaderCube('ifs')
fraggenCube = GenerateFragments('fragment_generation')
fraggen_failure = DatasetWriterCube('fraggen_failure')
confgenCube = GenerateStartingConfs('starting_conf_gen')
confgen_failure = DatasetWriterCube('confgen_failure')
torsgenCube = GenerateTorsionalConfs('torsional_conf_gen')
assembler = ProfileAssembler('assembler')
# geometry optimization using smaller basis set (e.g. minix)
psi4EnergyCube1 = ParallelPsi4EnergyCalculation(
'parallel_psi4_energy_calculation1')
psi4EnergyCube1.title = 'Psi4_Cube1'
psi4EnergyCube1.set_parameters(opt_method='hf3c')
An MMPBSA analysis is carried out on trajectory OEMols for protein, ligand
and possibly waters.
Required Input Parameters:
--------------------------
.oedb of records containing protein, ligand and possibly water
trajectory OEMols from an MD Short Trajectory run.
Outputs:
--------
out (.oedb file): file of the Analysis results for all ligands.
"""
job.uuid = "2717cf39-5bdd-4a1e-880e-5208bb232959"
job.classification = [['Analysis']]
job.tags = [tag for lists in job.classification for tag in lists]
ifs = DatasetReaderCube("ifs")
ifs.promote_parameter("data_in",
promoted_name="in",
title="System Input OERecord",
description="OERecord file name")
ofs = DatasetWriterCube('ofs', title='MD Out')
ofs.promote_parameter("data_out", promoted_name="out")
fail = DatasetWriterCube('fail', title='Failures')
fail.promote_parameter("data_out", promoted_name="fail")
IntECube = ParallelTrajInteractionEnergyCube("TrajInteractionEnergyCube")
Ex: python floes/openmm_MDprep.py --ligands ligands.oeb --protein protein.oeb --ofs-data_out prep.oeb
Parameters:
-----------
ligands (file): oeb file of ligand posed in the protein active site.
protein (file): oeb file of the protein structure, assumed to be pre-prepared
Optionals:
-----------
Outputs:
--------
ofs: Outputs a ready system to MD production run
"""
job.classification = [['Complex Setup', 'FrosstMD']]
job.tags = [tag for lists in job.classification for tag in lists]
# Ligand setting
iligs = LigandReader("LigandReader", title="Ligand Reader")
iligs.promote_parameter("data_in",
promoted_name="ligands",
title="Ligand Input File",
description="Ligand file name")
chargelig = LigChargeCube("LigCharge")
chargelig.promote_parameter(
'max_conformers',
promoted_name='max_conformers',
description="Set the max number of conformers per ligand",
default=800)
the complex is formed with each ligand/conformer
separately.
Required Input Parameters:
--------------------------
ligands (file): dataset of prepared ligands posed in the protein active site.
protein (file): dataset of the prepared protein structure.
Outputs:
--------
out (.oedb file): file of the protein-ligand complexes with parameters.
"""
# Locally the floe can be invoked by running the terminal command:
# python floes/LigReadPrep.py --ligands ligands.oeb --protein protein.oeb --out prod.oeb
job.classification = [['Molecular Dynamics']]
# job.uuid = "372e1890-d053-4027-970a-85b209e4676f"
job.tags = [tag for lists in job.classification for tag in lists]
# Ligand setting
iligs = DatasetReaderCube("LigandReader", title="Ligand Reader")
iligs.promote_parameter("data_in",
promoted_name="ligands",
title="Ligand Input Dataset",
description="Ligand Dataset")
ligset = LigandSetting("LigandSetting", title="Ligand Setting")
ligset.set_parameters(lig_res_name='LIG')
chargelig = ParallelLigandChargeCube("LigCharge", title="Ligand Charge")
chargelig.promote_parameter('charge_ligands',
from __future__ import unicode_literals
"""
Copyright (C) 2016 OpenEye Scientific Software
"""
from floe.api import WorkFloe, OEMolIStreamCube, OEMolOStreamCube
from PlatformTestCubes.cubes import PlatformTestCube
job = WorkFloe("OpenMMPlatforms")
job.description = """
**Check available OpenMM Platforms**
Based on OpenMM SimTK installation check script
"""
job.classification = [
["OpenMM", "Platforms"]
]
job.tags = [tag for lists in job.classification for tag in lists]
ifs = OEMolIStreamCube("ifs")
ofs = OEMolOStreamCube("ofs")
# Promotes the parameter to something we can specify from the command line as "--ifs=..."
ifs.promote_parameter("data_in", promoted_name="ifs")
# this is hardwiring the filename to the molecules coming out of ofs
ofs.set_parameters(data_out="openmmPlatformCheck.oeb")
# the name of the object has to match the string: this is the name of myself
platformTester = PlatformTestCube("platformTester")
