def setup_design(numcores, conf_spaces, eps, num_seqs, algo_index, data):
"""setup_design
Set up the classes required to run the design
conf_spaces: A dictionary containing protein, ligand, and complex conf
spaces along with force-field parameters
num_seqs: The number of top sequences we want (usually 5)
Returns a BBKStar instance, ready to run
"""
parallelism = osprey.Parallelism(cpuCores=numcores)
data['numCpus'] = numcores
# how should we compute energies of molecules?
minimizingEcalc = osprey.EnergyCalculator(conf_spaces['complex'],
conf_spaces['ffparams'],
parallelism=parallelism,
isMinimizing=True)
# record the seq tree file name
data['seq_tree_name'] = data["design name"] + "seqTree.tree"
# configure basic inputs for SHARKStar
design = osprey.BBKStar(
conf_spaces['protein'],
conf_spaces['ligand'],
conf_spaces['complex'],
numBestSequences=num_seqs,
epsilon=
eps, # you proabably want something more precise in your real designs
showPfuncProgress=True,
maxSimultaneousMutations=9,
numConfsPerBatch=BBK_BATCH_SIZE,
maxNumConfsPerBatch=8,
#printSequenceTree=data["seq_tree_name"]
)
# configure SHARK*/BBK* inputs for each conf space
configure_bbk(design, minimizingEcalc, "shark", data["design name"],
algo_index)
return design
# how should we define energies of conformations?
def confEcalcFactory(confSpace, ecalc):
eref = osprey.ReferenceEnergies(confSpace, ecalc)
return osprey.ConfEnergyCalculator(confSpace,
ecalc,
referenceEnergies=eref)
# configure BBK*
bbkstar = osprey.BBKStar(
proteinConfSpace,
ligandConfSpace,
complexConfSpace,
numBestSequences=2,
epsilon=
0.99, # you proabably want something more precise in your real designs
writeSequencesToConsole=True,
writeSequencesToFile='bbkstar.results.tsv')
# configure BBK* inputs for each conf space
for info in bbkstar.confSpaceInfos():
# how should we define energies of conformations?
eref = osprey.ReferenceEnergies(info.confSpace, minimizingEcalc)
info.confEcalcMinimized = osprey.ConfEnergyCalculator(
info.confSpace, minimizingEcalc, referenceEnergies=eref)
# compute the energy matrix
ematMinimized = osprey.EnergyMatrix(info.confEcalcMinimized,
rigidEcalc = osprey.SharedEnergyCalculator(minimizingEcalc, isMinimizing=False)
# how should we define energies of conformations?
def confEcalcFactory(confSpace, ecalc):
eref = osprey.ReferenceEnergies(confSpace, ecalc)
return osprey.ConfEnergyCalculator(confSpace,
ecalc,
referenceEnergies=eref)
# configure BBK*
bbkstar = osprey.BBKStar(proteinConfSpace,
ligandConfSpace,
complexConfSpace,
numBestSequences=5,
epsilon=0.683,
writeSequencesToConsole=True,
writeSequencesToFile='bbkstar.results.tsv')
# configure BBK* inputs for each conf space
for info in bbkstar.confSpaceInfos():
# how should we define energies of conformations?
eref = osprey.ReferenceEnergies(info.confSpace, minimizingEcalc)
info.confEcalcMinimized = osprey.ConfEnergyCalculator(
info.confSpace, minimizingEcalc, referenceEnergies=eref)
# compute the energy matrix
ematMinimized = osprey.EnergyMatrix(info.confEcalcMinimized,
cacheFile='emat.%s.dat' % info.id)
eref = osprey.ReferenceEnergies(confSpace, ecalc)
return osprey.ConfEnergyCalculator(confSpace,
ecalc,
referenceEnergies=eref)
# how should confs be ordered and searched?
def astarFactory(emat, rcs):
return osprey.AStarMPLP(emat, rcs, numIterations=5)
# run K*
bbkstar = osprey.BBKStar(proteinConfSpace,
ligandConfSpace,
complexConfSpace,
rigidEcalc,
minimizingEcalc,
confEcalcFactory,
astarFactory,
numBestSequences=2,
epsilon=0.99,
energyMatrixCachePattern='emat.*.dat',
writeSequencesToConsole=True,
writeSequencesToFile='bbkstar.results.tsv')
scoredSequences = bbkstar.run()
# use results
for scoredSequence in scoredSequences:
print("result:")
print("\tsequence: %s" % scoredSequence.sequence)
print("\tscore: %s" % scoredSequence.score)
import osprey
osprey.start()
# run this example AFTER you've trained the three LUTE models using LUTE.train.py
# import our conf spaces from another file
exec(open("LUTE.confSpaces.py").read())
# configure BBK*
bbkstar = osprey.BBKStar(confSpaces['protein'],
confSpaces['ligand'],
confSpaces['complex'],
epsilon=0.01,
numBestSequences=2,
writeSequencesToConsole=True)
# configure BBK* inputs for each conf space
for info in bbkstar.confSpaceInfos():
# read the trained LUTE model
pmat = osprey.DEE_read(info.confSpace, 'LUTE.pmat.%s.dat' % info.id)
model = osprey.LUTE_read('LUTE.%s.dat' % info.id)
# make a LUTE energy calculator from the model
luteEcalc = osprey.LUTE_ConfEnergyCalculator(info.confSpace, model)
info.confEcalcMinimized = luteEcalc
# how should confs be ordered and searched?
# (since we're in a loop, need capture variables above by using defaulted arguments)
def makeAStar(rcs, luteEcalc=luteEcalc, pmat=pmat):