def __init__(self,atomset1,atomset2):
""" """
EnergyScorer.__init__(self,atomset1,atomset2)
self.weight = None
self.weightLabel = None
self.scorer = AutoDock305Scorer()
self.prop = self.scorer.prop
bothAts = atomset1 + atomset2
for a in bothAts:
if a.parent.type + '_' + a.name in pep_aromList:
a.autodock_element=='A'
a.AtSolPar = .1027
elif a.autodock_element=='A':
a.AtSolPar = .1027
elif a.autodock_element=='C':
a.AtSolPar = .6844
else:
a.AtSolPar = 0.0
self.r = self.ms.add_entities(atomset1)
self.l = self.ms.add_entities(atomset2)
self.scorer.set_molecular_system(self.ms)
def setUp(self):
# setup MolecularSystem
self.setup_hsg1_ind() # defines self.ms, self.hsg1, self.ind
# setup Scorer and get the (atom-atom) score_array
self.ads = AutoDock305Scorer()
self.ads.set_molecular_system(self.ms)
def get_energy_breakdown(receptor_atoms, ligand_atoms, coords_to_use):
ms = MolecularSystem()
ms.add_entities(receptor_atoms)
ms.add_entities(ligand_atoms)
ms.set_coords(1, coords_to_use)
#ms.set_coords(1, c.getCoords()[:])
ad_scorer = AutoDock305Scorer()
ad_scorer.set_molecular_system(ms)
score_list = ad_scorer.get_score_per_term()
ostr = ""
for score in score_list:
ostr = ostr + "% 8.4f," % score
return ostr
if build_hydrogen_bonds or report_energy_breakdown:
d.ligMol.buildBondsByDistance()
receptor_filename = directory + '/' + receptor_filename
receptor = Read(receptor_filename)[0]
receptor.buildBondsByDistance()
if build_hydrogen_bonds:
hbondBuilder = HydrogenBondBuilder()
#do next two lines for each conf
#d.ligMol.set_conformation(conf)
#atDict = hbondBuilder.build(receptor.allAtoms, d.ligMol.allAtoms)
#num_hbonds= len(atDict)
if report_energy_breakdown:
ms = MolecularSystem()
ms.add_entities(receptor.allAtoms)
ms.add_entities(d.ligMol.allAtoms)
ad305scorer = AutoDock305Scorer()
ad305scorer.set_molecular_system(ms)
#whenever the ligMol changes conf,
#need to call ms.set_coords(1, new_coords)
mode = 'w'
first = True
if append_to_outputfile:
mode = 'a'
first = not os.path.exists(outputfilename)
fptr = open(outputfilename, mode)
num_clusters = len(d.clusterer.clustering_dict[rms_tolerance])
#output a header if file opened with mode 'w' or if this is first use of outputfile...
if verbose: print("first is ", first)
if first:
tstr = ""