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 = ""