def getRMSD(self, nSeqUnbound, seqUnboundAli, nSeqBound, seqBoundAli):
'''
Computes rmsd for nSeqUnbound chain unbound and nSeqBound bound chain
@param nSeqUnbound: int. The index of the bound sequence that will be aligned
@param seqUnboundAli: str. The alignment result for unbound sequence number nSeqUnbound
@param nSeqBound: int. The index of the bound sequence that will be aligned
@param seqBoundAli: str. The alignment result for bound sequence number nSeqBound
@return rmsd. float. Root mean square deviation of CA of both imput chains
@return boundToUnboundResDict. {Bio.PDB.Residue_bound --> Bio.PDB.Residue_unbound}
'''
boundToUnboundResDict, atomBoundToUnboundMap = self.build2SeqsDictMap(
nSeqUnbound, seqUnboundAli, nSeqBound, seqBoundAli)
atoms_x, atoms_y = zip(*atomBoundToUnboundMap)
coords_x = np.array([elem.get_coord() for elem in atoms_x])
coords_y = np.array([elem.get_coord() for elem in atoms_y])
sup = SVDSuperimposer()
rmsd = sup._rms(coords_x, coords_y)
# print(boundToUnboundResDict)
return rmsd, boundToUnboundResDict
def calc_DockQ(model, native, use_CA_only=False):
exec_path = os.path.dirname(os.path.abspath(sys.argv[0]))
atom_for_sup = ['CA', 'C', 'N', 'O']
if (use_CA_only):
atom_for_sup = ['CA']
cmd_fnat = exec_path + '/fnat ' + model + ' ' + native + ' 5'
#cmd_interface=exec_path + '/fnat ' + model + ' ' + native + ' 10 backbone'
cmd_interface = exec_path + '/fnat ' + model + ' ' + native + ' 10'
#fnat_out = os.popen(cmd_fnat).readlines()
fnat_out = commands.getoutput(cmd_fnat)
# sys.exit()
(fnat, nat_correct, nat_total, fnonnat, nonnat_count, model_total,
interface5A) = parse_fnat(fnat_out)
assert fnat != -1, "Error running cmd: %s\n" % (cmd_fnat)
# inter_out = os.popen(cmd_interface).readlines()
inter_out = commands.getoutput(cmd_interface)
(fnat_bb, nat_correct_bb, nat_total_bb, fnonnat_bb, nonnat_count_bb,
model_total_bb, interface) = parse_fnat(inter_out)
assert fnat_bb != -1, "Error running cmd: %s\n" % (cmd_interface)
#print fnat
#Use same interface as for fnat for iRMS
#interface=interface5A
# Start the parser
pdb_parser = Bio.PDB.PDBParser(QUIET=True)
# Get the structures
ref_structure = pdb_parser.get_structure("reference", native)
sample_structure = pdb_parser.get_structure("model", model)
# Use the first model in the pdb-files for alignment
# Change the number 0 if you want to align to another structure
ref_model = ref_structure[0]
sample_model = sample_structure[0]
# Make a list of the atoms (in the structures) you wish to align.
# In this case we use CA atoms whose index is in the specified range
ref_atoms = []
sample_atoms = []
common_interface = []
chain_res = {}
#find atoms common in both sample and native
atoms_def_sample = []
atoms_def_in_both = []
#first read in sample
for sample_chain in sample_model:
# print sample_chain
chain = sample_chain.id
# print chain
for sample_res in sample_chain:
# print sample_res
if sample_res.get_id()[0] != ' ': #Skip hetatm.
continue
resname = sample_res.get_id()[1]
key = str(resname) + chain
for a in atom_for_sup:
atom_key = key + '.' + a
if a in sample_res:
if atom_key in atoms_def_sample:
print atom_key + ' already added (MODEL)!!!'
atoms_def_sample.append(atom_key)
#then read in native also present in sample
for ref_chain in ref_model:
chain = ref_chain.id
for ref_res in ref_chain:
#print ref_res
if ref_res.get_id()[0] != ' ': #Skip hetatm.
# print ref_res.get_id()
continue
resname = ref_res.get_id()[1]
key = str(resname) + chain
for a in atom_for_sup:
atom_key = key + '.' + a
if a in ref_res and atom_key in atoms_def_sample:
if atom_key in atoms_def_in_both:
print atom_key + ' already added (Native)!!!'
atoms_def_in_both.append(atom_key)
# print atoms_def_in_both
for sample_chain in sample_model:
chain = sample_chain.id
if chain not in chain_res.keys():
chain_res[chain] = []
for sample_res in sample_chain:
if sample_res.get_id()[0] != ' ': #Skip hetatm.
continue
resname = sample_res.get_id()[1]
key = str(resname) + chain
chain_res[chain].append(key)
if key in interface:
for a in atom_for_sup:
atom_key = key + '.' + a
if a in sample_res and atom_key in atoms_def_in_both:
sample_atoms.append(sample_res[a])
common_interface.append(key)
#print inter_pairs
chain_ref = {}
common_residues = []
# Iterate of all chains in the model in order to find all residues
for ref_chain in ref_model:
# Iterate of all residues in each model in order to find proper atoms
# print dir(ref_chain)
chain = ref_chain.id
if chain not in chain_ref.keys():
chain_ref[chain] = []
for ref_res in ref_chain:
if ref_res.get_id()[0] != ' ': #Skip hetatm.
continue
resname = ref_res.get_id()[1]
key = str(resname) + chain
#print ref_res
# print key
# print chain_res.values()
if key in chain_res[chain]: # if key is present in sample
#print key
for a in atom_for_sup:
atom_key = key + '.' + a
if a in ref_res and atom_key in atoms_def_in_both:
chain_ref[chain].append(ref_res[a])
common_residues.append(key)
#chain_sample.append((ref_res['CA'])
if key in common_interface:
# Check if residue number ( .get_id() ) is in the list
# Append CA atom to list
#print key
for a in atom_for_sup:
atom_key = key + '.' + a
#print atom_key
if a in ref_res and atom_key in atoms_def_in_both:
ref_atoms.append(ref_res[a])
#get the ones that are present in native
chain_sample = {}
for sample_chain in sample_model:
chain = sample_chain.id
if chain not in chain_sample.keys():
chain_sample[chain] = []
for sample_res in sample_chain:
if sample_res.get_id()[0] != ' ': #Skip hetatm.
continue
resname = sample_res.get_id()[1]
key = str(resname) + chain
if key in common_residues:
for a in atom_for_sup:
atom_key = key + '.' + a
if a in sample_res and atom_key in atoms_def_in_both:
chain_sample[chain].append(sample_res[a])
#if key in common_residues:
# print key
#sample_atoms.append(sample_res['CA'])
#common_interface.append(key)
assert len(ref_atoms) != 0, "length of native is zero"
assert len(sample_atoms) != 0, "length of model is zero"
assert len(ref_atoms) == len(
sample_atoms
), "Different number of atoms in native and model %d %d\n" % (
len(ref_atoms), len(sample_atoms))
super_imposer = Bio.PDB.Superimposer()
super_imposer.set_atoms(ref_atoms, sample_atoms)
super_imposer.apply(sample_model.get_atoms())
# Print RMSD:
irms = super_imposer.rms
(chain1, chain2) = chain_sample.keys()
ligand_chain = chain1
receptor_chain = chain2
len1 = len(chain_res[chain1])
len2 = len(chain_res[chain2])
assert len1 != 0, "%s chain has zero length!\n" % chain1
assert len2 != 0, "%s chain has zero length!\n" % chain2
class1 = 'ligand'
class2 = 'receptor'
if (len(chain_sample[chain1]) > len(chain_sample[chain2])):
receptor_chain = chain1
ligand_chain = chain2
class1 = 'receptor'
class2 = 'ligand'
#print len1
#print len2
#print chain_sample.keys()
#Set to align on receptor
assert len(chain_ref[receptor_chain]) == len(
chain_sample[receptor_chain]
), "Different number of atoms in native and model receptor (chain %c) %d %d\n" % (
receptor_chain, len(
chain_ref[receptor_chain]), len(chain_sample[receptor_chain]))
super_imposer.set_atoms(chain_ref[receptor_chain],
chain_sample[receptor_chain])
super_imposer.apply(sample_model.get_atoms())
receptor_chain_rms = super_imposer.rms
#print receptor_chain_rms
#print dir(super_imposer)
#print chain1_rms
#Grep out the transformed ligand coords
#print ligand_chain
#print chain_ref[ligand_chain]
#print chain_sample[ligand_chain]
#l1=len(chain_ref[ligand_chain])
#l2=len(chain_sample[ligand_chain])
assert len(chain_ref[ligand_chain]) != 0 or len(
chain_sample[ligand_chain]
) != 0, "Zero number of equivalent atoms in native and model ligand (chain %s) %d %d.\nCheck that the residue numbers in model and native is consistent\n" % (
ligand_chain, len(
chain_ref[ligand_chain]), len(chain_sample[ligand_chain]))
assert len(chain_ref[ligand_chain]) == len(
chain_sample[ligand_chain]
), "Different number of atoms in native and model ligand (chain %c) %d %d\n" % (
ligand_chain, len(
chain_ref[ligand_chain]), len(chain_sample[ligand_chain]))
coord1 = np.array([atom.coord for atom in chain_ref[ligand_chain]])
coord2 = np.array([atom.coord for atom in chain_sample[ligand_chain]])
#coord1=np.array([atom.coord for atom in chain_ref[receptor_chain]])
#coord2=np.array([atom.coord for atom in chain_sample[receptor_chain]])
#print len(coord1)
#print len(coord2)
sup = SVDSuperimposer()
Lrms = sup._rms(
coord1,
coord2) #using the private _rms function which does not superimpose
#super_imposer.set_atoms(chain_ref[ligand_chain], chain_sample[ligand_chain])
#super_imposer.apply(sample_model.get_atoms())
#coord1=np.array([atom.coord for atom in chain_ref[receptor_chain]])
#coord2=np.array([atom.coord for atom in chain_sample[receptor_chain]])
#Rrms= sup._rms(coord1,coord2)
#should give same result as above line
#diff = coord1-coord2
#l = len(diff) #number of atoms
#from math import sqrt
#print sqrt(sum(sum(diff*diff))/l)
#print np.sqrt(np.sum(diff**2)/l)
DockQ = (float(fnat) + 1 / (1 + (irms / 1.5) * (irms / 1.5)) + 1 /
(1 + (Lrms / 8.5) * (Lrms / 8.5))) / 3
dict = {}
dict['DockQ'] = DockQ
dict['irms'] = irms
dict['Lrms'] = Lrms
dict['fnat'] = fnat
dict['nat_correct'] = nat_correct
dict['nat_total'] = nat_total
dict['fnonnat'] = fnonnat
dict['nonnat_count'] = nonnat_count
dict['model_total'] = model_total
dict['chain1'] = chain1
dict['chain2'] = chain2
dict['len1'] = len1
dict['len2'] = len2
dict['class1'] = class1
dict['class2'] = class2
return dict
def calc_DockQ(model,native,use_CA_only=False):
exec_path=os.path.dirname(os.path.abspath(sys.argv[0]))
atom_for_sup=['CA','C','N','O']
if(use_CA_only):
atom_for_sup=['CA']
cmd_fnat=exec_path + '/fnat ' + model + ' ' + native + ' 5'
#cmd_interface=exec_path + '/fnat ' + model + ' ' + native + ' 10 backbone'
cmd_interface=exec_path + '/fnat ' + model + ' ' + native + ' 10'
#fnat_out = os.popen(cmd_fnat).readlines()
fnat_out = commands.getoutput(cmd_fnat)
# sys.exit()
(fnat,nat_correct,nat_total,fnonnat,nonnat_count,model_total,interface5A)=parse_fnat(fnat_out)
assert fnat!=-1, "Error running cmd: %s\n" % (cmd_fnat)
# inter_out = os.popen(cmd_interface).readlines()
inter_out = commands.getoutput(cmd_interface)
(fnat_bb,nat_correct_bb,nat_total_bb,fnonnat_bb,nonnat_count_bb,model_total_bb,interface)=parse_fnat(inter_out)
assert fnat_bb!=-1, "Error running cmd: %s\n" % (cmd_interface)
#print fnat
#Use same interface as for fnat for iRMS
#interface=interface5A
# Start the parser
pdb_parser = Bio.PDB.PDBParser(QUIET = True)
# Get the structures
ref_structure = pdb_parser.get_structure("reference", native)
sample_structure = pdb_parser.get_structure("model", model)
# Use the first model in the pdb-files for alignment
# Change the number 0 if you want to align to another structure
ref_model = ref_structure[0]
sample_model = sample_structure[0]
# Make a list of the atoms (in the structures) you wish to align.
# In this case we use CA atoms whose index is in the specified range
ref_atoms = []
sample_atoms = []
common_interface=[]
chain_res={}
#find atoms common in both sample and native
atoms_def_sample=[]
atoms_def_in_both=[]
#first read in sample
for sample_chain in sample_model:
# print sample_chain
chain=sample_chain.id
# print chain
for sample_res in sample_chain:
# print sample_res
if sample_res.get_id()[0] != ' ': #Skip hetatm.
continue
resname=sample_res.get_id()[1]
key=str(resname) + chain
for a in atom_for_sup:
atom_key=key + '.' + a
if a in sample_res:
if atom_key in atoms_def_sample:
print atom_key + ' already added (MODEL)!!!'
atoms_def_sample.append(atom_key)
#then read in native also present in sample
for ref_chain in ref_model:
chain=ref_chain.id
for ref_res in ref_chain:
#print ref_res
if ref_res.get_id()[0] != ' ': #Skip hetatm.
# print ref_res.get_id()
continue
resname=ref_res.get_id()[1]
key=str(resname) + chain
for a in atom_for_sup:
atom_key=key + '.' + a
if a in ref_res and atom_key in atoms_def_sample:
if atom_key in atoms_def_in_both:
print atom_key + ' already added (Native)!!!'
atoms_def_in_both.append(atom_key)
# print atoms_def_in_both
for sample_chain in sample_model:
chain=sample_chain.id
if chain not in chain_res.keys():
chain_res[chain]=[]
for sample_res in sample_chain:
if sample_res.get_id()[0] != ' ': #Skip hetatm.
continue
resname=sample_res.get_id()[1]
key=str(resname) + chain
chain_res[chain].append(key)
if key in interface:
for a in atom_for_sup:
atom_key=key + '.' + a
if a in sample_res and atom_key in atoms_def_in_both:
sample_atoms.append(sample_res[a])
common_interface.append(key)
#print inter_pairs
chain_ref={}
common_residues=[]
# Iterate of all chains in the model in order to find all residues
for ref_chain in ref_model:
# Iterate of all residues in each model in order to find proper atoms
# print dir(ref_chain)
chain=ref_chain.id
if chain not in chain_ref.keys():
chain_ref[chain]=[]
for ref_res in ref_chain:
if ref_res.get_id()[0] != ' ': #Skip hetatm.
continue
resname=ref_res.get_id()[1]
key=str(resname) + chain
#print ref_res
# print key
# print chain_res.values()
if key in chain_res[chain]: # if key is present in sample
#print key
for a in atom_for_sup:
atom_key=key + '.' + a
if a in ref_res and atom_key in atoms_def_in_both:
chain_ref[chain].append(ref_res[a])
common_residues.append(key)
#chain_sample.append((ref_res['CA'])
if key in common_interface:
# Check if residue number ( .get_id() ) is in the list
# Append CA atom to list
#print key
for a in atom_for_sup:
atom_key=key + '.' + a
#print atom_key
if a in ref_res and atom_key in atoms_def_in_both:
ref_atoms.append(ref_res[a])
#get the ones that are present in native
chain_sample={}
for sample_chain in sample_model:
chain=sample_chain.id
if chain not in chain_sample.keys():
chain_sample[chain]=[]
for sample_res in sample_chain:
if sample_res.get_id()[0] != ' ': #Skip hetatm.
continue
resname=sample_res.get_id()[1]
key=str(resname) + chain
if key in common_residues:
for a in atom_for_sup:
atom_key=key + '.' + a
if a in sample_res and atom_key in atoms_def_in_both:
chain_sample[chain].append(sample_res[a])
#if key in common_residues:
# print key
#sample_atoms.append(sample_res['CA'])
#common_interface.append(key)
assert len(ref_atoms)!=0, "length of native is zero"
assert len(sample_atoms)!=0, "length of model is zero"
assert len(ref_atoms)==len(sample_atoms), "Different number of atoms in native and model %d %d\n" % (len(ref_atoms),len(sample_atoms))
super_imposer = Bio.PDB.Superimposer()
super_imposer.set_atoms(ref_atoms, sample_atoms)
super_imposer.apply(sample_model.get_atoms())
# Print RMSD:
irms=super_imposer.rms
(chain1,chain2)=chain_sample.keys()
ligand_chain=chain1
receptor_chain=chain2
len1=len(chain_res[chain1])
len2=len(chain_res[chain2])
assert len1!=0, "%s chain has zero length!\n" % chain1
assert len2!=0, "%s chain has zero length!\n" % chain2
class1='ligand'
class2='receptor'
if(len(chain_sample[chain1]) > len(chain_sample[chain2])):
receptor_chain=chain1
ligand_chain=chain2
class1='receptor'
class2='ligand'
#print len1
#print len2
#print chain_sample.keys()
#Set to align on receptor
assert len(chain_ref[receptor_chain])==len(chain_sample[receptor_chain]), "Different number of atoms in native and model receptor (chain %c) %d %d\n" % (receptor_chain,len(chain_ref[receptor_chain]),len(chain_sample[receptor_chain]))
super_imposer.set_atoms(chain_ref[receptor_chain], chain_sample[receptor_chain])
super_imposer.apply(sample_model.get_atoms())
receptor_chain_rms=super_imposer.rms
#print receptor_chain_rms
#print dir(super_imposer)
#print chain1_rms
#Grep out the transformed ligand coords
#print ligand_chain
#print chain_ref[ligand_chain]
#print chain_sample[ligand_chain]
#l1=len(chain_ref[ligand_chain])
#l2=len(chain_sample[ligand_chain])
assert len(chain_ref[ligand_chain])!=0 or len(chain_sample[ligand_chain])!=0, "Zero number of equivalent atoms in native and model ligand (chain %s) %d %d.\nCheck that the residue numbers in model and native is consistent\n" % (ligand_chain,len(chain_ref[ligand_chain]),len(chain_sample[ligand_chain]))
assert len(chain_ref[ligand_chain])==len(chain_sample[ligand_chain]), "Different number of atoms in native and model ligand (chain %c) %d %d\n" % (ligand_chain,len(chain_ref[ligand_chain]),len(chain_sample[ligand_chain]))
coord1=np.array([atom.coord for atom in chain_ref[ligand_chain]])
coord2=np.array([atom.coord for atom in chain_sample[ligand_chain]])
#coord1=np.array([atom.coord for atom in chain_ref[receptor_chain]])
#coord2=np.array([atom.coord for atom in chain_sample[receptor_chain]])
#print len(coord1)
#print len(coord2)
sup=SVDSuperimposer()
Lrms = sup._rms(coord1,coord2) #using the private _rms function which does not superimpose
#super_imposer.set_atoms(chain_ref[ligand_chain], chain_sample[ligand_chain])
#super_imposer.apply(sample_model.get_atoms())
#coord1=np.array([atom.coord for atom in chain_ref[receptor_chain]])
#coord2=np.array([atom.coord for atom in chain_sample[receptor_chain]])
#Rrms= sup._rms(coord1,coord2)
#should give same result as above line
#diff = coord1-coord2
#l = len(diff) #number of atoms
#from math import sqrt
#print sqrt(sum(sum(diff*diff))/l)
#print np.sqrt(np.sum(diff**2)/l)
DockQ=(float(fnat) + 1/(1+(irms/1.5)*(irms/1.5)) + 1/(1+(Lrms/8.5)*(Lrms/8.5)))/3
dict={}
dict['DockQ']=DockQ
dict['irms']=irms
dict['Lrms']=Lrms
dict['fnat']=fnat
dict['nat_correct']=nat_correct
dict['nat_total']=nat_total
dict['fnonnat']=fnonnat
dict['nonnat_count']=nonnat_count
dict['model_total']=model_total
dict['chain1']=chain1
dict['chain2']=chain2
dict['len1']=len1
dict['len2']=len2
dict['class1']=class1
dict['class2']=class2
return dict
