def extract_ligand_records(self, pdb_file, relevant_ligands):
"""
Extract all instances of fragment-containing small molecules bound to a PDB
:param pdb_file: Path to the PDB file containing the fragment-containing ligand
:param relevant_ligands: dict of Ideal_Ligand_PDB_Containers for ligands to find in pdb_file
:return: relevant_ligands_prody_dict - structured [resname, chain, resnum] : ligand prody
"""
pdb_header = prody.parsePDB(pdb_file, model=0, header=True)
# Find resnums for all instances of fragment-containing small molecules bound to this PDBrelevant_ligands
relevant_ligand_resnums = [(res.chain, res.resnum, res)
for res in pdb_header['chemicals']
if res.resname in relevant_ligands.keys()]
# Pull all relevant ligands from PDB as a prody AtomGroup objects
pdb_prody_hv = prody.parsePDB(pdb_file, altloc=True).getHierView()
relevant_ligands_prody_dict = dict()
for ligand_chain, ligand_resnum, res in relevant_ligand_resnums:
ligand_pdb_prody = pdb_prody_hv.getResidue(ligand_chain,
ligand_resnum)
# Issues occur when alternate coordinates are parsed for a ligand... we don't want those anyways
if ligand_pdb_prody is None:
continue
relevant_ligands_prody_dict[(res.resname, ligand_chain,
ligand_resnum)] = ligand_pdb_prody
return relevant_ligands_prody_dict
def get_alphashape(pdb, chain=None, plot=False):
'''
Returns an AlphaShape object of a pdb file, outlining its general
shape for use in a clash filter.
'''
if chain:
atoms = prody.parsePDB(pdb, chain=chain)
else:
atoms = prody.parsePDB(pdb)
atoms = atoms.select('not chain A')
# For some reason there is a level which is not populated. This may
# be for proteins w/ multiple chains.
coordsets = atoms.getCoordsets()
coords = []
for coordset in coordsets:
coords.extend(coordset)
# coords = [(0., 0.), (0., 1.), (1., 1.), (1., 0.), (0.5, 0.5)]
alpha_shape = alphashape.alphashape(coords, 0.18)
if plot:
helix = prody.parsePDB(pdb, chain='A')
helixcoords = helix.getCoordsets()[0]
fig = plt.figure()
# ax = fig.add_subplot(projection='3d')
ax = Axes3D(fig)
ax.scatter(*zip(*coords))
ax.scatter(*zip(*helixcoords))
# # ax.add_patch(PolygonPatch(alpha_shape, alpha=0.2))
ax.plot_trisurf(*zip(*alpha_shape.vertices),
triangles=alpha_shape.faces,
alpha=0.3)
plt.show()
return alpha_shape
def prepare_pdb22(self, out_prefix, csets=None, **kwargs):
csets = self._make_csets(csets)
nmin, psf = self._prepare_pdb22_one_frame(out_prefix, **kwargs)
nmin_ag = prody.parsePDB(nmin)
if len(csets) == 1:
self.ag = nmin_ag
self.save(nmin)
return self, psf
if nmin_ag.numAtoms() == self.ag.numAtoms():
if list(nmin_ag.getNames()) != list(self.ag.getNames()):
nmin_ag = self._match_by_residue_position(self.ag, nmin_ag)
else:
nmin_ag.setCoords(self.ag.getCoordsets())
else:
logger.info('Molecule was altered during preparation, preparing each frame separately')
new_csets = []
for cset in csets:
nmin_frame, psf_frame = self._prepare_pdb22_one_frame(out_prefix + '-%i-tmp' % cset, cset=0, **kwargs)
ag_frame = prody.parsePDB(nmin_frame)
assert (list(nmin_ag.getNames()) == list(ag_frame.getNames()))
new_csets.append(ag_frame.getCoords())
nmin_frame.remove()
psf_frame.remove()
nmin_ag.setCoords(np.array(new_csets))
self.ag = nmin_ag
self.save(nmin)
return self, psf
def get_vdms(self, df, path_to_vdm=None):
path = path_to_vdm or self._directory.split('csv')[0] + 'vdM/'
with os.scandir(path) as it:
for entry in it:
if entry.name[0] != '.':
filename_end = '_'.join(entry.name.split('_')[4:])
break
if 'query_name' in df.columns:
for n, row in df[['iFG_count', 'vdM_count',
'query_name']].iterrows():
try:
yield pr.parsePDB(path + 'iFG_' + str(row['iFG_count']) +
'_vdM_' + str(row['vdM_count']) + '_' +
filename_end)
except Exception:
traceback.print_exc()
else:
for n, row in df[['iFG_count', 'vdM_count']].iterrows():
try:
yield pr.parsePDB(path + 'iFG_' + str(row['iFG_count']) +
'_vdM_' + str(row['vdM_count']) + '_' +
filename_end)
except Exception:
traceback.print_exc()
def native_contact(rec_path, reorder_path, dock_path):
parsed_docked = prody.parsePDB(dock_path).select('not hydrogen')
parsed_crystal = prody.parsePDB(reorder_path).select('not hydrogen')
parsed_rec = prody.parsePDB(rec_path).select('not hydrogen')
cry_atom_num = parsed_crystal.numAtoms()
lig_atom_num = parsed_docked.numAtoms()
assert cry_atom_num == lig_atom_num
docked_coords = parsed_docked.getCoordsets()
crystal_coord = parsed_crystal.getCoords()
rec_coord = parsed_rec.getCoords()
exp_crystal_coord = np.expand_dims(crystal_coord, -2)
cry_diff = exp_crystal_coord - rec_coord
cry_distance = np.sqrt(np.sum(np.square(cry_diff), axis=-1))
exp_docked_coords = np.expand_dims(docked_coords, -2)
docked_diff = exp_docked_coords - rec_coord
docked_distance = np.sqrt(np.sum(np.square(docked_diff), axis=-1))
cry_contact = (cry_distance < distance_threshold).astype(int)
num_contact = np.sum(cry_contact).astype(float)
lig_contact = (docked_distance < distance_threshold).astype(int)
contact_ratio = np.sum(cry_contact * lig_contact,
axis=(-1, -2)) / num_contact
return [list(contact_ratio)]
def __init__(self, pdb_file=None, pdb_list=None, ag=None):
if pdb_file is not None:
if isinstance(pdb_file, prody.Atomic):
self.ag = pdb_file
elif isinstance(pdb_file, str):
self.ag = prody.parsePDB(pdb_file)
else:
raise ValueError('Wrong type of parameter `pdb_file` ({})'.format(type(pdb_file)))
elif pdb_list is not None:
ag_first = prody.parsePDB(pdb_list[0])
new_csets = []
for f in pdb_list:
ag = prody.parsePDB(f)
assert (list(ag.getNames()) == list(ag_first.getNames()))
new_csets.append(ag.getCoords())
ag_first.setCoords(np.array(new_csets))
self.ag = ag_first
elif ag is not None:
self.ag = ag.copy()
else:
raise ValueError('No molecules specified')
def overlap(reorder_path, dock_path):
"""
calculate overlap for the docking result
args:
reorder_path:: str
path of reorder ligand
dock_path:: str
path of docking result
returns:
overlap:: float
overlap value
"""
docked_coords = prody.parsePDB(dock_path).getCoordsets()
crystal_coords = prody.parsePDB(reorder_path).getCoords()
expanded_docked = np.expand_dims(docked_coords, -2)
diff = expanded_docked - crystal_coords
distance = np.sqrt(np.sum(np.power(diff, 2), axis=-1))
all_clash = (distance < clash_cutoff_A).astype(float)
atom_clash = (np.sum(all_clash, axis=-1) > 0).astype(float)
position_clash_ratio = np.mean(atom_clash, axis=-1)
return [list(position_clash_ratio)]
def assign_pcs(args):
fn, topf, eda, pcs, sel, outf = args
if fn.endswith("pdb"):
pdb = prody.parsePDB(fn)
pdb = pdb.select(sel).copy()
ensemble = prody.Ensemble('A single pdb file ensemble')
ensemble.setCoords(pdb.getCoords())
ensemble.addCoordset(pdb.getCoordsets())
ensemble.iterpose()
PCs = prody.calcProjection(ensemble, eda[pcs])
print(PCs)
return
elif fn.endswith(".dcd"):
structure = prody.parsePDB(topf)
str_sel = structure.select(sel)
#dcd = prody.DCDFile(fn)
dcd = prody.Trajectory(fn)
dcd.link(structure)
dcd.setCoords(structure)
dcd.setAtoms(str_sel)
PCs = prody.calcProjection(dcd, eda[pcs])
if outf is not None:
header = " ".join(["PC%d" % (i + 1) for i in pcs])
np.savetxt(outf, PCs, fmt="%.4f", header=header, comments="")
else:
print("Unsupport file type: %s" % fn)
return None
return PCs
def createPCAMOdes(base_path, protein_list):
for protein in protein_list:
receptor = os.path.join(base_path,
protein) + "/{}A-unbound.pdb".format(protein)
ligand = os.path.join(base_path,
protein) + "/{}B-unbound.pdb".format(protein)
pca_rec_folder = "{}/{}/input/pca/concoord/receptor".format(
base_path, protein)
pca_lig_folder = "{}/{}/input/pca/concoord/ligand".format(
base_path, protein)
dist_rec = "{}/{}A-dist".format(pca_rec_folder, protein)
dist_lig = "{}/{}B-dist".format(pca_lig_folder, protein)
disco_rec = "{}/{}A-disco.pdb".format(pca_rec_folder, protein)
disco_lig = "{}/{}B-disco.pdb".format(pca_lig_folder, protein)
nmdfile_rec = "{}/{}A-nmd".format(pca_rec_folder, protein)
nmdfile_lig = "{}/{}B-nmd".format(pca_lig_folder, protein)
os.system("mkdir -p {}".format(pca_rec_folder))
os.system("mkdir -p {}".format(pca_lig_folder))
#pwd = os.getcwd()
os.chdir(pca_rec_folder)
p = Popen([
"/home/glenn/Documents/Masterarbeit/concoord/bin/dist", "-p",
receptor
],
stdin=PIPE) #, shell=True #,"-op",dist_rec
p.communicate(input=b'1\n1\n')
os.system(
"/home/glenn/Documents/Masterarbeit/concoord/bin/disco -on {} -n 200 -i 1000 -viol 1. -bump "
.format(disco_rec))
os.chdir(pca_lig_folder)
p = Popen([
"/home/glenn/Documents/Masterarbeit/concoord/bin/dist", "-p",
ligand
],
stdin=PIPE) #, shell=True
p.communicate(input=b'1\n1\n')
os.system(
"/home/glenn/Documents/Masterarbeit/concoord/bin/disco -on {} -n 200 -i 1000 -viol 1. -bump "
.format(disco_lig))
try:
pca_rec = calcPCA(disco_rec)
atoms_rec = dy.parsePDB(receptor, subset='ca')
dy.writeNMD(nmdfile_rec, pca_rec, atoms_rec)
except:
pass
try:
pca_lig = calcPCA(disco_lig)
atoms_lig = dy.parsePDB(ligand, subset='ca')
dy.writeNMD(nmdfile_lig, pca_lig, atoms_lig)
except:
pass
def rmsd(reorder_path, dock_path):
docked_coords = prody.parsePDB(dock_path).getCoordsets()
crystal_coords = prody.parsePDB(reorder_path).getCoords()
rmsd = np.sqrt(np.mean(np.sum(np.square(docked_coords - crystal_coord), axis=1), axis=-1))
return [list(rmsd)]
def Resid(self, pdbFile, pdbChain1, pdbChain2, ligandName, runfolder):
"""
Return a list of residue in `Receptor` which distance from `Ligand` is less or equal 5 angstroms.
"""
# print "wthelelelelle"
print self.runfolder
os.chdir(runfolder)
# acpype = glob('*.acpype')
if pdbChain2 != '':
a = prody.parsePDB(
str(pdbFile)).select('(' + pdbChain1 + ')' +
' and within 5 of chain ' + pdbChain2)
residList = np.array(list(sorted(set(a.getResnums()))),
dtype='str')
residlist = " ".join(residList)
# for i in range(1, len(residList)):
# residlist += ' ' + str(residList[i])
with open('cutoff-resid-5angstroms', 'w') as residfile:
residfile.write(residlist)
return residList, str(runfolder + '/cutoff-resid-5angstroms')
elif ligandName != '':
receptor = prody.parsePDB(str(pdbFile))
Ligand = []
# for i in range(len(acpype)):
# Ligand.append(str(acpype[i].strip('.acpype')))
# print "is it a bug here??"
# print Ligand
# ligand = []
# for i in range(len(Ligand)):
Ligand.append(
prody.parsePDB(
str(ligandName) + '.acpype/' + str(ligandName) +
'_NEW.pdb'))
# print ligand
protein = receptor
# print "this is protein before add ligand[i]"
# print protein
haha = []
for i in range(len(Ligand)):
protein += Ligand[i]
haha = np.array(list(sorted(set(Ligand[i].getResnames()))),
dtype='str')
# print "this is protein after add ligand[i]"
# print protein
# print haha
# print type(haha)
ligands = ' or resname '.join(haha)
hoho = protein.select('(' + pdbChain1 + ')' +
' and within 5 of resname ' + ligands)
residList = list(sorted(set(hoho.getResnums())))
residList = np.array(residList, dtype='str')
residlist = " ".join(residList)
# print residlist
# for i in range(1, len(residList)):
# residlist += ' ' + str(residList[i])
with open('cutoff-resid-5angstroms', 'w') as residfile:
residfile.write(residlist)
return residList, str(runfolder + '/cutoff-resid-5angstroms')
def write_superposed_pdbs(self, output_pdb_folder, alignments: dict = None):
"""
Superposes PDBs according to alignment and writes transformed PDBs to files
(View with Pymol)
Parameters
----------
alignments
output_pdb_folder
"""
if alignments is None:
alignments = self.alignment
output_pdb_folder = Path(output_pdb_folder)
if not output_pdb_folder.exists():
output_pdb_folder.mkdir()
reference_name = self.structures[0].name
reference_pdb = pd.parsePDB(
str(self.output_folder / f"cleaned_pdb/{self.structures[0].name}.pdb")
)
core_indices = np.array(
[
i
for i in range(len(alignments[reference_name]))
if -1 not in [alignments[n][i] for n in alignments]
]
)
aln_ref = alignments[reference_name]
ref_coords_core = (
reference_pdb[helper.get_alpha_indices(reference_pdb)]
.getCoords()
.astype(np.float64)[np.array([aln_ref[c] for c in core_indices])]
)
ref_centroid = helper.nb_mean_axis_0(ref_coords_core)
ref_coords_core -= ref_centroid
transformation = pd.Transformation(np.eye(3), -ref_centroid)
reference_pdb = pd.applyTransformation(transformation, reference_pdb)
pd.writePDB(str(output_pdb_folder / f"{reference_name}.pdb"), reference_pdb)
for i in range(1, len(self.structures)):
name = self.structures[i].name
pdb = pd.parsePDB(
str(self.output_folder / f"cleaned_pdb/{self.structures[i].name}.pdb")
)
aln_name = alignments[name]
common_coords_2 = (
pdb[helper.get_alpha_indices(pdb)]
.getCoords()
.astype(np.float64)[np.array([aln_name[c] for c in core_indices])]
)
(
rotation_matrix,
translation_matrix,
) = superposition_functions.svd_superimpose(
ref_coords_core, common_coords_2
)
transformation = pd.Transformation(rotation_matrix.T, translation_matrix)
pdb = pd.applyTransformation(transformation, pdb)
pd.writePDB(str(output_pdb_folder / f"{name}.pdb"), pdb)
def paste_loop(path_to_loop,
path_to_pdb,
query_selection_N,
query_selection_C,
query_length_N=4,
query_length_C=4,
include_sidechains=False):
loop = pr.parsePDB(path_to_loop)
loop.setSegnames('A')
loop_bb = loop.select('backbone')
pdb = pr.parsePDB(path_to_pdb)
query_N = pdb.select(query_selection_N)
query_N_bb = query_N.select('name N C CA')
query_C = pdb.select(query_selection_C)
query_C_bb = query_C.select('name N C CA')
first_resnum_loop = loop_bb.getResnums()[0]
last_resnum_loop = loop_bb.getResnums()[-1]
loop_N_bb = loop_bb.select('name N C CA and resnum `' +
str(first_resnum_loop) + 'to' +
str(first_resnum_loop + query_length_N - 1) +
'`')
loop_C_bb = loop_bb.select('name N C CA and resnum `' +
str(last_resnum_loop - query_length_C + 1) +
'to' + str(last_resnum_loop) + '`')
try:
coords_diff_N = loop_N_bb.getCoords() - query_N_bb.getCoords()
coords_diff_C = loop_C_bb.getCoords() - query_C_bb.getCoords()
except ValueError:
print('Loop failure')
ind_match_N = np.argmin([np.linalg.norm(i) for i in coords_diff_N])
ind_match_C = np.argmin([np.linalg.norm(i) for i in coords_diff_C])
loop_N_bb_index = loop_N_bb.getIndices()[ind_match_N]
loop_C_bb_index = loop_C_bb.getIndices()[ind_match_C]
query_N_bb_index = query_N_bb.getIndices()[ind_match_N]
query_C_bb_index = query_C_bb.getIndices()[ind_match_C]
first_index_pdb = pdb.select('backbone').getIndices()[0]
last_index_pdb = pdb.select('backbone').getIndices()[-1]
loop_slice = loop_bb.select('index ' + str(loop_N_bb_index) + 'to' +
str(loop_C_bb_index))
if not include_sidechains:
pdb_N = pdb.select('backbone and index ' + str(first_index_pdb) +
'to' + str(query_N_bb_index - 1))
pdb_C = pdb.select('backbone and index ' + str(query_C_bb_index + 1) +
'to' + str(last_index_pdb))
else:
pdb_N = pdb.select('index ' + str(first_index_pdb) + 'to' +
str(query_N_bb_index - 1))
pdb_C = pdb.select('index ' + str(query_C_bb_index + 1) + 'to' +
str(last_index_pdb))
return pdb_N, loop_slice, pdb_C
def _import_pdbs(self):
"""
For each fragment ensemble, converts each residue in all processed PDBs into objects with representative
matrices and other relevant information
:return:
"""
processsed_residue_list = []
for pdb in pdb_check(self.processed_PDBs_dir):
# Make sure I can load things...
try:
prody_protein = prody.parsePDB(pdb)
# Check that residues exist within cutoff distance provided in alignments, otherwise pass
prody_protein_selection = prody_protein.select(
'protein and not hetatm')
if prody_protein_selection == None:
continue
else:
prody_protein_hv = prody_protein_selection.getHierView()
except Exception as e:
print(e)
continue
pdb_info = os.path.basename(os.path.normpath(pdb))
prody_ligand = prody.parsePDB(pdb).select(
'hetatm and resname {}'.format(pdb_info.split('_')[1]))
# todo: CATCH THIS!!!
if prody_ligand is None: continue
# Iterate over residues in contacts and generate representative vector with weights applied
processsed_residue_list += [
fragment_PDB(
residue,
pdb_info,
prody_ligand,
) for residue in prody_protein_hv.iterResidues()
]
processsed_residue_list_cleaned = [
residue for residue in processsed_residue_list
if residue.viable is not None
]
print(
f'Unique processed and viable residues: {len(processsed_residue_list_cleaned)}'
)
return processsed_residue_list_cleaned
def get_chain_from_astral_id(astral_id, d):
"""Given an ASTRAL ID and the ASTRAL->PDB/chain mapping dictionary, this function
attempts to return the relevant, parsed ProDy object."""
pdbid, chain = d[astral_id]
assert "," not in chain, f"Issue parsing {astral_id} with chain {chain} and pdbid " \
f"{pdbid}."
chain, resnums = chain.split(":")
if astral_id == "d4qrye_" or astral_id in ASTRAL_IDS_INCORRECTLY_PARSED:
chain = "A"
resnums = ""
# Handle special case https://github.com/prody/ProDy/issues/1197
if astral_id == "d1tocr1":
# a = pr.performDSSP("1toc")
a = pr.parsePDB("1toc", chain="R")
a = a.select("(chain R) and (resnum 2 to 59 or resnum 1A)"
) # Note there is no 1B
return a
a = pr.parsePDB(pdbid, chain=chain)
if resnums != "":
# This pattern matches ASTRAL number ranges like 1-100, 1A-100, -1-39, -4--1, etc.
p = re.compile(
r"((?P<d1>-?\d+)(?P<ic1>\w?))-((?P<d2>-?\d+)(?P<ic2>\w?))")
match = p.match(resnums)
start, start_icode = int(match.group("d1")), match.group("ic1")
end, end_icode = int(match.group("d2")), match.group("ic2")
# Ranges with negative numbers must be escaped with ` character
range_str = f"{start} to {end}"
if start < 0 or end < 0:
range_str = f"`{range_str}`"
if not start_icode and not end_icode:
# There are no insertion codes. Easy case.
selection_str = f"resnum {range_str}"
elif (start_icode and not end_icode) or (not start_icode
and end_icode):
# If there's only one insertion code, this selection is not well defined
# and must be handled by special cases above.
raise ValueError(f"Unsupported ASTRAL range {astral_id}.")
elif start_icode and end_icode:
if start_icode == end_icode:
selection_str = f"resnum {range_str} and icode {start_icode}"
else:
raise ValueError(f"Unsupported ASTRAL range {astral_id}.")
a = a.select(selection_str)
return a
def fix_openmm():
# get the whole crystal structure
# get only the ATOM records
# and HETAM records for MSE
# convert MSE to MET
with open('no_smet.pdb', 'w') as outfile:
with open('experimental.pdb') as infile:
for line in infile:
if line.startswith('ATOM'):
outfile.write(line)
if line.startswith('HETATM'):
if line[17:20] == 'MSE':
atom_name = line[12:17]
if atom_name == 'SE ':
atom_name = ' SD '
line_fixed = 'ATOM ' + line[
6:12] + atom_name + 'MET' + line[20:67] + '\n'
outfile.write(line_fixed)
# load the file into prody
p = prody.parsePDB('no_smet.pdb')
p = p.select('not hydrogen')
# get one of the rosetta models
r = prody.parsePDB('rosetta.pdb')
# perform an alignment to find out what part of the crystal structure
# corresponds to the rosetta file
match = prody.matchChains(r, p, subset='all', overlap=25,
pwalign=True)[0][1]
print len(match)
prody.writePDB('chain.pdb', match)
# now clean it up with pdb fixer
subprocess.check_call('python ~/Source/PdbFixer/pdbfixer.py chain.pdb',
shell=True)
# now load it with zam
p = protein.Protein('output.pdb')
p.Dehydrogen()
disulfide_pairs = find_disulfide(p)
for r1, r2 in disulfide_pairs:
print ' added disulfide between {} and {}'.format(r1, r2)
p.Res[r1].FullName = 'CYX'
p.Res[r2].FullName = 'CYX'
p.WritePdb('start.pdb')
# now run tleap
print ' running tleap'
run_tleap(disulfide_pairs)
def prody_contacts(**kwargs):
"""Identify contacts of a target structure with one or more ligands.
Contacting atoms (or extended subset of atoms, such as residues) are
outputted in PDB file format.
:arg target: target PDB identifier or filename
:arg ligand: ligand PDB identifier(s) or filename(s)
:arg select: atom selection string for target structure
:arg radius: contact radius (Å), default is ``4.0``
:arg extend: output same ``'residue'``, ``'chain'``, or ``'segment'`` along
with contacting atoms
:arg prefix: prefix for output file, default is *target* filename
:arg suffix: output filename suffix, default is *ligand* filename"""
import prody
LOGGER = prody.LOGGER
target = prody.parsePDB(kwargs['target'])
title = kwargs.get('prefix') or target.getTitle()
selstr = kwargs.get('select')
if selstr:
target = target.select(selstr)
contacts = prody.Contacts(target)
suffix = kwargs.get('suffix', '_contacts')
extend = kwargs.get('extend')
radius = float(kwargs.get('radius', 4.0))
ligands = kwargs.get('ligand')
if len(ligands) > 1:
outfn = lambda fn: title + suffix + '_' + fn + '.pdb'
else:
outfn = lambda fn: title + suffix + '.pdb'
for pdb in ligands:
ligand = prody.parsePDB(pdb)
sel = contacts(radius, ligand)
if sel:
LOGGER.info('{0} atoms from {1} contact {2}.'.format(
len(sel), pdb, str(target)))
if extend:
sel = target.select('same ' + extend + ' as sel', sel=sel)
LOGGER.info('Selection is extended to {0} atoms of the same '
'{1}(s).'.format(len(sel), extend))
pdbfn = outfn(ligand.getTitle())
LOGGER.info('Writing contacts into ' + pdbfn)
prody.writePDB(pdbfn, sel)
def rmsd(bucket, table_idx, param, input_data):
'''
Calculate rmsd and insert the result into database
Args:
table_idx: int, id for native contact table
param: dict, parameters
{
'input_docked_foler':'...',
'input_crystal_folder':'...',
}
input_data: list
[receptor, chain, resnum ,resname]
Returns:
'''
try:
receptor, chain, resnum, resname = input_data
input_docked_folder = param['input_docked_folder']
input_crystal_folder = param['input_crystal_folder']
lig_name = '_'.join([receptor, chain, resnum, resname, 'ligand'
]) + '.pdb'
input_docked_dir = os.path.join(data_dir, input_docked_folder,
receptor)
input_docked_path = os.path.join(input_docked_dir, lig_name)
input_crystal_dir = os.path.join(data_dir, input_crystal_folder,
receptor)
input_crystal_path = os.path.join(input_crystal_dir, lig_name)
docked_coords = prody.parsePDB(input_docked_path).getCoordsets()
crystal_coord = prody.parsePDB(input_crystal_path).getCoords()
rmsd = np.sqrt(
np.mean(np.sum(np.square(docked_coords - crystal_coord), axis=1),
axis=-1))
# todo (maksym) RMSDs not rmsd
records = []
for i, rd in enumerate(rmsd):
records.append(input_data + [i + 1, rd, 1, 'success'])
db.insert(table_idx, records, bucket=bucket)
except Exception as e:
record = input_data + [1, 0, 0, str(e)]
records = [record]
db.insert(table_idx, records, bucket=bucket)
def prody_contacts(**kwargs):
"""Identify contacts of a target structure with one or more ligands.
Contacting atoms (or extended subset of atoms, such as residues) are
outputted in PDB file format.
:arg target: target PDB identifier or filename
:arg ligand: ligand PDB identifier(s) or filename(s)
:arg select: atom selection string for target structure
:arg radius: contact radius (Å), default is ``4.0``
:arg extend: output same ``'residue'``, ``'chain'``, or ``'segment'`` along
with contacting atoms
:arg prefix: prefix for output file, default is *target* filename
:arg suffix: output filename suffix, default is *ligand* filename"""
import prody
LOGGER = prody.LOGGER
target = prody.parsePDB(kwargs['target'])
title = kwargs.get('prefix') or target.getTitle()
selstr = kwargs.get('select')
if selstr:
target = target.select(selstr)
contacts = prody.Contacts(target)
suffix = kwargs.get('suffix', '_contacts')
extend = kwargs.get('extend')
radius = float(kwargs.get('radius', 4.0))
ligands = kwargs.get('ligand')
if len(ligands) > 1:
outfn = lambda fn: title + suffix + '_' + fn + '.pdb'
else:
outfn = lambda fn: title + suffix + '.pdb'
for pdb in ligands:
ligand = prody.parsePDB(pdb)
sel = contacts(radius, ligand)
if sel:
LOGGER.info('{0} atoms from {1} contact {2}.'
.format(len(sel), pdb, str(target)))
if extend:
sel = target.select('same ' + extend + ' as sel', sel=sel)
LOGGER.info('Selection is extended to {0} atoms of the same '
'{1}(s).'.format(len(sel), extend))
pdbfn = outfn(ligand.getTitle())
LOGGER.info('Writing contacts into ' + pdbfn)
prody.writePDB(pdbfn, sel)
def Resid(self, pdbFile, pdbChain1, pdbChain2, ligandName, runfolder):
"""
Return a list of residue in `Receptor` which distance from `Ligand` is less or equal 5 angstroms.
"""
# print "wthelelelelle"
print self.runfolder
os.chdir(runfolder)
# acpype = glob('*.acpype')
if pdbChain2 != '':
a = prody.parsePDB(str(pdbFile)).select('('+pdbChain1+')' + ' and within 5 of chain ' + pdbChain2)
residList = np.array(list(sorted(set(a.getResnums()))), dtype='str')
residlist = " ".join(residList)
# for i in range(1, len(residList)):
# residlist += ' ' + str(residList[i])
with open('cutoff-resid-5angstroms', 'w') as residfile:
residfile.write(residlist)
return residList, str(runfolder + '/cutoff-resid-5angstroms')
elif ligandName != '':
receptor = prody.parsePDB(str(pdbFile))
Ligand = []
# for i in range(len(acpype)):
# Ligand.append(str(acpype[i].strip('.acpype')))
# print "is it a bug here??"
# print Ligand
# ligand = []
# for i in range(len(Ligand)):
Ligand.append(prody.parsePDB(str(ligandName) + '.acpype/' + str(ligandName) + '_NEW.pdb'))
# print ligand
protein = receptor
# print "this is protein before add ligand[i]"
# print protein
haha = []
for i in range(len(Ligand)):
protein += Ligand[i]
haha = np.array(list(sorted(set(Ligand[i].getResnames()))), dtype='str')
# print "this is protein after add ligand[i]"
# print protein
# print haha
# print type(haha)
ligands = ' or resname '.join(haha)
hoho = protein.select('('+pdbChain1+')' + ' and within 5 of resname ' + ligands)
residList = list(sorted(set(hoho.getResnums())))
residList = np.array(residList, dtype='str')
residlist = " ".join(residList)
# print residlist
# for i in range(1, len(residList)):
# residlist += ' ' + str(residList[i])
with open('cutoff-resid-5angstroms', 'w') as residfile:
residfile.write(residlist)
return residList, str(runfolder + '/cutoff-resid-5angstroms')
def rmsd(reorder_outpath, dock_outpath, init='rmsd_init'):
init = eval(init)
reorder_path = os.path.join(init.data_dir, reorder_outpath)
dock_path = os.path.join(init.data_dir, dock_outpath)
docked_coords = prody.parsePDB(dock_path).getCoordsets()
crystal_coords = prody.parsePDB(reorder_path).getCoords()
rmsd = np.sqrt(
np.mean(np.sum(np.square(docked_coords - crystal_coord), axis=1),
axis=-1))
return [list(rmsd)]
def overlap(reorder_path, dock_path):
docked_coords = prody.parsePDB(dock_path).getCoordsets()
crystal_coords = prody.parsePDB(reorder_path).getCoords()
expanded_docked = np.expand_dims(docked_coords, -2)
diff = expanded_docked - crystal_coords
distance = np.sqrt(np.sum(np.power(diff, 2), axis=-1))
all_clash = (distance < clash_cutoff_A).astype(float)
atom_clash = (np.sum(all_clash, axis=-1) > 0).astype(float)
position_clash_ratio = np.mean(atom_clash, axis=-1)
return [list(position_clash_ratio)]
def native_contact(rec_path, reorder_path, dock_path):
"""
calculate native contact ratio for the docking result
args:
rec_path:: str
path of splited receptor
reorder_path:: str
path of reorder ligand
dock_path:: str
path of docking result
returns:
native_contact:: float
native contact value
"""
parsed_docked = prody.parsePDB(dock_path).select('not hydrogen')
parsed_crystal = prody.parsePDB(reorder_path).select('not hydrogen')
parsed_rec = prody.parsePDB(rec_path).select('not hydrogen')
cry_atom_num = parsed_crystal.numAtoms()
lig_atom_num = parsed_docked.numAtoms()
assert cry_atom_num == lig_atom_num
docked_coords = parsed_docked.getCoordsets()
crystal_coord = parsed_crystal.getCoords()
rec_coord = parsed_rec.getCoords()
exp_crystal_coord = np.expand_dims(crystal_coord, -2)
cry_diff = exp_crystal_coord - rec_coord
cry_distance = np.sqrt(np.sum(np.square(cry_diff), axis=-1))
exp_docked_coords = np.expand_dims(docked_coords, -2)
docked_diff = exp_docked_coords - rec_coord
docked_distance = np.sqrt(np.sum(np.square(docked_diff),axis=-1))
cry_contact = (cry_distance < distance_threshold).astype(int)
num_contact = np.sum(cry_contact).astype(float)
lig_contact = (docked_distance < distance_threshold).astype(int)
contact_ratio = np.sum(cry_contact * lig_contact, axis=(-1,-2)) / num_contact
return [list(contact_ratio)]
def clean_pair(bound_pdb, bound_chains, peptide_chains, unbound_pdb, unbound_chains):
# 2
bound_receptor = parsePDB(bound_pdb, chain=bound_chains+peptide_chains)
writePDB('b.pdb',bound_receptor.select('protein and chain %s' % ' '.join(list(bound_chains))))
writePDB('p.pdb',bound_receptor.select('protein and chain %s' % peptide_chains))
#3
unbound_receptor = parsePDB(unbound_pdb, chain=unbound_chains)
alignment_results = compare.matchAlign(unbound_receptor, bound_receptor)
unbound_receptor = alignment_results[0]
writePDB('unb.pdb',unbound_receptor.select('protein'))
writePDB('b.pdb',bound_receptor.select('protein and chain %s' % ' '.join(list(bound_chains))))
writePDB('p.pdb',bound_receptor.select('protein and chain %s' % peptide_chains))
writePDB('up.pdb',unbound_receptor.select('protein') | bound_receptor.select('protein and chain %s' % peptide_chains))
return 0
def align_by_resid(input_pdb_path, target_pdb_path):
input_mol = parsePDB(input_pdb_path)
target_mol = parsePDB(target_pdb_path)
target_resid, input_resid = target_mol.select(
'calpha').getResnums(), input_mol.select('calpha').getResnums()
target_index = np.where(np.in1d(input_resid, target_resid))[0]
native_index = np.where(np.in1d(target_resid, input_resid))[0]
if len(input_mol.select('name CA').getSequence()) < 25 or len(
target_mol.select('name CA').getSequence()) < 25 or len(
target_index) < 25:
return None, None, None
input_mol = input_mol.select(
'resindex ' + reduce(lambda a, b: str(a) + ' ' + str(b), target_index))
return input_mol, target_index, native_index
def compare_pdb_files(file1, file2):
"""Returns the RMSD between two PDB files of the same protein.
Args:
file1 (str): Path to first PDB file.
file2 (str): Path to second PDB file. Must be the same protein as in file1.
Returns:
float: Root Mean Squared Deviation (RMSD) between the two structures.
"""
s1 = pr.parsePDB(file1)
s2 = pr.parsePDB(file2)
transformation = pr.calcTransformation(s1, s2)
s1_aligned = transformation.apply(s1)
return pr.calcRMSD(s1_aligned, s2)
def prody_align(opt):
"""Align models in a PDB file or a PDB file onto others."""
import prody
LOGGER = prody.LOGGER
args = opt.pdb
if len(args) == 1:
pdb = args[0]
LOGGER.info('Aligning multiple models in: ' + pdb)
selstr, prefix, model = opt.select, opt.prefix, opt.model
pdb = prody.parsePDB(pdb)
pdbselect = pdb.select(selstr)
if pdbselect is None:
opt.subparser.error('Selection {0:s} do not match any atoms.'
.format(repr(selstr)))
LOGGER.info('{0:d} atoms will be used for alignment.'
.format(len(pdbselect)))
pdbselect.setACSIndex(model-1)
prody.printRMSD(pdbselect, msg='Before alignment ')
prody.alignCoordsets(pdbselect)
prody.printRMSD(pdbselect, msg='After alignment ')
if prefix == '':
prefix = pdb.getTitle() + '_aligned'
outfn = prefix + '.pdb'
LOGGER.info('Writing file: ' + outfn)
prody.writePDB(outfn, pdb)
else:
reffn = args.pop(0)
seqid=opt.seqid
overlap=opt.overlap
LOGGER.info('Aligning structures onto: ' + reffn)
ref = prody.parsePDB(reffn)
for arg in args:
if arg == reffn:
continue
if '_aligned.pdb' in arg:
continue
pdb = prody.parsePDB(arg)
result = prody.matchAlign(pdb, ref, seqid=seqid, overlap=overlap,
tarsel=opt.select, allcsets=True,
cslabel='Model', csincr=1)
if result:
outfn = pdb.getTitle() + '_aligned.pdb'
LOGGER.info('Writing file: ' + outfn)
prody.writePDB(outfn, pdb)
else:
LOGGER.warning('Failed to align ' + arg)
def calc_pocket_rmsd(rec, lig, root):
"""
Calculate difference between the ligand reference receptor and
the receptor it is being docked into.
From original script by David Koes
"""
ligrec = lig.replace("LIG_aligned.sdf", "PRO.pdb")
rec = prody.parsePDB(os.path.join(root, rec))
ligrec = prody.parsePDB(os.path.join(root, ligrec))
lig = next(pybel.readfile("sdf", os.path.join(root, lig)))
c = np.array([a.coords for a in lig.atoms])
nearby = rec.select("protein and same residue as within 3.5 of point",
point=c)
matches = []
for cutoff in range(90, 0, -10):
# can't just set a low cutoff since we'll end up with bad alignments
# try a whole bunch of alignments to maximize the likelihood we get the right one
m = prody.matchChains(rec,
ligrec,
subset="all",
overlap=cutoff,
seqid=cutoff,
pwalign=True)
if m:
matches += m
minrmsd = np.inf
minbackrmsd = np.inf
for rmap, lrmap, _, _ in matches:
try:
closeatoms = set(nearby.getIndices())
lratoms = []
ratoms = []
for i, idx in enumerate(rmap.getIndices()):
if idx in closeatoms:
lratoms.append(lrmap.getIndices()[i])
ratoms.append(idx)
if len(lratoms) == 0:
continue
rmsd = prody.calcRMSD(rec[ratoms], ligrec[lratoms])
backrmsd = prody.calcRMSD(rec[ratoms] & rec.ca,
ligrec[lratoms] & ligrec.ca)
if rmsd < minrmsd:
minrmsd = rmsd
minbackrmsd = backrmsd
except:
pass
return minrmsd, minbackrmsd
def reorder(bucket, table_idx, param,
input_data): # todo(maksym) smina_reorder
try:
receptor, chain, resnum, resname = input_data
output_folder = param['output_folder']
output_folder = '{}_{}'.format(table_idx, output_folder)
input_lig_folder = param['input_ligand_folder']
input_rec_folder = param['input_receptor_folder']
smina_pm = smina_param()
smina_pm.param_load(param['smina_param'])
out_dir = os.path.join(data_dir, output_folder, receptor)
_makedir(out_dir)
out_name = '_'.join(input_data + ['ligand']) + '.pdb'
out_path = os.path.join(out_dir, out_name)
input_lig_dir = os.path.join(data_dir, input_lig_folder,
receptor) # lig_dir = input_lig_dir
lig_name = '_'.join(input_data + ['ligand']) + '.pdb'
input_lig_path = os.path.join(input_lig_dir, lig_name)
input_rec_dir = os.path.join(data_dir, input_rec_folder,
receptor) # rec_dir = input_rec_dir
rec_name = '_'.join(input_data + ['receptor']) + '.pdb'
input_rec_path = os.path.join(input_rec_dir, rec_name)
kw = {
'receptor': input_rec_path,
'ligand': input_lig_path,
'autobox_ligand': input_lig_path,
'out': out_path
}
cmd = smina_pm.make_command(**kw) # todo(maksym) smina_cmd
cl = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE)
cl.wait()
prody.parsePDB(out_path)
record = input_data + [1, 'success']
records = [record]
db.insert(table_idx, records, bucket=bucket)
except Exception as e:
record = input_data + [0, str(e)]
records = [record]
db.insert(table_idx, records, bucket=bucket)
def preprocess_single(model, chain_name):
hv = model.getHierView()
for chain in hv.iterChains():
chain.setChids(chain_name)
prody.writePDB("tmp.pdb", model)
model = prody.parsePDB("tmp.pdb")
check_call(["rm", "tmp.pdb"])
pos = 1
apos = 1
hv = model.getHierView()
for chain in hv.iterChains():
# print "chain : " , chain
for res in chain.iterResidues():
res.setResnums(pos)
for atom in res:
atom.setSerial(apos)
apos += 1
if len(res.getIcode()) == 0:
pos += 1
hv.update()
return model
def __init__(self, pdbid, cfg, ):
self.pdbid = pdbid
self.config = cfg
self.svm = config.trainClassifier(self.config)
fn_pattern = BOUND_FILENAME_PATTERN if self.config.testing.is_bound \
else UNBOUND_FILENAME_PATTERN
self.pdb_filename = fn_pattern.format(pdb=self.pdbid)
self.receptor = prody.parsePDB(self.pdb_filename).protein.noh
self.ddgs = self.config.testing.label_data_df.ix[self.pdbid]
self.confidence = pd.Series(
data=config.predictClassifier(self.config),
index=self.config.testing.label_data_df.index,
).ix[self.pdbid]
self.mask_binding = self.ddgs > self.config.testing.ddg_cutoff
self.mask_positive = self.confidence > 0
self.surface_resnums = self.ddgs.index
self.binding_resnums = \
self.surface_resnums[self.mask_binding]
self.positive_resnums = \
self.surface_resnums[self.mask_positive]
def receptor_residues(resnums):
return self.receptor.select(
'resnum %s' % ' '.join(map(str, resnums)))
self.surface_residues = receptor_residues(self.surface_resnums)
self.positive_residues = receptor_residues(self.positive_resnums)
def phi2pdb(base_pdb, phi, save_path="./"):
pdb = prody.parsePDB(base_pdb)
atoms = [a for a in pdb]
for a in pdb:
a.setBeta(0.0)
phif = open(phi, "r")
phic = phif.readlines()
phif.close()
for l in phic:
x,y,z,k = l.strip().split(",")
x = np.float(x)
y = np.float(y)
z = np.float(z)
k = np.float(k)
for a in xrange(len(atoms)):
X,Y,Z = atoms[a].getCoords()
if X==x and Y==y and Z==z:
atoms[a].setBeta(k)
atoms.pop(a)
break
out_pdb = os.path.join(save_path, phi.split(os.sep)[-1][:-4]+".pdb")
prody.writePDB(out_pdb, pdb)
return out_pdb
def getPairInformation(pdbid,
reference_chain,
pair_chain,
cutoff=5,
covalent_bond_cutoff=5):
"""
1. reads pdb id from file
2. selects atoms from pair of chains within cutoff
# draws selection within interface (simplest possible view)
reference == oncogene,
pair == peptide
"""
atoms = prody.parsePDB(pdbid) # TODO: turn off debug
reference_atoms = atoms.select("chain %s and not water" % reference_chain)
pair_atoms = atoms.select("chain %s and not water" % pair_chain)
# next try to select everything
ref_contacts = prody.measure.contacts.Contacts(reference_atoms)
ref_selection = ref_contacts.select(cutoff,
pair_atoms) # we need these atoms
pair_contacts = prody.measure.contacts.Contacts(pair_atoms)
pair_selection = pair_contacts.select(cutoff, reference_atoms) # and these
sulfur_pairs = []
## 1. select Cys atoms on oncogene
for (r, ch2, distance) in prody.measure.contacts.findNeighbors(
reference_atoms, covalent_bond_cutoff, pair_atoms):
if r.getResname() in 'CYS': # and r.getElement() in ['S'] :
sulfur_pairs.append((r.getSerial(), ch2.getSerial()))
# filtering: if there is no Cys, return nothing
if len(sulfur_pairs) < 1:
return None
return (pdbid, reference_chain, set(ref_selection.getResnums()),
pair_chain, set(pair_selection.getResnums()), sulfur_pairs)
def __init__(self, pdbid, context='bound'):
self.pdbid = pdbid.upper()
self.context = context
bound = True if context=='bound' else False
self.DATA_PATH = j(PEPTIDB_DATA_PATH, self.context)
self.PDB_DATA_PATH = j(self.DATA_PATH, self.context+'Set', 'mainChain' if bound else '')
self.FTMAP_DATA_PATH = j(self.DATA_PATH, 'FTMapAnalysis', 'ftmapData')
self.CONSURF_DATA_PATH = j(self.DATA_PATH, 'ConSurfAnalysis', 'data')
self.receptor_filename = j(self.PDB_DATA_PATH, '%s.pdb' % self.pdbid)
self.receptor_atoms = prody.parsePDB(self.receptor_filename).protein
self.receptor_chain = self.receptor_atoms.getHierView().iterChains().next()
#print "###%s###" % self.receptor_filename
self.resnum_index = pd.MultiIndex.from_tuples(
zip(
[pdbid]*self.receptor_chain.numResidues(),
self.receptor_chain.ca.getResnums()
),
names = [
'PDB identifier',
'Residue number',
]
)
self.df = pd.DataFrame(index=self.resnum_index)
print self.pdbid
def prody_biomol(pdbname,**kwargs):
"""Generate biomolecule coordinates.
:arg pdb: :term:`PDB` identifier or filename
:arg prefix: prefix for output files, default is :file:`_biomol`
:arg biomol: index of the biomolecule, by default all are generated"""
import prody
LOGGER = prody.LOGGER
prefix, biomol = kwargs.get('prefix',None), kwargs.get('biomol')
pdb, header = prody.parsePDB(pdbname, header=True)
if not prefix:
prefix = pdb.getTitle()
biomols = prody.buildBiomolecules(header, pdb, biomol=biomol)
if not isinstance(biomols, list):
biomols = [biomols]
for i, biomol in enumerate(biomols):
if isinstance(biomol, prody.Atomic):
outfn = '{0:s}_biomol_{1:d}.pdb'.format(prefix, i+1)
LOGGER.info('Writing {0:s}'.format(outfn))
prody.writePDB(outfn, biomol)
elif isinstance(biomol, tuple):
for j, part in enumerate(biomol):
outfn = ('{0:s}_biomol_{1:d}_part_{2:d}.pdb'
.format(prefix, i+1, j+1))
LOGGER.info('Writing {0:s}'.format(outfn))
prody.writePDB(outfn, part)
def main():
import sys
import getopt
import csv
import prody as pr
#usage = \
"""
Copyright (c) 2007 Bosco Ho
Calculates the total Accessible Surface Area (ASA) of atoms in a
PDB file.
Usage: asa.py -s n_sphere in_pdb [out_pdb]
- out_pdb PDB file in which the atomic ASA values are written
to the b-factor column.
-s n_sphere number of points used in generating the spherical
dot-density for the calculation (default=960). The
more points, the more accurate (but slower) the
calculation.
"""
#opts, args = getopt.getopt(sys.argv[1:], "n:")
#if len(args) < 1:
# print usage
# return
#mol = molecule.Molecule(args[0])
#pdb = molecule.Molecule('dimers/1R0R.pdb')
pdb = pr.parsePDB('dimers/1R0R.pdb')
#atoms = mol.atoms()
#molecule.add_radii(atoms)
data = []
#for o, a in opts:
# if '-n' in o:
# n_sphere = int(a)
# print "Points on sphere: ", n_sphere
#
#n_sphere = [500]
n_sphere = range(10,2000,10)
for n in n_sphere:
asas = calculate_asa_np(pdb, 1.4, n)
data.append(asas)
#print "%i, %.1f angstrom squared." % n, sum(asas)
print(str(n) + ", " + str(sum(asas)) + " angstrom squared.")
f_test = open('perturbation_analysis.csv','w')
c = csv.writer(f_test)
for i in xrange(len(data)):
c.writerow(data[i])
def computeEachAtomAllTrajectoriesMean(trajectories):
"""
Computes the mean of each atom's position in all the trajectories
"""
ri = []
riMeasures = []
for i, traj in enumerate(trajectories):
#trajectory = prody.parsePDB(traj)
trajectory = prody.parsePDB(traj, subset='calpha')
coordinates = trajectory.getCoordsets()
ensembleTrajectory = prody.PDBEnsemble("Complex")
ensembleTrajectory.setAtoms(trajectory)
ensembleTrajectory.addCoordset(coordinates[INITIAL_FRAME:])
ensembleTrajectory.setCoords(coordinates[0]) #reference
ensembleTrajectory.superpose()
#ensembleTrajectory = trajectory
sri, sriMeasures = addTrajectoryCoordinates(ensembleTrajectory.getCoordsets(), trajectory.numAtoms())
#sri, sriMeasures = addTrajectoryCoordinates(trajectory.getCoordsets(), trajectory.numAtoms())
ri.append(sri)
riMeasures.append(sriMeasures)
return average(ri, riMeasures)
def computeEachAtomsUnnormalisedAutocorrelation(trajectories, avgR):
"""
Computes the autocorrelation with the formula:
C(k) = 1/[(n-k)] \sum_{t=1}^{n-k} (Xt - mu)(Xt+k - mu)
To normalise it wihitn [-1:1]
c(k) = C(k) / var
When the true mean \mu and variance \sigma^2 are known, this estimate is unbiased.
"""
rirj = []
rirjMeasures = []
for i, traj in enumerate(trajectories):
#trajectory = prody.parsePDB(traj)
trajectory = prody.parsePDB(traj, subset='calpha')
coordinates = trajectory.getCoordsets()
#superpose
ensembleTrajectory = prody.PDBEnsemble("Complex")
ensembleTrajectory.setAtoms(trajectory)
ensembleTrajectory.addCoordset(coordinates[INITIAL_FRAME:])
ensembleTrajectory.setCoords(coordinates[0]) #reference
ensembleTrajectory.superpose()
#ensembleTrajectory = trajectory
srirj, sMeasures = computeEachAtomsUnnormalisedAutocorrelationForASingleTrajectory(ensembleTrajectory.getCoordsets(), avgR)
rirj.append(srirj)
rirjMeasures.append(sMeasures)
rirj = sumOverTrajectories(rirj)
rirjMeasures = sumOverTrajectories(rirjMeasures)
return rirj/rirjMeasures
def find_close(native_name, traj_name, skip_frames):
native = prody.parsePDB(native_name)
traj = prody.parsePDB(traj_name)
ensemble = prody.Ensemble('ensemble')
ensemble.setCoords(native.getCoords())
ensemble.addCoordset(traj.getCoordsets()[skip_frames:, ...]) # skip the first 10 frames
ensemble.superpose()
native_coords = native.getCoords()
ensemble_coords = ensemble.getCoordsets()
diff2 = (ensemble_coords - native_coords) ** 2
diff2 = numpy.sum(diff2, axis=2)
min_dev = numpy.min(diff2, axis=0)
return numpy.sqrt(numpy.sum(min_dev) / float(min_dev.shape[0]))
def generate_neighborhood_atom_list(input_pdbs, neighbors, acceptable_atoms_wt_set, acceptable_atoms_mut_set, input_type):
coordinates = []
for input_pdb in input_pdbs:
if 'WT.' not in input_pdb:
atom_list = []
neighborhood = prody.parsePDB(input_pdb)
neighborhood_hv = neighborhood.getHierView()
res_list = [neighborhood_hv[neighbor[1], neighbor[0][1]] for neighbor in neighbors]
for res in res_list:
# Check if numbering should be for WT or Mutant
# Check that residues are present in acceptable_residues
# Check that atom coordinates are present in acceptable_atoms
for atom in res:
if input_type == 'Mutant PDB':
# print (mut_to_wt_chains[res.getChid()], res.getResname(),
# int(residue_maps_reverse[(res.getChid(), mut_to_wt_chains[res.getChid()])][
# '%s %s ' % (res.getChid(), (' ' + str(res.getResnum()))[-3:])].split()[1]),
# atom.getName())
if (res.getChid(), res.getResname(),int(res.getResnum()), atom.getName()) in acceptable_atoms_mut_set:
if atom.getElement() != 'H':
atom_list.append(str(atom.getIndex()))
else:
if (res.getChid(), res.getResname(), res.getResnum(), atom.getName()) in acceptable_atoms_wt_set:
if atom.getElement() != 'H':
atom_list.append(str(atom.getIndex()))
coordinates.append(neighborhood.select('index ' + ' '.join(atom_list)))
return coordinates
def generate_point_atom_list(input_pdbs, mutations, acceptable_atoms_wt_set, acceptable_atoms_mut_set, mut_key_dict, input_type):
mutation_dict = {}
for counter, mutation in enumerate(mutations):
temp = []
for input_pdb in input_pdbs:
if 'WT.' not in input_pdb:
atom_list = []
point_mutant = prody.parsePDB(input_pdb)
point_mutant_hv = point_mutant.getHierView()
res_list = [point_mutant_hv[mutation[1], int(mutation[0])]]
for res in res_list:
# Check if numbering should be for WT or Mutant
# Check that residues are present in acceptable_residues
# Check that atom coordinates are present in acceptable_atoms
for atom in res:
if input_type == 'Mutant PDB':
if (res.getChid(), res.getResname(),int(res.getResnum()), atom.getName()) in acceptable_atoms_mut_set:
if atom.getElement() != 'H':
atom_list.append(str(atom.getIndex()))
if input_type == 'RosettaOut':
if (res.getChid(), res.getResname(), res.getResnum(), atom.getName()) in acceptable_atoms_wt_set:
if atom.getElement() != 'H':
atom_list.append(str(atom.getIndex()))
temp.append(point_mutant.select('index ' + ' '.join(atom_list)))
if input_type == 'Mutant PDB':
mutation_dict[mut_key_dict[mutation[1] + str(mutation[0])]] = temp
if input_type == 'RosettaOut':
mutation_dict[mutation[1] + str(mutation[0])] = temp
return mutation_dict
def runThrough(pfile):
# initial setup
print "Running through " + pfile + "..."
numMdls = getNumMdls(pfile)
#print numMdls
appf = pr.parsePDB(pfile, model=numMdls, secondary=True, chain='A', altLoc=False)
los = sheets.initializeList(pfile)
parseHelices(appf)
if(los != None):
parseSheets(appf,los)
# get them means
COLUMN_D_ON_AV = [sum(x)/len(x) if len(x) > 0 else 0 for x in COLUMN_D_ON]
COLUMN_D_OH_AV = [sum(x)/len(x) if len(x) > 0 else 0 for x in COLUMN_D_OH]
COLUMN_A_NHO_AV = [sum(x)/len(x) if len(x) > 0 else 0 for x in COLUMN_A_NHO]
COLUMN_A_HOC_AV = [sum(x)/len(x) if len(x) > 0 else 0 for x in COLUMN_A_HOC]
COLUMN_BETA_AV = [sum(x)/len(x) if len(x) > 0 else 0 for x in COLUMN_BETA]
COLUMN_GAMMA_AV = [sum(x)/len(x) if len(x) > 0 else 0 for x in COLUMN_GAMMA]
# get the std devs, nasty shit
COLUMN_D_ON_STD = [np.std(x) if len(x) > 0 else 0 for x in COLUMN_D_ON]
COLUMN_D_OH_STD = [np.std(x) if len(x) > 0 else 0 for x in COLUMN_D_OH]
COLUMN_A_NHO_STD = [np.std(x) if len(x) > 0 else 0 for x in COLUMN_A_NHO]
COLUMN_A_HOC_STD = [np.std(x) if len(x) > 0 else 0 for x in COLUMN_A_HOC]
COLUMN_BETA_STD = [np.std(x) if len(x) > 0 else 0 for x in COLUMN_BETA]
COLUMN_GAMMA_STD = [np.std(x) if len(x) > 0 else 0 for x in COLUMN_GAMMA]
TABLE = [COLUMN_D_ON_AV, COLUMN_D_OH_AV, COLUMN_A_NHO_AV, COLUMN_A_HOC_AV, COLUMN_BETA_AV, COLUMN_GAMMA_AV]
STDS = [COLUMN_D_ON_STD, COLUMN_D_OH_STD, COLUMN_A_NHO_STD, COLUMN_A_HOC_STD, COLUMN_BETA_STD, COLUMN_GAMMA_STD]
#print ' D_ON D_OH ANGLE(NHO) ANGLE(HOC) BETA GAMMA '
#print np.array(TABLE).T
#print np.array(STDS).T
return (TABLE,STDS)
def test_sasa_3():
ag = prody.parsePDB(TEST_DATA / '1atom.pdb')
sasa = calc_sasa(ag,
normalize=False,
change_radii={'N': 0.3},
probe_radius=0.001)
assert abs(sasa[0] - 4 * 3.14 * 0.3**2) < 0.1
def get_voxel(input_path, buffer, width):
input_mol = parsePDB(input_path)
input_mol = input_mol.select(
'element C or element N or element O or element S')
occus = make_voxel(input_mol=input_mol, buffer=buffer, width=width)
return occus, input_mol.select('name CA').getResnames(), input_mol.select(
'name CA').getResnums()
def align_fasta(input_pdb_path, target_fasta_path):
pdb = parsePDB(input_pdb_path)
input_fasta_path = tempfile.mktemp(suffix='.fasta')
f = open(input_fasta_path, 'w')
f.write('>temp\n')
if len(pdb.select('name CA').getSequence()) < 25:
return None, None, None
else:
f.write(reduce(lambda a, b: a + b,
pdb.select('name CA').getSequence()))
f.close()
needle_path = tempfile.mktemp(suffix='.needle')
cmd = [
'needle', '-outfile', needle_path, '-asequence', input_fasta_path,
'-bsequence', target_fasta_path, '-gapopen', '10', '-gapextend',
'0.5'
]
subprocess.call(cmd)
needle_result = list(AlignIO.parse(needle_path, 'emboss'))[0]
input_seq, target_seq = np.array(list(str(
needle_result[0].seq))), np.array(list(str(needle_result[1].seq)))
input_seq, target_seq = input_seq[np.where(
target_seq != '-')], target_seq[np.where(input_seq != '-')]
input_align_indices = np.where(target_seq != '-')[0]
target_align_indices = np.where(input_seq != '-')[0]
align_pdb = pdb.select(
'resindex ' +
reduce(lambda a, b: str(a) + ' ' + str(b), input_align_indices))
input_mol = input_mol.select(
'element C or element N or element O or element S')
return align_pdb, input_align_indices, target_align_indices
def inline_fitness(pdb_id, verbose):
structure = parsePDB(pdb_id)
for chain in structure.iterChains():
if not is_rna(chain): continue
for residue in chain.iterResidues():
try:
fitness = calc_inline_fitness(residue, verbose)
except AttributeError:
# end of the chain
continue
# pos = the position *downstream* of the examined
# internucleotide.
chain_id = chain.getChid()
res_num = residue.getResnum()
res_id = residue.getResname()
fields = (chain_id, res_id, res_num, fitness)
print '\t'.join(map(str, fields))
def prody_biomol(opt):
"""Generate biomolecule coordinates based on command line arguments."""
import prody
LOGGER = prody.LOGGER
prefix, biomol = opt.prefix, opt.biomol
pdb, header = prody.parsePDB(opt.pdb, header=True)
if not prefix:
prefix = pdb.getTitle()
biomols = prody.buildBiomolecules(header, pdb, biomol=biomol)
if not isinstance(biomols, list):
biomols = [biomols]
for i, biomol in enumerate(biomols):
if isinstance(biomol, prody.Atomic):
outfn = '{0:s}_biomol_{1:d}.pdb'.format(prefix, i+1)
LOGGER.info('Writing {0:s}'.format(outfn))
prody.writePDB(outfn, biomol)
elif isinstance(biomol, tuple):
for j, part in enumerate(biomol):
outfn = ('{0:s}_biomol_{1:d}_part_{2:d}.pdb'
.format(prefix, i+1, j+1))
LOGGER.info('Writing {0:s}'.format(outfn))
prody.writePDB(outfn, part)
def _generate_sidechains_scwrl(self):
if not self.rec is None:
rec = self.rec.copy()
rec.setChids('A')
lig = self._tpl.copy()
rec = BasePDB(ag=rec)
lig = BasePDB(ag=lig)
merged = rec.add_mol(lig, keep_resi=False, keep_chains=True)
merged.save(self._mrg_file)
self._make_scwrl_sequence_file()
call = [
define.SCWRL_EXE, '-h', '-i', self._mrg_file, '-o',
self._scw_file, '-s', self._seq_file
]
else:
prody.writePDB(self._mrg_file, self._tpl)
call = [
define.SCWRL_EXE, '-h', '-i', self._mrg_file, '-o',
self._scw_file
]
# scwrl wants rosetta hydrogen naming
BasePDB(self._mrg_file).to_rosetta().save(self._mrg_file)
helpers.shell_call(call)
pep = prody.parsePDB(self._scw_file)
# extract peptide and renumber
pep = BasePDB(ag=pep.select('chain B').copy()).renumber(
keep_resi=False).ag
self.pep = pep
def fix_openmm():
# get the whole crystal structure
# get only the ATOM records
# and HETAM records for MSE
# convert MSE to MET
with open('no_smet.pdb', 'w') as outfile:
with open('experimental.pdb') as infile:
for line in infile:
if line.startswith('ATOM'):
outfile.write(line)
if line.startswith('HETATM'):
if line[17:20] == 'MSE':
atom_name = line[12:17]
if atom_name == 'SE ':
atom_name = ' SD '
line_fixed = 'ATOM ' + line[6:12] + atom_name + 'MET' + line[20:67] + '\n'
outfile.write(line_fixed)
# load the file into prody
p = prody.parsePDB('no_smet.pdb')
p = p.select('not hydrogen')
# get one of the rosetta models
r = prody.parsePDB('rosetta.pdb')
# perform an alignment to find out what part of the crystal structure
# corresponds to the rosetta file
match = prody.matchChains(r, p, subset='all', overlap=25, pwalign=True)[0][1]
print len(match)
prody.writePDB('chain.pdb', match)
# now clean it up with pdb fixer
subprocess.check_call('python ~/Source/PdbFixer/pdbfixer.py chain.pdb', shell=True)
# now load it with zam
p = protein.Protein('output.pdb')
p.Dehydrogen()
disulfide_pairs = find_disulfide(p)
for r1, r2 in disulfide_pairs:
print ' added disulfide between {} and {}'.format(r1, r2)
p.Res[r1].FullName = 'CYX'
p.Res[r2].FullName = 'CYX'
p.WritePdb('start.pdb')
# now run tleap
print ' running tleap'
run_tleap(disulfide_pairs)
def prody_catdcd(opt):
"""Concatenate DCD files."""
import prody
LOGGER = prody.LOGGER
if opt.num:
num = []
for dcd in opt.dcd:
dcd = prody.DCDFile(dcd)
num.append(dcd.numFrames())
for n in num:
print(n)
print(sum(num))
return
align = opt.align
ag = opt.psf or opt.pdb
if ag:
if os.path.splitext(ag)[1].lower() == '.psf':
ag = prody.parsePSF(ag)
else:
ag = prody.parsePDB(ag)
elif align:
raise ValueError('one of PSF or PDB files must be provided for '
'align option to work')
dcd = opt.dcd
traj = prody.Trajectory(dcd.pop(0))
while dcd:
traj.addFile(dcd.pop(0))
if ag:
traj.setAtoms(ag)
select = traj.select(opt.select)
LOGGER.info('{0:d} atoms are selected for writing output.'
.format(len(select)))
if align:
_ = traj.select(align)
LOGGER.info('{0:d} atoms are selected for aligning frames.'
.format(len(_)))
out = prody.DCDFile(opt.output, 'w')
count = 0
goto = False
if opt.stride != 1:
goto = True
slc = slice(opt.first, opt.last, opt.stride).indices(len(traj)+1)
for i in range(*slc):
if goto:
traj.goto(i)
frame = traj.next()
if align:
frame.superpose()
out.write(select._getCoords(), frame.getUnitcell())
else:
out.write(frame._getCoords(), frame.getUnitcell())
count += 1
traj.close()
out.close()
LOGGER.info("{0:d} frames are written into '{1:s}'."
.format(count, opt.output))
def combine_structures(directory_with_pdbs, output_filename):
search_string = join(directory_with_pdbs, '*.pdb')
path_list = glob(search_string)
if len(path_list) > MAX_FRAMES:
raise RuntimeError('Got %d frames, but only up to %d frames are allowed.')
else:
pass
atom_group = parsePDB(path_list[0])
for i, path in enumerate(path_list):
if i == 0:
continue
else:
p = parsePDB(path)
atom_group.addCoordset(p)
writePDB(output_filename, atom_group)
def load(self,filename):
self.filname = filename
self.model = prody.parsePDB(self.filname, model=1)
print ("self.center",self.center)
if self.center :
# c = calcCenter(self.model)
moveAtoms(self.model, to=numpy.zeros(3))
self.ca_model = self.model.select('protein and name CA')#what about DNA
def prody_align(opt):
"""Align models in a PDB file or a PDB file onto others."""
import prody
LOGGER = prody.LOGGER
args = opt.pdb
if len(args) == 1:
pdb = args[0]
LOGGER.info('Aligning multiple models in: ' + pdb)
selstr, prefix, model = opt.select, opt.prefix, opt.model
pdb = prody.parsePDB(pdb)
pdbselect = pdb.select(selstr)
if pdbselect is None:
LOGGER.warning('Selection "{0:s}" do not match any atoms.'
.format(selstr))
sys.exit(-1)
LOGGER.info('{0:d} atoms will be used for alignment.'
.format(len(pdbselect)))
pdb.setACSIndex(model-1)
prody.alignCoordsets(pdb, selstr=selstr)
rmsd = prody.calcRMSD(pdb)
LOGGER.info('Max RMSD: {0:0.2f} Mean RMSD: {1:0.2f}'
.format(rmsd.max(), rmsd.mean()))
if prefix == '':
prefix = pdb.getTitle() + '_aligned'
outfn = prefix + '.pdb'
LOGGER.info('Writing file: ' + outfn)
prody.writePDB(outfn, pdb)
else:
reffn = args.pop(0)
LOGGER.info('Aligning structures onto: ' + reffn)
ref = prody.parsePDB(reffn)
for arg in args:
if arg == reffn:
continue
if '_aligned.pdb' in arg:
continue
pdb = prody.parsePDB(arg)
if prody.matchAlign(pdb, ref):
outfn = pdb.getTitle() + '_aligned.pdb'
LOGGER.info('Writing file: ' + outfn)
prody.writePDB(outfn, pdb)
else:
LOGGER.warning('Failed to align ' + arg)
def test_calc_matrix(self):
pdb_structure = prody.parsePDB("data/3_models.pdb")
expected = [ 35.01002624, 47.60315215, 88.64981522, 32.90471145, 87.13023459, 85.76106107]
product_matrix = DihedralRMSDMatrixCalculator.build(pdb_structure)
# print "out", product_matrix.get_data()
# print "out", product_matrix.get_data()
# print product_matrix.get_data()[0]
# print product_matrix[0,1]
numpy.testing.assert_almost_equal(expected, product_matrix.get_data(),8)
def conservation(self,):
consurf_pdb_filename = os.path.join(self.CONSURF_DATA_PATH, self.pdbid, 'pdbFILE_view_ConSurf.pdb')
p = prody.parsePDB(consurf_pdb_filename)
consurf_chain = p.getHierView().iterChains().next()
return pd.DataFrame(
columns=['Conservation-score'],
index=self.resnum_index,
data=consurf_chain.ca.getBetas()[:len(self.resnum_index)],
)
def preprocess_pdb(args):
pdb_file = args[1]
output = "./" + args[2]+"/"+args[2]
create_directory("./" + args[2])
cluster_frames = get_frame_numbers(args)
pdb = prody.parsePDB(pdb_file)
# Get a copy of the pdb coords
input_coordsets = numpy.array(pdb.getCoordsets()[cluster_frames])
# Empty pdb
pdb.delCoordset(range(pdb.numCoordsets()))
# Build another pdb to store it
input_pdb = prody.parsePDB(pdb_file)
input_pdb.delCoordset(range(input_pdb.numCoordsets()))
# And add the chosen coordsets
for i in range(len(cluster_frames)):
input_pdb.addCoordset(input_coordsets[i])
prody.writePDB(output+"_ini.pdb", input_pdb)
print_matrix(input_pdb.select("name CA").getCoordsets(), output)
return pdb, input_coordsets, cluster_frames, output
def _from_file(path):
"""
Load a ProDy AtomGroup from a pdb file.
Parameters:
path - a string containing a filepath to a PDB file
Returns:
A ProDy AtomGroup
"""
return prody.parsePDB(path)
def read_naccess_asa(asa_filename):
'''
Reads per-atom ASA values from ``asa_filename``.
Returns a pandas.Series object containing the data.
'''
# asa file is a PDB file with ASA values as
# occupancy and VDW radii as B-factor
atoms = prody.parsePDB(asa_filename).protein
atoms_asa = atoms.getOccupancies()
atoms_asa_series = pd.Series(data=atoms_asa, name='per_atom_asa')
return atoms_asa_series
def setUp(self):
self.output = join(TEMPDIR, 'test_prody_catdcd.dcd')
self.dcdpath = pathDatafile('dcd')
self.pdbpath = pathDatafile('multi_model_truncated')
self.dcd = DCDFile(self.dcdpath)
self.ag = parsePDB(self.pdbpath, model=1)
self.command = 'catdcd -o ' + self.output
self.tearDown()
def _find_ligand(self):
self._get_file_path(ligand=True)
protein = parsePDB(self.file_path)
try:
seq = protein['A'].getSequence()
except:
pass
else:
ligand = protein.select('not protein and not water')
repr(ligand)
if ligand:
self.out_filename = self.file_path.split('.')[0] + '_ligand.pdb'
writePDB(self.out_filename, ligand)