def Create_Example(pdb_file, folder):
"""Parse pdb files into files containing individual pairwise interactions
Input:
-pdb_file = target pdb file
-folder = target folder housing the output
Output:
-folder containing the parsed files containing individual pairwise interactions
"""
pdb_parser = pdb.PDBParser(PERMISSIVE=s.options.strict, QUIET=True)
file_name = os.path.basename(pdb_file)
s.Check_folder(folder)
output_folder = os.path.join(folder, file_name[:-4])
s.Check_folder(output_folder)
pdb_structure = pdb_parser.get_structure(file_name, pdb_file)
s.argprint("File have been parsed.", s.options.verbose, s.options.quiet, 1)
chain_names = []
for model in pdb_structure:
empty_model = cp.deepcopy(pdb_structure[model.get_id()])
for chain in model:
id_name = chain.get_id()
chain_names.append(id_name)
empty_model.detach_child(id_name)
for number in range(len(chain_names)):
if number % 2 == 1 and number != 0:
interaction = chain_names[number - 1] + chain_names[number]
new_model = cp.deepcopy(empty_model)
new_model.add(model[chain_names[number - 1]])
new_model.add(model[chain_names[number]])
io = pdb.PDBIO()
io.set_structure(new_model)
io.save(
os.path.join(output_folder,
"%s_%s.pdb" % (file_name, interaction)))
s.argprint("%s_%s.pdb created" % (file_name, interaction),
s.options.verbose, s.options.quiet, 2)
try:
interaction = chain_names[number] + chain_names[number + 1]
new_model = cp.deepcopy(empty_model)
new_model.add(model[chain_names[number]])
new_model.add(model[chain_names[number + 1]])
io = pdb.PDBIO()
io.set_structure(new_model)
io.save(
os.path.join(output_folder,
"%s_%s.pdb" % (file_name, interaction)))
s.argprint("%s_%s.pdb created" % (file_name, interaction),
s.options.verbose, s.options.quiet, 2)
except:
s.argprint("Example complete!", s.options.verbose,
s.options.quiet, 0)
def output_multiple_chains(chains, filename, file_type="pdb"):
'''
Dump multiple chains to an output file. Remove the hydrogen atoms.
:param chains: An iterable of Bio.PDB.Chain to dump.
:param filename: The place to dump it.
'''
class HSelect(bpdb.Select):
def accept_atom(self, atom):
if atom.name.find('H') >= 0:
return False
else:
return True
m = bpdb.Model.Model(0)
s = bpdb.Structure.Structure('stru')
for chain in chains:
log.debug("Adding chain %s with %s residues", chain.id, len(chain))
m.add(chain)
if file_type=="pdb" and len(chain.id)!=1:
raise ValueError("Cannot save chain with name %s (not a single character) "
"in PDB format. Use cif format instead!")
s.add(m)
if file_type == "pdb":
io = bpdb.PDBIO()
else:
io = bpdb.MMCIFIO()
io.set_structure(s)
try:
io.save(filename, HSelect())
except Exception as e:
with log_to_exception(log, e):
log.error("Could not output PDB with chains and residues:")
for chain in s[0]:
log.error("%s: %s", chain.id, [r.id for r in chain])
raise
def save_results(out_models, output, directory, verbose):
"""Saves the resulting models into PDB files. Creates a specific directory for the model if it does not exist.
Additionally, each chain receives a new ID in order to distinguish those chains that were equivalent.
Keyword arguments:
out_models -- list of the resulting model objects created by the program
output -- name of the output model/file given by the user
verbose -- boolean, prints to stderr the progress of the program"""
u = 1
if verbose:
sys.stderr.write("Saving models...\n")
io = PDB.PDBIO()
if not os.path.exists(directory):
os.makedirs(directory)
for i in range(len(out_models)):
id_list = []
final_model = UpdModel(str(i))
old_model = out_models[i]
for chain in old_model.get_chains():
new_chain = chain.copy()
new_chain.id = new_id(id_list)
id_list.append(new_chain.id)
final_model.add(new_chain)
io.set_structure(final_model)
io.save(directory + "/" + output + "_" + str(u) + ".pdb")
if verbose:
sys.stderr.write(" " + output + "_" + str(u) + ".pdb saved\n")
u += 1
def write_model(coords, target_seq, filename):
cc_model = new_cc(target_seq, coords)
io = PDB.PDBIO()
io.set_structure(cc_model)
io.save(filename + ".pdb")
return
def capture_mutant_pdb(out_name, mutant, chain_letter):
parser = PDB.PDBParser()
structure = parser.get_structure('working_pdb', mutant)
writer = PDB.PDBIO()
writer.set_structure(structure)
writer.save(out_name, select=SelectChains(chain_letter))
def split_chain(pdb_filename, out_path, pdb_id, chain_letters):
parser = PDB.PDBParser()
writer = PDB.PDBIO()
chain_letters = [chain.upper() for chain in chain_letters]
struct = parser.get_structure (pdb_id, pdb_filename)
writer.set_structure(struct)
writer.save(out_path, select=SelectChains(chain_letters))
def get_chain_length_and_position (pdb_filename, pdb_id):
parser = PDB.PDBParser()
writer = PDB.PDBIO()
struct = parser.get_structure (pdb_id, pdb_filename)
chain_dic = {}
for model in struct:
for chain in model:
chain_id = chain.get_id()
chain_lenth = len(chain)
#if chain_id not in chain_dic:
# chain_dic[chain_id] = []
#if chain_id in chain_dic:
# 1/0
chain_x_coord = 0
chain_y_coord = 0
chain_z_coord = 0
total_number_of_atoms = 0
for residue in chain:
for atom in residue:
chain_x_coord += atom.get_coord()[0]
chain_y_coord += atom.get_coord()[1]
chain_z_coord += atom.get_coord()[2]
total_number_of_atoms += 1
average_x = chain_x_coord/(total_number_of_atoms)
average_y = chain_y_coord/(total_number_of_atoms)
average_z = chain_z_coord/(total_number_of_atoms)
average_position = (average_x, average_y, average_z)
chain_dic[chain_id] = [chain_lenth, average_position]
return chain_dic
def __init__(self, out_dir=None):
""" Create parsing and writing objects, specify output directory. """
self.parser = PDB.PDBParser()
self.writer = PDB.PDBIO()
if out_dir is None:
out_dir = os.path.join(os.getcwd(), "chain_PDBs")
self.out_dir = out_dir
def load_from_one_cath_pml_file(pml_file, scratch_path, superfamilies,
dssp_path):
'''Load data from a .pml file of superposed
homologous superfamilies from the CATH database.
'''
superfamilies.append([])
candidate_proteins = []
with open(pml_file, 'r') as f:
while True:
line = f.readline()
if not line: break
# Read one structure
if line.strip().startswith('cmd.read_pdbstr'):
pdb_lines = [line.strip()[19:].strip('\\')]
pdb_id = ''
while True:
line = f.readline()
if line.strip().startswith('"""'):
pdb_id = line.strip()[5:12]
break
pdb_line = line.strip().strip('\\')
if len(pdb_line) > 17:
pdb_line = pdb_line[0:16] + ' ' + pdb_line[
17:] # Remove all altLoc flags
pdb_lines.append(pdb_line) # Remove all altLoc flags
# Make a pdb file of the structure for DSSP analysis
structure = structure_from_pdb_string('\n'.join(pdb_lines),
pdb_id)
# Store structures without chain breaks
if len(topology.find_structure_chain_breaks(structure)) == 0:
structure_path = os.path.join(scratch_path,
pdb_id + '.pdb')
io = PDB.PDBIO()
io.set_structure(structure)
io.save(structure_path)
candidate_proteins.append({
'structure': structure,
'path': structure_path
})
for p in candidate_proteins:
try:
find_secondary_structures(p, dssp_path)
except:
continue
superfamilies[-1].append(
p) # Add a protein to a superfamily if there's no exception
def extract_chain(in_dir, filename, chain_of_int, out_dir):
'''saves chain of interest as separate structure'''
pdb_id = filename.split('.')[0]
struct = PDB.PDBParser().get_structure(pdb_id, os.path.join(in_dir,
filename))
io = PDB.PDBIO()
io.set_structure(struct[0][chain_of_int])
io.save(os.path.join(out_dir, filename))
def __init__(self, crystal_file, ligand_name, pdbid):
self._crystal = crystal_file
self._ligand_name = ligand_name
self._crystal_path = os.path.dirname(self._crystal)
self._crystal_name = os.path.basename(self._crystal)
parser = PDB.PDBParser()
writer = PDB.PDBIO()
self._biostruc = parser.get_structure (pdbid, crystal_file)
def run_one(self, pdb_path, output_dir):
# parse structure object (permissive flag but let's not lose any atoms by using a custom builder)
parser = PDB.PDBParser(PERMISSIVE=1,
QUIET=True,
structure_builder=NonUniqueStructureBuilder())
# only consider the first model in the pdb file
structure = parser.get_structure('X', pdb_path)
model = structure[0]
ligand_paths = [None]
if self.ligand_fname_pattern[0]:
ligand_paths[0] = pdb_path.replace(self.ligand_fname_pattern[0],
self.ligand_fname_pattern[1])
ligand = parser.get_structure('L', ligand_paths[0])
het_list = list(ligand.get_residues())
else:
# get het entries of interest (filter using static dictionaries)
het_list = get_het_residues_from_pdb(
model,
remove_duplicates=False,
min_lig_atoms=self.min_lig_atoms,
allowed_names=self.allowed_lig_names)
os.makedirs(output_dir, exist_ok=True)
io = PDB.PDBIO()
io.set_structure(model)
name, ext = os.path.basename(pdb_path).split('.', 1)
output_pockets = []
for n, het in enumerate(het_list):
if self.include_het_resname:
site_name = "{0}_site_{1}_{2}.{3}".format(
name, n + 1, het.resname, ext)
else:
site_name = "{0}_site_{1}.{2}".format(name, n + 1, ext)
fname = os.path.join(output_dir, site_name)
output_pockets.append(fname)
io.save(
fname,
NearLigandSelect(self.distance_threshold,
het,
keep_lig_in_site=False,
keep_water=self.keep_water,
keep_other_hets=self.keep_other_hets))
if not self.ligand_fname_pattern[0]:
if self.include_het_resname:
lig_name = "{0}_lig_{1}_{2}.{3}".format(
name, n + 1, het.resname, ext)
else:
lig_name = "{0}_lig_{1}.{2}".format(name, n + 1, ext)
io.save(os.path.join(output_dir, lig_name),
LigandOnlySelect(het))
ligand_paths.append(os.path.join(output_dir, lig_name))
if self.save_clean_structure:
io.save(os.path.join(output_dir, '{}_clean.{}'.format(name, ext)),
ChainOnlySelect())
def main():
parser = argparse.ArgumentParser(description='Re-orders chains in the PDB \
file based on the specified ordering')
parser.add_argument('-p',
'--pdb',
action='store',
nargs=1,
dest='pdb',
help='PDB file with chains to be ordered')
parser.add_argument('-r',
'--rank_order',
action='store',
nargs='*',
dest='order',
type=int,
help='Space separated rank \
order for chains. Top rank starts with 0 \
(instead of 1)')
parser.add_argument('-i',
'--input_directory',
action='store',
nargs=1,
dest='input',
default=['./'],
help='Directory where \
input pdb files are stored')
parser.add_argument('-o',
'--output_directory',
action='store',
nargs=1,
dest='output',
default=['./'],
help='Directory where \
output log should be written')
args = vars(parser.parse_args())
parser = PDB.PDBParser()
structure = parser.get_structure('pdb',
args['input'][0] + '/' + args['pdb'][0])
chain_list = []
for model in structure:
for chain in model:
chain_list.append(chain)
for chain in chain_list:
model.detach_child(chain.id)
new_order = args['order']
chain_list = [chain_list[i] for i in new_order]
for idx, chain in enumerate(chain_list):
model.insert(idx, chain)
io = PDB.PDBIO()
io.set_structure(structure)
io.save(args['output'][0] + '/' + args['pdb'][0])
def miscFixes(pdbfile):
"""Misc fixes to pdb"""
parser = PDB.PDBParser(QUIET=True)
structure = parser.get_structure(pdbfile, pdbfile)
model = structure[0]
chc = model['C']
resetChainNumbering(chc)
w = PDB.PDBIO()
w.set_structure(structure)
w.save(pdbfile)
return
def get_chain_pdb(id, chain, cache):
cache_path = './cache/data/' + id + chain + '.pdb'
if os.path.isfile(cache_path):
return cache_path
if cache:
print("CACHE MISS WHEN REQUIRED: ", id, chain)
parser = PDB.PDBParser()
writer = PDB.PDBIO()
writer.set_structure(parser.get_structure(id, get_pdb(id, cache)))
writer.save(cache_path, select=SelectChain(chain))
return cache_path
def insertPredsInBfactor(pdbFnameIn,
scoresFnameIn,
pdfFnameOut,
scale=False,
min_max_norm=False,
verbose=True):
parser = PDBParser(QUIET=True)
struct = parser.get_structure(os.path.basename(pdbFnameIn), pdbFnameIn)
scores = pd.read_table(scoresFnameIn,
sep='\s+',
header='infer',
comment="#",
dtype={
"chainIdL": str,
"chainIdR": str,
"resIdL": str,
"resIdR": str,
"chainId": str,
"resId": str,
"resId": str
})
if scale: scores["prediction"] = SCALING_RANGE * scores["prediction"]
if min_max_norm:
scores["prediction"] = (scores["prediction"] - np.min(
scores["prediction"])) / (np.max(scores["prediction"]) -
np.min(scores["prediction"]))
scoresDict = {}
for i in range(scores.shape[0]):
scoresDict[(scores["chainId"][i],
scores["resId"][i])] = scores["prediction"][i]
# print( sorted([ (key, scoresDict[key]) for key in scoresDict]))
for chain in struct[0]:
chainId = chain.get_id()
if chainId == " ": chainId = UNKNOWN_CHAIN_SYMB
for res in chain:
resId = res.get_id()
strResId = (str(resId[1]) + resId[2]).strip()
if (chainId, strResId) in scoresDict:
predVal = scoresDict[(chainId, strResId)]
else:
predVal = 0.0
if verbose:
print(chainId, strResId, (chainId, strResId) in scoresDict,
predVal)
for atom in res:
atom.set_bfactor(predVal)
writer = PDB.PDBIO()
writer.set_structure(struct)
writer.save(pdfFnameOut)
def topology_backbone(topology_prefix):
parser = PDBParser(PERMISSIVE=1)
structure = parser.get_structure('topology', '%s.pdb' % topology_prefix)
output = bpdb.PDBIO()
output.set_structure(structure)
output_filename = str(topology_prefix) + '_backbone.pdb'
output.save('%s' % output_filename, Backboneselect())
print("The backbone of selected topology file has been saved as %s" %
output_filename)
return output_filename
def protein_to_file(structure, fpath):
"""
Writes a PDB to file.
:param structure: a protein structure
:param fpath: path for output pdb file
:type structure: `Bio.PDB.Structure.Structure`
:type fpath: str
"""
io = PDB.PDBIO()
io.set_structure(structure)
io.save(fpath)
def normalize_pdb(inp_fn, out_fn, reverse=False, reverse_id=False, limit=None):
"""generate out_fn and returns array of tuples
(old_chain,old_res_id), (new_chain, new_res_id)"""
numbers = []
parser = PDB.PDBParser()
if re.match("^.*.gz",inp_fn):
inp = gzip.open(inp_fn)
else:
inp = open(inp_fn)
s = parser.get_structure("c",inp)
inp.close()
total = 0
for model in list(s):
n = len([r for r in model.get_residues()])
for chain in list(model):
for i, r in enumerate(list(chain)):
total += 1
if limit is not None and total>limit:
chain.detach_child(r.get_id())
continue
old_id = r.get_id()
old_num = (str(old_id[1])+str(old_id[2])).strip()
old_chain = r.get_parent().get_id()
if reverse_id:
new_num = n-i
else:
new_num = i
new_id = (old_id[0], new_num, ' ') # we ignore old_id[2]
numbers.append(((old_chain, old_num), (old_chain, str(new_num))))
# print old_id, new_id
r.id = new_id
if len(chain)==0:
model.detach_child(chain.id)
if len(model)==0:
s.detach_child(model.id)
if reverse:
s = reverse_structure(s)
if limit is not None:
c = len(list(s.get_residues()))
print("number of residues=%s" % c)
assert c<=limit
# from http://biopython.org/wiki/Remove_PDB_disordered_atoms
class NotDisordered(PDB.Select):
def accept_atom(self, atom):
return not atom.is_disordered() or atom.get_altloc()=='A'
io = PDB.PDBIO()
io.set_structure(s)
io.save(out_fn, select=NotDisordered())
return numbers
def write_structure_into_file(structure, name, format):
"""
Writes the strcuture into a file. The file can be either a pdb or a mmcif.
Format needs to be either "pdb" or "mmcif" depending on the desired output
file.
"""
if format == "pdb":
io = pdb.PDBIO()
elif format == "mmcif":
io = pdb.MMCIFIO()
io.set_structure(structure)
io.save(name)
def preparePDB(pdbfile, start=3):
"""Prepare an MHC pdb for use in docking by removing unused atoms,
combining A+B chains and renumbering chain C"""
print pdbfile
parser = PDB.PDBParser(QUIET=True)
structure = parser.get_structure(pdbfile, pdbfile)
model = structure[0]
remove = string.uppercase[3:]
for chain in model.child_list[:]:
if chain.id in remove:
model.detach_child(chain.id)
else:
for residue in chain.child_list[:]:
#remove all non-standard residues
if residue.id[0] != " ":
chain.detach_child(residue.id)
removeresidues = [('A', range(86,200)), ('B', range(96,200))]
for c in removeresidues:
chain = model[c[0]]
remove = c[1]
for residue in chain.child_list[:]:
id = residue.id
if id[1] in remove:
chain.detach_child(id)
#renumber chain A
chaina = model['A']
renumberChain(chaina)
#renumber chain B
chainb = model['B']
i = chaina.child_list[-1].id[1] + 1
for residue in chainb:
residue.id = (' ', i, ' ')
i+=1
chainb.id = 'A'
#model.detach_child('B')
#print '%s chains, length chain a=%s' %(len(model.child_list), len(chaina.child_list))
#renumber chain c
renumberChain(model['C'])
w = PDB.PDBIO()
w.set_structure(structure)
name = os.path.splitext(pdbfile)[0]
filename = name+'_alt.pdb'
w.save(filename, write_end=1)
#print 'saved file %s' %filename
#save unbound
model.detach_child('C')
filename2 = name+'_unbound.pdb'
w.save(filename2, write_end=1)
return filename, filename2
def superimpose(first, second):
superimpose = PDB.Superimposer()
superimpose.set_atoms(first, second)
model = second_model
superimpose.apply(model.get_atoms())
print("The RMSD is: ")
print(superimpose.rms)
structure = second_structure
io = PDB.PDBIO()
io.set_structure(structure)
saved_file = input("Name of output file (.pdb)?")
io.save(saved_file)
return saved_file
def save_structure(structure, filename, quiet=False):
"""Write a PDB file from structure"""
if not quiet:
print_line = "Writing " + filename + "..."
print(print_line, end="\r")
longest_line_len = max(longest_line_len, len(print_line))
io = PDB.PDBIO()
io.set_structure(structure)
io.save(filename)
if not quiet:
final_print_str = "Wrote " + filename + "."
num_spaces = max(0, longest_line_len - len(final_print_str))
print(final_print_str + " " * num_spaces)
def Read_PDB(filePtr):
parser = PDB.PDBParser()
io = PDB.PDBIO()
struct = parser.get_structure('temp', filePtr)
xyz = []
for model in struct:
for chain in model:
for residue in chain:
for atom in residue:
x, y, z = atom.get_coord()
xyz.append(np.array([x, y, z]))
return np.asarray(xyz)
def Separate_Chains(pdb_file):
"""Separate the two chains and return their name in a list
Input:
-pdb file = target file
Output:
-interaction = list with chain information
"""
folder = "pdb_chains"
if not Check_folder(folder):
return False
pdb_parser = pdb.PDBParser(PERMISSIVE=True, QUIET=True)
pdb_structure = pdb_parser.get_structure("pdb_file", pdb_file)
interaction = list(
pdb_file[:-4].split("_")[-1]
) # Obtain 2 length lists with the chain names from file name, the order of the letters need match with the order in the pdb file(format= something_chains.pdb)
if len(interaction) != 2: #if the length is not true, something goes wrong
print(settings.IncorrectName(interaction))
for model in pdb_structure:
for chain in model:
id = chain.get_id()
class chain(pdb.Select):
def accept_chain(self, chain):
if chain.get_id() == id:
return True
else:
return False
io = pdb.PDBIO()
io.set_structure(pdb_structure)
name = "%s_chain_%s.pdb" % (interaction[0] + interaction[1],
interaction[i])
file_name = os.path.join(folder, name)
io.save(file_name, chain())
return interaction
def save_pdb(self, file_name):
'''Save the sheet to a PDB file.'''
c = PDB.Chain.Chain('A')
for strand in self.strand_list:
for r in strand.residue_list:
c.add(r)
m = PDB.Model.Model(0)
m.add(c)
s = PDB.Structure.Structure('s')
s.add(m)
io = PDB.PDBIO()
io.set_structure(s)
io.save(file_name)
def PDBToNPY(self, fpathin):
parser = PDB.PDBParser()
io = PDB.PDBIO()
struct = parser.get_structure('1ABZ', fpathin)
allcoords1 = []
for model in struct:
for chain in model:
for residue in chain:
for atom in residue:
x, y, z = atom.get_coord()
cSet = []
cSet.append(x)
cSet.append(y)
cSet.append(z)
allcoords1.append(cSet)
return allcoords1
def get_cropped_pdb_subcluster(namechainres, length, is_pos, cluster,
subcluster, hetlist):
name = (namechainres)[0:4]
chain = (namechainres)[10:11]
#print(namechainres)
res = (namechainres)[12:15]
resid = (namechainres)[15:]
#print(type(resid))
resid = int(resid)
length = int(length)
print(name + '_chain' + str(chain) + '_' + str(res) + str(resid) +
'_cropped.pdb')
hetlist_full = ast.literal_eval(hetlist)
hetlist = []
for subl in hetlist_full:
hetlist.append(subl[1])
os.chdir("C:/Users/Shaheer Rizwan/Documents/ramachandran/ids_xray")
io = PDB.PDBIO()
structure = PDBParser().get_structure(name, 'pdb' + name + ".ent")
class HeterochiralSelect(Select):
def accept_residue(self, residue):
#print(residue.id)
if ((residue.id[1] in range(resid, length + resid))
or residue.id[1] in hetlist):
#print(residue['CA'].get_serial_number())
return 1
else:
return 0
io.set_structure(structure[0][chain])
if not os.path.exists(
"C:/Users/Shaheer Rizwan/Documents/ramachandran/cropped_pdb_subcluster_rev2/"
+ str(length) + str(is_pos) + str(cluster) + "_" +
str(subcluster)):
os.makedirs(
"C:/Users/Shaheer Rizwan/Documents/ramachandran/cropped_pdb_subcluster_rev2/"
+ str(length) + str(is_pos) + str(cluster) + "_" + str(subcluster))
os.chdir(
"C:/Users/Shaheer Rizwan/Documents/ramachandran/cropped_pdb_subcluster_rev2/"
+ str(length) + str(is_pos) + str(cluster) + "_" + str(subcluster))
io.save(
name + '_chain' + str(chain) + '_' + str(res) + str(resid) +
'_cropped.pdb', HeterochiralSelect())
def run_one(self, pdb_path, output_dir):
# parse structure object (permissive flag but let's not lose any atoms by using a custom builder)
parser = PDB.PDBParser(PERMISSIVE=1, QUIET=True, structure_builder=NonUniqueStructureBuilder())
# only consider the first model in the pdb file
structure = parser.get_structure('X', pdb_path)
model = structure[0]
# Get ligand (het) to extract the site around
if self.ligand_fname_pattern[0]:
ligand_path = pdb_path.replace(self.ligand_fname_pattern[0], self.ligand_fname_pattern[1])
ligand = parser.get_structure('L', ligand_path)
het_list = list(ligand.get_residues())
else:
# get het entries of interest (filter using static dictionaries)
het_list = get_het_residues_from_pdb(model, remove_duplicates=False, min_lig_atoms=self.min_lig_atoms,
allowed_names=self.allowed_lig_names)
# Setup a PDB writer and load protein
io = PDB.PDBIO()
io.set_structure(model)
# create output directory and split input pdb_path
os.makedirs(output_dir, exist_ok=True)
name, ext = os.path.basename(pdb_path).rsplit('.', 1)
for n, het in enumerate(het_list):
# Set name of output site file
if self.include_het_resname:
site_name = f"{name}_site_{n+1}_{het.resname}.{ext}"
else:
site_name = f"{name}_site_{n+1}.{ext}"
fname = os.path.join(output_dir, site_name)
io.save(fname, NearLigandSelect(self.distance_threshold, het, keep_lig_in_site=False,
keep_water=self.keep_water, keep_other_hets=self.keep_other_hets))
if not self.ligand_fname_pattern[0]:
if self.include_het_resname:
lig_name = f"{name}_lig_{n+1}_{het.resname}.{ext}"
else:
lig_name = f"{name}_lig_{n+1}.{ext}"
io.save(os.path.join(output_dir, lig_name), LigandOnlySelect(het))
if self.save_clean_structure:
io.save(os.path.join(output_dir, f'{name}_clean.{ext}'), ChainOnlySelect())
def topology_rmsfexclusion(topology_prefix, selection_residue, allatom):
rmsf_topology_prefix = '%s_rmsf_contained' % topology_prefix
parser = PDBParser(PERMISSIVE=1)
structure = parser.get_structure('topology',
'%s.pdb' % rmsf_topology_prefix)
#if allatom:
# structure = parser.get_structure('topology', '%s_model.pdb' % topology_prefix)
residues = structure.get_residues()
output = bpdb.PDBIO()
output.set_structure(structure)
output.save('%s_rmsf_exclusion.pdb' % rmsf_topology_prefix,
RMSFexclusion(selection_residue))
#if allatom:
# output.save('%s_rmsf_exclusion_allatom.pdb' % rmsf_topology_prefix, RMSFexclusion(selection_residue))
#else:
# output.save('%s_rmsf_exclusion.pdb' % rmsf_topology_prefix, RMSFexclusion(selection_residue))
print("The pdb file with rmsf value and trimmed version has been saved")
