def run(prefix):
"""
Exercise interaction graph construction.
"""
pdb_inp = iotbx.pdb.input(file_name= os.path.join(qr_unit_tests,"data_files","2lvr.pdb"))
ph = pdb_inp.construct_hierarchy()
yoink_utils.write_yoink_infiles("cluster.xml",
"qmmm.xml",
ph,
os.path.join(qrefine,"plugin","yoink","dat"))
pyoink=PYoink(os.path.join(qrefine,"plugin","yoink","Yoink-0.0.1.jar"),
os.path.join(qrefine,"plugin","yoink","dat"),
"cluster.xml")
interaction_list, weight = pyoink.get_interactions_list()
#print interaction_list
expected_list = [[24, 27], [19, 22], [5, 11], [18, 22], [25, 26], [23, 26], [2, 3], [5, 7], [9, 11], [27, 29],
[5, 10], [9, 10], [18, 21], [11, 12], [24, 25], [5, 12], [6, 10], [6, 7], [20, 21], [10, 11],
[21, 25], [5, 6], [21, 24], [17, 21], [6, 11], [12, 13], [5, 13], [17, 20], [4, 5], [3, 12],
[29, 30], [8, 9], [20, 24], [11, 13], [4, 13], [5, 19], [28, 29], [6, 9], [6, 19], [6, 8],
[13, 19], [7, 19], [6, 13], [13, 18], [15, 19], [7, 8], [4, 19], [16, 19], [6, 22], [15, 18],
[15, 16], [9, 31], [6, 31], [26, 27], [13, 14], [11, 22], [18, 19], [4, 15], [17, 18], [14, 15],
[16, 17], [22, 31], [8, 26], [4, 16], [8, 23], [15, 17], [6, 23], [7, 23], [13, 15], [22, 23],
[2, 4], [13, 22], [23, 24], [19, 23], [10, 12], [19, 20], [24, 30], [21, 22], [9, 26], [23, 27],
[4, 14], [1, 2], [16, 20], [26, 31], [25, 28], [27, 28], [22, 26], [24, 28], [20, 23], [17, 19],
[27, 30], [16, 18], [20, 22], [1, 3], [6, 26], [28, 30], [3, 13], [3, 5], [3, 4], [22, 25],
[3, 14], [9, 22]]
for e1, e2 in zip(expected_list, interaction_list):
e1.sort()
e2.sort()
expected_list.sort()
interaction_list.sort()
assert approx_equal(expected_list, interaction_list)
def __init__(self,
working_folder="ase",
clustering_method=None,
altloc_method=None,
maxnum_residues_in_cluster=20,
charge_embedding=False,
two_buffers=False,
pdb_hierarchy=None,
qm_engine_name=None,
crystal_symmetry=None,
clustering=True,
qm_run=True,
cif_objects=None,
debug=False,
charge_cutoff=8.0,
save_clusters=False):
self.charge_embedding = charge_embedding
self.two_buffers = two_buffers
self.crystal_symmetry = crystal_symmetry
self.working_folder = os.path.abspath(working_folder)
self.pdb_hierarchy = pdb_hierarchy
self.charge_cutoff = charge_cutoff
self.system_size = pdb_hierarchy.atoms_size()
self.qm_engine_name = qm_engine_name
self.clustering_method = clustering_method
self.altloc_method = altloc_method
self.debug = debug
self.maxnum_residues_in_cluster = maxnum_residues_in_cluster
self.save_clusters = save_clusters
raw_records = pdb_hierarchy.as_pdb_string(
crystal_symmetry=crystal_symmetry)
self.charge_service = charges_class(raw_records=raw_records,
ligand_cif_file_names=cif_objects)
if (os.path.exists(self.working_folder) is not True):
os.mkdir(self.working_folder)
self.backbone_connections = fragment_utils.get_backbone_connections(
self.pdb_hierarchy)
self.get_altloc_molecular_indices()
if (1):
self.altloc_atoms = [
atom for atom in list(pdb_hierarchy.atoms())
if atom.pdb_label_columns()[4] != " "
]
self.expansion = expand(pdb_hierarchy=self.pdb_hierarchy,
crystal_symmetry=self.crystal_symmetry,
select_within_radius=10.0)
self.pdb_hierarchy_super = self.expansion.ph_super_sphere
## write expansion.pdb as the reference for capping
self.expansion_file = "expansion.pdb"
self.expansion.write_super_cell_selected_in_sphere(
file_name=self.expansion_file)
if (clustering):
self.yoink_dat_path = os.path.join(qrefine, "plugin", "yoink",
"dat")
self.pyoink = PYoink(
os.path.join(qrefine, "plugin", "yoink", "Yoink-0.0.1.jar"))
self.qm_run = qm_run
#t0 = time.time()
self.set_up_cluster_qm()
def run(prefix):
"""
Exercise interaction graph construction.
"""
if 1: # to be deprecated (Java version)
from qrefine.utils import yoink_utils
from qrefine.plugin.yoink.pyoink import PYoink
ph = get_hierarchy()
yoink_utils.write_yoink_infiles("cluster.xml",
"qmmm.xml",
ph,
os.path.join(qrefine,"plugin","yoink","dat"))
pyoink=PYoink(os.path.join(qrefine,"plugin","yoink","Yoink-0.0.1.jar"),
os.path.join(qrefine,"plugin","yoink","dat"),
"cluster.xml")
interaction_list_java, weight = pyoink.get_interactions_list()
expected_list_java = [[24, 27], [19, 22], [5, 11], [18, 22], [25, 26], [23, 26], [2, 3], [5, 7], [9, 11], [27, 29],
[5, 10], [9, 10], [18, 21], [11, 12], [24, 25], [5, 12], [6, 10], [6, 7], [20, 21], [10, 11],
[21, 25], [5, 6], [21, 24], [17, 21], [6, 11], [12, 13], [5, 13], [17, 20], [4, 5], [3, 12],
[29, 30], [8, 9], [20, 24], [11, 13], [4, 13], [5, 19], [28, 29], [6, 9], [6, 19], [6, 8],
[13, 19], [7, 19], [6, 13], [13, 18], [15, 19], [7, 8], [4, 19], [16, 19], [6, 22], [15, 18],
[15, 16], [9, 31], [6, 31], [26, 27], [13, 14], [11, 22], [18, 19], [4, 15], [17, 18], [14, 15],
[16, 17], [22, 31], [8, 26], [4, 16], [8, 23], [15, 17], [6, 23], [7, 23], [13, 15], [22, 23],
[2, 4], [13, 22], [23, 24], [19, 23], [10, 12], [19, 20], [24, 30], [21, 22], [9, 26], [23, 27],
[4, 14], [1, 2], [16, 20], [26, 31], [25, 28], [27, 28], [22, 26], [24, 28], [20, 23], [17, 19],
[27, 30], [16, 18], [20, 22], [1, 3], [6, 26], [28, 30], [3, 13], [3, 5], [3, 4], [22, 25],
[3, 14], [9, 22]]
for e1, e2 in zip(expected_list_java, interaction_list_java):
e1.sort()
e2.sort()
expected_list_java.sort()
interaction_list_java.sort()
assert approx_equal(expected_list_java, interaction_list_java)
if 1:
ph = get_hierarchy()
interaction_list_cpp = pair_interaction.run(ph)
expected_list_cpp = [
(9, 26), (24, 30), (6, 9), (17, 20), (1, 3), (18, 19), (23, 26), (6, 7),
(4, 19), (24, 27), (6, 10), (11, 22), (25, 26), (7, 19), (27, 28), (15, 18),
(5, 11), (29, 30), (4, 16), (6, 23), (16, 19), (6, 26), (17, 18), (22, 25),
(3, 12), (4, 15), (16, 18), (6, 13), (19, 23), (15, 16), (21, 24), (22, 23),
(22, 26), (8, 9), (17, 21), (20, 21), (24, 28), (6, 11), (13, 19), (18, 21),
(23, 24), (3, 5), (5, 10), (7, 8), (5, 7), (24, 25), (16, 20), (13, 22),
(18, 22), (28, 29), (5, 13), (19, 22), (15, 19), (16, 17), (11, 12), (4, 13),
(9, 11), (11, 13), (20, 22), (13, 15), (2, 3), (8, 26), (6, 8), (20, 24),
(6, 31), (26, 31), (21, 22), (13, 18), (27, 30), (23, 27), (3, 4), (2, 4),
(10, 11), (8, 23), (5, 6), (9, 22), (5, 19), (22, 31), (3, 14), (27, 29),
(1, 2), (28, 30), (5, 12), (10, 12), (4, 5), (7, 10), (6, 22), (7, 23),
(17, 19), (22, 24), (3, 13), (4, 14), (9, 10), (9, 31), (19, 20), (25, 28),
(15, 17), (6, 19), (21, 25), (20, 23), (26, 27), (13, 14), (12, 13), (14, 15)]
assert approx_equal(expected_list_cpp, interaction_list_cpp)
def clusters(pdb_hierarchy):
yoink_utils.write_yoink_infiles(
"cluster.xml", "qmmm.xml", pdb_hierarchy,
os.path.join(qrefine, "plugin", "yoink", "dat"))
pyoink = PYoink(
os.path.join(qrefine, "plugin", "yoink", "Yoink-0.0.1.jar"),
os.path.join(qrefine, "plugin", "yoink", "dat"), "cluster.xml")
interaction_list, weight = pyoink.get_interactions_list()
for e in interaction_list:
e.sort()
interaction_list.sort()
cc = clustering.betweenness_centrality_clustering(
interaction_list, maxnum_residues_in_cluster=3)
print cc.get_clusters()
return cc.get_clusters()
def run(prefix):
"""
Exercise buffer region of cluster.
"""
pdb_inp = iotbx.pdb.input(
file_name=os.path.join(qr_unit_tests, "data_files", "2lvr.pdb"))
ph = pdb_inp.construct_hierarchy()
yoink_utils.write_yoink_infiles(
"cluster.xml", "qmmm.xml", ph,
os.path.join(qrefine, "plugin", "yoink", "dat"))
gn_clusters = [[1, 2], [3, 4, 5, 14, 13], [6, 7, 8, 9, 22, 23, 26, 31],
[10, 11, 12], [15, 16, 17, 18, 19, 20, 21],
[24, 25, 27, 28, 29, 30]]
bc_clusters = [[1, 2], [3, 4, 5, 13, 14, 15, 16, 17, 18, 19, 20, 21],
[6, 7, 8, 9, 10, 11, 12, 22, 23, 26, 31],
[24, 25, 27, 28, 29, 30]]
bc_qms = [[1, 2, 3, 4],
[
1, 2, 3, 4, 5, 6, 7, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24, 25
],
[
3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 18, 19, 20, 21, 22, 23, 24,
25, 26, 27, 31
], [20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]]
gn_qms = [[1, 2, 3, 4],
[1, 2, 3, 4, 5, 6, 7, 10, 11, 12, 13, 14, 15, 16, 18, 19, 22],
[
5, 6, 7, 8, 9, 10, 11, 13, 18, 19, 20, 21, 22, 23, 24, 25,
26, 27, 31
], [3, 5, 6, 9, 10, 11, 12, 13, 22],
[4, 5, 6, 7, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25],
[20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]]
pyoink = PYoink(
os.path.join(qrefine, "plugin", "yoink", "Yoink-0.0.1.jar"),
os.path.join(qrefine, "plugin", "yoink", "dat"), "qmmm.xml")
check_buffer(bc_clusters, bc_qms, pyoink)
class fragments(object):
def __init__(self,
working_folder="ase",
clustering_method=None,
altloc_method=None,
maxnum_residues_in_cluster=20,
charge_embedding=False,
two_buffers=False,
pdb_hierarchy=None,
qm_engine_name=None,
crystal_symmetry=None,
clustering=True,
qm_run=True,
cif_objects=None,
debug=False,
charge_cutoff=8.0,
save_clusters=False,
fast_interaction=False,
select_within_radius=10.0,
bond_with_altloc_flag=True):
self.bond_with_altloc_flag = bond_with_altloc_flag
self.clustering = clustering
self.select_within_radius = select_within_radius
self.fast_interaction = fast_interaction
self.charge_embedding = charge_embedding
self.two_buffers = two_buffers
self.crystal_symmetry = crystal_symmetry
self.working_folder = os.path.abspath(working_folder)
self.pdb_hierarchy = pdb_hierarchy
self.charge_cutoff = charge_cutoff
self.system_size = pdb_hierarchy.atoms_size()
self.qm_engine_name = qm_engine_name
self.clustering_method = clustering_method
self.altloc_method = altloc_method
self.debug = debug
self.maxnum_residues_in_cluster = maxnum_residues_in_cluster
self.save_clusters = save_clusters
raw_records = pdb_hierarchy.as_pdb_string(
crystal_symmetry=crystal_symmetry)
self.charge_service = charges_class(raw_records=raw_records,
ligand_cif_file_names=cif_objects)
if (os.path.exists(self.working_folder) is not True):
os.mkdir(self.working_folder)
self.backbone_connections = fragment_utils.get_backbone_connections(
self.pdb_hierarchy)
self.get_altloc_molecular_indices()
if (1):
self.altloc_atoms = [
atom for atom in list(pdb_hierarchy.atoms())
if atom.pdb_label_columns()[4] != " "
]
self.expansion = expand(pdb_hierarchy=self.pdb_hierarchy,
crystal_symmetry=self.crystal_symmetry,
select_within_radius=self.select_within_radius)
self.pdb_hierarchy_super = self.expansion.ph_super_sphere
## write expansion.pdb as the reference for capping
self.expansion_file = "expansion.pdb"
self.expansion.write_super_cell_selected_in_sphere(
file_name=self.expansion_file)
if (not self.fast_interaction):
from qrefine.plugin.yoink.pyoink import PYoink
from qrefine.utils.yoink_utils import write_yoink_infiles
self.yoink_dat_path = os.path.join(qrefine, "plugin", "yoink",
"dat")
self.pyoink = PYoink(
os.path.join(qrefine, "plugin", "yoink", "Yoink-0.0.1.jar"))
self.qm_run = qm_run
#t0 = time.time()
self.set_up_cluster_qm()
#print "time taken for interaction graph",(time.time() - t0)
def update_xyz(self, sites_cart):
self.pdb_hierarchy.atoms().set_xyz(sites_cart)
pre_atoms = [
atom.pdb_label_columns()
for atom in self.pdb_hierarchy_super.atoms()
]
self.expansion = self.expansion.update_xyz(sites_cart=sites_cart)
self.pdb_hierarchy_super = self.expansion.ph_super_sphere
new_atoms = [
atom.pdb_label_columns()
for atom in self.pdb_hierarchy_super.atoms()
]
#if(self.expansion.ph_super_sphere.atoms_size()!=pre_size):
if (pre_atoms != new_atoms):
if (self.debug):
print(
"the content of the super sphere has been changed,reset up fragments"
)
#Note: the atom size of self.expansion.ph_super_sphere gets changeed,
#while the atom size in super_sphere_geometry_restraints_manager
#does not get changed. Re-generate the object of expand
if (1):
self.expansion = expand(
pdb_hierarchy=self.pdb_hierarchy,
crystal_symmetry=self.crystal_symmetry,
select_within_radius=self.select_within_radius)
self.pdb_hierarchy_super = self.expansion.ph_super_sphere
self.get_fragments()
self.get_fragment_hierarchies_and_charges()
def get_altloc_molecular_indices(self):
self.altloc_molecular_indices = []
index = 0
for chain in self.pdb_hierarchy.chains():
for residue_group in chain.residue_groups():
index += 1
if (residue_group.have_conformers()):
self.altloc_molecular_indices.append(index)
def set_up_cluster_qm(self, sites_cart=None):
if (sites_cart is not None):
## update the selection of expansion_sphere, and its
## geometry_restraints_manager and pdb_hierarchy
self.pdb_hierarchy_super = self.expansion.update(
sites_cart=sites_cart).ph_super_sphere
###get clusters and their buffer regions using yoink and graph clustering.
try:
pre_clusters = self.clusters
except:
pre_clusters = None
self.get_clusters()
if (self.qm_run is True and pre_clusters != self.clusters):
self.get_fragments()
self.get_fragment_hierarchies_and_charges()
def get_clusters(self):
#print(self.clustering)
n_residues = len(list(self.pdb_hierarchy.residue_groups()))
if (not self.clustering):
return (range(1, n_residues + 1, 1))
if (self.fast_interaction):
self.interaction_list = pair_interaction.run(
copy.deepcopy(self.pdb_hierarchy)) ##deepcopy
else: # to be deprecated.
self.cluster_file_name = self.working_folder + "/cluster.xml"
self.qmmm_file_name = self.working_folder + "/qmmm.xml"
## write yoink input file to get interactions
if (not self.fast_interaction):
from qrefine.utils.yoink_utils import write_yoink_infiles
write_yoink_infiles(self.cluster_file_name, self.qmmm_file_name,
self.pdb_hierarchy, self.yoink_dat_path)
self.pyoink.input_file = self.cluster_file_name
self.pyoink.update()
self.interaction_list, weight = self.pyoink.get_interactions_list()
self.interacting_pairs = len(self.interaction_list)
self.interaction_list += self.backbone_connections
## isolate altloc molecules
new_interaction_list = []
if (0):
for item in self.interaction_list:
contain_altlocs = set(
self.altloc_molecular_indices) & set(item)
if (len(contain_altlocs) == 0):
new_interaction_list.append(item)
self.interaction_list = new_interaction_list
from . import clustering
# t0 = time.time()
self.clustering = clustering.betweenness_centrality_clustering(
self.interaction_list,
size=n_residues,
maxnum_residues_in_cluster=self.maxnum_residues_in_cluster)
clusters = self.clustering.get_clusters()
# self.clusters=sorted(clusters, key=len, reverse=True)
self.clusters = sorted(
clusters,
key=cmp_to_key(lambda x, y: 1 if len(x) < len(y) else -1
if len(x) > len(y) else 0))
# self.clusters = sorted(clusters,
# lambda x, y: 1 if len(x) < len(y) else -1 if len(x) > len(y) else 0)
# print "time taken for clustering", (time.time() - t0)
def get_fragments(self):
def selected_atom_indices_in_entire_ph(selected_atom_indices_in_sub_ph,
sub_ph):
selected_atom_indices_in_entire_ph = []
for index, number in enumerate(sub_ph.atoms().extract_serial()):
if (index + 1 in selected_atom_indices_in_sub_ph):
selected_atom_indices_in_entire_ph.append(int(number))
return selected_atom_indices_in_entire_ph
self.pdb_hierarchy_super.atoms_reset_serial()
phs = [self.pdb_hierarchy_super]
altloc_size = self.pdb_hierarchy_super.altloc_indices().size()
if (altloc_size > 1):
## generate pdb_hierarchy for each altloc case
phs = []
asc = self.pdb_hierarchy_super.atom_selection_cache()
if (self.debug):
self.pdb_hierarchy_super.write_pdb_file(file_name="super.pdb")
## the first one altloc is " ", A B.. altlocs start from 1
altlocs = self.pdb_hierarchy_super.altloc_indices().keys()
altlocs.sort()
for altloc in altlocs:
if (altloc == ""): continue
sel = asc.selection("altloc '%s' or altloc '' or altloc ' '" %
altloc)
ph_altloc = self.pdb_hierarchy_super.select(sel)
phs.append(ph_altloc)
if (self.debug):
ph_altloc.write_pdb_file(file_name="super-" + str(altloc) +
".pdb")
cluster_atoms_in_phs = []
fragment_super_atoms_in_phs = []
clusters = self.clusters ##from graph clustring, molecular indices
##loop over each cluster in every pdb_hierarchy to define buffer region
##fragment consists of cluster and buffer
##all pdb_hierarchies have the same clusters at molecular level
for ph in phs:
cluster_atoms_in_ph = []
fragment_super_atoms_in_ph = []
molecules_in_fragments = []
## write yoink input file to get fragment
if (not self.fast_interaction):
pyoink = self.pyoink
from qrefine.utils.yoink_utils import write_yoink_infiles
write_yoink_infiles(self.cluster_file_name,
self.qmmm_file_name, ph,
self.yoink_dat_path)
for i in range(len(clusters)):
# print 'processing cluster', i
if (self.fast_interaction):
atoms_in_one_cluster, atoms_in_one_fragment, molecules_in_one_fragment = \
pair_interaction.run(copy.deepcopy(ph), clusters[i]) ##deepcopy
# print("clusters[i]",clusters[i])
# print("molecules_in_one_fragment:", molecules_in_one_fragment)
# print("atoms_in_one_fragment",atoms_in_one_fragment)
else:
pyoink.input_file = self.qmmm_file_name
pyoink.update(clusters[i])
atoms_in_one_cluster = pyoink.qm_core_fixed_indices
atoms_in_one_fragment, molecules_in_one_fragment = pyoink.get_qm_indices(
)
atoms_in_one_cluster = selected_atom_indices_in_entire_ph(
atoms_in_one_cluster, ph)
cluster_atoms_in_ph.append(atoms_in_one_cluster)
atoms_in_one_fragment = selected_atom_indices_in_entire_ph(
atoms_in_one_fragment, ph)
fragment_super_atoms_in_ph.append(atoms_in_one_fragment)
molecules_in_fragments.append(molecules_in_one_fragment)
if (0):
print(i, "atoms in cluster: ", atoms_in_one_cluster)
if True:
atoms = self.pdb_hierarchy_super.atoms()
check_selection_integrity(atoms, atoms_in_one_cluster)
# print "cluster->fragments done"
if (self.two_buffers): ## define a second buffer layer
# print "adding second layer"
fragment_super_atoms_in_ph = []
for molecules in molecules_in_fragments:
if (self.fast_interaction):
junk1, atoms_in_one_fragment, junk2 = pair_interaction.run(
copy.deepcopy(ph), molecules)
else:
pyoink.input_file = self.qmmm_file_name
pyoink.update(list(molecules))
atoms_in_one_fragment, junk = pyoink.get_qm_indices()
atoms_in_one_fragment = selected_atom_indices_in_entire_ph(
atoms_in_one_fragment, ph)
fragment_super_atoms_in_ph.append(atoms_in_one_fragment)
cluster_atoms_in_phs.append(cluster_atoms_in_ph)
fragment_super_atoms_in_phs.append(fragment_super_atoms_in_ph)
#
##always collect the clustering result from phs[0]
self.cluster_atoms = []
self.fragment_super_atoms = []
self.fragment_scales = []
for i_cluster in range(len(clusters)):
self.collect_cluster_and_fragment(cluster_atoms_in_phs,
fragment_super_atoms_in_phs,
i_cluster, 0)
##check alternative locations and get all clusters and fragments
overlap_clusters = {}
overlap_fragments_super = {}
if (len(phs) > 1):
for i_cluster in range(len(clusters)):
for j_ph in range(1, len(phs)):
fragment_same = (set(
fragment_super_atoms_in_phs[0][i_cluster]) == set(
fragment_super_atoms_in_phs[j_ph][i_cluster]))
# two same fragments for same non-altloc clusters
if (fragment_same): continue
# check the overlap
overlap_atoms_in_one_cluster = self.atoms_overlap(
cluster_atoms_in_phs, i_cluster, j_ph)
empty_overlap_cluster = (
len(overlap_atoms_in_one_cluster) == 0)
#substract the contribution from overlap
if (self.altloc_method == "subtract"):
self.collect_cluster_and_fragment(
cluster_atoms_in_phs, fragment_super_atoms_in_phs,
i_cluster, j_ph)
# different fragments for different altloc clusters
if (empty_overlap_cluster): continue
else:
# two same non-altloc clusters, the overlap is a cluster
# two different altloc clusters, the overlap is part of a residue,
# even an atom
# the cluster overlap will cause troubles for QM calculation,
# expecially when it is an atom
atoms = self.pdb_hierarchy_super.atoms()
overlap_atoms_in_one_fragment = self.atoms_overlap(
fragment_super_atoms_in_phs, i_cluster, j_ph)
check_selection_integrity(
atoms, overlap_atoms_in_one_fragment)
self.cluster_atoms.append(
list(overlap_atoms_in_one_cluster))
self.fragment_super_atoms.append(
list(overlap_atoms_in_one_fragment))
scale_list = [-1.0] * sum(
i <= self.system_size
for i in overlap_atoms_in_one_fragment)
self.fragment_scales.append(scale_list)
##average the contributions from overlap
elif (self.altloc_method == "average"):
# different fragments for different altloc clusters
if (empty_overlap_cluster):
self.collect_cluster_and_fragment(
cluster_atoms_in_phs,
fragment_super_atoms_in_phs, i_cluster, j_ph)
else:
# two same non-altloc clusters, the overlap is a cluster
# two different altloc clusters, the overlap is part of a residue, even an atom
# collect all overlap clusters and fragments
try:
overlap_clusters[i_cluster] = overlap_clusters[
i_cluster].append(
cluster_atoms_in_phs[j_ph][i_cluster])
overlap_fragments_super[i_cluster] = \
overlap_fragments_super[i_cluster].append(
fragment_super_atoms_in_phs[j_ph][i_cluster])
except:
overlap_clusters[i_cluster] = \
[cluster_atoms_in_phs[j_ph][i_cluster]]
overlap_fragments_super[i_cluster] = \
[fragment_super_atoms_in_phs[j_ph][i_cluster]]
overlap_atoms = []
for i_cluster, overlap_cluster in overlap_clusters.items():
overlap_atoms = overlap_atoms + list(
itertools.chain.from_iterable(overlap_cluster +
[self.cluster_atoms[i_cluster]])
) #[atom_index, atom_index]
frequency_overlap_atoms = {
x: overlap_atoms.count(x)
for x in overlap_atoms
} #{atom_index,frequency}
for i_cluster, clusters in overlap_clusters.items():
## reset the fragment scale for the ith fragment in ph[0]
for index, atom in enumerate([
i for i in self.fragment_super_atoms[i_cluster]
if i <= self.system_size
]):
if (atom in self.cluster_atoms[i_cluster]
and atom in frequency_overlap_atoms.keys()
and not self.bond_with_altloc(
atom, self.bond_with_altloc_flag)):
self.fragment_scales[i_cluster][index] = \
1.0/frequency_overlap_atoms[atom]
## add overlap clusters and fragments
for index, fragment_super in enumerate(
overlap_fragments_super[i_cluster]):
scale_list = []
for atom in [
i for i in fragment_super if i <= self.system_size
]:
if (atom in clusters[index]
and atom in frequency_overlap_atoms.keys()
and not self.bond_with_altloc(
atom, self.bond_with_altloc_flag)):
scale_list.append(1.0 / frequency_overlap_atoms[atom])
else:
scale_list.append(1.0)
self.cluster_atoms.append(clusters[index])
self.fragment_super_atoms.append(fragment_super)
self.fragment_scales.append(scale_list)
def bond_with_altloc(self, atom_index, bond_with_altloc_flag):
if (not bond_with_altloc_flag): return False
ph_atoms = list(self.pdb_hierarchy.atoms())
ph_atom = ph_atoms[atom_index - 1]
bond = False
for altloc_atom in self.altloc_atoms:
distance = ph_atom.distance(altloc_atom)
if (distance < 1.7):
bond = True
break
##TODO
##check bond, better from bond topology
return bond
def atoms_overlap(self, cluster_atoms_in_phs, i_cluster, j_ph):
overlap_atoms_in_one_cluster = set(cluster_atoms_in_phs[0][i_cluster]) & \
set(cluster_atoms_in_phs[j_ph][i_cluster])
return overlap_atoms_in_one_cluster
def collect_cluster_and_fragment(self, cluster_atoms_in_phs,
fragment_super_atoms_in_phs, i_cluster,
j_ph):
self.cluster_atoms.append(cluster_atoms_in_phs[j_ph][i_cluster])
self.fragment_super_atoms.append(
fragment_super_atoms_in_phs[j_ph][i_cluster])
scale_list = [1.0] * sum(
i <= self.system_size
for i in fragment_super_atoms_in_phs[j_ph][i_cluster])
self.fragment_scales.append(scale_list)
def get_fragment_hierarchies_and_charges(self):
def pdb_hierarchy_select(atoms_size, selection):
selection_array = flex.bool(atoms_size, False)
for item in selection:
if (item <= atoms_size):
selection_array[item - 1] = True
return selection_array
self.fragment_selections = []
self.fragment_super_selections = []
self.fragment_charges = []
self.cluster_selections = []
self.buffer_selections = []
self.cluster_selections = []
self.fragment_capped_initial = []
for i in range(len(self.fragment_super_atoms)):
fragment_selection = pdb_hierarchy_select(
self.pdb_hierarchy.atoms_size(), self.fragment_super_atoms[i])
## QM part is fragment_super
fragment_super_selection = pdb_hierarchy_select(
self.pdb_hierarchy_super.atoms_size(),
self.fragment_super_atoms[i])
fragment_super_hierarchy = self.pdb_hierarchy_super.select(
fragment_super_selection)
if (self.debug):
fragment_super_hierarchy.write_pdb_file(file_name=str(i) +
"-origin-cs.pdb")
fragment_super_hierarchy.write_pdb_file(
file_name=str(i) + ".pdb",
crystal_symmetry=self.expansion.cs_box)
charge_hierarchy = completion.run(
pdb_hierarchy=fragment_super_hierarchy,
crystal_symmetry=self.expansion.cs_box,
model_completion=False,
original_pdb_filename=self.expansion_file)
self.fragment_capped_initial.append(charge_hierarchy)
raw_records = charge_hierarchy.as_pdb_string(
crystal_symmetry=self.expansion.cs_box)
if (1):
charge_hierarchy.write_pdb_file(
file_name=str(i) + "_capping_tmp.pdb",
crystal_symmetry=self.expansion.cs_box)
self.charge_service.update_pdb_hierarchy(
charge_hierarchy,
self.expansion.cs_box,
)
#TODO: do not why self.charge_service could not right charge
#charge = self.charge_service.get_total_charge()
charge = charges_class(pdb_filename=str(i) +
"_capping_tmp.pdb").get_total_charge()
#the capping pdb file causes problem for tests, remove it
os.remove(str(i) + "_capping_tmp.pdb")
self.fragment_super_selections.append(fragment_super_selection)
#
self.fragment_selections.append(fragment_selection)
self.fragment_charges.append(charge)
cluster_selection = pdb_hierarchy_select(
self.pdb_hierarchy.atoms_size(), self.cluster_atoms[i])
self.cluster_selections.append(cluster_selection)
s = fragment_selection == cluster_selection
buffer_selection = fragment_selection.deep_copy().set_selected(
s, False)
self.buffer_selections.append(buffer_selection)
# if(self.debug):
# fragment_super_hierarchy.write_pdb_file(file_name=str(i)+"_frag.pdb",
# crystal_symmetry=self.expansion.cs_box)
# cluster_pdb_hierarchy = self.pdb_hierarchy.select(cluster_selection)
# cluster_pdb_hierarchy.write_pdb_file(file_name=str(i)+"_cluster.pdb",
# crystal_symmetry=self.expansion.cs_box)
check_hierarchy(fragment_super_hierarchy)