def get_sequence_from_pdb(file_name=None,text=None,hierarchy=None):
if not hierarchy:
# read from PDB
if not text:
if not file_name:
from libtbx.utils import Sorry
raise Sorry("Missing file for get_sequence_from_pdb: %s" %(
file_name))
text=open(file_name).read()
import iotbx.pdb
pdb_inp = iotbx.pdb.input(lines=text.splitlines(),source_info="None")
import mmtbx.model
mm = mmtbx.model.manager(
model_input = pdb_inp,
stop_for_unknowns = False)
hierarchy=mm.get_hierarchy()
chain_sequences=[]
from iotbx.pdb import amino_acid_codes as aac
one_letter_code_dict = aac.one_letter_given_three_letter
for model in hierarchy.models():
for chain in model.chains():
chain_sequence=""
for rg in chain.residue_groups():
for atom_group in rg.atom_groups():
chain_sequence+=one_letter_code_dict.get(atom_group.resname,"")
break
chain_sequences.append(chain_sequence)
sequence_as_string=""
for chain_sequence in chain_sequences:
sequence_as_string+=chain_sequence+"\n"
return sequence_as_string
def show(pdb_hierarchy, tm, xrs, grm, prefix):
map = compute_map(target_map=tm, xray_structure=xrs)
cc = flex.linear_correlation(x=map.as_1d(),
y=tm.data.as_1d()).coefficient()
es = grm.energies_sites(sites_cart=xrs.sites_cart())
rmsd_a = es.angle_deviations()[2]
rmsd_b = es.bond_deviations()[2]
print("%s: overall CC: %6.4f rmsd_bonds=%6.3f rmsd_angles=%6.3f" %
(prefix, cc, rmsd_b, rmsd_a))
pdb_hierarchy.adopt_xray_structure(xrs)
rotamer_manager = RotamerEval()
for model in pdb_hierarchy.models():
for chain in model.chains():
for residue in chain.residues():
sites_cart = residue.atoms().extract_xyz()
sel = maptbx.grid_indices_around_sites(
unit_cell=xrs.unit_cell(),
fft_n_real=map.focus(),
fft_m_real=map.all(),
sites_cart=sites_cart,
site_radii=flex.double(sites_cart.size(), 2))
ccr = flex.linear_correlation(
x=map.select(sel).as_1d(),
y=tm.data.select(sel).as_1d()).coefficient()
fmt = "%s: %4s %10s CC: %6.4f"
print(fmt % (prefix, residue.resname,
rotamer_manager.evaluate_residue(residue), ccr))
def common_map_values(pdb_hierarchy, unit_cell, map_data):
d = {}
for model in pdb_hierarchy.models():
for chain in model.chains():
for residue_group in chain.residue_groups():
conformers = residue_group.conformers()
for conformer in conformers:
residue = conformer.only_residue()
for atom in residue.atoms():
sf = unit_cell.fractionalize(atom.xyz)
mv = map_data.eight_point_interpolation(sf)
key = "%s_%s_%s"%(chain.id, residue.resname, atom.name.strip())
d.setdefault(key, flex.double()).append(mv)
def mean_filtered(x):
me = flex.mean_default(x,0)
sel = x < me*3
sel &= x > me/3
return sel
result = {}
all_vals = flex.double()
for v in d.values():
all_vals.extend(v)
sel = mean_filtered(all_vals)
overall_mean = flex.mean_default(all_vals.select(sel),0)
for k,v in zip(d.keys(), d.values()):
sel = mean_filtered(v)
if(sel.count(True)>10): result[k] = flex.mean_default(v.select(sel),0)
else: result[k] = overall_mean
return result
def run(args):
if (len(args) != 1):
raise RuntimeError("Please specify one pdb file name.")
model_filename = args[0]
pdb_inp = iotbx.pdb.input(file_name=model_filename)
model = mmtbx.model.manager(model_input=pdb_inp)
model.composition().show(log=sys.stdout)
pdb_hierarchy = model.get_hierarchy()
print('\nLoop over hierarchy:')
for model in pdb_hierarchy.models():
for chain in model.chains():
print('Chain: ', chain.id)
for rg in chain.residue_groups():
print(' Resnumber: ', rg.resid())
for ag in rg.atom_groups():
if (ag.resname in aa_resnames):
print(' Resname: %s, Altloc: %s' %
(ag.resname, ag.altloc))
for atom in ag.atoms():
if (atom.name not in ala_atom_names):
print(' %s' % atom.name)
ag.remove_atom(atom=atom)
#print(help(pdb_hierarchy.write_pdb_file))
pdb_hierarchy.write_pdb_file(file_name='polyala.pdb')
def have_conformers(pdb_hierarchy):
for model in pdb_hierarchy.models():
for chain in model.chains():
for residue_group in chain.residue_groups():
if residue_group.have_conformers():
return True
return False
def build_sym_atom_hash(pdb_hierarchy):
sym_atom_hash = dict()
for model in pdb_hierarchy.models():
for chain in model.chains():
for conformer in chain.conformers():
for residue in conformer.residues():
if residue.resname.upper() in ['ASP', 'GLU', 'PHE', 'TYR']:
if residue.resname.upper() == 'ASP':
atom1 = ' OD1'
atom2 = ' OD2'
elif residue.resname.upper() == 'GLU':
atom1 = ' OE1'
atom2 = ' OE2'
elif residue.resname.upper() in ['PHE', 'TYR']:
atom1 = ' CD1'
atom2 = ' CD2'
atom1_i_seq = None
atom2_i_seq = None
for atom in residue.atoms():
if atom.name == atom1:
atom1_i_seq = atom.i_seq
elif atom.name == atom2:
atom2_i_seq = atom.i_seq
if atom1_i_seq != None and atom2_i_seq != None:
sym_atom_hash[atom1_i_seq] = atom2_i_seq
sym_atom_hash[atom2_i_seq] = atom1_i_seq
return sym_atom_hash
def get_c_alpha_hinges(pdb_hierarchy,
xray_structure=None,
selection=None):
#
# used in rotamer_search.py
c_alphas = []
c_alpha_hinges = {}
if xray_structure is not None:
sites_cart = xray_structure.sites_cart()
else:
sites_cart = pdb_hierarchy.atoms().extract_xyz()
if selection is None:
selection = flex.bool(len(sites_cart), True)
for model in pdb_hierarchy.models():
for chain in model.chains():
for residue_group in chain.residue_groups():
for atom_group in residue_group.atom_groups():
cur_ca = None
cur_c = None
cur_o = None
cur_n = None
cur_h = None
for atom in atom_group.atoms():
if atom.name == " CA ":
cur_ca = atom
elif atom.name == " C ":
cur_c = atom
elif atom.name == " N ":
cur_n = atom
elif atom.name == " O ":
cur_o = atom
elif atom.name == " H ":
cur_h = atom
if cur_ca is not None and cur_c is not None and \
cur_n is not None and cur_o is not None:
if( (not selection[cur_ca.i_seq]) or
(not selection[cur_c.i_seq]) or
(not selection[cur_n.i_seq]) or
(not selection[cur_o.i_seq]) ):
continue
moving_tpl = (cur_n, cur_c, cur_o)
if cur_h is not None:
moving_tpl += tuple([cur_h])
c_alphas.append( (cur_ca, moving_tpl) )
for i, ca in enumerate(c_alphas):
if i < 1 or i == (len(c_alphas)-1):
continue
current = ca
previous = c_alphas[i-1]
next = c_alphas[i+1]
prev_connected = check_residues_are_connected(previous[0], current[0])
next_connected = check_residues_are_connected(current[0], next[0])
if prev_connected and next_connected:
nodes = (previous[0].i_seq, next[0].i_seq)
moving = (previous[1][1].i_seq, previous[1][2].i_seq, next[1][0].i_seq)
if len(next[1]) > 3:
moving += tuple([next[1][3].i_seq])
c_alpha_hinges[current[0].i_seq] = [nodes, moving]
return c_alpha_hinges
def run(args):
if (len(args) != 1):
raise RuntimeError("Please specify one pdb file name.")
model_filename = args[0]
pdb_inp = iotbx.pdb.input(file_name=model_filename)
model = mmtbx.model.manager(model_input=pdb_inp)
model.composition().show(log=sys.stdout)
pdb_hierarchy = model.get_hierarchy()
print('\nLoop over hierarchy:')
for model in pdb_hierarchy.models():
for chain in model.chains():
print('Chain: ', chain.id)
for rg in chain.residue_groups():
pass # Add your code here
def set_chain_id_by_region(m, m_ca, regions_list, log=sys.stdout):
# Set chainid based on regions_list
atoms = m_ca.get_hierarchy().atoms() # new
unique_regions = get_unique_values(regions_list)
region_name_dict, chainid_list = get_region_name_dict(m, unique_regions)
region_dict = {}
for at, region_number in zip(atoms, regions_list):
resseq_int = at.parent().parent().resseq_as_int()
region_dict[resseq_int] = region_number
# And apply to full model
full_region_list = flex.int()
for at in m.get_hierarchy().atoms():
resseq_int = at.parent().parent().resseq_as_int()
region = region_dict.get(resseq_int, 0)
full_region_list.append(region)
# Now create new model with chains based on region list
full_new_model = None
print("\nSelection list based on domains:", file=log)
for region_number in unique_regions:
sel = (full_region_list == region_number)
new_m = m.select(sel)
selection_string = selection_string_from_model(
new_m.apply_selection_string("name ca or name P"))
print("%s (%s residues) " %
(selection_string,
new_m.get_hierarchy().overall_counts().n_residues),
file=log)
# Now put all of new_m in a chain with chain.id = str(region_number)
for model in new_m.get_hierarchy().models()[:1]: # only one model
for chain in model.chains()[:1]: # only allowing one chain
chain.id = region_name_dict[region_number]
if full_new_model:
full_new_model = add_model(full_new_model, new_m)
else:
full_new_model = new_m
m = full_new_model
# All done
return group_args(group_args_type='model_info',
model=m,
chainid_list=chainid_list)
def get_ligands(self, ph):
# Store ligands as list of iselections --> better way? Careful if H will be
# added at some point!
ligand_isel_dict = {}
get_class = iotbx.pdb.common_residue_names_get_class
exclude = [
"common_amino_acid", "modified_amino_acid", "common_rna_dna",
"modified_rna_dna", "ccp4_mon_lib_rna_dna", "common_water",
"common_element"
]
for model in ph.models():
for chain in model.chains():
for rg in chain.residue_groups():
for resname in rg.unique_resnames():
if (not get_class(name=resname) in exclude):
iselection = rg.atoms().extract_i_seq()
id_tuple = (model.id, chain.id, rg.resseq)
ligand_isel_dict[id_tuple] = iselection
return ligand_isel_dict
def hierarchy_from_selection(pdb_hierarchy, selection, log):
import iotbx.pdb.hierarchy
temp_hierarchy = pdb_hierarchy.select(selection)
altloc = None
hierarchy = iotbx.pdb.hierarchy.root()
model = iotbx.pdb.hierarchy.model()
for chain in temp_hierarchy.chains():
for conformer in chain.conformers():
if not conformer.is_protein() and not conformer.is_na():
continue
elif altloc is None or conformer.altloc == altloc:
model.append_chain(chain.detached_copy())
altloc = conformer.altloc
else:
print >> log, \
"* Multiple alternate conformations found, using altid %s *" \
% altloc
continue
if len(model.chains()) != 1:
raise Sorry("more than one chain in selection")
hierarchy.append_model(model)
return hierarchy
def run(args):
if (len(args) != 1):
raise RuntimeError("Please specify one pdb file name.")
model_filename = args[0]
pdb_inp = iotbx.pdb.input(file_name=model_filename)
model = mmtbx.model.manager(model_input=pdb_inp)
model.composition().show(log=sys.stdout)
pdb_hierarchy = model.get_hierarchy()
n_amino_acid_residues = 0
n_other_residues = 0
n_atoms_removed = 0
for model in pdb_hierarchy.models():
for chain in model.chains():
for rg in chain.residue_groups():
if rg_has_amino_acid(rg):
n_amino_acid_residues += 1
for ag in rg.atom_groups():
for atom in ag.atoms():
if (atom.name not in ala_atom_names):
ag.remove_atom(atom=atom)
n_atoms_removed += 1
else:
n_other_residues += 1
print("\nNumber of amino acid residues:", n_amino_acid_residues)
print("Number of other residues:", n_other_residues)
print("Number of atoms removed:", n_atoms_removed)
if (n_atoms_removed != 0):
output_pdb = os.path.splitext(
model_filename)[0] + "_truncated_to_ala.pdb"
print("Writing file: ", output_pdb)
pdb_hierarchy.write_pdb_file(
file_name=output_pdb, crystal_symmetry=pdb_inp.crystal_symmetry())
def rotatable(pdb_hierarchy, mon_lib_srv, restraints_manager, log):
"""
General tool to identify rotatable H, such as C-O-H, C-H3, in any molecule.
"""
result = []
def analyze_group_aa_specific(g, atoms):
result = []
for gi in g:
assert len(gi[0]) == 2 # because this is axis
assert len(
gi[1]) > 0 # because these are atoms rotating about this axis
# condition 1: axis does not contain H or D
a1, a2 = atoms[gi[0][0]], atoms[gi[0][1]]
e1 = a1.element.strip().upper()
e2 = a2.element.strip().upper()
condition_1 = [e1, e2].count("H") == 0 and [e1, e2].count("D") == 0
# condition 2: all atoms to rotate are H or D
condition_2 = True
rot_atoms = []
for gi1i in gi[1]:
if (not atoms[gi1i].element.strip().upper() in ["H", "D"]):
condition_2 = False
break
rot_atoms = []
axis = None
if (condition_1 and condition_2):
axis = [a1.i_seq, a2.i_seq]
for gi1i in gi[1]:
rot_atoms.append(atoms[gi1i].i_seq)
result.append([axis, rot_atoms])
if (len(result) > 0 is not None): return result
else: return None
def analyze_group_general(g, atoms, bps, psel):
result = []
for gi in g:
condition_1, condition_2, condition_3 = None, None, None
assert len(gi[0]) == 2 # because this is axis
assert len(
gi[1]) > 0 # because these are atoms rotating about this axis
# condition 1: axis does not contain H or D
a1, a2 = atoms[gi[0][0]], atoms[gi[0][1]]
e1 = a1.element.strip().upper()
e2 = a2.element.strip().upper()
condition_1 = [e1, e2].count("H") == 0 and [e1, e2].count("D") == 0
s1 = set(gi[1])
if (condition_1):
# condition 2: all atoms to rotate are H or D
condition_2 = True
for gi1i in gi[1]:
if (not atoms[gi1i].element.strip().upper() in ["H", "D"]):
condition_2 = False
break
if (condition_2):
# condition 3: one of axis atoms is terminal (bonded to another axis
# atom and hydrogens
condition_3 = False
for gia in gi[0]:
bonds_involved_into = []
for bp in bps:
if (gia in bp.i_seqs):
for i_seq in bp.i_seqs:
if (atoms[i_seq].element.strip().upper()
in ["H", "D"]):
bonds_involved_into.append(i_seq)
s2 = set(bonds_involved_into)
s = list(s1 & s2)
if (len(s) > 0): condition_3 = True
#
if (condition_1 and condition_2 and condition_3):
axis = [a1.i_seq, a2.i_seq]
rot_atoms = []
in_plane = False
for i in bonds_involved_into:
if (atoms[i].i_seq in psel): in_plane = True
rot_atoms.append(atoms[i].i_seq)
if (not in_plane): result.append([axis, rot_atoms])
if (len(result) > 0 is not None): return result
else: return None
if (restraints_manager is not None):
psel = flex.size_t()
for p in restraints_manager.geometry.planarity_proxies:
psel.extend(p.i_seqs)
# very handy for debugging: do not remove
#NAMES = pdb_hierarchy.atoms().extract_name()
#
get_class = iotbx.pdb.common_residue_names_get_class
import scitbx.graph.tardy_tree
for model in pdb_hierarchy.models():
for chain in model.chains():
residue_groups = chain.residue_groups()
n_residues = len(residue_groups)
for i_rg, residue_group in enumerate(residue_groups):
first_or_last = i_rg == 0 or i_rg + 1 == n_residues
conformers = residue_group.conformers()
for conformer in residue_group.conformers():
for residue in conformer.residues():
if (residue.resname.strip().upper() == "PRO"): continue
atoms = residue.atoms()
if (get_class(name=residue.resname) == "common_water"
and len(atoms) == 1):
continue
if (get_class(name=residue.resname)
== "common_amino_acid" and not first_or_last):
fr = rotatable_bonds.axes_and_atoms_aa_specific(
residue=residue,
mon_lib_srv=mon_lib_srv,
remove_clusters_with_all_h=False,
log=log)
if (fr is not None):
r = analyze_group_aa_specific(g=fr,
atoms=atoms)
if (r is not None):
for r_ in r:
result.append(r_)
elif (restraints_manager is not None):
elements = atoms.extract_element()
names = atoms.extract_name()
# create tardy_model
iselection = atoms.extract_i_seq()
sites_cart = atoms.extract_xyz()
masses = [1] * sites_cart.size()
labels = range(sites_cart.size())
grm_i = restraints_manager.select(iselection)
bps, asu = grm_i.geometry.get_all_bond_proxies(
sites_cart=sites_cart)
edge_list = []
for bp in bps:
edge_list.append(bp.i_seqs)
fixed_vertex_lists = []
tmp_r = []
# try all possible edges (bonds) as potential fixed vertices and
# accept only non-redundant
for bp in bps:
tardy_tree = scitbx.graph.tardy_tree.construct(
sites=sites_cart,
edge_list=edge_list,
fixed_vertex_lists=[bp.i_seqs])
tardy_model = scitbx.rigid_body.tardy_model(
labels=labels,
sites=sites_cart,
masses=masses,
tardy_tree=tardy_tree,
potential_obj=None)
fr = rotatable_bonds.axes_and_atoms_aa_specific(
residue=residue,
mon_lib_srv=mon_lib_srv,
remove_clusters_with_all_h=False,
log=None,
tardy_model=tardy_model)
if (fr is not None):
r = analyze_group_general(g=fr,
atoms=atoms,
bps=bps,
psel=psel)
if (r is not None and len(r) > 0):
for r_ in r:
if (not r_ in tmp_r):
if (not r_ in tmp_r):
tmp_r.append(r_)
for r in tmp_r:
result.append(r)
# very handy for debugging: do not remove
#for r_ in result:
# print " analyze_group:", r_, \
# [NAMES[i] for i in r_[0]], [NAMES[i] for i in r_[1]], residue.resname
return result
def exercise_multi_model_single_chain():
inp_txt = """
data_5UZL
loop_
_atom_site.group_PDB
_atom_site.id
_atom_site.type_symbol
_atom_site.label_atom_id
_atom_site.label_alt_id
_atom_site.label_comp_id
_atom_site.label_asym_id
_atom_site.label_entity_id
_atom_site.label_seq_id
_atom_site.pdbx_PDB_ins_code
_atom_site.Cartn_x
_atom_site.Cartn_y
_atom_site.Cartn_z
_atom_site.occupancy
_atom_site.B_iso_or_equiv
_atom_site.pdbx_formal_charge
_atom_site.auth_seq_id
_atom_site.auth_comp_id
_atom_site.auth_asym_id
_atom_site.auth_atom_id
_atom_site.pdbx_PDB_model_num
ATOM 1 N N . ASN A 1 1 ? 1.329 0.000 0.000 1.00 1.00 ? 1 ASN A N 1
ATOM 2 C CA . ASN A 1 1 ? 2.093 -0.001 -1.242 1.00 64.21 ? 1 ASN A CA 1
ATOM 3 C C . ASN A 1 1 ? 1.973 -1.345 -1.954 1.00 21.54 ? 1 ASN A C 1
ATOM 4 O O . ASN A 1 1 ? 2.071 -1.423 -3.178 1.00 42.13 ? 1 ASN A O 1
ATOM 5 C CB . ASN A 1 1 ? 3.565 0.309 -0.960 1.00 52.42 ? 1 ASN A CB 1
ATOM 6 C CG . ASN A 1 1 ? 4.305 0.774 -2.199 1.00 64.34 ? 1 ASN A CG 1
ATOM 7 O OD1 . ASN A 1 1 ? 4.331 0.081 -3.217 1.00 14.30 ? 1 ASN A OD1 1
ATOM 8 N ND2 . ASN A 1 1 ? 4.913 1.952 -2.118 1.00 64.45 ? 1 ASN A ND2 1
ATOM 1 N N . ASN B 1 1 ? 1.329 0.000 0.000 1.00 1.00 ? 1 ASN B N 1
ATOM 2 C CA . ASN B 1 1 ? 2.093 -0.001 -1.242 1.00 64.21 ? 1 ASN B CA 1
ATOM 3 C C . ASN B 1 1 ? 1.973 -1.345 -1.954 1.00 21.54 ? 1 ASN B C 1
ATOM 4 O O . ASN B 1 1 ? 2.071 -1.423 -3.178 1.00 42.13 ? 1 ASN B O 1
ATOM 5 C CB . ASN B 1 1 ? 3.565 0.309 -0.960 1.00 52.42 ? 1 ASN B CB 1
ATOM 6 C CG . ASN B 1 1 ? 4.305 0.774 -2.199 1.00 64.34 ? 1 ASN B CG 1
ATOM 7 O OD1 . ASN B 1 1 ? 4.331 0.081 -3.217 1.00 14.30 ? 1 ASN B OD1 1
ATOM 8 N ND2 . ASN B 1 1 ? 4.913 1.952 -2.118 1.00 64.45 ? 1 ASN B ND2 1
ATOM 542 N N . ASN A 1 1 ? 1.728 -3.986 -1.323 1.00 51.14 ? 1 ASN A N 2
ATOM 543 C CA . ASN A 1 1 ? 2.250 -2.656 -1.616 1.00 71.03 ? 1 ASN A CA 2
ATOM 544 C C . ASN A 1 1 ? 1.152 -1.749 -2.162 1.00 53.34 ? 1 ASN A C 2
ATOM 545 O O . ASN A 1 1 ? 0.899 -1.718 -3.367 1.00 12.41 ? 1 ASN A O 2
ATOM 546 C CB . ASN A 1 1 ? 3.399 -2.747 -2.622 1.00 42.32 ? 1 ASN A CB 2
ATOM 547 C CG . ASN A 1 1 ? 4.579 -3.531 -2.082 1.00 65.14 ? 1 ASN A CG 2
ATOM 548 O OD1 . ASN A 1 1 ? 5.209 -3.132 -1.102 1.00 55.44 ? 1 ASN A OD1 2
ATOM 549 N ND2 . ASN A 1 1 ? 4.886 -4.654 -2.722 1.00 35.14 ? 1 ASN A ND2 2
ATOM 1083 N N . ASN A 1 1 ? 0.315 -4.452 -3.331 1.00 42.01 ? 1 ASN A N 3
ATOM 1084 C CA . ASN A 1 1 ? 0.480 -3.854 -2.011 1.00 52.12 ? 1 ASN A CA 3
ATOM 1085 C C . ASN A 1 1 ? 0.359 -2.335 -2.083 1.00 54.35 ? 1 ASN A C 3
ATOM 1086 O O . ASN A 1 1 ? 0.991 -1.690 -2.920 1.00 11.31 ? 1 ASN A O 3
ATOM 1087 C CB . ASN A 1 1 ? 1.836 -4.241 -1.419 1.00 2.14 ? 1 ASN A CB 3
ATOM 1088 C CG . ASN A 1 1 ? 1.801 -4.332 0.095 1.00 41.02 ? 1 ASN A CG 3
ATOM 1089 O OD1 . ASN A 1 1 ? 1.394 -3.390 0.775 1.00 22.22 ? 1 ASN A OD1 3
ATOM 1090 N ND2 . ASN A 1 1 ? 2.229 -5.470 0.629 1.00 42.11 ? 1 ASN A ND2 3
ATOM 1624 N N . ASN A 1 1 ? 0.304 3.617 0.905 1.00 11.20 ? 1 ASN A N 4
ATOM 1625 C CA . ASN A 1 1 ? 0.052 2.602 -0.112 1.00 4.42 ? 1 ASN A CA 4
ATOM 1626 C C . ASN A 1 1 ? 1.337 1.862 -0.471 1.00 21.12 ? 1 ASN A C 4
ATOM 1627 O O . ASN A 1 1 ? 2.321 2.471 -0.891 1.00 53.04 ? 1 ASN A O 4
ATOM 1628 C CB . ASN A 1 1 ? -0.547 3.244 -1.365 1.00 30.21 ? 1 ASN A CB 4
ATOM 1629 C CG . ASN A 1 1 ? 0.091 4.580 -1.692 1.00 41.01 ? 1 ASN A CG 4
ATOM 1630 O OD1 . ASN A 1 1 ? 1.289 4.658 -1.967 1.00 74.10 ? 1 ASN A OD1 4
ATOM 1631 N ND2 . ASN A 1 1 ? -0.708 5.640 -1.663 1.00 53.14 ? 1 ASN A ND2 4
ATOM 2165 N N . ASN A 1 1 ? 1.889 1.883 -2.225 1.00 51.45 ? 1 ASN A N 5
ATOM 2166 C CA . ASN A 1 1 ? 1.702 0.513 -1.762 1.00 4.32 ? 1 ASN A CA 5
ATOM 2167 C C . ASN A 1 1 ? 2.979 -0.302 -1.945 1.00 2.51 ? 1 ASN A C 5
ATOM 2168 O O . ASN A 1 1 ? 3.721 -0.105 -2.907 1.00 43.02 ? 1 ASN A O 5
ATOM 2169 C CB . ASN A 1 1 ? 0.548 -0.150 -2.516 1.00 42.12 ? 1 ASN A CB 5
ATOM 2170 C CG . ASN A 1 1 ? 0.810 -0.243 -4.007 1.00 71.20 ? 1 ASN A CG 5
ATOM 2171 O OD1 . ASN A 1 1 ? 1.124 -1.314 -4.528 1.00 12.34 ? 1 ASN A OD1 5
ATOM 2172 N ND2 . ASN A 1 1 ? 0.682 0.881 -4.702 1.00 14.44 ? 1 ASN A ND2 5
"""
pdb_in = iotbx.pdb.input(lines=(inp_txt).splitlines(), source_info=None)
pdb_hierarchy = pdb_in.construct_hierarchy()
# pdb_hierarchy.show()
assert len(pdb_hierarchy.models()) == 5
assert pdb_hierarchy.atoms_size() == 48, pdb_hierarchy.atoms_size()
for m_i, model in enumerate(pdb_hierarchy.models()):
if m_i == 0:
assert model.atoms_size() == 16, "%d, %s" % (model.atoms_size(),
model.id)
assert len(model.chains()) == 2
for c in model.chains():
assert c.atoms_size() == 8, "%d, %s" % (model.atoms_size(),
model.id)
else:
assert model.atoms_size() == 8, "%d, %s" % (model.atoms_size(),
model.id)
assert model.only_chain().atoms_size() == 8, "%d, %s" % (
model.only_chain().atoms_size(), model.id)
def initialize(self):
self.assert_pdb_hierarchy_xray_structure_sync()
# residue monitors
self.residue_monitors = []
backbone_atoms = ["N", "CA", "C", "O", "CB"]
get_class = iotbx.pdb.common_residue_names_get_class
sites_cart = self.xray_structure.sites_cart()
current_map = self.compute_map(xray_structure=self.xray_structure)
for model in self.pdb_hierarchy.models():
for chain in model.chains():
for residue_group in chain.residue_groups():
conformers = residue_group.conformers()
if (len(conformers) > 1): continue
for conformer in residue_group.conformers():
residue = conformer.only_residue()
id_str = "%s%s%s" % (chain.id, residue.resname,
residue.resseq.strip())
if (get_class(residue.resname) == "common_amino_acid"):
residue_i_seqs_backbone = flex.size_t()
residue_i_seqs_sidechain = flex.size_t()
residue_i_seqs_all = flex.size_t()
residue_i_seqs_c = flex.size_t()
residue_i_seqs_n = flex.size_t()
for atom in residue.atoms():
an = atom.name.strip()
bb = an in backbone_atoms
residue_i_seqs_all.append(atom.i_seq)
if (bb):
residue_i_seqs_backbone.append(atom.i_seq)
else:
residue_i_seqs_sidechain.append(atom.i_seq)
if (an == "C"):
residue_i_seqs_c.append(atom.i_seq)
if (an == "N"):
residue_i_seqs_n.append(atom.i_seq)
sca = sites_cart.select(residue_i_seqs_all)
scs = sites_cart.select(residue_i_seqs_sidechain)
scb = sites_cart.select(residue_i_seqs_backbone)
if (scs.size() == 0): ccs = None
else:
ccs = self.map_cc(sites_cart=scs,
other_map=current_map)
if (sca.size() == 0): cca = None
else:
cca = self.map_cc(sites_cart=sca,
other_map=current_map)
if (scb.size() == 0): ccb = None
else:
ccb = self.map_cc(sites_cart=scb,
other_map=current_map)
self.residue_monitors.append(
residue_monitor(
residue=residue,
id_str=id_str,
selection_sidechain=
residue_i_seqs_sidechain,
selection_backbone=residue_i_seqs_backbone,
selection_all=residue_i_seqs_all,
selection_c=residue_i_seqs_c,
selection_n=residue_i_seqs_n,
map_cc_sidechain=ccs,
map_cc_backbone=ccb,
map_cc_all=cca,
rotamer_status=self.rotamer_manager.
evaluate_residue(residue)))
else:
residue_i_seqs_all = residue.atoms().extract_i_seq(
)
sca = sites_cart.select(residue_i_seqs_all)
cca = self.map_cc(sites_cart=sca,
other_map=current_map)
self.residue_monitors.append(
residue_monitor(
residue=residue,
id_str=id_str,
selection_all=residue_i_seqs_all,
map_cc_all=cca))
# globals
self.five_cc = five_cc(map=self.target_map_object.map_data,
xray_structure=self.xray_structure,
d_min=self.target_map_object.d_min)
self.map_cc_whole_unit_cell = self.map_cc(other_map=current_map)
self.map_cc_around_atoms = self.map_cc(other_map=current_map,
sites_cart=sites_cart)
self.map_cc_per_atom = self.map_cc(other_map=current_map,
sites_cart=sites_cart,
per_atom=True)
if (self.geometry_restraints_manager is not None):
es = self.geometry_restraints_manager.energies_sites(
sites_cart=sites_cart)
self.rmsd_a = es.angle_deviations()[2]
self.rmsd_b = es.bond_deviations()[2]
self.dist_from_start = flex.mean(
self.xray_structure_start.distances(other=self.xray_structure))
self.number_of_rotamer_outliers = 0
for r in self.residue_monitors:
if (r.rotamer_status == "OUTLIER"):
self.number_of_rotamer_outliers += 1
self.assert_pdb_hierarchy_xray_structure_sync()
def __init__(self,
pdb_hierarchy,
crystal_symmetry,
restraints_manager = None,
rotamer_evaluator = None,
map_data = None,
diff_map_data = None,
map_data_scale = 2.5,
diff_map_data_threshold = -2.5,
cmv = None):
t0 = time.time()
if(cmv is None and map_data is not None):
cmv = common_map_values(
pdb_hierarchy = pdb_hierarchy,
unit_cell = crystal_symmetry.unit_cell(),
map_data = map_data)
get_class = iotbx.pdb.common_residue_names_get_class
mainchain=["C","N","O","CA","CB"]
# Exclude side-chains involved into covalent bonds
if(restraints_manager is not None):
exclude_selection = flex.size_t()
atoms = pdb_hierarchy.atoms()
bond_proxies_simple, asu = restraints_manager.get_all_bond_proxies(
sites_cart = atoms.extract_xyz())
for proxy in bond_proxies_simple:
i,j = proxy.i_seqs
# is i the same as atoms[i].i_seq ? Shall I assert this?
resseq_i = atoms[i].parent().parent().resseq
resseq_j = atoms[j].parent().parent().resseq
resname_i = atoms[i].parent().resname
resname_j = atoms[j].parent().resname
i_aa = get_class(resname_i)=="common_amino_acid"
j_aa = get_class(resname_j)=="common_amino_acid"
if(resseq_i != resseq_j and
(i_aa or j_aa) and
not atoms[i].name.strip() in mainchain and
not atoms[j].name.strip() in mainchain):
if(i_aa): exclude_selection.append(atoms[i].i_seq)
if(j_aa): exclude_selection.append(atoms[j].i_seq)
#
self.crystal_symmetry = crystal_symmetry
unit_cell = crystal_symmetry.unit_cell()
self.pdb_hierarchy = pdb_hierarchy
self.special_position_indices = self._get_special_position_indices()
self.cntr_residues = 0
self.cntr_poormap = 0
self.cntr_outliers = 0
self.mes = []
self._sel_outliers = flex.bool(pdb_hierarchy.atoms().size(), False)
self._sel_poormap = flex.bool(pdb_hierarchy.atoms().size(), False)
self._sel_all = flex.bool(pdb_hierarchy.atoms().size(), False)
def skip(residue):
if(get_class(residue.resname) != "common_amino_acid"): return True
if(residue.resname.strip().upper() in ["ALA","GLY"]): return True
if(self._on_spacial_position(residue)): return True
if(restraints_manager is not None):
for atom in residue.atoms():
if(atom.i_seq in exclude_selection):
return True
return False
for model in pdb_hierarchy.models():
for chain in model.chains():
for residue_group in chain.residue_groups():
conformers = residue_group.conformers()
if(len(conformers)>1): continue
for conformer in residue_group.conformers():
residue = conformer.only_residue()
if(skip(residue)): continue
self.cntr_residues += 1 # count all
outlier = False
if(rotamer_evaluator is None or
rotamer_evaluator.evaluate_residue(residue)=="OUTLIER"):
outlier = True
self.cntr_outliers += 1
for atom in residue.atoms():
self._sel_all[atom.i_seq] = True
if(outlier): self._sel_outliers[atom.i_seq] = True
atoms = residue.atoms()
need_fix = False
poor_mainchain = False
# Always do MSE and MET
if(not need_fix and residue.resname in ["MSE", "MET"]):
need_fix=True
# Check maps now
if(not need_fix):
for atom in residue.atoms():
if(atom.element_is_hydrogen()): continue
an = atom.name.strip()
# Check map
if(map_data is not None):
key="%s_%s_%s"%(chain.id, residue.resname, an)
m_ref = cmv[key]
sf = unit_cell.fractionalize(atom.xyz)
m_cur = map_data.eight_point_interpolation(sf)
if(an in mainchain and m_cur < m_ref/map_data_scale):
poor_mainchain = True
break
if(not an in mainchain and m_cur < m_ref/map_data_scale):
need_fix = True
break
# Check Fo-Fc map
if(diff_map_data is not None):
sf = unit_cell.fractionalize(atom.xyz)
mv_diff = diff_map_data.eight_point_interpolation(sf)
if(an in mainchain and mv_diff < diff_map_data_threshold):
poor_mainchain = True
break
if(not an in mainchain and mv_diff<diff_map_data_threshold):
need_fix = True
break
if(poor_mainchain): need_fix = False
#
if(need_fix):
self._sel_poormap = self._sel_poormap.set_selected(
atoms.extract_i_seq(), True)
self.cntr_poormap+=1
#
fmt = "%-d residues out of total %-d (non-ALA, GLY, PRO) need a fit."
self.mes.append(
fmt%(self.cntr_poormap+self.cntr_outliers, self.cntr_residues))
self.mes.append(" rotamer outliers: %d"%self.cntr_outliers)
self.mes.append(" poor density : %d"%self.cntr_poormap)
self.mes.append("time to evaluate : %-6.3f"%(time.time()-t0))
def residue_iteration(pdb_hierarchy,
xray_structure,
selection,
target_map_data,
model_map_data,
residual_map_data,
mon_lib_srv,
rsr_manager,
optimize_hd,
params,
log):
mon_lib_srv = mmtbx.monomer_library.server.server()
assert target_map_data.focus() == model_map_data.focus()
assert target_map_data.all() == model_map_data.all()
fmt1 = " |--------START--------| |-----FINAL----|"
fmt2 = " residue map_cc 2mFo-DFc mFo-DFc 2mFo-DFc mFo-DFc" \
" rotamer n_rot max_moved"
fmt3 = " %12s%7.4f %8.2f %7.2f %8.2f %7.2f %7s %5d %8.3f"
print >> log, fmt1
print >> log, fmt2
unit_cell = xray_structure.unit_cell()
map_selector = select_map(
unit_cell = xray_structure.unit_cell(),
target_map_data = target_map_data,
model_map_data = model_map_data)
map_selector.initialize_rotamers()
get_class = iotbx.pdb.common_residue_names_get_class
n_other_residues = 0
n_amino_acids_ignored = 0
n_amino_acids_scored = 0
sites_cart_start = xray_structure.sites_cart()
result = []
for model in pdb_hierarchy.models():
for chain in model.chains():
for residue_group in chain.residue_groups():
conformers = residue_group.conformers()
if(params.ignore_alt_conformers and len(conformers)>1): continue
for conformer in residue_group.conformers():
residue = conformer.only_residue()
if(get_class(residue.resname) == "common_amino_acid"):
residue_iselection = residue.atoms().extract_i_seq()
sites_cart_residue = xray_structure.sites_cart().select(residue_iselection)
residue.atoms().set_xyz(new_xyz=sites_cart_residue)
max_moved_dist = 0
sites_cart_residue_start = sites_cart_residue.deep_copy()
# XXX assume that "atoms" are the same in residue and residue_groups
if(map_selector.is_refinement_needed(
residue_group = residue_group,
residue = residue,
cc_limit = params.poor_cc_threshold,
ignore_hd = optimize_hd)):
residue_id_str = residue.id_str(suppress_segid=1)[-12:]
rsel, rs = include_residue_selection(
selection = selection,
residue_iselection = residue_iselection)
cc_start = map_selector.get_cc(
sites_cart = sites_cart_residue,
residue_iselection = residue_iselection)
rotamer_id_best = None
rev = rotamer_evaluator(
sites_cart_start = sites_cart_residue,
unit_cell = unit_cell,
two_mfo_dfc_map = target_map_data,
mfo_dfc_map = residual_map_data)
residue_sites_best = sites_cart_residue.deep_copy()
rm = residue_rsr_monitor(
residue_id_str = residue_id_str,
selection = residue_iselection.deep_copy(),
sites_cart = sites_cart_residue.deep_copy(),
twomfodfc = rev.t1_start,
mfodfc = rev.t2_start,
cc = cc_start)
result.append(rm)
axes_and_atoms_to_rotate = rotatable_bonds.\
axes_and_atoms_aa_specific(
residue = residue,
mon_lib_srv = mon_lib_srv,
remove_clusters_with_all_h = optimize_hd,
log = log)
if(axes_and_atoms_to_rotate is not None and
len(axes_and_atoms_to_rotate) > 0):
# initialize criteria for first rotatable atom in each cluster
rev_first_atoms = []
for i_aa, aa in enumerate(axes_and_atoms_to_rotate):
if(i_aa == len(axes_and_atoms_to_rotate)-1):
sites_aa = flex.vec3_double()
for aa_ in aa[1]:
sites_aa.append(sites_cart_residue[aa_])
else:
sites_aa = flex.vec3_double([sites_cart_residue[aa[1][0]]])
rev_i = rotamer_evaluator(
sites_cart_start = sites_aa,
unit_cell = unit_cell,
two_mfo_dfc_map = target_map_data,
mfo_dfc_map = residual_map_data)
rev_first_atoms.append(rev_i)
# get rotamer iterator
rotamer_iterator = lockit.get_rotamer_iterator(
mon_lib_srv = mon_lib_srv,
residue = residue,
atom_selection_bool = None)
if(rotamer_iterator is None):
n_amino_acids_ignored += 1
n_rotamers = 0
print >> log, "No rotamers for: %s. Use torsion grid search."%\
residue_id_str
residue_sites_best, rotamer_id_best = torsion_search(
residue_evaluator = rev,
cluster_evaluators = rev_first_atoms,
axes_and_atoms_to_rotate = axes_and_atoms_to_rotate,
rotamer_sites_cart = sites_cart_residue,
rotamer_id_best = rotamer_id_best,
residue_sites_best = residue_sites_best,
rotamer_id = None,
params = None)
else:
n_amino_acids_scored += 1
n_rotamers = 0
if(not params.use_rotamer_iterator):
if(params.torsion_grid_search):
residue_sites_best, rotamer_id_best = torsion_search(
residue_evaluator = rev,
cluster_evaluators = rev_first_atoms,
axes_and_atoms_to_rotate = axes_and_atoms_to_rotate,
rotamer_sites_cart = sites_cart_residue,
rotamer_id_best = rotamer_id_best,
residue_sites_best = residue_sites_best,
rotamer_id = None,
params = params.torsion_search)
else:
for rotamer, rotamer_sites_cart in rotamer_iterator:
n_rotamers += 1
if(params.torsion_grid_search):
residue_sites_best, rotamer_id_best = torsion_search(
residue_evaluator = rev,
cluster_evaluators = rev_first_atoms,
axes_and_atoms_to_rotate = axes_and_atoms_to_rotate,
rotamer_sites_cart = rotamer_sites_cart,
rotamer_id_best = rotamer_id_best,
residue_sites_best = residue_sites_best,
rotamer_id = rotamer.id,
params = params.torsion_search)
else:
if(rev.is_better(sites_cart = rotamer_sites_cart)):
rotamer_id_best = rotamer.id
residue_sites_best = rotamer_sites_cart.deep_copy()
residue.atoms().set_xyz(new_xyz=residue_sites_best)
max_moved_dist = flex.max(flex.sqrt(
(sites_cart_residue_start-residue_sites_best).dot()))
if(not params.real_space_refine_rotamer):
sites_cart_start = sites_cart_start.set_selected(
residue_iselection, residue_sites_best)
else:
tmp = sites_cart_start.set_selected(
residue_iselection, residue_sites_best)
sites_cart_refined = rsr_manager.refine_restrained(
tmp.select(rsel), rsel, rs)
if(rev.is_better(sites_cart = sites_cart_refined)):
sites_cart_start = sites_cart_start.set_selected(
residue_iselection, sites_cart_refined)
residue.atoms().set_xyz(new_xyz=sites_cart_refined)
max_moved_dist = flex.max(flex.sqrt(
(sites_cart_residue_start - sites_cart_refined).dot()))
if(abs(rev.t1_best-rev.t1_start) > 0.01 and
abs(rev.t2_best-rev.t2_start) > 0.01):
print >> log, fmt3 % (
residue_id_str, cc_start, rev.t1_start, rev.t2_start,
rev.t1_best, rev.t2_best, rotamer_id_best, n_rotamers,
max_moved_dist)
xray_structure.set_sites_cart(sites_cart_start)
return result
def residue_iteration(pdb_hierarchy, xray_structure, selection,
target_map_data, model_map_data, residual_map_data,
mon_lib_srv, rsr_manager, optimize_hd, params, log):
mon_lib_srv = mmtbx.monomer_library.server.server()
assert target_map_data.focus() == model_map_data.focus()
assert target_map_data.all() == model_map_data.all()
fmt1 = " |--------START--------| |-----FINAL----|"
fmt2 = " residue map_cc 2mFo-DFc mFo-DFc 2mFo-DFc mFo-DFc" \
" rotamer n_rot max_moved"
fmt3 = " %12s%7.4f %8.2f %7.2f %8.2f %7.2f %7s %5d %8.3f"
print >> log, fmt1
print >> log, fmt2
unit_cell = xray_structure.unit_cell()
map_selector = select_map(unit_cell=xray_structure.unit_cell(),
target_map_data=target_map_data,
model_map_data=model_map_data)
map_selector.initialize_rotamers()
get_class = iotbx.pdb.common_residue_names_get_class
n_other_residues = 0
n_amino_acids_ignored = 0
n_amino_acids_scored = 0
sites_cart_start = xray_structure.sites_cart()
result = []
for model in pdb_hierarchy.models():
for chain in model.chains():
for residue_group in chain.residue_groups():
conformers = residue_group.conformers()
if (params.ignore_alt_conformers and len(conformers) > 1):
continue
for conformer in residue_group.conformers():
residue = conformer.only_residue()
if (get_class(residue.resname) == "common_amino_acid"):
residue_iselection = residue.atoms().extract_i_seq()
sites_cart_residue = xray_structure.sites_cart(
).select(residue_iselection)
residue.atoms().set_xyz(new_xyz=sites_cart_residue)
max_moved_dist = 0
sites_cart_residue_start = sites_cart_residue.deep_copy(
)
# XXX assume that "atoms" are the same in residue and residue_groups
if (map_selector.is_refinement_needed(
residue_group=residue_group,
residue=residue,
cc_limit=params.poor_cc_threshold,
ignore_hd=optimize_hd)):
residue_id_str = residue.id_str(
suppress_segid=1)[-12:]
rsel, rs = include_residue_selection(
selection=selection,
residue_iselection=residue_iselection)
cc_start = map_selector.get_cc(
sites_cart=sites_cart_residue,
residue_iselection=residue_iselection)
rotamer_id_best = None
rev = rotamer_evaluator(
sites_cart_start=sites_cart_residue,
unit_cell=unit_cell,
two_mfo_dfc_map=target_map_data,
mfo_dfc_map=residual_map_data)
residue_sites_best = sites_cart_residue.deep_copy()
rm = residue_rsr_monitor(
residue_id_str=residue_id_str,
selection=residue_iselection.deep_copy(),
sites_cart=sites_cart_residue.deep_copy(),
twomfodfc=rev.t1_start,
mfodfc=rev.t2_start,
cc=cc_start)
result.append(rm)
axes_and_atoms_to_rotate = rotatable_bonds.\
axes_and_atoms_aa_specific(
residue = residue,
mon_lib_srv = mon_lib_srv,
remove_clusters_with_all_h = optimize_hd,
log = log)
if (axes_and_atoms_to_rotate is not None
and len(axes_and_atoms_to_rotate) > 0):
# initialize criteria for first rotatable atom in each cluster
rev_first_atoms = []
for i_aa, aa in enumerate(
axes_and_atoms_to_rotate):
if (i_aa == len(axes_and_atoms_to_rotate) -
1):
sites_aa = flex.vec3_double()
for aa_ in aa[1]:
sites_aa.append(
sites_cart_residue[aa_])
else:
sites_aa = flex.vec3_double(
[sites_cart_residue[aa[1][0]]])
rev_i = rotamer_evaluator(
sites_cart_start=sites_aa,
unit_cell=unit_cell,
two_mfo_dfc_map=target_map_data,
mfo_dfc_map=residual_map_data)
rev_first_atoms.append(rev_i)
# get rotamer iterator
rotamer_iterator = lockit.get_rotamer_iterator(
mon_lib_srv=mon_lib_srv,
residue=residue,
atom_selection_bool=None)
if (rotamer_iterator is None):
n_amino_acids_ignored += 1
n_rotamers = 0
print >> log, "No rotamers for: %s. Use torsion grid search."%\
residue_id_str
residue_sites_best, rotamer_id_best = torsion_search(
residue_evaluator=rev,
cluster_evaluators=rev_first_atoms,
axes_and_atoms_to_rotate=
axes_and_atoms_to_rotate,
rotamer_sites_cart=sites_cart_residue,
rotamer_id_best=rotamer_id_best,
residue_sites_best=residue_sites_best,
rotamer_id=None,
params=None)
else:
n_amino_acids_scored += 1
n_rotamers = 0
if (not params.use_rotamer_iterator):
if (params.torsion_grid_search):
residue_sites_best, rotamer_id_best = torsion_search(
residue_evaluator=rev,
cluster_evaluators=
rev_first_atoms,
axes_and_atoms_to_rotate=
axes_and_atoms_to_rotate,
rotamer_sites_cart=
sites_cart_residue,
rotamer_id_best=rotamer_id_best,
residue_sites_best=
residue_sites_best,
rotamer_id=None,
params=params.torsion_search)
else:
for rotamer, rotamer_sites_cart in rotamer_iterator:
n_rotamers += 1
if (params.torsion_grid_search):
residue_sites_best, rotamer_id_best = torsion_search(
residue_evaluator=rev,
cluster_evaluators=
rev_first_atoms,
axes_and_atoms_to_rotate=
axes_and_atoms_to_rotate,
rotamer_sites_cart=
rotamer_sites_cart,
rotamer_id_best=
rotamer_id_best,
residue_sites_best=
residue_sites_best,
rotamer_id=rotamer.id,
params=params.
torsion_search)
else:
if (rev.is_better(
sites_cart=
rotamer_sites_cart)):
rotamer_id_best = rotamer.id
residue_sites_best = rotamer_sites_cart.deep_copy(
)
residue.atoms().set_xyz(
new_xyz=residue_sites_best)
max_moved_dist = flex.max(
flex.sqrt((sites_cart_residue_start -
residue_sites_best).dot()))
if (not params.real_space_refine_rotamer):
sites_cart_start = sites_cart_start.set_selected(
residue_iselection, residue_sites_best)
else:
tmp = sites_cart_start.set_selected(
residue_iselection, residue_sites_best)
sites_cart_refined = rsr_manager.refine_restrained(
tmp.select(rsel), rsel, rs)
if (rev.is_better(
sites_cart=sites_cart_refined)):
sites_cart_start = sites_cart_start.set_selected(
residue_iselection,
sites_cart_refined)
residue.atoms().set_xyz(
new_xyz=sites_cart_refined)
max_moved_dist = flex.max(
flex.sqrt(
(sites_cart_residue_start -
sites_cart_refined).dot()))
if (abs(rev.t1_best - rev.t1_start) > 0.01 and
abs(rev.t2_best - rev.t2_start) > 0.01):
print >> log, fmt3 % (
residue_id_str, cc_start, rev.t1_start,
rev.t2_start, rev.t1_best, rev.t2_best,
rotamer_id_best, n_rotamers,
max_moved_dist)
xray_structure.set_sites_cart(sites_cart_start)
return result
def exercise(d_min=5, random_seed=1111111):
inp = get_pdb_inputs(pdb_str=pdb_str)
xrs_good = inp.xrs.deep_copy_scatterers()
target_map = get_tmo(inp=inp, d_min=d_min)
inp.ph.write_pdb_file(file_name="start.pdb")
show(prefix="GOOD",
pdb_hierarchy=inp.ph,
tm=target_map,
xrs=xrs_good,
grm=inp.grm.geometry)
#
sites_cart_reference = []
selections_reference = []
pdb_hierarchy_reference = inp.ph.deep_copy()
pdb_hierarchy_reference.reset_i_seq_if_necessary()
for model in inp.ph.models():
for chain in model.chains():
for residue in chain.residues():
sites_cart_reference.append(residue.atoms().extract_xyz())
selections_reference.append(residue.atoms().extract_i_seq())
#
sites_cart_reference_for_chi_only = []
selections_reference_for_chi_only = []
for model in inp.ph.models():
for chain in model.chains():
for residue in chain.residues():
s1 = flex.vec3_double()
s2 = flex.size_t()
for atom in residue.atoms():
if (not atom.name.strip().upper() in ["O"]):
s1.append(atom.xyz)
s2.append(atom.i_seq)
sites_cart_reference_for_chi_only.append(s1)
selections_reference_for_chi_only.append(s2)
#
xrs_poor = shake_sites(xrs=xrs_good.deep_copy_scatterers(),
random=False,
shift=2.0,
grm=inp.grm)
inp.ph.adopt_xray_structure(xrs_poor)
inp.ph.write_pdb_file(file_name="poor.pdb")
#
for use_reference_torsion in [
"no", "yes_add_once", "yes_add_per_residue", "yes_manual"
]:
es = inp.grm.energies_sites(
sites_cart=xrs_good.sites_cart()) # it's essential to update grm
inp.ph.adopt_xray_structure(xrs_poor)
random.seed(random_seed)
flex.set_random_seed(random_seed)
print("*" * 79)
print("use_reference_torsion:", use_reference_torsion)
print("*" * 79)
show(prefix="START",
pdb_hierarchy=inp.ph,
tm=target_map,
xrs=xrs_poor,
grm=inp.grm.geometry)
#
if (use_reference_torsion == "yes_add_per_residue"):
inp.grm.geometry.remove_chi_torsion_restraints_in_place()
for sites_cart, selection in zip(sites_cart_reference,
selections_reference):
inp.grm.geometry.add_chi_torsion_restraints_in_place(
pdb_hierarchy=pdb_hierarchy_reference,
sites_cart=sites_cart,
selection=selection,
chi_angles_only=True,
sigma=1)
if (use_reference_torsion == "yes_add_once"):
inp.grm.geometry.remove_chi_torsion_restraints_in_place()
inp.grm.geometry.add_chi_torsion_restraints_in_place(
pdb_hierarchy=pdb_hierarchy_reference,
sites_cart=xrs_good.sites_cart(),
chi_angles_only=True,
sigma=1)
if (use_reference_torsion == "yes_manual"):
inp.grm.geometry.remove_chi_torsion_restraints_in_place()
for sites_cart, selection in zip(
sites_cart_reference_for_chi_only,
selections_reference_for_chi_only):
inp.grm.geometry.add_chi_torsion_restraints_in_place(
pdb_hierarchy=pdb_hierarchy_reference,
sites_cart=sites_cart,
selection=selection,
chi_angles_only=True,
sigma=1)
#
tmp = xrs_poor.deep_copy_scatterers()
rsr_simple_refiner = individual_sites.simple(
target_map=target_map.data,
selection=flex.bool(tmp.scatterers().size(), True),
real_space_gradients_delta=d_min / 4,
max_iterations=500,
geometry_restraints_manager=inp.grm.geometry)
refined = individual_sites.refinery(refiner=rsr_simple_refiner,
optimize_weight=True,
xray_structure=tmp,
start_trial_weight_value=50,
rms_bonds_limit=0.02,
rms_angles_limit=2.0)
assert refined.sites_cart_result is not None
tmp = tmp.replace_sites_cart(refined.sites_cart_result)
inp.ph.adopt_xray_structure(tmp)
show(prefix="FINAL",
pdb_hierarchy=inp.ph,
tm=target_map,
xrs=tmp,
grm=inp.grm.geometry)
inp.ph.write_pdb_file(file_name="final_%s.pdb" %
str(use_reference_torsion))
def split_model_with_pae(model,
m,
pae_matrix,
maximum_domains=None,
pae_power=1.,
pae_cutoff=5.,
pae_graph_resolution=1.,
minimum_domain_length=10,
log=sys.stdout):
"""
Function to identify groups of atoms in a model that form compact units
using a predicted alignment error matrix (pae_matrix).
Normally used after trimming low-confidence regions in
predicted models to isolate domains that are likely to have indeterminate
relationships.
m: cctbx.model.model object containing information about the input model
after trimming
model: model before trimming
pae_matrix: matrix of predicted aligned errors (e.g., from AlphaFold2), NxN
matrix of RMSD values, N = number of residues in model.
maximum_domains: If more than this many domains, merge closest ones until
reaching this number
pae_power (default=1): each edge in the graph will be weighted
proportional to (1/pae**pae_power)
pae_cutoff (optional, default=5): graph edges will only be created for
residue pairs with pae<pae_cutoff
pae_graph_resolution (optional, default = 1): regulates how aggressively
the clustering algorithm is. Smaller values lead to larger clusters.
Value should be larger than zero, and values larger than 5 are
unlikely to be useful
minimum_domain_length: if a region is smaller than this, skip completely
Output:
group_args object with members:
m: new model with chainid values from 0 to N where there are N domains
chainid 1 to N are the N domains, roughly in order along the chain.
chainid_list: list of all the chainid values
On failure: returns None
"""
print("\nSelecting domains with predicted alignment error estimates",
file=log)
# Select CA and P atoms with B-values in range
selection_string = '(name ca or name p)'
m_ca = m.apply_selection_string(selection_string)
n = model.apply_selection_string(
selection_string).get_hierarchy().overall_counts().n_residues
# Make sure matrix matches
if tuple(pae_matrix.shape) != (n, n):
raise Sorry("The pae matrix has a size of (%s,%s) " %
(tuple(pae_matrix.shape)) +
"but the number of residues in the model is %s" % (n))
from mmtbx.secondary_structure.find_ss_from_ca import get_first_resno
first_resno = get_first_resno(model.get_hierarchy())
# Assign all CA in model to a region
from mmtbx.domains_from_pae import get_domain_selections_from_pae_matrix
selection_list = get_domain_selections_from_pae_matrix(
pae_matrix=pae_matrix,
pae_power=pae_power,
pae_cutoff=pae_cutoff,
graph_resolution=pae_graph_resolution,
first_resno=first_resno,
)
# And apply to full model
unique_regions = list(range(len(selection_list)))
keep_list = []
good_selections = []
ph = m.get_hierarchy()
for selection_string, region_number in zip(selection_list, unique_regions):
asc1 = ph.atom_selection_cache()
sel = asc1.selection(selection_string)
if sel.count(True) >= minimum_domain_length:
keep_list.append(True)
good_selections.append(selection_string)
else:
keep_list.append(False)
print("Skipping region '%s' with size of only %s residues" %
(selection_string, sel.count(True)),
file=log)
region_name_dict, chainid_list = get_region_name_dict(m,
unique_regions,
keep_list=keep_list)
print("\nSelection list based on PAE values:", file=log)
# Now create new model with chains based on region list
full_new_model = None
for keep, selection_string, region_number in zip(keep_list, selection_list,
unique_regions):
if not keep: continue
new_m = m.apply_selection_string(selection_string)
print("%s (%s residues) " %
(selection_string,
new_m.get_hierarchy().overall_counts().n_residues),
file=log)
# Now put all of new_m in a chain with chain.id = str(region_number)
for model in new_m.get_hierarchy().models()[:1]: # only one model
for chain in model.chains()[:1]: # only allowing one chain
chain.id = region_name_dict[region_number]
if full_new_model:
full_new_model = add_model(full_new_model, new_m)
else:
full_new_model = new_m
m = full_new_model
# All done
return group_args(group_args_type='model_info',
model=m,
chainid_list=chainid_list)
return set_chain_id_by_region(m, m_ca, regions_list, log=log)
