def align_model (self, i_model) :
from scitbx.array_family import flex
from scitbx.math import superpose
hierarchy_moving = self.related_chains[i_model].pdb_hierarchy
mov_atoms = hierarchy_moving.atoms()
mov_atoms.reset_i_seq()
sel_cache = hierarchy_moving.atom_selection_cache()
mov_atom_selection = sel_cache.selection(self.atom_selection_string)
mov_chain = hierarchy_moving.only_model().only_chain()
sel_ref = flex.size_t()
sel_mov = flex.size_t()
for residue_group in mov_chain.residue_groups() :
for atom in residue_group.only_atom_group().atoms() :
if (not mov_atom_selection[atom.i_seq]) :
continue
resid = residue_group.resid()
ref_name = "%s %s" % (resid, atom.name.strip())
if (ref_name in self.atoms_ref) :
sel_mov.append(atom.i_seq)
sel_ref.append(self.atoms_ref.index(ref_name))
if (len(sel_ref) == 0) :
assert (self.atom_selection_string is not None)
return None
assert (len(sel_ref) > 0) and (len(sel_ref) == len(sel_mov))
xyz_mov = mov_atoms.extract_xyz()
sites_mov = xyz_mov.select(sel_mov)
sites_ref = self.reference_sites.select(sel_ref)
if (self.sieve_fit) :
return superpose.sieve_fit(
sites_fixed=sites_ref,
sites_moving=sites_mov,
frac_discard=self.frac_discard)
else :
return superpose.least_squares_fit(
reference_sites=sites_ref,
other_sites=sites_mov)
def align_model(self, i_model):
from scitbx.array_family import flex
from scitbx.math import superpose
hierarchy_moving = self.related_chains[i_model].pdb_hierarchy
mov_atoms = hierarchy_moving.atoms()
mov_atoms.reset_i_seq()
sel_cache = hierarchy_moving.atom_selection_cache()
mov_atom_selection = sel_cache.selection(self.atom_selection_string)
mov_chain = hierarchy_moving.only_model().only_chain()
sel_ref = flex.size_t()
sel_mov = flex.size_t()
for residue_group in mov_chain.residue_groups():
for atom in residue_group.only_atom_group().atoms():
if (not mov_atom_selection[atom.i_seq]):
continue
resid = residue_group.resid()
ref_name = "%s %s" % (resid, atom.name.strip())
if (ref_name in self.atoms_ref):
sel_mov.append(atom.i_seq)
sel_ref.append(self.atoms_ref.index(ref_name))
if (len(sel_ref) == 0):
assert (self.atom_selection_string is not None)
return None
assert (len(sel_ref) > 0) and (len(sel_ref) == len(sel_mov))
xyz_mov = mov_atoms.extract_xyz()
sites_mov = xyz_mov.select(sel_mov)
sites_ref = self.reference_sites.select(sel_ref)
if (self.sieve_fit):
return superpose.sieve_fit(
sites_fixed=sites_ref,
sites_moving=sites_mov,
frac_discard=self.frac_discard)
else :
return superpose.least_squares_fit(
reference_sites=sites_ref,
other_sites=sites_mov)
def morph_models (params, out=None, debug=False) :
assert len(params.morph.pdb_file) > 1
assert (len(params.morph.frames) == (len(params.morph.pdb_file) - 1))
if (out is None) : out = sys.stdout
from mmtbx.monomer_library import pdb_interpretation, server
from iotbx import file_reader
pdb_hierarchies = []
for pdb_file in params.morph.pdb_file :
pdb_in = file_reader.any_file(pdb_file, force_type="pdb")
pdb_in.check_file_type("pdb")
hierarchy = pdb_in.file_object.hierarchy
pdb_hierarchies.append(hierarchy)
new_pdb = homogenize_structures(
pdb_hierarchies=pdb_hierarchies,
delete_waters=params.morph.delete_waters,
debug=debug)
mon_lib_srv = server.server()
ener_lib = server.ener_lib()
for cif_file in params.morph.cif_file :
print "Loading CIF file %s" % cif_file
cif_object = server.read_cif(file_name=cif_file)
mon_lib_serv.process_cif_object(cif_object=cif_object, file_name=cif_file)
ener_lib.process_cif_object(cif_object=cif_object, file_name=cif_file)
if (params.morph.minimization.interpolate_dihedrals) :
params.pdb_interpretation.peptide_link.discard_psi_phi = False
processed_pdb_file = pdb_interpretation.process(
mon_lib_srv=mon_lib_srv,
ener_lib=ener_lib,
params=params.pdb_interpretation,
pdb_inp=new_pdb[0],
substitute_non_crystallographic_unit_cell_if_necessary=True)
all_chain_proxies = processed_pdb_file.all_chain_proxies
static_coords = [ all_chain_proxies.pdb_hierarchy.atoms().extract_xyz() ]
for pdb_inp in new_pdb[1:] :
sites = pdb_inp.atoms().extract_xyz()
static_coords.append(sites)
if (params.morph.fitting.align_atoms is not None) :
print >> out, "Superposing on initial structure..."
selection_cache = all_chain_proxies.pdb_hierarchy.atom_selection_cache()
selection = selection_cache.selection(params.morph.fitting.align_atoms)
if (selection.count(True) == 0) :
raise Sorry("No atoms in alignment selection!")
i_ref = params.morph.fitting.reference_structure
sites_fixed = static_coords[i_ref]
j = 0
while (j < len(static_coords)) :
if (j != i_ref) :
sites_moving = static_coords[j]
assert (len(sites_moving) == len(sites_fixed) > 0)
if (params.morph.fitting.sieve_fit) :
from scitbx.math import superpose
lsq_fit = superpose.sieve_fit(
sites_fixed=sites_fixed,
sites_moving=sites_moving,
selection=selection)
sites_moving_new = lsq_fit.r.elems * sites_moving + lsq_fit.t.elems
else :
sites_moving_new = fit_sites(
sites_fixed=sites_fixed,
sites_moving=sites_moving,
selection=selection)
assert (sites_moving_new.size() == sites_moving.size())
static_coords[j] = sites_moving_new
j += 1
print >> out, "Ready to morph"
morphs = []
restraints_manager = processed_pdb_file.geometry_restraints_manager()
for i in range(len(params.morph.pdb_file) - 1) :
morph = adiabatic_mapping(
pdb_hierarchy=all_chain_proxies.pdb_hierarchy,
restraints_manager=restraints_manager,
start_coords = static_coords[i],
end_coords = static_coords[i+1],
params = params.morph.minimization,
nsteps = params.morph.frames[i],
out=out)
morphs.append(morph)
serial = 1
if (params.morph.output_directory is not None) :
output_base = os.path.join(params.morph.output_directory,
params.morph.output_prefix)
else :
output_base = params.morph.output_prefix
for i, morph in enumerate(morphs) :
serial = morph.write_pdb_files(
output_base=output_base,
serial=serial,
serial_format=params.morph.serial_format,
pause=params.morph.pause,
pause_at_end=(i == (len(morphs) - 1)),
log=out)
f = open("%s.pml" % output_base, "w")
for i in range(1, serial) :
format_base = "%s_%s" % (output_base, params.morph.serial_format)
print >> f, "load %s.pdb, morph" % (format_base % i)
f.close()
print >> out, "PyMOL script is %s.pml" % output_base
def find_ncs_operators (pdb_hierarchy, max_rmsd=2.0, try_sieve_fit=True,
log=None) :
"""
Determines all possible NCS transformation matrices for the input structure,
based on sequence alignemnt and simple C-alpha superposition. There may be
multiple sets of operators but these will eventually become a flat list.
:param max_rmsd: maximum allowable RMSD between NCS-related chains for use
in ligand superposition
:param try_sieve_fit: also perform a sieve fit between chains and use the
resulting operator if the RMSD is lower than the global fit
:param log: filehandle-like object
:returns: list of lists of group_operators objects
"""
import iotbx.ncs
from scitbx.math import superpose
from scitbx.array_family import flex
ncs_obj = iotbx.ncs.input(hierarchy=pdb_hierarchy)
ncs_groups = []
for k,v in ncs_obj.ncs_to_asu_selection.iteritems():
ncs_groups.append([k]+v)
if (len(ncs_groups) == 0) :
raise Sorry("No NCS present in the input model.")
for k, group in enumerate(ncs_groups) :
print >> log, "Group %d:" % (k+1)
for sele in group :
print >> log, " %s" % sele
selection_cache = pdb_hierarchy.atom_selection_cache()
pdb_atoms = pdb_hierarchy.atoms()
sites_cart = pdb_atoms.extract_xyz()
operators = []
def get_selection (sele_str) :
sele_str = "(%s) and name CA and (altloc ' ' or altloc A)" % sele_str
return selection_cache.selection(sele_str).iselection()
for restraint_group in ncs_groups :
group_ops = []
assert (len(restraint_group) >= 2)
# XXX This is currently an all-vs-all loop, which means that each
# NCS relationship will be calculated (and stored) twice. Need to figure
# out whether this actually matters in practice.
for j, sele_str in enumerate(restraint_group) :
sele_j = get_selection(sele_str)
group = group_operators(sele_j, sele_str, sites_cart)
assert (len(sele_j) > 0)
calpha_ids = []
for i_seq in sele_j :
resid = resid_str(pdb_atoms[i_seq])
if (not resid in calpha_ids) :
calpha_ids.append(resid)
for k, sele_str_k in enumerate(restraint_group) :
if (k == j) : continue
sele_k = get_selection(sele_str_k)
group_sele = flex.size_t()
group_ids = set([])
assert (len(sele_k) > 0)
# poor man's sequence alignment
for i_seq in sele_k :
id_str = resid_str(pdb_atoms[i_seq])
if (id_str in group_ids) :
continue
group_ids.add(id_str)
if (id_str in calpha_ids) :
group_sele.append(i_seq)
first_sele_copy = flex.size_t() #first_sele.deep_copy()
delete_indices = []
for i_seq, id_str in zip(sele_j, calpha_ids) :
if (id_str in group_ids) :
first_sele_copy.append(i_seq)
assert (len(first_sele_copy) == len(group_sele))
assert (len(group_sele) > 0)
sites_ref = sites_cart.select(first_sele_copy)
sites_group = sites_cart.select(group_sele).deep_copy()
lsq_fit = superpose.least_squares_fit(
reference_sites=sites_ref,
other_sites=sites_group)
sites_fit = lsq_fit.r.elems * sites_group + lsq_fit.t.elems
rmsd = sites_ref.rms_difference(sites_fit)
if (try_sieve_fit) :
lsq_fit_2 = superpose.sieve_fit(
sites_fixed=sites_ref,
sites_moving=sites_group,
frac_discard=0.25)
sites_fit_2 = lsq_fit_2.r.elems * sites_group + lsq_fit_2.t.elems
rmsd_2 = sites_ref.rms_difference(sites_fit)
if (rmsd_2 < rmsd) :
print >> log, " using sieve fit (RMSD = %.3f, RMSD(all) = %.3f)" %\
(rmsd_2, rmsd)
lsq_fit = lsq_fit_2
rmsd = rmsd_2
print >> log, " %d versus %d RMSD = %.3f" % (j+1, k+1, rmsd)
if (rmsd <= max_rmsd) :
group.add_operator(lsq_fit.rt().inverse(), sele_str_k)
else :
print >> log, " exceeds cutoff, will not use this operator"
group_ops.append(group)
operators.append(group_ops)
return operators
