'''
(c) 2011 Thomas Holder, MPI for Developmental Biology
License: BSD-2-Clause
'''
if not __name__.endswith('.fitting'):
raise Exception("Must do 'import psico.fitting' instead of 'run ...'")
from pymol import cmd, CmdException
from .mcsalign import mcsalign # noqa: F401
ALL_STATES = 0
CURRENT_STATE = -1
_auto_arg0_align = cmd.auto_arg[0]['align']
_auto_arg1_align = cmd.auto_arg[1]['align']
[docs]
def alignwithanymethod(mobile, target, methods=None, async_=1, quiet=1,
*, _self=cmd, **kwargs):
'''
DESCRIPTION
Align copies of mobile to target with several alignment methods
ARGUMENTS
mobile = string: atom selection
target = string: atom selection
methods = string: space separated list of PyMOL commands which take
arguments "mobile" and "target" (in any order) {default: align super
cealign tmalign theseus}
'''
import threading
import time
if methods is None:
methods = align_methods
else:
methods = methods.split()
async_, quiet = int(kwargs.pop('async', async_)), int(quiet)
mobile_obj = _self.get_object_list('first (' + mobile + ')')[0]
def myalign(method):
newmobile = _self.get_unused_name(mobile_obj + '_' + method)
_self.create(newmobile, mobile_obj)
start = time.time()
_self.do('%s mobile=%s in %s, target=%s' % (method, newmobile, mobile, target))
if not quiet:
print('Finished: %s (%.2f sec)' % (method, time.time() - start))
for method in methods:
if method not in cmd.keyword:
if not quiet:
print('No such method:', method)
continue
if async_:
t = threading.Thread(target=myalign, args=(method,))
t.setDaemon(1)
t.start()
else:
myalign(method)
[docs]
@cmd.extendaa(_auto_arg0_align, _auto_arg1_align)
def usalign(mobile: str,
target: str,
mobile_state: int = 1,
target_state: int = 1,
*,
mm: int = 1,
ter: int = 0,
args: str = '',
exe='USalign',
_self=cmd,
**_kwargs):
'''
DESCRIPTION
USalign wrapper. See `tmalign` for details.
Reference: Zhang et al., Nature Methods 2022 19: 1109-1115
https://zhanggroup.org/US-align/
ARGUMENTS
mm = int: 1=multi-chain, 3=circularly permutated {default: 1}
ter = int: 3=stop at the first TER record {default: 0}
EXAMPLE
fetch 1y39 2zjr
usalign 1y39 & chain A+C, 2zjr & chain F+X, object=aln
'''
args += f" -ter {ter} -mm {mm}"
ter = int(ter) == 3
return tmalign(mobile, target, mobile_state, target_state, args,
exe, ter, **_kwargs, _self=_self)
[docs]
def tmalign(mobile, target, mobile_state=1, target_state=1, args='',
exe='TMalign', ter=0, transform=1, object=None, quiet=0, *, _self=cmd):
'''
DESCRIPTION
TMalign wrapper. You may also use this as a TMscore or MMalign wrapper
if you privide the corresponding executable with the "exe" argument.
Reference: Y. Zhang and J. Skolnick, Nucl. Acids Res. 2005 33, 2302-9
http://zhanglab.ccmb.med.umich.edu/TM-align/
ARGUMENTS
mobile, target = string: atom selections
mobile_state, target_state = int: object states {default: 1}
args = string: Extra arguments like -d0 5 -L 100
exe = string: Path to TMalign (or TMscore, MMalign, USalign) executable
{default: TMalign}
ter = 0/1: If ter=0, then ignore chain breaks because TMalign will stop
at first TER record {default: 0}
'''
import pymol.exporting
import subprocess, tempfile, os, re
from .exporting import save_pdb_without_ter
ter, quiet = int(ter), int(quiet)
mobile_filename = tempfile.mktemp('.pdb', 'mobile')
target_filename = tempfile.mktemp('.pdb', 'target')
matrix_filename = tempfile.mktemp('.txt', 'matrix')
mobile_sele = f'({mobile}) and (not hetatm) and alt +A'
target_sele = f'({target}) and (not hetatm) and alt +A'
mobile_ca_sele = f'({mobile_sele}) and guide'
target_ca_sele = f'({target_sele}) and guide'
if ter:
save = pymol.exporting.save
else:
save = save_pdb_without_ter
save(mobile_filename, mobile_sele, state=mobile_state, _self=_self)
save(target_filename, target_sele, state=target_state, _self=_self)
exe = cmd.exp_path(exe)
args = [exe, mobile_filename, target_filename, '-m', matrix_filename] + args.split()
try:
process = subprocess.Popen(args, stdout=subprocess.PIPE,
universal_newlines=True)
lines = process.stdout.readlines()
except OSError as ex:
raise CmdException(
f'Cannot execute "{exe}", please provide full path to TMscore or TMalign executable'
) from ex
finally:
os.remove(mobile_filename)
os.remove(target_filename)
# TMalign >= 2012/04/17
if os.path.exists(matrix_filename):
lines += open(matrix_filename).readlines()
os.remove(matrix_filename)
r = None
re_score = re.compile(r'TM-score\s*=\s*(\d*\.\d*)')
rowcount = 0
matrix = []
line_it = iter(lines)
headercheck = False
alignment = [""] * 3
for line in line_it:
if 4 >= rowcount > 0:
if rowcount >= 2:
a = list(map(float, line.split()))
matrix.extend(a[2:5])
matrix.append(a[1])
rowcount += 1
elif not headercheck and line.startswith(' * '):
a = line.split(None, 2)
if len(a) == 3:
headercheck = a[1]
elif line.lower().startswith(' -------- rotation matrix'):
rowcount = 1
elif line.startswith('------ The rotation matrix'):
rowcount = 1
elif line.startswith('(":" denotes'):
alignment = [next(line_it).rstrip() for i in range(3)]
else:
match = re_score.search(line)
if match is not None:
r = float(match.group(1))
if not quiet:
print(line.rstrip())
if not matrix:
raise CmdException("incomplete output")
if not quiet:
for i in range(0, len(alignment[0]) - 1, 78):
for line in alignment:
print(line[i:i + 78])
print('')
assert len(matrix) == 3 * 4
matrix.extend([0, 0, 0, 1])
if int(transform):
for model in _self.get_object_list(mobile):
_self.transform_object(model, matrix, state=0, homogenous=1)
# alignment object
if object is not None:
mobile_idx, target_idx = [], []
space = {'mobile_idx': mobile_idx, 'target_idx': target_idx}
_self.iterate_state(mobile_state, mobile_ca_sele, 'mobile_idx.append("%s`%d" % (model, index))', space=space)
_self.iterate_state(target_state, target_ca_sele, 'target_idx.append("%s`%d" % (model, index))', space=space)
for i, aa in enumerate(alignment[0]):
if aa in ('-', '*'):
mobile_idx.insert(i, None)
for i, aa in enumerate(alignment[2]):
if aa in ('-', '*'):
target_idx.insert(i, None)
if (len(mobile_idx) == len(target_idx) == len(alignment[2])):
_self.rms_cur(
' '.join(idx for (idx, m) in zip(mobile_idx, alignment[1]) if m in ':.'),
' '.join(idx for (idx, m) in zip(target_idx, alignment[1]) if m in ':.'),
cycles=0, matchmaker=4, object=object)
else:
print('Could not load alignment object')
if not quiet:
if headercheck:
print('Finished Program:', headercheck)
if r is not None:
print('Found in output TM-score = %.4f' % (r))
return r
[docs]
def dyndom_parse_info(filename, selection='(all)', quiet=0, *, _self=cmd):
import re
fixed = False
fixed_name = None
dom_nr = 0
color = 'none'
bending = list()
for line in open(filename):
if line.startswith('FIXED DOMAIN'):
fixed = True
continue
if line.startswith('MOVING DOMAIN'):
fixed = False
continue
m = re.match(r'DOMAIN NUMBER: *(\d+) \(coloured (\w+)', line)
if m:
dom_nr = m.group(1)
color = m.group(2)
continue
m = re.match(r'RESIDUE NUMBERS :(.*)', line)
if m:
resi = m.group(1)
resi = resi.replace(',', '+')
resi = resi.replace(' ', '')
if not quiet:
print('Domain ' + dom_nr + ' (' + color + '): resi ' + resi)
name = 'domain_' + dom_nr
_self.select(name, '(%s) and (resi %s)' % (selection, resi), 0)
_self.color(color, name)
if fixed:
fixed_name = name
continue
m = re.match(r'BENDING RESIDUES:(.*)', line)
if m:
resi = m.group(1)
resi = resi.replace(',', '+')
resi = resi.replace(' ', '')
bending.append(resi)
if len(bending) > 0:
name = 'bending'
_self.select(name, '(%s) and (resi %s)' % (selection, '+'.join(bending)), 0)
_self.color('green', name)
return fixed_name
[docs]
def dyndom(mobile, target, window=5, domain=20, ratio=1.0, exe='', transform=1,
quiet=1, mobile_state=1, target_state=1, match='align', preserve=0,
*, _self=cmd):
'''
DESCRIPTION
DynDom wrapper
DynDom is a program to determine domains, hinge axes and hinge bending
residues in proteins where two conformations are available.
http://fizz.cmp.uea.ac.uk/dyndom/
USAGE
dyndom mobile, target [, window [, domain [, ratio ]]]
'''
import tempfile, subprocess, os, shutil, sys
from .exporting import save_pdb_without_ter
window, domain, ratio = int(window), int(domain), float(ratio)
transform, quiet = int(transform), int(quiet)
mobile_state, target_state = int(mobile_state), int(target_state)
mm = MatchMaker(
'(%s) & polymer & state %d' % (mobile, mobile_state),
'(%s) & polymer & state %d' % (target, target_state), match, _self=_self)
chains = _self.get_chains(mm.mobile)
if len(chains) != 1:
raise CmdException('mobile selection must be single chain')
chain1id = chains[0]
chains = _self.get_chains(mm.target)
if len(chains) != 1:
raise CmdException('target selection must be single chain')
chain2id = chains[0]
if not exe:
from . import which
exe = which('DynDom', 'dyndom')
if not exe:
raise CmdException('Cannot find DynDom executable')
else:
exe = cmd.exp_path(exe)
tempdir = tempfile.mkdtemp()
try:
filename1 = os.path.join(tempdir, 'mobile.pdb')
filename2 = os.path.join(tempdir, 'target.pdb')
commandfile = os.path.join(tempdir, 'command.txt')
infofile = os.path.join(tempdir, 'out_info')
save_pdb_without_ter(filename1, mm.mobile, state=mobile_state, _self=_self)
save_pdb_without_ter(filename2, mm.target, state=target_state, _self=_self)
f = open(commandfile, 'w')
f.write('title=out\nfilename1=%s\nchain1id=%s\nfilename2=%s\nchain2id=%s\n'
'window=%d\ndomain=%d\nratio=%.4f\n' % (filename1, chain1id,
filename2, chain2id, window, domain, ratio))
f.close()
process = subprocess.Popen([exe, commandfile], cwd=tempdir,
universal_newlines=True,
stderr=subprocess.STDOUT, stdout=subprocess.PIPE)
for line in process.stdout:
if not quiet:
sys.stdout.write(line)
if process.poll() != 0:
raise CmdException('"%s" failed with status %d' % (exe, process.returncode))
_self.color('gray', mobile)
fixed_name = dyndom_parse_info(infofile, mm.mobile, quiet)
except OSError as ex:
raise CmdException(
f'Cannot execute "{exe}", please provide full path to DynDom executable'
) from ex
finally:
if not int(preserve):
shutil.rmtree(tempdir)
elif not quiet:
print(' Not deleting temporary directory:', tempdir)
if transform and fixed_name is not None:
_self.align(fixed_name, target)
[docs]
def gdt_ts(mobile, target, cutoffs='1 2 4 8', quiet=1, *, _self=cmd):
'''
DESCRIPTION
Global Distance Test Total Score (GDT_TS)
'''
cutoffs = list(map(float, cutoffs.split()))
quiet = int(quiet)
mobile = '(' + mobile + ') and guide'
target = '(' + target + ') and guide'
ts = 0
N = min(_self.count_atoms(mobile), _self.count_atoms(target))
for cutoff in cutoffs:
x = _self.align(mobile, target, cutoff=cutoff, transform=0)
p = float(x[1]) / N
if not quiet:
print(' GDT_TS: GDT_P%.1f = %.2f' % (cutoff, p))
ts += p
ts /= len(cutoffs)
if not quiet:
print(' GDT_TS: Total Score = %.2f' % (ts))
return ts
[docs]
def get_rmsd_func():
'''
DESCRIPTION
API only. Returns a function that uses either numpy (fast) or chempy.cpv
(slow) to calculate the rmsd fit of two nx3 arrays.
'''
try:
# this is much faster than cpv.fit
from numpy import dot, sqrt, array
from numpy.linalg import svd
def rmsd(X, Y):
X = X - X.mean(0)
Y = Y - Y.mean(0)
R_x = (X**2).sum()
R_y = (Y**2).sum()
L = svd(dot(Y.T, X))[1]
return sqrt((R_x + R_y - 2 * L.sum()) / len(X))
rmsd.array = array
except ImportError:
from chempy import cpv
def rmsd(X, Y):
return cpv.fit(X, Y)[-1]
rmsd.array = lambda x: x
return rmsd
[docs]
class MatchMaker(object):
'''
DESCRIPTION
API only. Matches two atom selections and provides two matched
subselections with equal atom count. May involve temporary objects
or named selections which will be automatically deleted.
ARGUMENTS
mobile = string: first atom selection
target = string: second atom selection
match = string: method how to match atoms
* none: (dummy)
* in: match atoms by "in" operator
* like: match atoms by "like" operator
* align: match atoms by cmd.align (without refinement)
* super: match atoms by cmd.super (without refinement)
* <name of alignment object>: use given alignment
RESULT
Properties "mobile" and "target" hold the matched subselections as
selection strings.
'''
def __init__(self, mobile, target, match, *, autodelete=True, _self=cmd):
self._self = _self
self.autodelete = autodelete
self.temporary = []
if match == 'none':
self.mobile = mobile
self.target = target
elif match in ['in', 'like']:
self.mobile = '(%s) %s (%s)' % (mobile, match, target)
self.target = '(%s) %s (%s)' % (target, match, mobile)
elif match in ['align', 'super']:
self.align(mobile, target, match)
elif match in _self.get_names('all') and _self.get_type(match) in ('object:', 'object:alignment'):
self.from_alignment(mobile, target, match)
else:
raise CmdException('unkown match method', match)
[docs]
def check(self):
return self._self.count_atoms(self.mobile) == self._self.count_atoms(self.target)
[docs]
def align(self, mobile, target, match):
'''
Align mobile to target using the alignment method given by "match"
'''
aln_obj = self._self.get_unused_name('_')
self.temporary.append(aln_obj)
align = cmd.keyword[match][0]
align(mobile, target, cycles=0, transform=0, object=aln_obj, _self=self._self)
self._self.disable(aln_obj)
self.from_alignment(mobile, target, aln_obj)
[docs]
def from_alignment(self, mobile, target, aln_obj):
'''
Use alignment given by "aln_obj" (name of alignment object)
'''
from .selecting import wait_for
wait_for(aln_obj, _self=self._self)
self.mobile = '(%s) and %s' % (mobile, aln_obj)
self.target = '(%s) and %s' % (target, aln_obj)
if self.check():
return
# difficult: if selections spans only part of the alignment or
# if alignment object covers more than the two objects, then we
# need to pick those columns that have no gap in any of the two
# given selections
mobileidx = set(self._self.index(mobile))
targetidx = set(self._self.index(target))
mobileidxsel = []
targetidxsel = []
for column in self._self.get_raw_alignment(aln_obj):
mobiles = mobileidx.intersection(column)
if len(mobiles) == 1:
targets = targetidx.intersection(column)
if len(targets) == 1:
mobileidxsel.extend(mobiles)
targetidxsel.extend(targets)
self.mobile = self._self.get_unused_name('_mobile')
self.target = self._self.get_unused_name('_target')
self.temporary.append(self.mobile)
self.temporary.append(self.target)
mobile_objects = set(idx[0] for idx in mobileidxsel)
target_objects = set(idx[0] for idx in targetidxsel)
if len(mobile_objects) == len(target_objects) == 1:
mobile_index_list = [idx[1] for idx in mobileidxsel]
target_index_list = [idx[1] for idx in targetidxsel]
self._self.select_list(self.mobile, mobile_objects.pop(), mobile_index_list, mode='index')
self._self.select_list(self.target, target_objects.pop(), target_index_list, mode='index')
else:
self._self.select(self.mobile, ' '.join('%s`%d' % idx for idx in mobileidxsel))
self._self.select(self.target, ' '.join('%s`%d' % idx for idx in targetidxsel))
def __enter__(self):
return self
def __exit__(self, type_, value, traceback):
self._cleanup()
self.autodelete = False
def __del__(self):
self._cleanup()
def _cleanup(self):
if not self.autodelete:
return
for name in self.temporary:
self._self.delete(name)
[docs]
def local_rms(mobile, target, window=20, mobile_state=1, target_state=1,
match='align', load_b=1, visualize=1, quiet=1, *, _self=cmd):
'''
DESCRIPTION
"local_rms" computes the C-alpha RMS fit within a sliding window along the
backbone. The obtained RMS is assigned as a pseudo b-factor to the residue
in the middle of the window. This is useful to visualize hinge-regions.
The result is very sensitive to window size.
USAGE
local_rms mobile, target [, window ]
ARGUMENTS
mobile = string: object to assign b-factors and to visualize as putty cartoon
target = string: object to superimpose mobile to
window = integer: width of sliding window {default: 20}
match = string: in, like, align, none or the name of an alignment object
{default: align}
* in: match all atom identifiers (segi,chain,resn,resi,name)
* like: match residue number (resi)
* align: do a sequence alignment
* none: assume same number of atoms in both selections
* name of alignment object: take sequence alignment from object
EXAMPLE
fetch 2x19 2xwu, async=0
remove not chain B or not polymer
local_rms 2x19, 2xwu, 40
'''
rmsd = get_rmsd_func()
array = rmsd.array
window = int(window)
mobile_state, target_state = int(mobile_state), int(target_state)
load_b, visualize, quiet = int(load_b), int(visualize), int(quiet)
w2 = window // 2
w4 = window // 4
mm = MatchMaker('(%s) and guide' % (mobile),
'(%s) and guide' % (target), match, _self=_self)
model_mobile = _self.get_model(mm.mobile, mobile_state)
model_target = _self.get_model(mm.target, target_state)
if len(model_mobile.atom) != len(model_mobile.atom):
raise CmdException('number of atoms differ, please check match method')
seq_start = model_mobile.atom[0].resi_number
seq_end = model_mobile.atom[-1].resi_number
resv2i = dict((a.resi_number, i) for (i, a) in enumerate(model_mobile.atom))
resv2b = dict()
X_mobile = array(model_mobile.get_coord_list())
X_target = array(model_target.get_coord_list())
for resv in range(seq_start, seq_end + 1):
for resv_from in range(resv - w2, resv + 1):
i_from = resv2i.get(resv_from)
if i_from is not None:
break
for resv_to in range(resv + w2, resv - 1, -1):
i_to = resv2i.get(resv_to)
if i_to is not None:
break
if i_from is None or i_to is None:
continue
if i_to - i_from < w4:
continue
x = X_mobile[i_from:i_to + 1]
y = X_target[i_from:i_to + 1]
resv2b[resv] = rmsd(x, y)
if not quiet:
print(' resi %4d: RMS = %6.3f (%4d atoms)' % (resv, resv2b[resv], i_to - i_from + 1))
if load_b:
_self.alter(mobile, 'b=resv2b.get(resv, -1.0)', space={'resv2b': resv2b})
if load_b and visualize:
_self.color('yellow', '(%s) and b < -0.5' % (mobile))
_self.spectrum('b', 'blue_white_red', '(%s) and b > -0.5' % (mobile))
_self.show_as('cartoon', mobile)
_self.hide('cartoon', '(%s) and b < -0.5' % (mobile))
_self.cartoon('putty', mobile)
return resv2b
def _run_theseus(args, tempdir, preserve, quiet):
'''
DESCRIPTION
Helper function for theseus and intra_theseus
'''
import subprocess, os
translations = []
rotations = []
t_type = float
try:
if quiet:
subprocess.call(args, cwd=tempdir)
else:
import re
unesc = re.compile('\x1b' + r'\[[\d;]+m').sub
process = subprocess.Popen(args, cwd=tempdir, stdout=subprocess.PIPE,
universal_newlines=True)
for line in process.stdout:
print(unesc('', line.rstrip()))
filename = os.path.join(tempdir, 'theseus_transf2.txt')
if not os.path.exists(filename):
# THESEUS 3.x
filename = os.path.join(tempdir, 'theseus_transf.txt')
if not os.path.exists(filename):
raise CmdException('no theseus_transf2.txt or '
'theseus_transf.txt output file')
def t_type(t):
return float(t) * -1.
handle = open(filename)
for line in handle:
if line[10:13] == ' t:':
translations.append(list(map(t_type, line[13:].split())))
elif line[10:13] == ' R:':
rotations.append(list(map(float, line[13:].split())))
handle.close()
except OSError as ex:
raise CmdException('Cannot execute "%s"' % (args[0])) from ex
finally:
if not preserve:
import shutil
shutil.rmtree(tempdir)
elif not quiet:
print(' Not deleting temporary directory:', tempdir)
return translations, rotations
[docs]
def theseus(mobile, target, match='align', cov=0, cycles=200,
mobile_state=1, target_state=1, exe='theseus', preserve=0, quiet=1,
*, _self=cmd):
'''
DESCRIPTION
Structural superposition of two molecules with maximum likelihood.
THESEUS: Maximum likelihood multiple superpositioning
http://www.theseus3d.org
ARGUMENTS
mobile = string: atom selection for mobile atoms
target = string: atom selection for target atoms
match = string: in, like, align, none or the name of an alignment object
(see "local_rms" help for details) {default: align}
cov = 0/1: 0 is variance weighting, 1 is covariance weighting (slower)
{default: 0}
SEE ALSO
align, super, cealign
'''
import tempfile, os
cov, cycles = int(cov), int(cycles)
mobile_state, target_state = int(mobile_state), int(target_state)
preserve, quiet = int(preserve), int(quiet)
tempdir = tempfile.mkdtemp()
mobile_filename = os.path.join(tempdir, 'mobile.pdb')
target_filename = os.path.join(tempdir, 'target.pdb')
mm = MatchMaker(mobile, target, match, _self=_self)
_self.save(mobile_filename, mm.mobile, mobile_state)
_self.save(target_filename, mm.target, target_state)
exe = cmd.exp_path(exe)
args = [exe, '-a0', '-c' if cov else '-v', '-i%d' % cycles,
mobile_filename, target_filename]
translations, rotations = _run_theseus(args, tempdir, preserve, quiet)
matrices = [R[0:3] + [i * t[0]] + R[3:6] + [i * t[1]] + R[6:9] + [i * t[2], 0, 0, 0, 1]
for (R, t, i) in zip(rotations, translations, [-1, 1])]
obj_list = _self.get_object_list(mobile)
for obj in obj_list:
_self.transform_object(obj, matrices[0], 0, transpose=1)
_self.transform_object(obj, matrices[1], 0)
if not quiet:
print(' theseus: done')
[docs]
def intra_theseus(selection, state=1, cov=0, cycles=200,
exe='theseus', preserve=0, quiet=1, *, _self=cmd):
'''
DESCRIPTION
Fits all states of an object to an atom selection with maximum likelihood.
THESEUS: Maximum likelihood multiple superpositioning
http://www.theseus3d.org
ARGUMENTS
selection = string: atoms to fit
state = integer: keep transformation of this state unchanged {default: 1}
cov = 0/1: 0 is variance weighting, 1 is covariance weighting (slower)
{default: 0}
SEE ALSO
intra_fit, intra_rms_cur
'''
import tempfile, os
state, cov, cycles = int(state), int(cov), int(cycles)
preserve, quiet = int(preserve), int(quiet)
tempdir = tempfile.mkdtemp()
filename = os.path.join(tempdir, 'mobile.pdb')
_self.save(filename, selection, 0)
exe = cmd.exp_path(exe)
args = [exe, '-a0', '-c' if cov else '-v', '-i%d' % cycles, filename]
translations = []
rotations = []
translations, rotations = _run_theseus(args, tempdir, preserve, quiet)
matrices = [R[0:3] + [-t[0]] + R[3:6] + [-t[1]] + R[6:9] + [-t[2], 0, 0, 0, 1]
for (R, t) in zip(rotations, translations)]
# intra fit states
obj_list = _self.get_object_list(selection)
for i, m in enumerate(matrices):
for obj in obj_list:
_self.transform_object(obj, m, i + 1, transpose=1)
# fit back to given state
if 0 < state <= len(matrices):
m = list(matrices[state - 1])
for i in [3, 7, 11]:
m[i] *= -1
for obj in obj_list:
_self.transform_object(obj, m, 0)
if not quiet:
print(' intra_theseus: %d states aligned' % (len(matrices)))
[docs]
def prosmart(mobile, target, mobile_state=1, target_state=1,
exe='prosmart', transform=1, object=None, quiet=0, *, _self=cmd):
'''
DESCRIPTION
ProSMART wrapper.
http://www2.mrc-lmb.cam.ac.uk/groups/murshudov/
'''
import subprocess, tempfile, os, shutil, glob
quiet = int(quiet)
tempdir = tempfile.mkdtemp()
mobile_filename = os.path.join(tempdir, 'mobile.pdb')
target_filename = os.path.join(tempdir, 'target.pdb')
_self.save(mobile_filename, mobile, state=mobile_state)
_self.save(target_filename, target, state=target_state)
exe = cmd.exp_path(exe)
args = [exe, '-p1', mobile_filename, '-p2', target_filename, '-a']
def xglob(x):
return glob.glob(
os.path.join(tempdir, 'ProSMART_Output/Output_Files', x))
try:
subprocess.check_call(args, cwd=tempdir)
transfiles = xglob('Superposition/Transformations/*/*.txt')
with open(transfiles[0]) as f:
f = iter(f)
for line in f:
if line.startswith('ROTATION'):
matrix = [list(map(float, next(f).split())) + [0] for _ in range(3)]
elif line.startswith('TRANSLATION'):
matrix.append([-float(v) for v in next(f).split()] + [1])
break
if int(transform):
matrix = [v for m in matrix for v in m]
assert len(matrix) == 4 * 4
for model in _self.get_object_list(mobile):
_self.transform_object(model, matrix, state=0)
if object:
from .importing import load_aln
alnfiles = xglob('Residue_Alignment_Scores/*/*.txt')
alnfiles = [x for x in alnfiles if not x.endswith('_clusters.txt')]
load_aln(alnfiles[0], object, mobile, target, _self=_self)
except OSError as ex:
raise CmdException(
f'Cannot execute "{exe}", please provide full path to prosmart executable'
) from ex
finally:
shutil.rmtree(tempdir)
if not quiet:
print(' prosmart: done')
def _bfit_get_prior(distribution, em=0):
from csb.statistics import scalemixture as sm
if distribution == 'student':
prior = sm.GammaPrior()
if em:
prior.estimator = sm.GammaPosteriorMAP()
elif distribution == 'k':
prior = sm.InvGammaPrior()
if em:
prior.estimator = sm.InvGammaPosteriorMAP()
else:
raise AttributeError('distribution')
return prior
[docs]
def xfit(mobile, target, mobile_state=-1, target_state=-1, load_b=0,
cycles=10, match='align', guide=1, seed=0, quiet=1,
bfit=0, distribution='student', _self=cmd):
'''
DESCRIPTION
Weighted superposition of the model in the first selection on to the model
in the second selection. The weights are estimated with maximum likelihood.
The result should be very similar to "theseus".
Requires CSB, https://github.com/csb-toolbox/CSB
ARGUMENTS
mobile = string: atom selection
target = string: atom selection
mobile_state = int: object state of mobile selection {default: current}
target_state = int: object state of target selection {default: current}
load_b = 0 or 1: save -log(weights) into B-factor column {default: 0}
SEE ALSO
intra_xfit, align, super, fit, cealign, theseus
'''
from numpy import asarray, identity, log, dot, zeros
from csb.bio.utils import distance_sq, wfit, fit
from . import querying
cycles, quiet = int(cycles), int(quiet)
mobile_state, target_state = int(mobile_state), int(target_state)
mobile_obj = querying.get_object_name(mobile, 1, _self=_self)
if mobile_state < 1:
mobile_state = querying.get_object_state(mobile_obj, _self=_self)
if target_state < 1:
target_state = querying.get_selection_state(target, _self=_self)
if int(guide):
mobile = '(%s) and guide' % (mobile)
target = '(%s) and guide' % (target)
mm = MatchMaker(mobile, target, match, _self=_self)
Y = asarray(_self.get_coords(mm.mobile, mobile_state))
X = asarray(_self.get_coords(mm.target, target_state))
if int(seed):
R, t = identity(3), zeros(3)
else:
R, t = fit(X, Y)
if int(bfit):
# adapted from csb.apps.bfit
from csb.bio.utils import distance, probabilistic_fit
from csb.statistics.scalemixture import ScaleMixture
mixture = ScaleMixture(scales=X.shape[0],
prior=_bfit_get_prior(distribution), d=3)
for _ in range(cycles):
data = distance(Y, dot(X - t, R))
mixture.estimate(data)
R, t = probabilistic_fit(X, Y, mixture.scales)
scales = mixture.scales
else:
for _ in range(cycles):
data = distance_sq(Y, dot(X - t, R))
scales = 1.0 / data.clip(1e-3)
R, t = wfit(X, Y, scales)
m = identity(4)
m[0:3, 0:3] = R
m[0:3, 3] = t
_self.transform_object(mobile_obj, list(m.flat))
if int(load_b):
b_iter = iter(-log(scales))
_self.alter(mm.mobile, 'b = next(b_iter)', space={'b_iter': b_iter, 'next': next})
if not quiet:
print(' xfit: %d atoms aligned' % (len(X)))
[docs]
def intra_xfit(selection, load_b=0, cycles=20, guide=1, seed=0, quiet=1,
bfit=0, distribution='student', _self=cmd):
'''
DESCRIPTION
Weighted superposition of all states of an object to the intermediate
structure over all states. The weights are estimated with maximum
likelihood.
The result should be very similar to "intra_theseus".
Requires CSB, https://github.com/csb-toolbox/CSB
ARGUMENTS
selection = string: atom selection
load_b = 0 or 1: save -log(weights) into B-factor column {default: 0}
NOTE
Assumes all states to have identical number of CA-atoms.
SEE ALSO
xfit, intra_fit, intra_theseus
'''
from numpy import asarray, identity, log, dot, zeros
from csb.bio.utils import wfit, fit
from .querying import get_ensemble_coords
cycles, quiet = int(cycles), int(quiet)
if int(guide):
selection = '(%s) and guide' % (selection)
mobile_objs = _self.get_object_list(selection)
n_states_objs = []
X = []
for obj in mobile_objs:
X_obj = get_ensemble_coords('({}) & {}'.format(selection, obj), _self=_self)
if X and len(X_obj) and len(X[0]) != len(X_obj[0]):
raise CmdException('objects have different number of atoms')
X.extend(X_obj)
n_states_objs.append(len(X_obj))
n_models = len(X)
X = asarray(X)
R, t = [identity(3)] * n_models, [zeros(3)] * n_models
if int(bfit):
# adapted from csb.apps.bfite
from csb.bio.utils import average_structure, distance
from csb.statistics.scalemixture import ScaleMixture
average = average_structure(X)
mixture = ScaleMixture(scales=X.shape[1],
prior=_bfit_get_prior(distribution), d=3)
for i in range(n_models):
R[i], t[i] = fit(X[i], average)
for _ in range(cycles):
data = asarray([distance(average, dot(X[i] - t[i], R[i])) for i in range(n_models)])
mixture.estimate(data.T)
for i in range(n_models):
R[i], t[i] = wfit(X[i], average, mixture.scales)
scales = mixture.scales
else:
if int(seed):
ensemble = X
else:
ensemble = []
for i in range(n_models):
R[i], t[i] = fit(X[i], X[0])
ensemble.append(dot(X[i] - t[i], R[i]))
for _ in range(cycles):
ensemble = asarray(ensemble)
average = ensemble.mean(0)
data = ensemble.var(0).sum(1)
scales = 1.0 / data.clip(1e-3)
ensemble = []
for i in range(n_models):
R[i], t[i] = wfit(X[i], average, scales)
ensemble.append(dot(X[i] - t[i], R[i]))
m = identity(4)
back = identity(4)
back[0:3, 0:3] = R[0]
back[0:3, 3] = t[0]
transformation_i = 0
for mobile_obj, n_states in zip(mobile_objs, n_states_objs):
for state_i in range(n_states):
m[0:3, 0:3] = R[transformation_i].T
m[3, 0:3] = -t[transformation_i]
_self.transform_object(mobile_obj, list(m.flat), state=state_i + 1)
transformation_i += 1
# fit back to first state
_self.transform_object(mobile_obj, list(back.flat), state=0)
if int(load_b):
b_iter = iter(-log(scales))
_self.alter('({}) & {} & state 1'.format(selection, mobile_obj),
'b = next(b_iter)',
space={'b_iter': b_iter, 'next': next})
if not quiet:
print(' intra_xfit: %d atoms in %d states aligned' % (len(X[0]), n_models))
[docs]
def promix(mobile, target, K=0, prefix=None, mobile_state=-1, target_state=-1,
match='align', guide=1, quiet=1, async_=-1, _self=cmd, **kwargs):
'''
DESCRIPTION
Finds rigid segments in two objects with different conformation.
Requires CSB, https://github.com/csb-toolbox/CSB
ARGUMENTS
mobile, target = string: atom selections
K = integer: Number of segments {default: guess}
prefix = string: Prefix of named segment selections to make
SEE ALSO
intra_promix
REFERENCE
Mixture models for protein structure ensembles
Hirsch M, Habeck M. - Bioinformatics. 2008 Oct 1;24(19):2184-92
'''
from numpy import asarray
from csb.statistics.mixtures import SegmentMixture as Mixture # noqa: F401 imported but unused
from .querying import get_object_name
K, guide, quiet = int(K), int(guide), int(quiet)
async_ = int(kwargs.pop('async', async_))
mobile_state, target_state = int(mobile_state), int(target_state)
if async_ < 0:
async_ = not quiet
if isinstance(target, str) and target.isdigit() and \
_self.count_atoms('?' + target) == 0 and _self.count_states(mobile) > 1:
print(' Warning: sanity test suggest you want "intra_promix"')
return intra_promix(mobile, target, prefix, 0, guide, quiet, async_)
if guide:
mobile = '(%s) and guide' % (mobile)
target = '(%s) and guide' % (target)
_self.color('gray', mobile)
obj = get_object_name(mobile, _self=_self)
mm = MatchMaker(mobile, target, match, _self=_self)
selection = mm.mobile
X = asarray([
_self.get_coords(mm.mobile, mobile_state),
_self.get_coords(mm.target, target_state),
])
if not async_:
_promix(**locals())
else:
import threading
t = threading.Thread(target=_promix, kwargs=locals())
t.setDaemon(1)
t.start()
[docs]
def intra_promix(selection, K=0, prefix=None, conformers=0, guide=1,
quiet=1, async_=-1, _self=cmd, **kwargs):
'''
DESCRIPTION
Finds rigid segments in a multi-state object.
Requires CSB, https://github.com/csb-toolbox/CSB
ARGUMENTS
selection = string: atom selection
K = integer: Number of segments {default: guess}
prefix = string: Prefix of named segment selections to make
SEE ALSO
promix
REFERENCE
Mixture models for protein structure ensembles
Hirsch M, Habeck M. - Bioinformatics. 2008 Oct 1;24(19):2184-92
'''
from numpy import asarray
from csb.statistics import mixtures
from .querying import get_ensemble_coords, get_object_name
K, conformers = int(K), int(conformers)
guide, quiet, async_ = int(guide), int(quiet), int(kwargs.pop('async', async_))
if async_ < 0:
async_ = not quiet
Mixture = mixtures.ConformerMixture if conformers else mixtures.SegmentMixture
obj = get_object_name(selection, _self=_self)
n_models = _self.count_states(obj)
if guide:
selection = '(%s) and guide' % (selection)
if n_models < 2:
raise CmdException('object needs multiple states')
X = asarray(get_ensemble_coords(selection, _self=_self))
assert X.shape == (n_models, _self.count_atoms(selection), 3)
if not async_:
_promix(**locals())
else:
import threading
t = threading.Thread(target=_promix, kwargs=locals())
t.setDaemon(1)
t.start()
def _promix(conformers=0, prefix=None,
obj=NotImplemented, selection=NotImplemented,
X=NotImplemented, K=NotImplemented, Mixture=NotImplemented,
_self=cmd,
**_):
if not prefix:
if conformers:
prefix = obj + '_conformer'
else:
prefix = obj + '_segment'
_self.delete(prefix + '_*')
id_list = []
_self.iterate(selection, 'id_list.append(ID)', space=locals())
mixture = Mixture.new(X, K)
membership = mixture.membership
if conformers:
states_list = [0] * mixture.K
for (i, k) in enumerate(membership):
states_list[k] += 1
name = '%s_%d' % (prefix, k + 1)
_self.create(name, obj, i + 1, states_list[k])
else:
_self.color('gray', selection)
for k in range(mixture.K):
name = '%s_%d' % (prefix, k + 1)
id_list_k = [i for (i, m) in zip(id_list, membership) if m == k]
_self.select_list(name, obj, id_list_k)
_self.disable(name)
_self.color(k + 2, name)
for k, (sigma, w) in enumerate(zip(mixture.sigma, mixture.w)):
print(' %s_%d: sigma = %6.3f, w = %.3f' % (prefix, k + 1, sigma, w))
print(' BIC: %.2f' % (mixture.BIC))
print(' Log Likelihood: %.2f' % (mixture.log_likelihood))
[docs]
def intra_boxfit(selection="polymer", center=(0.5, 0.5, 0.5), *, _self=cmd):
"""
DESCRIPTION
Center selection in simulation box.
ARGUMENTS
selection = str: atom selection to center {default: polymer}
center = list-of-3-floats: Target position in fractional space
{default: [0.5, 0.5, 0.5]}
"""
from numpy import dot
from .xtal import cellbasis
if isinstance(center, str):
center = _self.safe_list_eval(center)
objects = _self.get_object_list(selection)
for state in range(1, _self.count_states(selection) + 1):
selecenter = _self.get_coords(selection, state).mean(0)
for obj in objects:
sym = _self.get_symmetry(obj, state)
if not sym:
raise CmdException("no symmetry")
basis = cellbasis(sym[3:6], sym[0:3])[:3, :3]
cset = _self.get_coordset(obj, state, copy=0)
cset += dot(basis, center) - selecenter
_self.rebuild(selection)
[docs]
@cmd.extend
def intra_center(selection: str = "polymer",
state: int = CURRENT_STATE,
*,
_self=cmd):
"""
DESCRIPTION
Center frames on a selection. Like intra_fit, but without rotation.
ARGUMENTS
selection = str: atom selection to center {default: polymer}
state = int: reference state {default: current state}
"""
objects = _self.get_object_list(selection)
center = _self.get_coords(selection, state).mean(0)
for state in range(1, _self.count_states(selection) + 1):
offset = center - _self.get_coords(selection, state).mean(0)
offset = offset.tolist()
for obj in objects:
_self.translate(offset, 'none', state,
camera=0, object=obj, object_mode=1)
# all those have kwargs: mobile, target, mobile_state, target_state
align_methods = ['align', 'super', 'cealign', 'tmalign', 'theseus',
'prosmart', 'xfit', 'mcsalign']
align_methods_sc = cmd.Shortcut(align_methods)
# pymol commands
cmd.extend('alignwithanymethod', alignwithanymethod)
cmd.extend('tmalign', tmalign)
cmd.extend('dyndom', dyndom)
cmd.extend('gdt_ts', gdt_ts)
cmd.extend('local_rms', local_rms)
if 'extra_fit' not in cmd.keyword:
cmd.extend('extra_fit', extra_fit)
cmd.extend('intra_theseus', intra_theseus)
cmd.extend('theseus', theseus)
cmd.extend('prosmart', prosmart)
cmd.extend('xfit', xfit)
cmd.extend('intra_xfit', intra_xfit)
cmd.extend('promix', promix)
cmd.extend('intra_promix', intra_promix)
cmd.extend('intra_boxfit', intra_boxfit)
# autocompletion
cmd.auto_arg[0].update([
('alignwithanymethod', _auto_arg0_align),
('tmalign', _auto_arg0_align),
('dyndom', _auto_arg0_align),
('gdt_ts', _auto_arg0_align),
('local_rms', _auto_arg0_align),
('extra_fit', _auto_arg0_align),
('theseus', _auto_arg0_align),
('intra_theseus', _auto_arg1_align),
('prosmart', _auto_arg0_align),
('xfit', _auto_arg0_align),
('intra_xfit', _auto_arg0_align),
('promix', _auto_arg0_align),
('intra_promix', _auto_arg0_align),
('intra_boxfit', _auto_arg1_align),
('intra_center', _auto_arg1_align),
])
cmd.auto_arg[1].update([
('alignwithanymethod', _auto_arg1_align),
('tmalign', _auto_arg1_align),
('dyndom', _auto_arg1_align),
('gdt_ts', _auto_arg1_align),
('local_rms', _auto_arg1_align),
('extra_fit', cmd.auto_arg[0]['disable']),
('theseus', _auto_arg1_align),
('prosmart', _auto_arg1_align),
('xfit', _auto_arg1_align),
('promix', _auto_arg0_align),
])
cmd.auto_arg[2].update([
('extra_fit', [align_methods_sc, 'alignment method', '']),
])
# vi: ts=4:sw=4:smarttab:expandtab