def fetchPDBLigand(cci, filename=None):
"""Fetch PDB ligand data from PDB_ for chemical component *cci*.
*cci* may be 3-letter chemical component identifier or a valid XML
filename. If *filename* is given, XML file will be saved with that name.
If you query ligand data frequently, you may configure ProDy to save XML
files in your computer. Set ``ligand_xml_save`` option **True**, i.e.
``confProDy(ligand_xml_save=True)``. Compressed XML files will be save
to ProDy package folder, e.g. :file:`/home/user/.prody/pdbligands`. Each
file is around 5Kb when compressed.
This function is compatible with PDBx/PDBML v 4.0.
Ligand data is returned in a dictionary. Ligand coordinate atom data with
*model* and *ideal* coordinate sets are also stored in this dictionary.
Note that this dictionary will contain data that is present in the XML
file and all Ligand Expo XML files do not contain every possible data
field. So, it may be better if you use :meth:`dict.get` instead of
indexing the dictionary, e.g. to retrieve formula weight (or relative
molar mass) of the chemical component use ``data.get('formula_weight')``
instead of ``data['formula_weight']`` to avoid exceptions when this data
field is not found in the XML file. URL and/or path of the XML file are
returned in the dictionary with keys ``url`` and ``path``, respectively.
Following example downloads data for ligand STI (a.k.a. Gleevec and
Imatinib) and calculates RMSD between model (X-ray structure 1IEP) and
ideal (energy minimized) coordinate sets:
.. ipython:: python
from prody import *
ligand_data = fetchPDBLigand('STI')
ligand_data['model_coordinates_db_code']
ligand_model = ligand_data['model']
ligand_ideal = ligand_data['ideal']
transformation = superpose(ligand_ideal.noh, ligand_model.noh)
calcRMSD(ligand_ideal.noh, ligand_model.noh)"""
if not isinstance(cci, str):
raise TypeError('cci must be a string')
if isfile(cci):
inp = openFile(cci)
xml = inp.read()
inp.close()
url = None
path = cci
cci = splitext(splitext(split(cci)[1])[0])[0].upper()
elif len(cci) > 4 or not cci.isalnum():
raise ValueError('cci must be 3-letters long and alphanumeric or '
'a valid filename')
else:
xml = None
cci = cci.upper()
if SETTINGS.get('ligand_xml_save'):
folder = join(getPackagePath(), 'pdbligands')
if not isdir(folder):
makePath(folder)
xmlgz = path = join(folder, cci + '.xml.gz')
if isfile(xmlgz):
with openFile(xmlgz) as inp:
xml = inp.read()
else:
path = None
#url = ('http://ligand-expo.rcsb.org/reports/{0[0]}/{0}/{0}'
# '.xml'.format(cci.upper()))
url = 'http://www.pdb.org/pdb/files/ligand/{0}.xml'.format(cci.upper())
if not xml:
#'http://www.pdb.org/pdb/files/ligand/{0}.xml'
try:
inp = openURL(url)
except IOError:
raise IOError('XML file for ligand {0} is not found online'
.format(cci))
else:
xml = inp.read()
inp.close()
if filename:
out = openFile(filename, mode='w', folder=folder)
out.write(xml)
out.close()
if SETTINGS.get('ligand_xml_save'):
with openFile(xmlgz, 'w') as out:
out.write(xml)
import xml.etree.cElementTree as ET
root = ET.XML(xml)
if (root.get('{http://www.w3.org/2001/XMLSchema-instance}'
'schemaLocation') !=
'http://pdbml.pdb.org/schema/pdbx-v40.xsd pdbx-v40.xsd'):
LOGGER.warn('XML is not in PDBx/PDBML v 4.0 format, resulting '
'dictionary may not contain all data fields')
ns = root.tag[:root.tag.rfind('}')+1]
len_ns = len(ns)
dict_ = {'url': url, 'path': path}
for child in list(root.find(ns + 'chem_compCategory')[0]):
tag = child.tag[len_ns:]
if tag.startswith('pdbx_'):
tag = tag[5:]
dict_[tag] = child.text
dict_['formula_weight'] = float(dict_.get('formula_weight'))
identifiers_and_descriptors = []
results = root.find(ns + 'pdbx_chem_comp_identifierCategory')
if results:
identifiers_and_descriptors.extend(results)
results = root.find(ns + 'pdbx_chem_comp_descriptorCategory')
if results:
identifiers_and_descriptors.extend(results)
for child in identifiers_and_descriptors:
program = child.get('program').replace(' ', '_')
type_ = child.get('type').replace(' ', '_')
dict_[program + '_' + type_] = child[0].text
dict_[program + '_version'] = child.get('program_version')
dict_['audits'] = [(audit.get('action_type'), audit.get('date'))
for audit in
list(root.find(ns + 'pdbx_chem_comp_auditCategory'))]
atoms = list(root.find(ns + 'chem_comp_atomCategory'))
n_atoms = len(atoms)
ideal_coords = np.zeros((n_atoms, 3))
model_coords = np.zeros((n_atoms, 3))
atomnames = np.zeros(n_atoms, dtype=ATOMIC_FIELDS['name'].dtype)
elements = np.zeros(n_atoms, dtype=ATOMIC_FIELDS['element'].dtype)
resnames = np.zeros(n_atoms, dtype=ATOMIC_FIELDS['resname'].dtype)
charges = np.zeros(n_atoms, dtype=ATOMIC_FIELDS['charge'].dtype)
resnums = np.ones(n_atoms, dtype=ATOMIC_FIELDS['charge'].dtype)
alternate_atomnames = np.zeros(n_atoms, dtype=ATOMIC_FIELDS['name'].dtype)
leaving_atom_flags = np.zeros(n_atoms, np.bool)
aromatic_flags = np.zeros(n_atoms, np.bool)
stereo_configs = np.zeros(n_atoms, np.bool)
ordinals = np.zeros(n_atoms, int)
name2index = {}
for i, atom in enumerate(atoms):
data = dict([(child.tag[len_ns:], child.text) for child in list(atom)])
name = data.get('pdbx_component_atom_id', 'X')
name2index[name] = i
atomnames[i] = name
elements[i] = data.get('type_symbol', 'X')
resnames[i] = data.get('pdbx_component_comp_id', 'UNK')
charges[i] = float(data.get('charge', 0))
alternate_atomnames[i] = data.get('alt_atom_id', 'X')
leaving_atom_flags[i] = data.get('pdbx_leaving_atom_flag') == 'Y'
aromatic_flags[i] = data.get('pdbx_atomatic_flag') == 'Y'
stereo_configs[i] = data.get('pdbx_stereo_config') == 'Y'
ordinals[i] = int(data.get('pdbx_ordinal', 0))
model_coords[i, 0] = float(data.get('model_Cartn_x', 0))
model_coords[i, 1] = float(data.get('model_Cartn_y', 0))
model_coords[i, 2] = float(data.get('model_Cartn_z', 0))
ideal_coords[i, 0] = float(data.get('pdbx_model_Cartn_x_ideal', 0))
ideal_coords[i, 1] = float(data.get('pdbx_model_Cartn_y_ideal', 0))
ideal_coords[i, 2] = float(data.get('pdbx_model_Cartn_z_ideal', 0))
pdbid = dict_.get('model_coordinates_db_code')
if pdbid:
model = AtomGroup(cci + ' model ({0})'.format(pdbid))
else:
model = AtomGroup(cci + ' model')
model.setCoords(model_coords)
model.setNames(atomnames)
model.setResnames(resnames)
model.setResnums(resnums)
model.setElements(elements)
model.setCharges(charges)
model.setFlags('leaving_atom_flags', leaving_atom_flags)
model.setFlags('aromatic_flags', aromatic_flags)
model.setFlags('stereo_configs', stereo_configs)
model.setData('ordinals', ordinals)
model.setData('alternate_atomnames', alternate_atomnames)
dict_['model'] = model
ideal = model.copy()
ideal.setTitle(cci + ' ideal')
ideal.setCoords(ideal_coords)
dict_['ideal'] = ideal
bonds = []
warned = set()
for bond in list(root.find(ns + 'chem_comp_bondCategory') or bonds):
name_1 = bond.get('atom_id_1')
name_2 = bond.get('atom_id_2')
try:
bonds.append((name2index[name_1], name2index[name_2]))
except KeyError:
if name_1 not in warned and name_1 not in name2index:
warned.add(name_1)
LOGGER.warn('{0} specified {1} in bond category is not '
'a valid atom name.'.format(repr(name_1), cci))
if name_2 not in warned and name_2 not in name2index:
warned.add(name_2)
LOGGER.warn('{0} specified {1} in bond category is not '
'a valid atom name.'.format(repr(name_2), cci))
if bonds:
bonds = np.array(bonds, int)
model.setBonds(bonds)
ideal.setBonds(bonds)
return dict_
class exANM(ANMBase):
"""Class for explicit ANM (exANM) method ([FT00]_).
Optional arguments build a membrane lattice permit analysis of membrane
effect on elastic network models in *exANM* method described in [TL12]_.
.. [TL12] Lezon TR, Bahar I, Constraints Imposed by the Membrane
Selectively Guide the Alternating Access Dynamics of the Glutamate
Transporter GltPh
"""
def __init__(self, name='Unknown'):
super(exANM, self).__init__(name)
self._membrane = None
self._combined = None
def buildMembrane(self, coords, **kwargs):
"""Build Hessian matrix for given coordinate set.
:arg coords: a coordinate set or an object with ``getCoords`` method
:type coords: :class:`numpy.ndarray`
:arg membrane_hi: the maximum z coordinate of the pdb default is 13.0
:type membrane_hi: float
:arg membrane_lo: the minimum z coordinate of the pdb default is -13.0
:type membrane_lo: float
:arg R: radius of all membrane in x-y direction default is 80.
:type R: float
:arg r: radius of individual barrel-type membrane protein default is 2.5.
:type
:arg lat: lattice type which could be FCC(face-centered-cubic)(default),
SC(simple cubic), SH(simple hexagonal)
:type lat: str
"""
if type(coords) is AtomGroup:
buildAg = True
else:
buildAg = False
try:
coords = (coords._getCoords() if hasattr(coords, '_getCoords') else
coords.getCoords())
except AttributeError:
try:
checkCoords(coords)
except TypeError:
raise TypeError('coords must be a Numpy array or an object '
'with `getCoords` method')
self._n_atoms = natoms = int(coords.shape[0])
pxlo = min(np.append(coords[:,0],10000))
pxhi = max(np.append(coords[:,0],-10000))
pylo = min(np.append(coords[:,1],10000))
pyhi = max(np.append(coords[:,1],-10000))
pzlo = min(np.append(coords[:,2],10000))
pzhi = max(np.append(coords[:,2],-10000))
membrane_hi = float(kwargs.get('membrane_hi', 13.0))
membrane_lo = float(kwargs.get('membrane_lo', -13.0))
R = float(kwargs.get('R', 80))
r = float(kwargs.get('r', 5))
lat = str(kwargs.get('lat', 'FCC'))
lpv = assign_lpvs(lat)
imax = (R + lpv[0,2] * (membrane_hi - membrane_lo)/2.)/r
jmax = (R + lpv[1,2] * (membrane_hi - membrane_lo)/2.)/r
kmax = (R + lpv[2,2] * (membrane_hi - membrane_lo)/2.)/r
#print pxlo, pxhi, pylo, pyhi, pzlo, pzhi
#print lpv[0,2],lpv[1,2],lpv[2,2]
#print R,r,imax,jmax,kmax
membrane = zeros((1,3))
LOGGER.timeit('_membrane')
membrane = zeros((1,3))
atm = 0
for i in range(-int(imax),int(imax+1)):
for j in range(-int(jmax),int(jmax+1)):
for k in range(-int(kmax),int(kmax+1)):
X = zeros((1,3))
for p in range(3):
X[0,p]=2.*r*(i*lpv[0,p]+j*lpv[1,p]+k*lpv[2,p])
dd=0
for p in range(3):
dd += X[0,p] ** 2
if dd<R**2 and X[0,2]>membrane_lo and X[0,2]<membrane_hi:
if X[0,0]>pxlo-R/2 and X[0,0]<pxhi+R/2 and X[0,1]>pylo-R/2 and X[0,1]<pyhi+R/2 and X[0,2]>pzlo and X[0,2]<pzhi:
if checkClash(X, coords[:natoms,:], radius=5):
if atm == 0:
membrane = X
else:
membrane = np.append(membrane, X, axis=0)
atm = atm + 1
#print atm
self._membrane = AtomGroup(title="Membrane")
self._membrane.setCoords(membrane)
self._membrane.setResnums(range(atm))
self._membrane.setResnames(["NE1" for i in range(atm)])
self._membrane.setChids(["Q" for i in range(atm)])
self._membrane.setElements(["Q1" for i in range(atm)])
self._membrane.setNames(["Q1" for i in range(atm)])
LOGGER.report('Membrane was built in %2.fs.', label='_membrane')
def buildHessian(self, coords, cutoff=15., gamma=1., **kwargs):
"""Build Hessian matrix for given coordinate set.
:arg coords: a coordinate set or an object with ``getCoords`` method
:type coords: :class:`numpy.ndarray`
:arg cutoff: cutoff distance (Å) for pairwise interactions,
default is 15.0 Å
:type cutoff: float
:arg gamma: spring constant, default is 1.0
:type gamma: float
:arg membrane_hi: the maximum z coordinate of the pdb default is 13.0
:type membrane_hi: float
:arg membrane_lo: the minimum z coordinate of the pdb default is -13.0
:type membrane_lo: float
:arg R: radius of all membrane in x-y direction default is 80.
:type R: float
:arg r: radius of individual barrel-type membrane protein default is 2.5.
:type
:arg lat: lattice type which could be FCC(face-centered-cubic)(default),
SC(simple cubic), SH(simple hexagonal)
:type lat: str
"""
try:
coords = (coords._getCoords() if hasattr(coords, '_getCoords') else
coords.getCoords())
except AttributeError:
try:
checkCoords(coords)
except TypeError:
raise TypeError('coords must be a Numpy array or an object '
'with `getCoords` method')
self._n_atoms = natoms = int(coords.shape[0])
if self._membrane is None:
membrane_hi = float(kwargs.get('membrane_hi', 13.0))
membrane_lo = float(kwargs.get('membrane_lo', -13.0))
R = float(kwargs.get('R', 80))
r = float(kwargs.get('r', 5))
lat = str(kwargs.get('lat', 'FCC'))
buildMembrane(self,coords,membrane_hi=membrane_hi, membrane_lo=membrane_lo,R=R,r=r,lat=lat)
LOGGER.timeit('_exanm')
coords = np.concatenate((coords,self._membrane.getCoords()),axis=0)
self._combined_coords = coords
total_natoms = int(coords.shape[0])
self._hessian = np.zeros((natoms*3, natoms*3), float)
total_hessian = np.zeros((total_natoms*3, total_natoms*3), float)
cutoff, g, gamma = checkENMParameters(cutoff, gamma)
cutoff2 = cutoff * cutoff
for i in range(total_natoms):
res_i3 = i*3
res_i33 = res_i3+3
i_p1 = i+1
i2j_all = coords[i_p1:, :] - coords[i]
for j, dist2 in enumerate((i2j_all ** 2).sum(1)):
if dist2 > cutoff2:
continue
i2j = i2j_all[j]
j += i_p1
g = gamma(dist2, i, j)
res_j3 = j*3
res_j33 = res_j3+3
super_element = np.outer(i2j, i2j) * (- g / dist2)
total_hessian[res_i3:res_i33, res_j3:res_j33] = super_element
total_hessian[res_j3:res_j33, res_i3:res_i33] = super_element
total_hessian[res_i3:res_i33, res_i3:res_i33] = total_hessian[res_i3:res_i33, res_i3:res_i33] - super_element
total_hessian[res_j3:res_j33, res_j3:res_j33] = total_hessian[res_j3:res_j33, res_j3:res_j33] - super_element
ss = total_hessian[:natoms*3, :natoms*3]
so = total_hessian[:natoms*3, natoms*3+1:]
os = total_hessian[natoms*3+1:,:natoms*3]
oo = total_hessian[natoms*3+1:, natoms*3+1:]
self._hessian = ss - np.dot(so, np.dot(linalg.inv(oo), os))
LOGGER.report('Hessian was built in %.2fs.', label='_exanm')
self._dof = self._hessian.shape[0]
def calcModes(self, n_modes=20, zeros=False, turbo=True):
"""Calculate normal modes. This method uses :func:`scipy.linalg.eigh`
function to diagonalize the Hessian matrix. When Scipy is not found,
:func:`numpy.linalg.eigh` is used.
:arg n_modes: number of non-zero eigenvalues/vectors to calculate.
If ``None`` is given, all modes will be calculated.
:type n_modes: int or None, default is 20
:arg zeros: If ``True``, modes with zero eigenvalues will be kept.
:type zeros: bool, default is ``False``
:arg turbo: Use a memory intensive, but faster way to calculate modes.
:type turbo: bool, default is ``True``
"""
super(exANM, self).calcModes(n_modes, zeros, turbo)
def getMembrane(self):
"""Returns a copy of the membrane coordinates."""
if self._membrane is not None:
return self._membrane.copy()
def _getMembrane(self):
return self._membrane
def combineMembraneProtein(self, coords):
try:
if type(coords) is AtomGroup:
self._combined = coords + self._membrane
except TypeError:
raise TypeError('coords must be an AtomGroup object '
'with `getCoords` method')
def writeCombinedPDB(self,filename):
""" Given membrane coordinates it will write a pdb file with membrane coordinates.
:arg filename: filename for the pdb file.
:type filename: str
:arg membrane: membrane coordinates or the membrane structure.
:type membrane: nd.array
"""
if self._combined is None:
combineMembraneProtein(self,coords)
try:
writePDB(filename,self._combined)
except TypeError:
raise "Membrane not found. Use buildMembrane() function."
def parseEMDStream(stream, **kwargs):
"""Parse lines of data stream from an EMD/MRC2014 file and
optionally return an :class:`.AtomGroup` containing TRN
nodes based on it.
:arg stream: Any object with the method ``readlines``
(e.g. :class:`file`, buffer, stdin)
"""
cutoff = kwargs.get('cutoff', None)
min_cutoff = kwargs.get('min_cutoff', cutoff)
if min_cutoff is not None:
if isinstance(min_cutoff, Number):
min_cutoff = float(min_cutoff)
else:
raise TypeError('min_cutoff should be a number or None')
max_cutoff = kwargs.get('max_cutoff', None)
if max_cutoff is not None:
if isinstance(max_cutoff, Number):
max_cutoff = float(max_cutoff)
else:
raise TypeError('max_cutoff should be a number or None')
n_nodes = kwargs.get('n_nodes', 0)
num_iter = int(kwargs.get('num_iter', 20))
map = kwargs.get('map', False)
if not isinstance(n_nodes, int):
raise TypeError('n_nodes should be an integer')
if n_nodes > 0:
make_nodes = True
else:
make_nodes = False
map = True
LOGGER.info('As n_nodes is less than or equal to 0, no nodes will be'
' made and the raw map will be returned')
emd = EMDMAP(stream, min_cutoff, max_cutoff)
if make_nodes:
title_suffix = kwargs.get('title_suffix', '')
atomgroup = AtomGroup(
str(kwargs.get('title', 'Unknown')) + title_suffix)
atomgroup._n_atoms = n_nodes
coordinates = np.zeros((n_nodes, 3), dtype=float)
atomnames = np.zeros(n_nodes, dtype=ATOMIC_FIELDS['name'].dtype)
resnames = np.zeros(n_nodes, dtype=ATOMIC_FIELDS['resname'].dtype)
resnums = np.zeros(n_nodes, dtype=ATOMIC_FIELDS['resnum'].dtype)
chainids = np.zeros(n_nodes, dtype=ATOMIC_FIELDS['chain'].dtype)
trn = TRNET(n_nodes=n_nodes)
trn.inputMap(emd, sample='density')
trn.run(tmax=num_iter)
for i in range(n_nodes):
coordinates[i, :] = trn.W[i, :]
atomnames[i] = 'B'
resnames[i] = 'CGB'
resnums[i] = i + 1
chainids[i] = 'X'
atomgroup.setCoords(coordinates)
atomgroup.setNames(atomnames)
atomgroup.setResnames(resnames)
atomgroup.setResnums(resnums)
atomgroup.setChids(chainids)
if make_nodes:
if map:
return atomgroup, emd
else:
return atomgroup
else:
return emd
def parseNMD(filename, type=NMA):
"""Returns :class:`.NMA` and :class:`.AtomGroup` instances storing data
parsed from *filename* in :file:`.nmd` format. Type should be :class:`.NMA`
or a subclass such as :class:`.PCA`, :class:`.ANM`, or :class:`.GNM`."""
if isinstance(type, str):
type = type.upper().strip()
if 'ANM' in type:
type = ANM
elif 'GNM' in type:
type = GNM
elif 'PCA' in type or 'EDA' in type:
type = PCA
elif type == 'NMA':
type = NMA
else:
type = None
if not issubclass(type, NMA):
raise TypeError('type must be NMA, ANM, GNM, or PCA')
atomic = {}
atomic.update([(label, None) for label in NMD_LABEL_MAP])
atomic['coordinates'] = None
atomic['name'] = None
modes = []
with open(filename) as nmd:
for i, line in enumerate(nmd):
try:
label, data = line.split(None, 1)
except ValueError:
pass
if label == 'mode':
modes.append((i + 1, data))
elif label in atomic:
if atomic[label] is None:
atomic[label] = (i + 1, data)
else:
LOGGER.warn('Data label {0} is found more than once in '
'{1}.'.format(repr(label), repr(filename)))
name = atomic.pop('name', '')[1].strip() or splitext(split(filename)[1])[0]
ag = AtomGroup(name)
dof = None
n_atoms = None
line, coords = atomic.pop('coordinates', None)
if coords is not None:
coords = np.fromstring(coords, dtype=float, sep=' ')
dof = coords.shape[0]
if dof % 3 != 0:
LOGGER.warn('Coordinate data in {0} at line {1} is corrupt '
'and will be omitted.'.format(repr(filename), line))
else:
n_atoms = dof // 3
coords = coords.reshape((n_atoms, 3))
ag.setCoords(coords)
from prody.atomic import ATOMIC_FIELDS
for label, data in atomic.items(): # PY3K: OK
if data is None:
continue
line, data = data
data = data.split()
if n_atoms is None:
n_atoms = len(data)
dof = n_atoms * 3
elif len(data) != n_atoms:
LOGGER.warn('Data with label {0} in {1} at line {2} is '
'corrupt, expected {2} values, parsed {3}.'.format(
repr(label), repr(filename), line, n_atoms,
len(data)))
continue
label = NMD_LABEL_MAP[label]
data = np.array(data, dtype=ATOMIC_FIELDS[label].dtype)
ag.setData(label, data)
if not modes:
return None, ag
length = len(modes[0][1].split())
is3d = length > n_atoms + 2
if dof is None:
dof = length - (length % 3)
elif not is3d: # GNM
dof = n_atoms
array = np.zeros((dof, len(modes)))
less = 0
eigvals = []
count = 0
for i, (line, mode) in enumerate(modes):
mode = np.fromstring(mode, dtype=float, sep=' ')
diff = len(mode) - dof
if diff < 0 or diff > 2:
LOGGER.warn('Mode data in {0} at line {1} is corrupt.'.format(
repr(filename), line))
continue
array[:, i - less] = mode[diff:]
count += 1
eigvals.append(mode[:diff])
if count == 0:
return None, ag
try:
eigvals = np.array(eigvals, dtype=float)
except TypeError:
LOGGER.warn('Failed to parse eigenvalues from {0}.'.format(
repr(filename)))
if eigvals.shape[1] > 2:
LOGGER.warn('Failed to parse eigenvalues from {0}.'.format(
repr(filename)))
eigvals = None
elif eigvals.shape[1] == 1:
if np.all(eigvals % 1 == 0):
LOGGER.warn('Failed to parse eigenvalues from {0}.'.format(
repr(filename)))
eigvals = None
else:
eigvals = eigvals.flatten()**2
else:
eigvals = eigvals[:, 1]**2
nma = type(name)
if type != PCA:
eigvals = 1. / eigvals
if count != array.shape[1]:
array = array[:, :count].copy()
nma.setEigens(array, eigvals)
return nma, ag
class exANM(ANMBase):
"""Class for explicit ANM (exANM) method ([FT00]_).
Optional arguments build a membrane lattice permit analysis of membrane
effect on elastic network models in *exANM* method described in [TL12]_.
.. [TL12] Lezon TR, Bahar I, Constraints Imposed by the Membrane
Selectively Guide the Alternating Access Dynamics of the Glutamate
Transporter GltPh
"""
def __init__(self, name='Unknown'):
super(exANM, self).__init__(name)
self._membrane = None
self._combined = None
def buildMembrane(self, coords, **kwargs):
"""Build Hessian matrix for given coordinate set.
:arg coords: a coordinate set or an object with ``getCoords`` method
:type coords: :class:`numpy.ndarray`
:arg membrane_hi: the maximum z coordinate of the pdb default is 13.0
:type membrane_hi: float
:arg membrane_lo: the minimum z coordinate of the pdb default is -13.0
:type membrane_lo: float
:arg R: radius of all membrane in x-y direction default is 80.
:type R: float
:arg r: radius of individual barrel-type membrane protein default is 2.5.
:type
:arg lat: lattice type which could be FCC(face-centered-cubic)(default),
SC(simple cubic), SH(simple hexagonal)
:type lat: str
"""
if type(coords) is AtomGroup:
buildAg = True
else:
buildAg = False
try:
coords = (coords._getCoords()
if hasattr(coords, '_getCoords') else coords.getCoords())
except AttributeError:
try:
checkCoords(coords)
except TypeError:
raise TypeError('coords must be a Numpy array or an object '
'with `getCoords` method')
self._n_atoms = natoms = int(coords.shape[0])
pxlo = min(np.append(coords[:, 0], 10000))
pxhi = max(np.append(coords[:, 0], -10000))
pylo = min(np.append(coords[:, 1], 10000))
pyhi = max(np.append(coords[:, 1], -10000))
pzlo = min(np.append(coords[:, 2], 10000))
pzhi = max(np.append(coords[:, 2], -10000))
membrane_hi = float(kwargs.get('membrane_hi', 13.0))
membrane_lo = float(kwargs.get('membrane_lo', -13.0))
R = float(kwargs.get('R', 80))
r = float(kwargs.get('r', 5))
lat = str(kwargs.get('lat', 'FCC'))
lpv = assign_lpvs(lat)
imax = (R + lpv[0, 2] * (membrane_hi - membrane_lo) / 2.) / r
jmax = (R + lpv[1, 2] * (membrane_hi - membrane_lo) / 2.) / r
kmax = (R + lpv[2, 2] * (membrane_hi - membrane_lo) / 2.) / r
#print pxlo, pxhi, pylo, pyhi, pzlo, pzhi
#print lpv[0,2],lpv[1,2],lpv[2,2]
#print R,r,imax,jmax,kmax
membrane = zeros((1, 3))
LOGGER.timeit('_membrane')
membrane = zeros((1, 3))
atm = 0
for i in range(-int(imax), int(imax + 1)):
for j in range(-int(jmax), int(jmax + 1)):
for k in range(-int(kmax), int(kmax + 1)):
X = zeros((1, 3))
for p in range(3):
X[0, p] = 2. * r * (i * lpv[0, p] + j * lpv[1, p] +
k * lpv[2, p])
dd = 0
for p in range(3):
dd += X[0, p]**2
if dd < R**2 and X[0,
2] > membrane_lo and X[0,
2] < membrane_hi:
if X[0, 0] > pxlo - R / 2 and X[
0, 0] < pxhi + R / 2 and X[
0, 1] > pylo - R / 2 and X[
0, 1] < pyhi + R / 2 and X[
0, 2] > pzlo and X[0, 2] < pzhi:
if checkClash(X, coords[:natoms, :], radius=5):
if atm == 0:
membrane = X
else:
membrane = np.append(membrane, X, axis=0)
atm = atm + 1
#print atm
self._membrane = AtomGroup(title="Membrane")
self._membrane.setCoords(membrane)
self._membrane.setResnums(list(range(atm)))
self._membrane.setResnames(["NE1" for i in range(atm)])
self._membrane.setChids(["Q" for i in range(atm)])
self._membrane.setElements(["Q1" for i in range(atm)])
self._membrane.setNames(["Q1" for i in range(atm)])
LOGGER.report('Membrane was built in %2.fs.', label='_membrane')
def buildHessian(self, coords, cutoff=15., gamma=1., **kwargs):
"""Build Hessian matrix for given coordinate set.
:arg coords: a coordinate set or an object with ``getCoords`` method
:type coords: :class:`numpy.ndarray`
:arg cutoff: cutoff distance (Å) for pairwise interactions,
default is 15.0 Å
:type cutoff: float
:arg gamma: spring constant, default is 1.0
:type gamma: float
:arg membrane_hi: the maximum z coordinate of the pdb default is 13.0
:type membrane_hi: float
:arg membrane_lo: the minimum z coordinate of the pdb default is -13.0
:type membrane_lo: float
:arg R: radius of all membrane in x-y direction default is 80.
:type R: float
:arg r: radius of individual barrel-type membrane protein default is 2.5.
:type
:arg lat: lattice type which could be FCC(face-centered-cubic)(default),
SC(simple cubic), SH(simple hexagonal)
:type lat: str
"""
try:
coords = (coords._getCoords()
if hasattr(coords, '_getCoords') else coords.getCoords())
except AttributeError:
try:
checkCoords(coords)
except TypeError:
raise TypeError('coords must be a Numpy array or an object '
'with `getCoords` method')
self._n_atoms = natoms = int(coords.shape[0])
if self._membrane is None:
membrane_hi = float(kwargs.get('membrane_hi', 13.0))
membrane_lo = float(kwargs.get('membrane_lo', -13.0))
R = float(kwargs.get('R', 80))
r = float(kwargs.get('r', 5))
lat = str(kwargs.get('lat', 'FCC'))
self.buildMembrane(coords,
membrane_hi=membrane_hi,
membrane_lo=membrane_lo,
R=R,
r=r,
lat=lat)
LOGGER.timeit('_exanm')
coords = np.concatenate((coords, self._membrane.getCoords()), axis=0)
self._combined_coords = coords
total_natoms = int(coords.shape[0])
self._hessian = np.zeros((natoms * 3, natoms * 3), float)
total_hessian = np.zeros((total_natoms * 3, total_natoms * 3), float)
cutoff, g, gamma = checkENMParameters(cutoff, gamma)
cutoff2 = cutoff * cutoff
for i in range(total_natoms):
res_i3 = i * 3
res_i33 = res_i3 + 3
i_p1 = i + 1
i2j_all = coords[i_p1:, :] - coords[i]
for j, dist2 in enumerate((i2j_all**2).sum(1)):
if dist2 > cutoff2:
continue
i2j = i2j_all[j]
j += i_p1
g = gamma(dist2, i, j)
res_j3 = j * 3
res_j33 = res_j3 + 3
super_element = np.outer(i2j, i2j) * (-g / dist2)
total_hessian[res_i3:res_i33, res_j3:res_j33] = super_element
total_hessian[res_j3:res_j33, res_i3:res_i33] = super_element
total_hessian[res_i3:res_i33, res_i3:res_i33] = total_hessian[
res_i3:res_i33, res_i3:res_i33] - super_element
total_hessian[res_j3:res_j33, res_j3:res_j33] = total_hessian[
res_j3:res_j33, res_j3:res_j33] - super_element
ss = total_hessian[:natoms * 3, :natoms * 3]
so = total_hessian[:natoms * 3, natoms * 3 + 1:]
os = total_hessian[natoms * 3 + 1:, :natoms * 3]
oo = total_hessian[natoms * 3 + 1:, natoms * 3 + 1:]
self._hessian = ss - np.dot(so, np.dot(linalg.inv(oo), os))
LOGGER.report('Hessian was built in %.2fs.', label='_exanm')
self._dof = self._hessian.shape[0]
def calcModes(self, n_modes=20, zeros=False, turbo=True):
"""Calculate normal modes. This method uses :func:`scipy.linalg.eigh`
function to diagonalize the Hessian matrix. When Scipy is not found,
:func:`numpy.linalg.eigh` is used.
:arg n_modes: number of non-zero eigenvalues/vectors to calculate.
If ``None`` is given, all modes will be calculated.
:type n_modes: int or None, default is 20
:arg zeros: If ``True``, modes with zero eigenvalues will be kept.
:type zeros: bool, default is ``False``
:arg turbo: Use a memory intensive, but faster way to calculate modes.
:type turbo: bool, default is ``True``
"""
super(exANM, self).calcModes(n_modes, zeros, turbo)
def getMembrane(self):
"""Returns a copy of the membrane coordinates."""
if self._membrane is not None:
return self._membrane.copy()
def _getMembrane(self):
return self._membrane
def combineMembraneProtein(self, coords):
try:
if type(coords) is AtomGroup:
self._combined = coords + self._membrane
except TypeError:
raise TypeError('coords must be an AtomGroup object '
'with `getCoords` method')
def writeCombinedPDB(self, filename):
""" Given membrane coordinates it will write a pdb file with membrane coordinates.
:arg filename: filename for the pdb file.
:type filename: str
:arg membrane: membrane coordinates or the membrane structure.
:type membrane: nd.array
"""
if self._combined is None:
combineMembraneProtein(self, coords)
try:
writePDB(filename, self._combined)
except TypeError:
raise "Membrane not found. Use buildMembrane() function."
class exANM(ANM):
"""Class for explicit ANM (exANM) method ([FT00]_).
Optional arguments build a membrane lattice permit analysis of membrane
effect on elastic network models in *exANM* method described in [TL12]_.
.. [TL12] Lezon TR, Bahar I, Constraints Imposed by the Membrane
Selectively Guide the Alternating Access Dynamics of the Glutamate
Transporter GltPh
"""
def __init__(self, name='Unknown'):
super(exANM, self).__init__(name)
self._membrane = None
self._combined = None
def buildMembrane(self, coords, **kwargs):
"""Build membrane lattice around **coords**.
:arg coords: a coordinate set or an object with ``getCoords`` method
:type coords: :class:`numpy.ndarray`
:arg membrane_high: the maximum z coordinate of the membrane. Default is **13.0**
:type membrane_high: float
:arg membrane_low: the minimum z coordinate of the membrane. Default is **-13.0**
:type membrane_low: float
:arg R: radius of all membrane in x-y direction. Default is **80**
:type R: float
:arg Ri: inner radius of the membrane in x-y direction if it needs to be hollow.
Default is **0**, which is not hollow
:type Ri: float
:arg r: radius of each membrane node. Default is **3.1**
:type r: float
:arg lat: lattice type which could be **FCC** (face-centered-cubic, default),
**SC** (simple cubic), **SH** (simple hexagonal)
:type lat: str
:arg exr: exclusive radius of each protein node. Default is **5.0**
:type exr: float
:arg hull: whether use convex hull to determine the protein's interior.
Turn it off if protein is multimer. Default is **True**
:type hull: bool
:arg center: whether transform the structure to the origin (only x- and y-axis).
Default is **True**
:type center: bool
"""
atoms = coords
try:
coords = (coords._getCoords()
if hasattr(coords, '_getCoords') else coords.getCoords())
except AttributeError:
try:
checkCoords(coords)
except TypeError:
raise TypeError('coords must be a Numpy array or an object '
'with `getCoords` method')
self._n_atoms = int(coords.shape[0])
LOGGER.timeit('_membrane')
depth = kwargs.pop('depth', None)
h = depth / 2 if depth is not None else None
h = kwargs.pop('h', h)
if h is not None:
h = float(h)
hu = h
hl = -h
else:
hu = kwargs.pop('membrane_high', 13.0)
hu = kwargs.pop('high', hu)
hu = float(hu)
hl = kwargs.pop('membrane_low', -13.0)
hl = kwargs.pop('low', hl)
hl = float(hl)
R = float(kwargs.pop('R', 80.))
Ri = float(kwargs.pop('Ri', 0.))
r = float(kwargs.pop('r', 3.1))
lat = str(kwargs.pop('lat', 'FCC'))
exr = float(kwargs.pop('exr', 5.))
use_hull = kwargs.pop('hull', True)
centering = kwargs.pop('center', True)
V = assign_lpvs(lat)
if centering:
c0 = coords.mean(axis=0)
c0[-1] = 0.
coords -= c0
# determine transmembrane part
torf = np.logical_and(coords[:, -1] < hu, coords[:, -1] > hl)
transmembrane = coords[torf, :]
if not np.any(torf):
raise ValueError(
'No region was identified as membrane. Please use a structure from opm/ppm.'
)
if use_hull:
from scipy.spatial import ConvexHull
hull = ConvexHull(transmembrane)
else:
hull = transmembrane
## determine the bound for ijk
imax = (R + V[0, 2] * (hu - hl) / 2.) / r
jmax = (R + V[1, 2] * (hu - hl) / 2.) / r
kmax = (R + V[2, 2] * (hu - hl) / 2.) / r
imax = int(ceil(imax))
jmax = int(ceil(jmax))
kmax = int(ceil(kmax))
membrane = []
atm = 0
for i in range(-imax, imax):
for j in range(-jmax, jmax):
for k in range(-kmax, kmax):
c = array([i, j, k])
xyz = 2. * r * dot(c, V)
if xyz[2]>hl and xyz[2]<hu and \
xyz[0]>-R and xyz[0]<R and \
xyz[1]>-R and xyz[1]<R:
dd = norm(xyz[:2])
if dd < R and dd > Ri:
if checkClash(xyz, hull, radius=exr):
membrane.append(xyz)
atm = atm + 1
membrane = array(membrane)
if len(membrane) == 0:
self._membrane = None
LOGGER.warn(
'no membrane is built. The protein should be transformed to the correct origin as in OPM'
)
return coords
else:
self._membrane = AtomGroup(title="Membrane")
self._membrane.setCoords(membrane)
self._membrane.setResnums(range(atm))
self._membrane.setResnames(["NE1" for i in range(atm)])
self._membrane.setChids(["Q" for i in range(atm)])
self._membrane.setElements(["Q1" for i in range(atm)])
self._membrane.setNames(["Q1" for i in range(atm)])
LOGGER.report('Membrane was built in %2.fs.', label='_membrane')
coords = self._combineMembraneProtein(atoms)
return coords
def buildHessian(self, coords, cutoff=15., gamma=1., **kwargs):
"""Build Hessian matrix for given coordinate set.
**kwargs** are passed to :method:`.buildMembrane`.
:arg coords: a coordinate set or an object with ``getCoords`` method
:type coords: :class:`numpy.ndarray`
:arg cutoff: cutoff distance (Å) for pairwise interactions,
default is 15.0 Å
:type cutoff: float
:arg gamma: spring constant, default is 1.0
:type gamma: float
"""
atoms = coords
turbo = kwargs.pop('turbo', True)
try:
coords = (coords._getCoords()
if hasattr(coords, '_getCoords') else coords.getCoords())
except AttributeError:
try:
checkCoords(coords)
except TypeError:
raise TypeError('coords must be a Numpy array or an object '
'with `getCoords` method')
n_atoms = int(coords.shape[0])
if self._membrane is None:
coords = self.buildMembrane(atoms, **kwargs)
else:
coords = self._combined.getCoords()
system = zeros(coords.shape[0], dtype=bool)
system[:n_atoms] = True
LOGGER.timeit('_exanm')
if turbo:
self._hessian = buildReducedHessian(coords, system, cutoff, gamma,
**kwargs)
else:
super(exANM, self).buildHessian(coords, cutoff, gamma, **kwargs)
system = np.repeat(system, 3)
self._hessian = _reduceModel(self._hessian, system)
LOGGER.report('Hessian was built in %.2fs.', label='_exanm')
self._dof = self._hessian.shape[0]
self._n_atoms = n_atoms
def calcModes(self, n_modes=20, zeros=False, turbo=True):
"""Calculate normal modes. This method uses :func:`scipy.linalg.eigh`
function to diagonalize the Hessian matrix. When Scipy is not found,
:func:`numpy.linalg.eigh` is used.
:arg n_modes: number of non-zero eigenvalues/vectors to calculate.
If **None** is given, all modes will be calculated.
:type n_modes: int or None, default is 20
:arg zeros: If **True**, modes with zero eigenvalues will be kept.
:type zeros: bool, default is **True**
:arg turbo: Use a memory intensive, but faster way to calculate modes.
:type turbo: bool, default is **True**
"""
super(exANM, self).calcModes(n_modes, zeros, turbo)
def getMembrane(self):
"""Returns a copy of the membrane coordinates."""
return self._membrane
def getCombined(self):
"""Returns a copy of the combined atoms or coordinates."""
return self._combined
def _combineMembraneProtein(self, coords):
if self._membrane is not None:
if isinstance(coords, Atomic):
self._combined = coords.copy() + self._membrane
coords = self._combined.getCoords()
else:
self._combined = coords = np.concatenate(
(coords, self._membrane.getCoords()), axis=0)
else:
self._combined = coords = copy(coords)
return coords
def fetchPDBLigand(cci, filename=None):
"""Fetch PDB ligand data from PDB_ for chemical component *cci*.
*cci* may be 3-letter chemical component identifier or a valid XML
filename. If *filename* is given, XML file will be saved with that name.
If you query ligand data frequently, you may configure ProDy to save XML
files in your computer. Set ``ligand_xml_save`` option **True**, i.e.
``confProDy(ligand_xml_save=True)``. Compressed XML files will be save
to ProDy package folder, e.g. :file:`/home/user/.prody/pdbligands`. Each
file is around 5Kb when compressed.
This function is compatible with PDBx/PDBML v 4.0.
Ligand data is returned in a dictionary. Ligand coordinate atom data with
*model* and *ideal* coordinate sets are also stored in this dictionary.
Note that this dictionary will contain data that is present in the XML
file and all Ligand Expo XML files do not contain every possible data
field. So, it may be better if you use :meth:`dict.get` instead of
indexing the dictionary, e.g. to retrieve formula weight (or relative
molar mass) of the chemical component use ``data.get('formula_weight')``
instead of ``data['formula_weight']`` to avoid exceptions when this data
field is not found in the XML file. URL and/or path of the XML file are
returned in the dictionary with keys ``url`` and ``path``, respectively.
Following example downloads data for ligand STI (a.k.a. Gleevec and
Imatinib) and calculates RMSD between model (X-ray structure 1IEP) and
ideal (energy minimized) coordinate sets:
.. ipython:: python
from prody import *
ligand_data = fetchPDBLigand('STI')
ligand_data['model_coordinates_db_code']
ligand_model = ligand_data['model']
ligand_ideal = ligand_data['ideal']
transformation = superpose(ligand_ideal.noh, ligand_model.noh)
calcRMSD(ligand_ideal.noh, ligand_model.noh)"""
if not isinstance(cci, str):
raise TypeError('cci must be a string')
if isfile(cci):
inp = openFile(cci)
xml = inp.read()
inp.close()
url = None
path = cci
cci = splitext(splitext(split(cci)[1])[0])[0].upper()
elif len(cci) > 4 or not cci.isalnum():
raise ValueError('cci must be 3-letters long and alphanumeric or '
'a valid filename')
else:
xml = None
cci = cci.upper()
if SETTINGS.get('ligand_xml_save'):
folder = join(getPackagePath(), 'pdbligands')
if not isdir(folder):
makePath(folder)
xmlgz = path = join(folder, cci + '.xml.gz')
if isfile(xmlgz):
with openFile(xmlgz) as inp:
xml = inp.read()
else:
path = None
#url = ('http://ligand-expo.rcsb.org/reports/{0[0]}/{0}/{0}'
# '.xml'.format(cci.upper()))
url = 'http://files.rcsb.org/ligands/download/{0}.xml'.format(
cci.upper())
if not xml:
#'http://www.pdb.org/pdb/files/ligand/{0}.xml'
try:
inp = openURL(url)
except IOError:
raise IOError(
'XML file for ligand {0} is not found online'.format(cci))
else:
xml = inp.read()
inp.close()
if filename:
out = openFile(filename, mode='w', folder=folder)
out.write(xml)
out.close()
if SETTINGS.get('ligand_xml_save'):
with openFile(xmlgz, 'w') as out:
out.write(xml)
import xml.etree.cElementTree as ET
root = ET.XML(xml)
if (root.get('{http://www.w3.org/2001/XMLSchema-instance}'
'schemaLocation') !=
'http://pdbml.pdb.org/schema/pdbx-v40.xsd pdbx-v40.xsd'):
LOGGER.warn('XML is not in PDBx/PDBML v 4.0 format, resulting '
'dictionary may not contain all data fields')
ns = root.tag[:root.tag.rfind('}') + 1]
len_ns = len(ns)
dict_ = {'url': url, 'path': path}
for child in list(root.find(ns + 'chem_compCategory')[0]):
tag = child.tag[len_ns:]
if tag.startswith('pdbx_'):
tag = tag[5:]
dict_[tag] = child.text
dict_['formula_weight'] = float(dict_.get('formula_weight'))
identifiers_and_descriptors = []
results = root.find(ns + 'pdbx_chem_comp_identifierCategory')
if results:
identifiers_and_descriptors.extend(results)
results = root.find(ns + 'pdbx_chem_comp_descriptorCategory')
if results:
identifiers_and_descriptors.extend(results)
for child in identifiers_and_descriptors:
program = child.get('program').replace(' ', '_')
type_ = child.get('type').replace(' ', '_')
dict_[program + '_' + type_] = child[0].text
dict_[program + '_version'] = child.get('program_version')
dict_['audits'] = [
(audit.get('action_type'), audit.get('date'))
for audit in list(root.find(ns + 'pdbx_chem_comp_auditCategory'))
]
atoms = list(root.find(ns + 'chem_comp_atomCategory'))
n_atoms = len(atoms)
ideal_coords = np.zeros((n_atoms, 3))
model_coords = np.zeros((n_atoms, 3))
atomnames = np.zeros(n_atoms, dtype=ATOMIC_FIELDS['name'].dtype)
elements = np.zeros(n_atoms, dtype=ATOMIC_FIELDS['element'].dtype)
resnames = np.zeros(n_atoms, dtype=ATOMIC_FIELDS['resname'].dtype)
charges = np.zeros(n_atoms, dtype=ATOMIC_FIELDS['charge'].dtype)
resnums = np.ones(n_atoms, dtype=ATOMIC_FIELDS['charge'].dtype)
alternate_atomnames = np.zeros(n_atoms, dtype=ATOMIC_FIELDS['name'].dtype)
leaving_atom_flags = np.zeros(n_atoms, np.bool)
aromatic_flags = np.zeros(n_atoms, np.bool)
stereo_configs = np.zeros(n_atoms, np.bool)
ordinals = np.zeros(n_atoms, int)
name2index = {}
for i, atom in enumerate(atoms):
data = dict([(child.tag[len_ns:], child.text) for child in list(atom)])
name = data.get('pdbx_component_atom_id', 'X')
name2index[name] = i
atomnames[i] = name
elements[i] = data.get('type_symbol', 'X')
resnames[i] = data.get('pdbx_component_comp_id', 'UNK')
charges[i] = float(data.get('charge', 0))
alternate_atomnames[i] = data.get('alt_atom_id', 'X')
leaving_atom_flags[i] = data.get('pdbx_leaving_atom_flag') == 'Y'
aromatic_flags[i] = data.get('pdbx_atomatic_flag') == 'Y'
stereo_configs[i] = data.get('pdbx_stereo_config') == 'Y'
ordinals[i] = int(data.get('pdbx_ordinal', 0))
model_coords[i, 0] = float(data.get('model_Cartn_x', 0))
model_coords[i, 1] = float(data.get('model_Cartn_y', 0))
model_coords[i, 2] = float(data.get('model_Cartn_z', 0))
ideal_coords[i, 0] = float(data.get('pdbx_model_Cartn_x_ideal', 0))
ideal_coords[i, 1] = float(data.get('pdbx_model_Cartn_y_ideal', 0))
ideal_coords[i, 2] = float(data.get('pdbx_model_Cartn_z_ideal', 0))
pdbid = dict_.get('model_coordinates_db_code')
if pdbid:
model = AtomGroup(cci + ' model ({0})'.format(pdbid))
else:
model = AtomGroup(cci + ' model')
model.setCoords(model_coords)
model.setNames(atomnames)
model.setResnames(resnames)
model.setResnums(resnums)
model.setElements(elements)
model.setCharges(charges)
model.setFlags('leaving_atom_flags', leaving_atom_flags)
model.setFlags('aromatic_flags', aromatic_flags)
model.setFlags('stereo_configs', stereo_configs)
model.setData('ordinals', ordinals)
model.setData('alternate_atomnames', alternate_atomnames)
dict_['model'] = model
ideal = model.copy()
ideal.setTitle(cci + ' ideal')
ideal.setCoords(ideal_coords)
dict_['ideal'] = ideal
bonds = []
warned = set()
for bond in list(root.find(ns + 'chem_comp_bondCategory') or bonds):
name_1 = bond.get('atom_id_1')
name_2 = bond.get('atom_id_2')
try:
bonds.append((name2index[name_1], name2index[name_2]))
except KeyError:
if name_1 not in warned and name_1 not in name2index:
warned.add(name_1)
LOGGER.warn('{0} specified {1} in bond category is not '
'a valid atom name.'.format(repr(name_1), cci))
if name_2 not in warned and name_2 not in name2index:
warned.add(name_2)
LOGGER.warn('{0} specified {1} in bond category is not '
'a valid atom name.'.format(repr(name_2), cci))
if bonds:
bonds = np.array(bonds, int)
model.setBonds(bonds)
ideal.setBonds(bonds)
return dict_
def parseNMD(filename, type=NMA):
"""Returns normal mode and atomic data parsed from an NMD file.
Normal mode data is returned in an :class:`~.NMA` instance. Atomic
data is returned in an :class:`~.AtomGroup` instance."""
assert not isinstance(type, NMA), 'type must be NMA, ANM, GNM, or PCA'
atomic = dict()
modes = []
nmd = open(filename)
for line in nmd:
split = line.find(' ')
if line[:split] == 'mode':
modes.append(line[split:].strip())
elif line[:split] in ('coordinates', 'atomnames', 'resnames',
'resnums', 'resids', 'chainids', 'bfactors',
'name'):
atomic[line[:split]] = line[split:].strip()
nmd.close()
name = atomic.pop('name', os.path.splitext(os.path.split(filename)[1])[0])
coords = atomic.pop('coordinates', None)
dof = None
if coords is not None:
coords = np.fromstring( coords, dtype=float, sep=' ')
dof = coords.shape[0]
ag = None
n_atoms = dof / 3
coords = coords.reshape((n_atoms, 3))
ag = AtomGroup(name)
ag.setCoords(coords)
data = atomic.pop('atomnames', None)
if data is not None:
ag.setNames(data.split())
data = atomic.pop('resnames', None)
if data is not None:
ag.setResnames(data.split())
data = atomic.pop('chainids', None)
if data is not None:
ag.setChids(data.split())
data = atomic.pop('resnums', None)
if data is not None:
ag.setResnums(np.fromstring(data, int, sep=' '))
data = atomic.pop('resids', None)
if data is not None:
ag.setResnums(np.fromstring(data, int, sep=' '))
data = atomic.pop('bfactors', None)
if data is not None:
ag.setBetas(np.fromstring(data, float, sep=' '))
nma = type(name)
for mode in modes:
items = mode.split()
diff = len(items) - dof
mode = np.array(items[diff:]).astype(float)
if len(mode) != dof:
pass
if diff == 1 and not items[0].isdigit():
value = float(items[0])
else:
if not items[0].isdigit():
value = float(items[0])
elif not items[1].isdigit():
value = float(items[1])
else:
value = 1.0
nma.addEigenpair(mode, value)
return nma, ag
def parseNMD(filename, type=None):
"""Return :class:`.NMA` and :class:`.AtomGroup` instances storing data
parsed from *filename* in :file:`.nmd` format. Type of :class:`.NMA`
instance, e.g. :class:`.PCA`, :class:`.ANM`, or :class:`.GNM` will
be determined based on mode data."""
assert not isinstance(type, NMA), 'type must be NMA, ANM, GNM, or PCA'
atomic = {}
atomic.update([(label, None) for label in NMD_LABEL_MAP])
atomic['coordinates'] = None
atomic['name'] = None
modes = []
with open(filename) as nmd:
for i, line in enumerate(nmd):
try:
label, data = line.split(None, 1)
except ValueError:
pass
if label == 'mode':
modes.append((i + 1, data))
elif label in atomic:
if atomic[label] is None:
atomic[label] = (i + 1, data)
else:
LOGGER.warn('Data label {0} is found more than once in '
'{1}.'.format(repr(label), repr(filename)))
name = atomic.pop('name', '')[1].strip() or splitext(split(filename)[1])[0]
ag = AtomGroup(name)
dof = None
n_atoms = None
line, coords = atomic.pop('coordinates', None)
if coords is not None:
coords = np.fromstring(coords, dtype=float, sep=' ')
dof = coords.shape[0]
if dof % 3 != 0:
LOGGER.warn('Coordinate data in {0} at line {1} is corrupt '
'and will be omitted.'.format(repr(filename), line))
else:
n_atoms = dof / 3
coords = coords.reshape((n_atoms, 3))
ag.setCoords(coords)
from prody.atomic import ATOMIC_FIELDS
for label, data in atomic.items(): # PY3K: OK
if data is None:
continue
line, data = data
data = data.split()
if n_atoms is None:
n_atoms = len(data)
dof = n_atoms * 3
elif len(data) != n_atoms:
LOGGER.warn('Data with label {0} in {1} at line {2} is '
'corrupt, expected {2} values, parsed {3}.'.format(
repr(label), repr(filename), line, n_atoms, len(data)))
continue
label = NMD_LABEL_MAP[label]
data = np.array(data, dtype=ATOMIC_FIELDS[label].dtype)
ag.setData(label, data)
if not modes:
return None, ag
length = len(modes[0][1].split())
is3d = length > n_atoms + 2
if dof is None:
dof = length - (length % 3)
elif not is3d: # GNM
dof = n_atoms
array = np.zeros((dof, len(modes)))
less = 0
eigvals = []
count = 0
for i, (line, mode) in enumerate(modes):
mode = np.fromstring(mode, dtype=float, sep=' ')
diff = len(mode) - dof
if diff < 0 or diff > 2:
LOGGER.warn('Mode data in {0} at line {1} is corrupt.'
.format(repr(filename), line))
continue
array[:, i - less] = mode[diff:]
count += 1
eigvals.append(mode[:diff])
if count == 0:
return None, ag
try:
eigvals = np.array(eigvals, dtype=float)
except TypeError:
LOGGER.warn('Failed to parse eigenvalues from {0}.'
.format(repr(filename)))
if eigvals.shape[1] > 2:
LOGGER.warn('Failed to parse eigenvalues from {0}.'
.format(repr(filename)))
eigvals = None
elif eigvals.shape[1] == 1:
if np.all(eigvals % 1 == 0):
LOGGER.warn('Failed to parse eigenvalues from {0}.'
.format(repr(filename)))
eigvals = None
else:
eigvals = eigvals.flatten() ** 2
else:
eigvals = eigvals[:, 1] ** 2
if is3d:
if eigvals is not None and np.all(eigvals[:-1] >= eigvals[1:]):
nma = PCA(name)
else:
nma = ANM(name)
else:
nma = GNM(name)
if count != array.shape[1]:
array = array[:, :count].copy()
nma.setEigens(array, eigvals)
return nma, ag
