def lindemann_per_frames(u: Universe, select_lang):
"""Calculate the lindemann index for each atom AND FRAME
Warning this can produce extremly large ndarrays in memory
depending on the size of the cluster and the ammount of frames.
Parameters
----------
u : MDA trajectory instance.
select_lang : select language.
Returns
-------
a ndarray of shape (len_frames, natoms, natoms)
"""
# natoms = natoms
sele_ori = u.select_atoms(select_lang)
natoms = len(sele_ori)
nframes = len(u.trajectory)
len_frames = len(u.trajectory)
array_mean = np.zeros((natoms, natoms))
array_var = np.zeros((natoms, natoms))
# array_distance = np.zeros((natoms, natoms))
iframe = 1
lindex_array = np.zeros((len_frames, natoms, natoms))
cluster = u.select_atoms(select_lang, updating=True)
for q, ts in enumerate(u.trajectory):
# print(ts)
coords = cluster.positions
n, p = coords.shape
array_distance = distance.cdist(coords, coords)
#################################################################################
# update mean and var arrays based on Welford algorithm suggested by Donald Knuth
#################################################################################
for i in range(natoms):
for j in range(i + 1, natoms):
xn = array_distance[i, j]
mean = array_mean[i, j]
var = array_var[i, j]
delta = xn - mean
# update mean
array_mean[i, j] = mean + delta / iframe
# update variance
array_var[i, j] = var + delta * (xn - array_mean[i, j])
iframe += 1
if iframe > nframes + 1:
break
for i in range(natoms):
for j in range(i + 1, natoms):
array_mean[j, i] = array_mean[i, j]
array_var[j, i] = array_var[i, j]
lindemann_indices = np.divide(np.sqrt(np.divide(array_var, nframes)),
array_mean)
# lindemann_indices = np.nanmean(np.sqrt(array_var/nframes)/array_mean, axis=1)
lindex_array[q] = lindemann_indices
return np.array([np.nanmean(i, axis=1) for i in lindex_array])
def classifyResiduesIntoTwo(apo_pdb, holo_pdb, ligname, cutoff=4.0):
S_aromatic_resname = set(['PHE', 'TRP', 'TYR', 'HIS'])
u_holo, u_apo = Universe(holo_pdb), Universe(apo_pdb)
ligand = u_holo.select_atoms(f'resname {ligname}')
holo = u_holo.select_atoms(f'not resname {ligname}')
apo = u_apo.select_atoms(f'protein')
resids = []
S_cryptic, S_not_cryptic = [], []
# -- calculate distances from atoms of a ligand to those of residues in an apo state
# -- the aim is to detect residues in a cryptic site.
# -- if the distance is less than a threshold (i.e., CRASHED!), then the aromatic residue is considered as cryptic one.
for iatom in ligand:
for jatom in apo:
distance = np.linalg.norm(iatom.position - jatom.position)
if distance <= cutoff and jatom.resname in S_aromatic_resname:
#print(f'{iatom.name}-{iatom.resname}, {jatom.name}-{jatom.resname}{jatom.resid}, {distance}')
resids.append(jatom.resid)
S_cryptic.append(f'{jatom.resname}{jatom.resid}')
S_cryptic = set(S_cryptic)
# -- a set of aromatic residue's names are generated here. note that this is specialised for aromatic residues
S_all_aroma = set([
f'{residue.resname}{residue.resid}' for residue in holo.residues
if residue.resname in S_aromatic_resname
])
S_not_cryptic = S_all_aroma - S_cryptic
return set(S_cryptic), set(S_not_cryptic)
def test_write_selection(self):
ref = Universe(mol2_molecule)
gr0 = ref.select_atoms("name C*")
gr0.write(self.outfile)
u = Universe(self.outfile)
gr1 = u.select_atoms("name C*")
assert_equal(len(gr0), len(gr1))
def main():
u = Universe(
'/Volumes/HD-siida/gtail_b1_sys/analysis/merged_aligned_complexes.pdb')
#u = Universe('complex_models.pdb')
print(u.atoms.segids)
ca_integrinAB = u.select_atoms('segid A B and name CA')
ca_lamininE8 = u.select_atoms('segid C D E and name CA')
lower, upper = 6.0, 10.0
with open('model_no.out', 'w') as fout:
fout.write(
f'#MODEL NO, nViolations (if r<{lower}), nContacts ({lower}<=r<={upper}) \n'
)
for i, frame in enumerate(tqdm(u.trajectory),
1): # Note that i starts with 1.
distances = distance.cdist(ca_integrinAB.positions,
ca_lamininE8.positions,
metric='euclidean')
#nViolations = len(distances[distances<=cutoff])
nViolations = len(distances[distances < lower])
nContacts = len(distances[(distances <= upper)
& (distances >= lower)])
# score = nContacts -nViolations
if nViolations != 0:
score = -0.59 * np.log(nContacts / nViolations)
else:
score = np.nan
fout.write(f'{i}, {nViolations}, {nContacts}, {score}\n')
def test_write_selection(self):
ref = Universe(mol2_molecule)
gr0 = ref.select_atoms("name C*")
gr0.write(self.outfile)
u = Universe(self.outfile)
gr1 = u.select_atoms("name C*")
assert_equal(len(gr0), len(gr1))
def distanceMatrix(trajFile, sele1, sele2, ref=None):
if ref == None:
u = Universe(trajFile)
else:
print("* Reference is given.")
u = Universe(ref, trajFile)
s1, s2 = u.select_atoms(sele1), u.select_atoms(sele2)
print("* pair 1: \n ", s1)
print("* pair 2: \n ", s2)
distances = []
for itraj in tqdm(u.trajectory):
pos1, pos2 = s1.positions, s2.positions
if sele1 == sele2: #if self-distance pair calculation
dist = np.triu(distance.cdist(
pos1, pos2,
metric='euclidean')) #symmetrical matrix if self-distances
dist = dist[dist != 0] # remove 0 elements
else: #if differnt distance pair calculation
dist = distance.cdist(pos1, pos2, metric='euclidean').flatten()
distances.append(dist)
return distances
def calc_tilt_end_to_end(universe: mda.Universe,
resid_up,
resid_down,
fname="TMD_tilt.dat"):
''' Calculate tilt related to angle between zaxis and resid_down --> resid_up
Takes COM of resids
'''
fstr2 = '{: <15}{: <20}'
fstr = '{: <15}{: <20.5f}'
with open(fname, "w") as outf:
print(fstr2.format("time", "tilt"), file=outf)
for t in range(universe.trajectory.n_frames):
time = universe.trajectory[t].time
LOGGER.info("At %s", time)
zaxis = np.array([0, 0, 1])
sel_u = universe.select_atoms("resid {}".format(resid_up))
sel_d = universe.select_atoms("resid {}".format(resid_down))
pos_u = sel_u.center_of_mass()
pos_d = sel_d.center_of_mass()
costilt = np.dot(
(pos_d - pos_u), zaxis) / np.linalg.norm(pos_d - pos_u)
angle = np.arccos(costilt) * (180 / np.pi)
if angle > 90:
angle -= 180
print(fstr.format(time, abs(angle)), file=outf)
def cluster_coordinates( # TODO: rewrite the method
nvt_run: Universe,
select_dict: Dict[str, str],
run_start: int,
run_end: int,
species: List[str],
distance: float,
basis_vectors: Optional[Union[List[np.ndarray], np.ndarray]] = None,
cluster_center: str = "center",
) -> np.ndarray:
"""Calculates the average position of a cluster.
Args:
nvt_run: An MDAnalysis ``Universe`` containing wrapped trajectory.
select_dict: A dictionary of atom species selection, where each atom species name is a key
and the corresponding values are the selection language.
run_start: Start frame of analysis.
run_end: End frame of analysis.
species: A list of species in the cluster.
distance: The coordination cutoff distance.
basis_vectors: The basis vector for normalizing the coordinates of the cluster atoms.
cluster_center: Cluster center atom species.
Returns:
An array of coordinates of the cluster atoms.
"""
trj_analysis = nvt_run.trajectory[run_start:run_end:]
cluster_center_atom = nvt_run.select_atoms(select_dict.get(cluster_center),
periodic=True)[0]
selection = ("(" + " or ".join(s for s in species) + ") and (around " +
str(distance) + " index " + str(cluster_center_atom.index) +
")")
shell = nvt_run.select_atoms(selection, periodic=True)
cluster = []
for atom in shell:
coord_list = []
for ts in trj_analysis:
coord_list.append(atom.position)
cluster.append(np.mean(np.array(coord_list), axis=0))
cluster_array = np.array(cluster)
if basis_vectors:
if len(basis_vectors) == 2:
vec1 = basis_vectors[0]
vec2 = basis_vectors[1]
vec3 = np.cross(vec1, vec2)
vec2 = np.cross(vec1, vec3)
elif len(basis_vectors) == 3:
vec1 = basis_vectors[0]
vec2 = basis_vectors[1]
vec3 = basis_vectors[2]
else:
raise ValueError("incorrect vector format")
vec1 = vec1 / np.linalg.norm(vec1)
vec2 = vec2 / np.linalg.norm(vec2)
vec3 = vec3 / np.linalg.norm(vec3)
basis_xyz = np.transpose([vec1, vec2, vec3])
cluster_norm = np.linalg.solve(basis_xyz, cluster_array.T).T
cluster_norm = cluster_norm - np.mean(cluster_norm, axis=0)
return cluster_norm
return cluster_array
def res_dict_from_select_dict(u: Universe,
select_dict: Dict[str, str]) -> Dict[str, str]:
"""
Infer res_dict (residue selection) from select_dict (atom selection) in a MDAnalysis.universe object.
Args:
u: The universe object to assign resnames to.
select_dict: A dictionary of atom species, where each atom species name is a key
and the corresponding values are the selection language.
return:
A dictionary of resnames.
"""
saved_select = []
res_dict = {}
for key, val in select_dict.items():
res_select = "same resid as (" + val + ")"
res_group = u.select_atoms(res_select)
if key in ["cation", "anion"] or res_group not in saved_select:
saved_select.append(res_group)
res_dict[key] = res_select
if ("cation" in res_dict and "anion" in res_dict and u.select_atoms(
res_dict.get("cation")) == u.select_atoms(res_dict.get("anion"))):
res_dict.pop("anion")
res_dict["salt"] = res_dict.pop("cation")
return res_dict
def save_systems(flex: mda.Universe, protein: mda.Universe,
crystal: mda.Universe, dir: str):
def sel(resnum, resname, segid, icode) -> str:
s = f"(resid {resnum}{icode} and resname {resname} and segid {segid})"
return s
flexres = flex.select_atoms("protein").residues
max_rmsd = -1
residues = []
for res in flexres:
ressel = (sel(res.resnum, res.resname, res.segid, res.icode) +
" and not (type H or name H*)")
# Select single residue
p_res = protein.select_atoms(ressel)
c_res = crystal.select_atoms(ressel)
assert p_res.n_atoms == c_res.n_atoms
pfname = os.path.join(
dir, f"pflex-{res.resname}-{res.segid}{res.resnum}{res.icode}.pdb")
cfname = os.path.join(
dir, f"cflex-{res.resname}-{res.segid}{res.resnum}{res.icode}.pdb")
# Write out PDB files
p_res.write(pfname)
c_res.write(cfname)
residues.append((res.resnum, res.resname, res.segid, res.icode))
# Check that all flexible residues are listed
assert len(residues) == len(flexres)
# TODO: Can be improved by using ressel
selection = "".join([
sel(id, name, chain, icode) + " or "
for id, name, chain, icode in residues
])
selection = selection[:-4] # Remove final " or "
# Remove H atoms
# TODO: Possibly need perception for atom name, when type is not present
selection = f"({selection}) and not (type H or name H*)"
p_atoms = protein.select_atoms(selection)
c_atoms = crystal.select_atoms(selection)
# Check that the number of atoms in the two selections is equal
assert len(p_atoms) == len(c_atoms)
pfname = os.path.join(dir, "pflex.pdb")
cfname = os.path.join(dir, "cflex.pdb")
p_atoms.write(pfname)
c_atoms.write(cfname)
def test_atomgroups(self):
u = Universe(self.filename)
segidB0 = len(u.select_atoms("segid B and (not altloc B)"))
segidB1 = len(u.select_atoms("segid B and (not altloc A)"))
assert_equal(segidB0, segidB1)
altlocB0 = len(u.select_atoms("segid B and (altloc A)"))
altlocB1 = len(u.select_atoms("segid B and (altloc B)"))
assert_equal(altlocB0, altlocB1)
sum = len(u.select_atoms("segid B"))
assert_equal(sum, segidB0 + altlocB0)
def num_of_neighbor_simple(
nvt_run: Universe,
center_atom: Atom,
distance_dict: Dict[str, float],
select_dict: Dict[str, str],
run_start: int,
run_end: int,
) -> Dict[str, np.ndarray]:
"""Calculates solvation structure type (1 for SSIP, 2 for CIP and 3 for AGG) with respect to the ``enter_atom``
in the specified frame range.
Args:
nvt_run: An MDAnalysis ``Universe`` containing wrapped trajectory.
center_atom: The solvation shell center atom.
distance_dict: A dict of coordination cutoff distance of the neighbor species.
select_dict: A dictionary of atom species selection, where each atom species name is a key
and the corresponding values are the selection language.
run_start: Start frame of analysis.
run_end: End frame of analysis.
Returns:
A dict with "total" as the key and an array of the solvation structure type in the specified frame range
as the value.
"""
time_count = 0
trj_analysis = nvt_run.trajectory[run_start:run_end:]
center_selection = "same type as index " + str(center_atom.index)
assert len(
distance_dict
) == 1, "Please only specify the counter-ion species in the distance_dict"
species = list(distance_dict.keys())[0]
cn_values = np.zeros(int(len(trj_analysis)))
for ts in trj_analysis:
selection = select_shell(select_dict, distance_dict, center_atom,
species)
shell = nvt_run.select_atoms(selection, periodic=True)
shell_len = len(shell)
if shell_len == 0:
cn_values[time_count] = 1
elif shell_len == 1:
selection_species = select_shell(center_selection, distance_dict,
shell.atoms[0], species)
shell_species = nvt_run.select_atoms(selection_species,
periodic=True)
shell_species_len = len(shell_species) - 1
if shell_species_len == 0:
cn_values[time_count] = 2
else:
cn_values[time_count] = 3
else:
cn_values[time_count] = 3
time_count += 1
cn_values = {"total": cn_values}
return cn_values
def split_molecules(
u: mda.Universe,
keep_ions: bool = False
) -> Dict[str, Union[mda.AtomGroup, List[mda.AtomGroup]]]:
"""
Split different molecules (protein, water, ligands, ...) within a structure in separate files.
Args:
u (mda.Universe): MDAnalysis universe
keep_ions (bool, optional): Flag to keep/ignore ions
Returns:
A dictionaty with the name of the selection and the corresponding ``mda.AtomGroup``
(or a list of ``mda.AtomGroup`` is there are multiple molecules with the same name).
"""
split = {}
# Select protein
protein = u.select_atoms("protein")
if len(protein.atoms) != 0: # Check if protein is present
split["protein"] = protein
# Select water molecules
for water_name in ["WAT", "HOH"]:
water = u.select_atoms(f"resname {water_name}")
if len(water.atoms) != 0:
break # If selection is not empty, stop
if len(water.atoms) != 0: # Check if water is present
split["water"] = water
# Other molecules
other = u.select_atoms("all") - protein - water
for res in other.residues: # Loop over all "other" residues
name = res.resname
if re.search("[A-Z]?[+-]", name) is not None and not keep_ions:
break # Skip ion if keep_ions=True
try:
old = split[name]
if type(old) is list:
split[name].append(res)
else:
split[name] = [old, res]
except KeyError:
split[name] = res
return split
def select_flexres(flex: mda.Universe, prot: mda.Universe) -> mda.AtomGroup:
"""
Given a protein and a series of flexible residues, selectss the full flexible
residues (including backbone atoms) from the protein structure.
Args:
flex (mda.Universe): flexible residues
prot (mda.Universe): protein
Returns:
An `mda.AtomGroup` containing the atoms corresponding to flexible residues
extracted from the protein (including backbone atoms)
"""
fres = []
for res in flex.residues:
fres.append((res.resnum, res.resname, res.icode, res.segid))
sel = "".join(
[
f"(resid {num}{icode} and resname {name} and segid {chain}) or "
for num, name, icode, chain in fres
]
)
# Sanitize selection and remove residues without Janin dihedrals
# Ignoring them explicitly removes a warning
sel = (
sel[:-4]
+ "and not (resname ALA or resname CYS or resname GLY or resname PRO or resname SER or resname THR or resname VAL)"
)
return prot.select_atoms(sel)
def test_bonds(self):
u = Universe(self.filename, guess_bonds=True)
# need to force topology to load before querying individual atom bonds
u.build_topology()
bonds0 = u.select_atoms("segid B and (altloc A)")[0].bonds
bonds1 = u.select_atoms("segid B and (altloc B)")[0].bonds
assert_equal(len(bonds0), len(bonds1))
def select(system: mda.Universe,
distance: float,
removeHs: bool = False) -> Tuple[np.ndarray, np.ndarray]:
"""
Select binding site.
Parameters
---------
system: mda.Universe
Protein-ligand complex
distance: float
Ligand-residues distance
removeHs: bool
Remove hydrogen atoms
Returns
-------
Tuple[np.ndarray, np.ndarray]
Array of elements and array of cartesian coordinate for ligand and protein
atoms within the binding site
Notes
-----
The binding site is defined by residues with at least one atom within
:code:`distance` from the ligand.
"""
resselection = system.select_atoms(
f"(byres (around {distance} (resname LIG))) or (resname LIG)")
if removeHs:
mask = resselection.elements != "H"
# Elements from PDB file needs MDAnalysis@develop (see #2648)
return resselection.elements[mask], resselection.positions[mask]
else:
return resselection.elements, resselection.positions
def test_bonds(self):
u = Universe(self.filename, guess_bonds=True)
# need to force topology to load before querying individual atom bonds
u.build_topology()
bonds0 = u.select_atoms("segid B and (altloc A)")[0].bonds
bonds1 = u.select_atoms("segid B and (altloc B)")[0].bonds
assert_equal(len(bonds0), len(bonds1))
def main():
# get options
options = parse_options()
psf = options.psf_file
dcd = options.dcd_file
chain1 = options.segid1
chain2 = options.segid2
selection1 = options.selection1
selection2 = options.selection2
co = options.cutoff
output = options.output_file
visu = options.pymol
pdbvisu = options.pymol_pdb
# use MDAnalysis to read trajectory
u = Universe(psf, dcd)
# get contact probability
cp = GetContacts(u)
contactprob, bio1, bio2 = cp.run(chain1, chain2, selection1, selection2,
co)
np.savetxt(output, contactprob, fmt='%4.2f', delimiter=" ")
# generate pymol scripts if needed
if visu == 'Y':
# if no pdb file is supplied, write one from trajectory, first frame
if pdbvisu == None:
seleforpymol = u.select_atoms("segid %s or segid %s" %
(chain1, chain2))
seleforpymol.write('forpymol.pdb', remarks=None)
pdbvisu = 'forpymol.pdb'
# check pdb file format for weird encoding
check_pdb(pdbvisu)
pymol_contact_visu(contactprob, pdbvisu, chain1, chain2, bio1, bio2)
def analyze_radgyr(u: mda.Universe) -> List[float]:
"""Extract the radius of gyration metric for each trajectory frame."""
trajectory_radgyr = []
atoms = u.select_atoms(STANDARD_SELECTION)
for _ in u.trajectory:
trajectory_radgyr.append(atoms.radius_of_gyration())
return trajectory_radgyr
def select_dict_from_resname(u: Universe) -> Dict[str, str]:
"""
Infer select_dict (possibly interested atom species selection) from resnames in a MDAnalysis.universe object.
The resname must be pre-assigned already.
Args:
u: The universe object to work with.
return:
A dictionary of atom species.
"""
select_dict: Dict[str, str] = {}
resnames = np.unique(u.residues.resnames)
for resname in resnames:
if resname == "":
continue
residue = u.select_atoms("resname " + resname).residues[0]
if np.isclose(residue.charge, 0,
atol=1e-5): # np.sum(residue.atoms.charges)
if len(residue.atoms.fragments) == 2:
for i, frag in enumerate(residue.atoms.fragments):
charge = np.sum(frag.charges)
if charge > 0.001:
extract_atom_from_ion(True, frag, select_dict)
elif charge < -0.001:
extract_atom_from_ion(False, frag, select_dict)
else:
extract_atom_from_molecule(resname,
frag,
select_dict,
number=i + 1)
elif len(residue.atoms.fragments) >= 2:
cation_number = 1
anion_number = 1
molecule_number = 1
for frag in residue.atoms.fragments:
charge = np.sum(frag.charges)
if charge > 0.001:
extract_atom_from_ion(True, frag, select_dict,
cation_number)
cation_number += 1
elif charge < -0.001:
extract_atom_from_ion(False, frag, select_dict,
anion_number)
anion_number += 1
else:
extract_atom_from_molecule(resname, frag, select_dict,
molecule_number)
molecule_number += 1
else:
extract_atom_from_molecule(resname, residue, select_dict)
elif residue.charge > 0:
extract_atom_from_ion(True, residue, select_dict)
else:
extract_atom_from_ion(False, residue, select_dict)
return select_dict
def output_pdb_w_index(self):
#This scales sigma. The reason for this is because PDB files accepts few significant digits/
# sigma is usually 10^2 ~ 10^3 order, so if sigma was 0.011, then the sigma value to be written would be 0.01 in the PDB. I want to avoid this.
scale_factor = 100.0
u = Universe(self.__ref)
#initialize the b-factor column
u.atoms.tempfactors = 0
for icalpha in u.atoms.select_atoms('name CA'):
if icalpha.resname in ['PHE','TRP','TYR','HIS']:
key = icalpha.resname + str(icalpha.resid) + icalpha.segid.replace('SYSTEM', 'A')
DF = self.cryptic_index[key][0]
sigma = self.cryptic_index[key][1]
print(key, DF, sigma)
if np.abs(DF) < self.__alpha:
# print(key, DF, sigma)
icalpha.tempfactor = sigma * scale_factor
u.select_atoms('protein').write(f'index_{self.__out_suffix}.pdb')
def check_contiguous_steps(
nvt_run: Universe,
center_atom: Atom,
distance_dict: Dict[str, float],
select_dict: Dict[str, str],
run_start: int,
run_end: int,
checkpoints: np.ndarray,
lag: int = 20,
) -> Dict[str, np.ndarray]:
"""Calculates the distance between the center atom and the neighbor atom
in the checkpoint +/- lag time range.
Args:
nvt_run: An MDAnalysis ``Universe`` containing wrapped trajectory.
center_atom: The center atom object.
distance_dict: A dictionary of Cutoff distance of neighbor for each species.
select_dict: A dictionary of atom species selection, where each atom species name is a key
and the corresponding values are the selection language.
run_start: Start frame of analysis.
run_end: End frame of analysis.
checkpoints: The frame numberings of interest to check for contiguous steps.
lag: The range (+/- lag) of the contiguous steps. Default to 20.
Returns:
An array of distance between the center atom and the neighbor atoms
in the checkpoint +/- lag time range.
"""
coord_num: Dict[str, Union[List[List[int]], np.ndarray]] = {
x: [[] for _ in range(lag * 2 + 1)]
for x in distance_dict
}
trj_analysis = nvt_run.trajectory[run_start:run_end:]
has = False
for i, ts in enumerate(trj_analysis):
log = False
checkpoint = -1
for j in checkpoints:
if abs(i - j) <= lag:
log = True
has = True
checkpoint = j
if log:
for kw in distance_dict:
selection = select_shell(select_dict, distance_dict,
center_atom, kw)
shell = nvt_run.select_atoms(selection, periodic=True)
coord_num[kw][i - checkpoint + lag].append(len(shell))
one_atom_ave = {}
if has:
for kw in coord_num:
np_arrays = np.array(
[np.array(time).mean() for time in coord_num[kw]])
one_atom_ave[kw] = np_arrays
return one_atom_ave
def _list_types(coordinates_file):
# Check the extension
_check_input_file(coordinates_file, extensions=[".gro"])
# Load the system
system = Universe(coordinates_file)
# List the residue names
resnames = system.select_atoms("all").resnames
return np.unique(resnames)
def analyze_sasa(u: mda.Universe) -> np.ndarray:
"""Extract SASA value for each trajectory frame."""
atoms = u.select_atoms(STANDARD_SELECTION)
positions = u.trajectory.timeseries(asel=atoms)
trajectory_sasa = []
atom_radius = list(map(get_atom_radius, atoms))
for frame in np.swapaxes(positions, 0, 1):
sasa = freesasa.calcCoord(frame.reshape(-1), atom_radius).totalArea()
trajectory_sasa.append(sasa)
return np.array(trajectory_sasa)
def analyze_pca(u: mda.Universe, n_dimensions=40):
"""Fetch PCA component contribution values for a single trajectory."""
pca_analysis = pca.PCA(u, select='backbone')
space = pca_analysis.run()
space_3 = space.transform(u.select_atoms('backbone'), 3)
w = pca.cosine_content(space_3, 0)
print(w)
return [
space.variance[:n_dimensions], space.cumulated_variance[:n_dimensions]
]
def calc_neigh_corr(
nvt_run: Universe,
distance_dict: Dict[str, float],
select_dict: Dict[str, str],
time_step: float,
run_start: int,
run_end: int,
center_atom: str = "cation",
) -> Tuple[np.ndarray, Dict[str, np.ndarray]]:
"""Calculates the neighbor auto-correlation function (ACF)
of selected species around center atom.
Args:
nvt_run: An MDAnalysis ``Universe``.
distance_dict:
select_dict:
time_step:
run_start: Start frame of analysis.
run_end: End frame of analysis.
center_atom: The center atom to calculate the ACF for. Default to "cation".
Returns:
A tuple containing the time series, and a dict of acf of neighbor species.
"""
# Set up times array
times = []
step = 0
center_atoms = nvt_run.select_atoms(select_dict[center_atom])
for ts in nvt_run.trajectory[run_start:run_end]:
times.append(step * time_step)
step += 1
times = np.array(times)
acf_avg = {}
for kw in distance_dict.keys():
acf_all = []
for atom in tqdm(center_atoms[::]):
distance = distance_dict.get(kw)
assert distance is not None
adjacency_matrix = neighbors_one_atom(
nvt_run,
atom,
kw,
select_dict,
distance,
run_start,
run_end,
)
acfs = calc_acf(adjacency_matrix)
for acf in acfs:
acf_all.append(acf)
acf_avg[kw] = np.mean(acf_all, axis=0)
return times, acf_avg
def generate_universe(topology, trajectory=None):
print('Generating Universe...')
if trajectory is None or trajectory == '':
u = Universe(topology)
else:
u = Universe(topology, trajectory)
x, y, z = u.dimensions[:3]
print(f'Universe with dimensions x: {x}, y: {y}, z: {z} loaded!')
n_waters = u.select_atoms('resname WAT').n_residues
print(f'{n_waters} water molecules detected!')
return u
def distance_to_cnt(u: Universe, selection_cluster, cluster_size):
"""For carbon nanotube included trajectories, analyze cluster atoms.
Parameters
----------
u : MDA trajectory instance.
selection_cluster : selection_cluster:
cluster_size : size of clusters
Returns
-------
"""
distances = np.zeros((len(u.trajectory), cluster_size))
cnt = u.select_atoms('name C', updating=True)
pt = u.select_atoms(selection_cluster, updating=True)
for q, ts in enumerate(u.trajectory):
cg = cnt.center_of_geometry()
for p, t in enumerate(pt.positions):
dis = distance.euclidean(t, [cg[0], cg[1], t[2]])
distances[q, p] = dis
return distances
def check_inputs(selection: list, start: int, stop: int, step: int,
universe: mda.Universe):
ag_sel = selection[0]
ag_names = selection[1]
ag_pair = selection[2]
# Testing names and selections
if len(ag_sel) > len(ag_names):
raise InputError('Not all selections are named')
elif len(ag_sel) < len(ag_names):
raise InputError('Too many selection names for number of selections')
for sel in ag_sel:
try:
ag = universe.select_atoms(sel)
except mda.SelectionError:
raise InputError('Error in selection: {}'.format(sel))
for pair in ag_pair:
if len(pair) != 4:
raise InputError(
'Pairs must be a python list of string with 4 items')
found0 = False
found1 = False
for name in ag_names:
if pair[0] == name:
found0 = True
if pair[1] == name:
found1 = True
if found0 is False:
raise InputError(
f'{pair[0]} in {pair} group_pair_selections is not in defined in atom_group_names'
)
if found1 is False:
raise InputError(
f'{pair[1]} in {pair} group_pair_selections is not in defined in atom_group_names'
)
if start >= stop:
raise InputError('Start is greater than or equal to stop')
if step >= stop:
raise InputError('Step is greater than or equal to stop')
if step == 0:
raise InputError('Step cannot be 0')
if len(universe.trajectory) < stop:
raise InputError(
f'Stop exceeds length of trajectory, trajectory is {len(universe.trajectory)} frames'
)
print('Input Parameters Accepted')
def main():
#24.1.2020
#they were downloaded by a certain rule via the adcanced serarch in rcsb pdb,
#but they contains more than 3 chains, which was out of my scope.
omited_pdbs = ['4e7u.pdb', '4e7t.pdb', '3exx.pdb', '4fka.pdb', '5ep6.pdb',
'4gxv.pdb', '4uqp.pdb', '3uqy.pdb', '4dg4.pdb', '4urh.pdb',
'6f4j.pdb', '1xd3.pdb', '3bog.pdb', '6mee.pdb', '4pj2.pdb',
'5bpk.pdb', '3cjs.pdb', '4c2v.pdb', '1pid.pdb', '6fu9.pdb',
'2oxg.pdb', '1svf.pdb', '6fc1.pdb', '1q7l.pdb', '4kn9.pdb',
'4b2b.pdb', '6g6k.pdb', '4m4l.pdb', '4b2c.pdb', '1ben.pdb',
'3tt8.pdb', '3fq9.pdb', '5nwg.pdb', '4uql.pdb', '2xkn.pdb',
'5nwd.pdb']
filenames = glob.glob('./interfaces/heterodimer/*.pdb')
whole_distances = []
for j, file in enumerate(filenames):
#print(j, file, file.split('/')[-1] in omited_pdbs)
if file.split('/')[-1] in omited_pdbs:
continue
else:
print(file.split('/')[-1].split('.')[0].upper()+",",end = '')
continue
u = Universe(file)
nchains = len(set(u.segments.segids))
if nchains != 2: sys.exit(f'The number of chains = {nchains}. that is out of scope for this program.')
chain_objs = []
for i, chain in enumerate(set(u.segments.segids)):
chain_objs.append(u.select_atoms(f'protein and segid {chain} and name CA'))
print(f' *{chain}')
# print(chain_objs[i].atoms)
whole_distances.append(distances(chain_objs[0], chain_objs[1]))
sys.exit('stop! you might have already done this. so i forced you to procced.')
print(whole_distances)
whole_distances = np.hstack(whole_distances)
filtered_dist = whole_distances[whole_distances<=50.0]
with open('whole_distances.pkl','wb') as f:
pickle.dump(whole_distances, f)
mean = np.mean(whole_distances)
std = np.std(whole_distances)
print(f'mean: {mean}, std:{std}')
def assign_resname(u: Universe, res_dict: Dict[str, str]):
"""
Assign resnames to residues in a MDAnalysis.universe object. The function will not overwrite existing resnames.
Args:
u: The universe object to assign resnames to.
res_dict: A dictionary of resnames, where each resname is a key
and the corresponding values are the selection language.
"""
u.add_TopologyAttr("resname")
for key, val in res_dict.items():
res_group = u.select_atoms(val)
res_names = res_group.residues.resnames
res_names[res_names == ""] = key
res_group.residues.resnames = res_names
def neighbor_distance(
nvt_run: Universe,
center_atom: Atom,
run_start: int,
run_end: int,
species: str,
select_dict: Dict[str, str],
distance: float,
) -> Dict[str, np.ndarray]:
"""
Calculates a dictionary of distances between the ``center_atom`` and neighbor atoms.
Args:
nvt_run: An MDAnalysis ``Universe`` containing wrapped trajectory.
center_atom: The center atom object.
run_start: Start frame of analysis.
run_end: End frame of analysis.
species: The neighbor species in the select_dict.
select_dict: A dictionary of atom species selection, where each atom species name is a key
and the corresponding values are the selection language.
distance: The neighbor cutoff distance.
Returns:
A dictionary of distance of neighbor atoms to the ``center_atom``. The keys are atom indexes in string type .
"""
dist_dict = {}
time_count = 0
trj_analysis = nvt_run.trajectory[run_start:run_end:]
species_selection = select_dict.get(species)
if species_selection is None:
raise ValueError("Invalid species selection")
for ts in trj_analysis:
selection = ("(" + species_selection + ") and (around " +
str(distance) + " index " + str(center_atom.index) + ")")
shell = nvt_run.select_atoms(selection, periodic=True)
for atom in shell.atoms:
if str(atom.index) not in dist_dict:
dist_dict[str(atom.index)] = np.full(run_end - run_start,
100.0)
time_count += 1
time_count = 0
for ts in trj_analysis:
for atom_index, val in dist_dict.items():
dist = distance_array(ts[center_atom.index], ts[int(atom_index)],
ts.dimensions)
val[time_count] = dist
time_count += 1
return dist_dict
def test_write_read(self):
u = Universe(self.filename)
u.select_atoms("all").write(self.outfile)
u2 = Universe(self.outfile)
assert_equal(len(u.atoms), len(u2.atoms))
args = parser.parse_args()
if not args.static:
header_string = "; Umbrella potential for a spherical shell cavity\n"\
"; Name Type Group Kappa Nstar mu width cutoff outfile nstout\n"\
"hydshell dyn_union_sph_sh OW 0.0 0 XXX 0.01 0.02 phiout.dat 50 \\\n"
else:
header_string = "; Umbrella potential for a spherical shell cavity\n"\
"; Name Type Group Kappa Nstar mu width cutoff outfile nstout\n"\
"hydshell union_sph_sh OW 0.0 0 XXX 0.01 0.02 phiout.dat 50 \\\n"
if args.traj is None:
u = Universe(args.gro)
if args.sspec is not None:
prot_heavies = u.select_atoms(args.sspec)
else:
# Select peptide heavies - exclude water's and ions
prot_heavies = u.select_atoms("not (name H* or type H or resname SOL) and not (name NA or name CL) and not (resname WAL) and not (resname DUM)")
fout = open(args.outfile, 'w')
fout.write(header_string)
if args.static:
for atm in prot_heavies:
fout.write("{:<10.1f} {:<10.1f} {:<10.3f} {:<10.3f} {:<10.3f}\\\n".format(-0.5, args.rad/10.0, atm.pos[0]/10.0, atm.pos[1]/10.0, atm.pos[2]/10.0))
else:
for atm in prot_heavies:
fout.write("{:<10.1f} {:<10.1f} {:d} \\\n".format(-0.5, args.rad/10.0, atm.index+1))
fout.close()
At this time, I wanted to confirm if the com of s100b was canceled.
Caution: this program is specialized for s100b-CTD system.
Usage: python conform_com_cancel.py [ PDB file name ]
"""
file_name = sys.argv[1]
print "Input file name : ", file_name
u = Universe(file_name)
f_out = open(file_name+"_comTraj.dat", "w")
print "No of snapshots: ", len(u.trajectory)
for i, ts in enumerate(u.trajectory):
#Select the all atoms constitute s100b
selected_atoms = u.select_atoms("resid 1-94")
print "atom ids: ", selected_atoms.ids
com = selected_atoms.center_of_mass()
cog = selected_atoms.center_of_geometry()
f_out.write(str(com[0]) + " " + str(com[1]) + " " + str(com[2]) + " \n")
.. SeeAlso:: :mod:`MDAnalysis.analysis.psa`
"""
from MDAnalysis import Universe
from MDAnalysis.analysis.align import rotation_matrix
from MDAnalysis.analysis.psa import PSAnalysis
if __name__ == '__main__':
print("Generating AdK CORE C-alpha reference coordinates and structure...")
# Read in closed/open AdK structures; work with C-alphas only
u_closed = Universe('structs/adk1AKE.pdb')
u_open = Universe('structs/adk4AKE.pdb')
ca_closed = u_closed.select_atoms('name CA')
ca_open = u_open.select_atoms('name CA')
# Move centers-of-mass of C-alphas of each structure's CORE domain to origin
adkCORE_resids = "(resid 1:29 or resid 60:121 or resid 160:214)"
u_closed.atoms.translate(-ca_closed.select_atoms(adkCORE_resids).center_of_mass())
u_open.atoms.translate(-ca_open.select_atoms(adkCORE_resids).center_of_mass())
# Get C-alpha CORE coordinates for each structure
closed_ca_core_coords = ca_closed.select_atoms(adkCORE_resids).positions
open_ca_core_coords = ca_open.select_atoms(adkCORE_resids).positions
# Compute rotation matrix, R, that minimizes rmsd between the C-alpha COREs
R, rmsd_value = rotation_matrix(open_ca_core_coords, closed_ca_core_coords)
# Rotate open structure to align its C-alpha CORE to closed structure's
from MDAnalysis import Universe, collection, Timeseries
from MDAnalysis.tests.datafiles import PSF, DCD
try:
import matplotlib
matplotlib.use('agg') # no interactive plotting, only save figures
from pylab import errorbar, legend, xlabel, ylabel, savefig, clf, gca, draw
have_matplotlib = True
except ImportError:
have_matplotlib = False
universe = Universe(PSF, DCD)
protein = universe.select_atoms("protein")
numresidues = protein.numberOfResidues()
collection.clear()
for res in range(2, numresidues - 1):
print "Processing residue {0:d}".format(res)
# selection of the atoms involved for the phi for resid '%d' %res
## select_atoms("atom 4AKE %d C"%(res-1), "atom 4AKE %d N"%res, "atom %d 4AKE CA"%res, "atom 4AKE %d C" % res)
phi_sel = universe.residues[res].phi_selection()
# selection of the atoms involved for the psi for resid '%d' %res
psi_sel = universe.residues[res].psi_selection()
# collect the timeseries of a dihedral
collection.addTimeseries(Timeseries.Dihedral(phi_sel))
import MDAnalysis
from MDAnalysis import Universe
from MDAnalysis.analysis.contacts import calculate_contacts
import numpy as np
import pandas as pd
ref = Universe("conf_protein.gro.bz2")
u = Universe("conf_protein.gro.bz2", "traj_protein_0.xtc")
x = len(ref.select_atoms("protein"))
selA = "not name H* and resid 72-95 and bynum {}:{}".format(1, x//2)
selB = "not name H* and resid 72-95 and bynum {}:{}".format(x//2, x)
data = calculate_contacts(ref, u, selA, selB)
df = pd.DataFrame(data, columns=["Time (ps)", "Q"])
print(df)