def capping(ref, ace, nma, output):
resids = ref.select_atoms("all").resids
resid_min, resid_max = min(resids), max(resids)
for r in ace.residues:
r.resid += resid_min - max(ace.atoms.resids)
for r in nma.residues:
r.resid = resid_max
# TODO pick the first residue in the protein (how should we cap the chains?)
# TODO consider a case when the protein resid is 1 and all peptide has to be shifted by +1, put that in docs as a
# post-processing step
alignto(ace, ref, select={
"mobile": "resid {0} and backbone".format(resid_min),
"reference": "resid {0} and backbone".format(resid_min)},
strict=True)
alignto(nma, ref, select={
"mobile": "resid {0} and backbone and not (resname NMA NME)".format(resid_max),
"reference": "resid {0} and (backbone or name OT2)".format(resid_max)},
strict=True)
# TODO remove the Hydrogen closest to ACE's oxygen
nma.residues.resids = 16
u = Merge(ace.select_atoms("resname ACE"),
ref.select_atoms(
"not (resid {0} and name HT*) and not (resid {1} and (name HT* OT1))"
"".format(resid_min, resid_max)),
nma.select_atoms("resname NME NMA"))
u.trajectory.ts.dimensions = ref.trajectory.ts.dimensions
u.atoms.write(output)
return u
def preliminaryAnalysis(obj, top):
aligner1 = align.AlignTraj(obj, obj, verbose=True, in_memory=True)
cAlpha = obj.select_atoms("name CA")
#computing Radius of gyration
print("Computing Radius of gyration along the trajectory:")
gyro_list = list()
for ts in obj.trajectory:
gyro_list.append(obj.atoms.radius_of_gyration())
s_gyro = pd.Series(gyro_list)
#computing rmsd with first frame as reference
print("Computing c-alphas RMSD with the first frame as reference:")
rmsd1 = RMSD(cAlpha, verbose=True).run()
rmsd_df = pd.DataFrame(rmsd1.rmsd)
#computind rmsf
print("Computing c-alphas RMSF:")
ref_coordinates = obj.trajectory.timeseries(asel=cAlpha).mean(axis=1)
ref = Merge(cAlpha).load_new(ref_coordinates[:, None, :], order="afc")
re_align = align.AlignTraj(obj, ref, select="name CA").run()
# need to write the trj to disk (issue 15)?
with Writer("rmsfit.xtc", n_atoms=obj.atoms.n_atoms) as w:
for ts in obj.trajectory:
w.write(obj.atoms)
#creating the fitted trj
rmsfObj = Universe("rmsfit.xtc", top)
#backboneRef = rmsfObj.select_atoms("backbone")
rmsf = RMSF(cAlpha, verbose=True).run()
rmsf_df = pd.DataFrame(rmsf.rmsf, index=cAlpha.resnums)
return s_gyro, rmsd_df, rmsf_df
def capping(ref, ace, nma, output):
resids = ref.selectAtoms("all").resids()
resid_min, resid_max = min(resids), max(resids)
# There is probably some cache i need to update both the atom and residues
for a in ace.atoms:
a.resid += resid_min - max(ace.atoms.resids())
for r in ace.residues:
r.id += resid_min - max(ace.atoms.resids())
for a in nma.atoms:
a.resid = resid_max
for r in nma.residues:
r.id = resid_max
# TODO pick the first residue in the protein (how should we cap the chains?)
# TODO consider a case when the protein resid is 1 and all peptide has to be shifted by +1, put that in docs as a
# post-processing step
alignto(ace,
ref,
select={
"mobile": "resid {} and backbone".format(resid_min),
"reference": "resid {} and backbone".format(resid_min)
})
alignto(nma,
ref,
select={
"mobile":
"resid {} and backbone and not (resname NMA or resname NME)".
format(resid_max),
"reference":
"resid {} and (backbone or name OT2)".format(resid_max)
})
# TODO remove the Hydrogen closest to ACE's oxygen
u = Merge(
ace.selectAtoms("resname ACE"),
ref.selectAtoms(
"not (resid {} and name HT*) and not (resid {} and (name HT* or name OT1))"
.format(resid_min, resid_max)),
nma.selectAtoms("resname NME or resname NMA"))
u.trajectory.ts.dimensions = ref.trajectory.ts.dimensions
u.atoms.write(output)
return u
def test_emptyAG_ValueError(self, u_protein):
a = AtomGroup([], u_protein)
b = AtomGroup([], u_protein)
with pytest.raises(ValueError):
Merge(a, b)
def test_nonsense_TypeError(self):
with pytest.raises(TypeError):
Merge(['1', 2])
def test_empty_ValueError(self):
with pytest.raises(ValueError):
Merge()
def test_segment_references(self, u_protein, u_ligand):
m = Merge(u_protein.atoms, u_ligand.atoms)
assert_equal(m.atoms.segments[0].universe, m,
"wrong universe reference for segments after Merge()")
def test_segment_references(self):
u1, u2, u3 = self.universes
m = Merge(u1.atoms, u2.atoms)
assert_equal(m.atoms.segments[0].universe, m,
"wrong universe reference for segments after Merge()")
def test_residue_references(self):
u1, u2, u3 = self.universes
m = Merge(u1.atoms, u2.atoms)
assert_equal(m.atoms.residues[0].universe, m,
"wrong universe reference for residues after Merge()")
#!/usr/bin/env python
from MDAnalysis import Universe, Merge
import argparse
parser = argparse.ArgumentParser(description='Demo')
parser.add_argument('-i', '--input', nargs="+")
parser.add_argument('-o', '--output')
args = parser.parse_args()
universes = [Universe(f).atoms for f in args.input]
#u1 = Universe("frame.gro")
#u2 = Universe("bilayer.gro")
u = Merge(*universes)
u.atoms.write(args.output)
def __init__(self,
nuclstr_instance,
time=None,
bps=None,
num_threads=1,
dir=None):
#take
#nuclstr_instance.u
self.resid_start = nuclstr_instance.seqs[
nuclstr_instance.dyad_top[0]]['resid_start']
self.DNA_length = nuclstr_instance.DNA_top_length
self.bp_dict = nuclstr_instance.bp_dict
if bps is None:
#MAJOR CHANGES - idea is that we use bp_dict object, which is derived at structure initialisation
# using allignment with rev compl bot strand
#self.bps=np.arange(self.resid_start,self.resid_start+self.DNA_length)
self.bps = np.array(list(nuclstr_instance.bp_dict['top'].keys()))
else:
logger.warning(
'With bps set manually, sugar information is incorrect!!!')
self.bps = []
for bp in bps:
if bp in (nuclstr_instance.bp_dict['top'].keys()):
self.bps.append(bp)
self.bps = np.array(self.bps)
self.p = nuclstr_instance
self.u = nuclstr_instance.u
if time is None:
if isinstance(nuclstr_instance.time, slice):
self.time = nuclstr_instance.time
else:
self.time = slice(*nuclstr_instance.time)
else:
if isinstance(time, slice):
self.time = time
else:
self.time = slice(*time)
# print(self.time)
self.sel = self.u.select_atoms('nucleic') #should be allright now
self.sel_u = Merge(self.sel)
#OLD WAY
# with tempfile.TemporaryDirectory() as TEMP:
# logger.debug('created temporary directory ' + TEMP)
# #At first we need to makup a couple of unique file names
# self.u.trajectory[self.time.start]
# self.sel.write(os.path.join(TEMP,'first.pdb'))
# self.bp_ref_file=DNAtools.X3DNA_find_pair(os.path.join(TEMP,'first.pdb'))
#####New way
self.bp_ref_file = self.gen_find_pair_file()
#Run X3DNA_analyze
if num_threads != 1:
with tempfile.TemporaryDirectory(dir=dir) as TEMP:
# print(TEMP)
pdbfiles = []
frames = []
for ts in self.u.trajectory[self.time]:
unique = str(uuid.uuid4())
pdb = unique + '.pdb'
pdbfiles.append(os.path.join(TEMP, pdb))
frames.append(ts.frame)
with Pool(num_threads) as pool:
# to be picklable we must construct zip with such an order pdb,frame,sel,bp_ref_file,bps
N = len(pdbfiles)
df_list = list(
tqdm(pool.imap(
X3DNA_analyze_runner,
zip(pdbfiles, frames, [self.sel] * N,
[self.bp_ref_file] * N, [self.bps] * N)),
total=len(pdbfiles)))
for df in df_list:
df['BPnum_dyad'] = self.bps - self.p.dyad_top[1]
df['segid'] = nuclstr_instance.dyad_top[0]
self.df_series = pd.concat(df_list)
#old singlethread
elif num_threads == 1:
self.df_series = pd.DataFrame()
with tempfile.TemporaryDirectory(dir=dir) as TEMP:
for ts in tqdm(self.u.trajectory[self.time]):
unique = str(uuid.uuid4())
pdb = unique + '.pdb'
self.sel.write(os.path.join(TEMP, pdb))
df = DNAtools.X3DNA_analyze(os.path.join(TEMP, pdb),
ref_fp_id=self.bp_ref_file)
# print(df)
df = df.iloc[0:len(self.bps)]
df['Frame'] = ts.frame
# df['Time']=ts.time
df['BPnum_dyad'] = self.bps - self.p.dyad_top[1]
df['segid'] = nuclstr_instance.dyad_top[0]
df['resid'] = self.bps
self.df_series = pd.concat([self.df_series, df])
#TODO: Add tricks if top and bottom in PDB are not in the the order top,bottom, but bottom,top
self.df_series = self.df_series.reset_index(drop=True)
self.df_series.replace('---', np.nan, inplace=True)
self.df_series['alpha_1'] = self.df_series['alpha_1'].astype(float)
self.df_series['beta_1'] = self.df_series['beta_1'].astype(float)
self.df_series['epsilon_1'] = self.df_series['epsilon_1'].astype(float)
self.df_series['zeta_1'] = self.df_series['zeta_1'].astype(float)
self.df_series['epsilon_2'] = self.df_series['epsilon_2'].astype(float)
self.df_series['e-z_1'] = self.df_series['e-z_1'].astype(float)
self.df_series['alpha_2'] = self.df_series['alpha_2'].astype(float)
self.df_series['beta_2'] = self.df_series['beta_2'].astype(float)
self.df_series['zeta_1'] = self.df_series['zeta_1'].astype(float)
self.df_series['e-z_2'] = self.df_series['e-z_2'].astype(float)
self.df_series['zeta_2'] = self.df_series['zeta_2'].astype(float)
self.df_series['ssZp_1'] = self.df_series['ssZp_1'].astype(float)
self.df_series['ssZp_2'] = self.df_series['ssZp_2'].astype(float)
self.df_series['Dp_1'] = self.df_series['Dp_1'].astype(float)
self.df_series['Dp_2'] = self.df_series['Dp_2'].astype(float)
try:
self.do_stats()
except:
logger.warning('statistics failed!')
class a_DNA:
"""
Analyze DNA parameters for a pynucl object
bps - define what basepairs to consider in calculating params.
None - all
[] - list of resids along the top chain
Will provide df, df_series, df_std
"""
def __init__(self,
nuclstr_instance,
time=None,
bps=None,
num_threads=1,
dir=None):
#take
#nuclstr_instance.u
self.resid_start = nuclstr_instance.seqs[
nuclstr_instance.dyad_top[0]]['resid_start']
self.DNA_length = nuclstr_instance.DNA_top_length
self.bp_dict = nuclstr_instance.bp_dict
if bps is None:
#MAJOR CHANGES - idea is that we use bp_dict object, which is derived at structure initialisation
# using allignment with rev compl bot strand
#self.bps=np.arange(self.resid_start,self.resid_start+self.DNA_length)
self.bps = np.array(list(nuclstr_instance.bp_dict['top'].keys()))
else:
logger.warning(
'With bps set manually, sugar information is incorrect!!!')
self.bps = []
for bp in bps:
if bp in (nuclstr_instance.bp_dict['top'].keys()):
self.bps.append(bp)
self.bps = np.array(self.bps)
self.p = nuclstr_instance
self.u = nuclstr_instance.u
if time is None:
if isinstance(nuclstr_instance.time, slice):
self.time = nuclstr_instance.time
else:
self.time = slice(*nuclstr_instance.time)
else:
if isinstance(time, slice):
self.time = time
else:
self.time = slice(*time)
# print(self.time)
self.sel = self.u.select_atoms('nucleic') #should be allright now
self.sel_u = Merge(self.sel)
#OLD WAY
# with tempfile.TemporaryDirectory() as TEMP:
# logger.debug('created temporary directory ' + TEMP)
# #At first we need to makup a couple of unique file names
# self.u.trajectory[self.time.start]
# self.sel.write(os.path.join(TEMP,'first.pdb'))
# self.bp_ref_file=DNAtools.X3DNA_find_pair(os.path.join(TEMP,'first.pdb'))
#####New way
self.bp_ref_file = self.gen_find_pair_file()
#Run X3DNA_analyze
if num_threads != 1:
with tempfile.TemporaryDirectory(dir=dir) as TEMP:
# print(TEMP)
pdbfiles = []
frames = []
for ts in self.u.trajectory[self.time]:
unique = str(uuid.uuid4())
pdb = unique + '.pdb'
pdbfiles.append(os.path.join(TEMP, pdb))
frames.append(ts.frame)
with Pool(num_threads) as pool:
# to be picklable we must construct zip with such an order pdb,frame,sel,bp_ref_file,bps
N = len(pdbfiles)
df_list = list(
tqdm(pool.imap(
X3DNA_analyze_runner,
zip(pdbfiles, frames, [self.sel] * N,
[self.bp_ref_file] * N, [self.bps] * N)),
total=len(pdbfiles)))
for df in df_list:
df['BPnum_dyad'] = self.bps - self.p.dyad_top[1]
df['segid'] = nuclstr_instance.dyad_top[0]
self.df_series = pd.concat(df_list)
#old singlethread
elif num_threads == 1:
self.df_series = pd.DataFrame()
with tempfile.TemporaryDirectory(dir=dir) as TEMP:
for ts in tqdm(self.u.trajectory[self.time]):
unique = str(uuid.uuid4())
pdb = unique + '.pdb'
self.sel.write(os.path.join(TEMP, pdb))
df = DNAtools.X3DNA_analyze(os.path.join(TEMP, pdb),
ref_fp_id=self.bp_ref_file)
# print(df)
df = df.iloc[0:len(self.bps)]
df['Frame'] = ts.frame
# df['Time']=ts.time
df['BPnum_dyad'] = self.bps - self.p.dyad_top[1]
df['segid'] = nuclstr_instance.dyad_top[0]
df['resid'] = self.bps
self.df_series = pd.concat([self.df_series, df])
#TODO: Add tricks if top and bottom in PDB are not in the the order top,bottom, but bottom,top
self.df_series = self.df_series.reset_index(drop=True)
self.df_series.replace('---', np.nan, inplace=True)
self.df_series['alpha_1'] = self.df_series['alpha_1'].astype(float)
self.df_series['beta_1'] = self.df_series['beta_1'].astype(float)
self.df_series['epsilon_1'] = self.df_series['epsilon_1'].astype(float)
self.df_series['zeta_1'] = self.df_series['zeta_1'].astype(float)
self.df_series['epsilon_2'] = self.df_series['epsilon_2'].astype(float)
self.df_series['e-z_1'] = self.df_series['e-z_1'].astype(float)
self.df_series['alpha_2'] = self.df_series['alpha_2'].astype(float)
self.df_series['beta_2'] = self.df_series['beta_2'].astype(float)
self.df_series['zeta_1'] = self.df_series['zeta_1'].astype(float)
self.df_series['e-z_2'] = self.df_series['e-z_2'].astype(float)
self.df_series['zeta_2'] = self.df_series['zeta_2'].astype(float)
self.df_series['ssZp_1'] = self.df_series['ssZp_1'].astype(float)
self.df_series['ssZp_2'] = self.df_series['ssZp_2'].astype(float)
self.df_series['Dp_1'] = self.df_series['Dp_1'].astype(float)
self.df_series['Dp_2'] = self.df_series['Dp_2'].astype(float)
try:
self.do_stats()
except:
logger.warning('statistics failed!')
def do_stats(self):
# old version does not work
#self.df=self.df_series.groupby(['BPnum','BPnum_dyad','segid','resid'])['x','y','z','Shear','Stretch','Stagger','Buckle','Prop-Tw','Opening','Shift', 'Slide', 'Rise', 'Tilt', 'Roll', 'Twist','Pairing', 'chi_1', 'alpha_1', 'beta_1', 'gamma_1', 'delta_1','epsilon_1', 'zeta_1', 'e-z_1', 'chi_2', 'alpha_2','beta_2', 'gamma_2', 'delta_2', 'epsilon_2', 'zeta_2', 'e-z_2', 'v0_1', 'v1_1', 'v2_1', 'v3_1', 'v4_1', 'tm_1', 'P_1', 'ssZp_1', 'Dp_1', 'v0_2', 'v1_2', 'v2_2', 'v3_2', 'v4_2','tm_2', 'P_2', 'ssZp_2', 'Dp_2'].apply(np.mean).reset_index()
#self.df=self.df_series.groupby(['BPnum','BPnum_dyad','segid','resid']).apply(np.mean).drop(['BPnum','BPnum_dyad','segid','resid'],level=4).unstack().reset_index()
self.df = self.df_series.groupby(
['BPnum', 'BPnum_dyad', 'segid',
'resid']).agg('mean').drop(columns=['Frame']).reset_index()
# refactor
#self.df_std=self.df_series.groupby(['BPnum','BPnum_dyad','segid','resid'])['x','y','z','Shear','Stretch','Stagger','Buckle','Prop-Tw','Opening','Shift', 'Slide', 'Rise', 'Tilt', 'Roll', 'Twist','Pairing', 'chi_1', 'alpha_1', 'beta_1', 'gamma_1', 'delta_1','epsilon_1', 'zeta_1', 'e-z_1', 'chi_2', 'alpha_2','beta_2', 'gamma_2', 'delta_2', 'epsilon_2', 'zeta_2', 'e-z_2', 'v0_1', 'v1_1', 'v2_1', 'v3_1', 'v4_1', 'tm_1', 'P_1', 'ssZp_1', 'Dp_1', 'v0_2', 'v1_2', 'v2_2', 'v3_2', 'v4_2','tm_2', 'P_2', 'ssZp_2', 'Dp_2'].agg('std').reset_index()
self.df_std = self.df_series.groupby(
['BPnum', 'BPnum_dyad', 'segid',
'resid']).agg('std').drop(columns=['Frame']).reset_index()
#self.df_min=self.df_series.groupby(['BPnum','BPnum_dyad','segid','resid'])['x','y','z','Shear','Stretch','Stagger','Buckle','Prop-Tw','Opening','Shift', 'Slide', 'Rise', 'Tilt', 'Roll', 'Twist','Pairing', 'chi_1', 'alpha_1', 'beta_1', 'gamma_1', 'delta_1','epsilon_1', 'zeta_1', 'e-z_1', 'chi_2', 'alpha_2','beta_2', 'gamma_2', 'delta_2', 'epsilon_2', 'zeta_2', 'e-z_2', 'v0_1', 'v1_1', 'v2_1', 'v3_1', 'v4_1', 'tm_1', 'P_1', 'ssZp_1', 'Dp_1', 'v0_2', 'v1_2', 'v2_2', 'v3_2', 'v4_2','tm_2', 'P_2', 'ssZp_2', 'Dp_2'].agg('min').reset_index()
self.df_min = self.df_series.groupby(
['BPnum', 'BPnum_dyad', 'segid',
'resid']).agg('min').drop(columns=['Frame']).reset_index()
#self.df_max=self.df_series.groupby(['BPnum','BPnum_dyad','segid','resid'])['x','y','z','Shear','Stretch','Stagger','Buckle','Prop-Tw','Opening','Shift', 'Slide', 'Rise', 'Tilt', 'Roll', 'Twist','Pairing', 'chi_1', 'alpha_1', 'beta_1', 'gamma_1', 'delta_1','epsilon_1', 'zeta_1', 'e-z_1', 'chi_2', 'alpha_2','beta_2', 'gamma_2', 'delta_2', 'epsilon_2', 'zeta_2', 'e-z_2', 'v0_1', 'v1_1', 'v2_1', 'v3_1', 'v4_1', 'tm_1', 'P_1', 'ssZp_1', 'Dp_1', 'v0_2', 'v1_2', 'v2_2', 'v3_2', 'v4_2','tm_2', 'P_2', 'ssZp_2', 'Dp_2'].agg('max').reset_index()
self.df_max = self.df_series.groupby(
['BPnum', 'BPnum_dyad', 'segid',
'resid']).agg('max').drop(columns=['Frame']).reset_index()
#for what ever reason .apply(lambda x: np.std(x.to_numpy(),ddof=1)).reset_index() does not work(!)
#Let's group x y z as coord array for compatibility
self.df_series['coord'] = [
np.array([x, y, z]) for x, y, z in zip(
self.df_series['x'], self.df_series['y'], self.df_series['z'])
]
self.df['coord'] = [
np.array([x, y, z])
for x, y, z in zip(self.df['x'], self.df['y'], self.df['z'])
]
def calc_DNA_pos_deviation(self, ref=0, only_average=False):
"""
Gets the deviation of DNA bp centers from reference
ref - should be time frame or an a_DNA.df - dataframe
"""
# if hasattr(self, 'contdf_series'):
# return self.contdf_series
if isinstance(ref, int):
ref = self.df_series[self.df_series['Frame'] == ref].copy()
if (only_average == False):
#This might probably be speeded up by using scipy.spatial.distance.cdist(dref,d,'sqeuclidean')
#But currently the timing is not that bad.
g = self.df_series.groupby(['Frame'])
dev = g.apply(lambda x: x[['x', 'y', 'z']].reset_index() - ref[
['x', 'y', 'z']].reset_index()).reset_index()
self.df_series['dx'] = dev['x']
self.df_series['dy'] = dev['y']
self.df_series['dz'] = dev['z']
self.df_series['pos_dev'] = np.sqrt(dev['x']**2 + dev['y']**2 +
dev['z']**2)
print('Added dx,dy,dz and pos_dev columns to self.df_series')
#Now calculate for average
self.df['dx'] = self.df['x'] - ref['x']
self.df['dy'] = self.df['y'] - ref['y']
self.df['dz'] = self.df['z'] - ref['z']
self.df['pos_dev'] = np.sqrt(self.df['dx']**2 + self.df['dy']**2 +
self.df['dz']**2)
print('Added dx,dy,dz and pos_dev columns to self.df')
#return self.df_series
def calc_DNA_bp_closest(self, ref=0):
"""
This will for every time frame calculate the closest base pair number in the reference and get its distance.
"""
d = self.df_series[['x', 'y', 'z']].to_numpy()
d = d.reshape(-1, self.p.DNA_top_length, 3)
if isinstance(ref, int):
ref = self.df_series[self.df_series['Frame'] == ref]
#else ref should be a df with x,y,z
dref = ref[['x', 'y', 'z']].to_numpy()
dref = dref.reshape(self.p.DNA_top_length, 3)
mindist = []
minbp = []
bpshift = []
for i in range(0,
int(self.df_series.shape[0] / self.p.DNA_top_length)):
cdist = scipy.spatial.distance.cdist(dref, d[i], 'euclidean')
mindist.extend(np.min(cdist, axis=0))
m = (cdist == np.min(cdist, axis=0))
r, c = np.where(m)
minbp.extend(r + self.p.dyad_top[1] - self.p.DNA_left_length)
bpshift.extend(r - np.arange(self.p.DNA_top_length))
self.df_series['BP_mindist'] = mindist
self.df_series['BP_id_closest'] = minbp
self.df_series['BP_id_shift'] = bpshift
print(
'BP_mindist, BP_id_closest and BP_id_shift added to df_series dataframe'
)
# return pm
# def calc_unwrapping(self,threshold=10):
# """
# This will
# """
def gen_find_pair_file(self):
"""
generate find pair file as buffer
"""
output = 'input.pdb' + '\n' + 'input.out\n'
output += ' 2 # duplex\n%5d # number of base-pairs\n 1 1 # explicit bp numbering/hetero atoms\n' % len(
self.bps)
# 729 1018 0 # 1 | ....>I:.-72_:[.DA]A-----T[.DT]:..72_:J<.... 1.82 1.59 28.50 9.48 4.43
for n, top_resid in enumerate(self.bps, 1):
bot_resid = self.bp_dict['top'][top_resid]
resindex_top = self.sel_u.select_atoms(
'segid %s and resnum %d' %
(self.p.DNA_top_segid, top_resid)).residues.resindices[0] + 1
resname_top = self.sel_u.select_atoms(
'segid %s and resnum %d' %
(self.p.DNA_top_segid, top_resid)).residues.resnames[0]
# comp_resid=self.p.dyad_bot[1]+self.p.dyad_top[1]-resid
# print(resid,comp_resid,self.p.dyad_bot[1],self.p.dyad_top[1])
resindex_bot = self.sel_u.select_atoms(
'segid %s and resnum %d' %
(self.p.DNA_bot_segid, bot_resid)).residues.resindices[0] + 1
resname_bot = self.sel_u.select_atoms(
'segid %s and resnum %d' %
(self.p.DNA_bot_segid, bot_resid)).residues.resnames[0]
output += "%5d %5d 0 #%5d | ....>%s:%s_:[%s]%1s-----%1s[%s]:%s_:%s<.... 0.00 0.00 00.00 0.00 0.00\n" % (
resindex_top, resindex_bot, n, self.p.DNA_top_segid,
('%4d' % top_resid).replace(' ', '.'),
('%3s' % resname_top).replace(' ', '.'), resname_top[-1],
resname_bot[-1], ('%3s' % resname_bot).replace(' ', '.'),
('%4d' % bot_resid).replace(' ', '.'), self.p.DNA_bot_segid)
#print('DEBUG')
#print(output)
buffer = StringIO()
buffer.write(output)
buffer.seek(0)
return (buffer)
