class Stereochem_analysis:
"""Class for performing the relative stereochemistry analysis."""
def __init__(self, stage=1, results_dir=None, num_ens=10000, num_models=10, configs=None, snapshot_dir='snapshots', snapshot_min=None, snapshot_max=None, pseudo=None, noe_file=None, noe_norm=None, rdc_name=None, rdc_file=None, rdc_spin_id1_col=None, rdc_spin_id2_col=None, rdc_data_col=None, rdc_error_col=None, bond_length=None, bond_length_file=None, log=None, bucket_num=200, lower_lim_noe=0.0, upper_lim_noe=600.0, lower_lim_rdc=0.0, upper_lim_rdc=1.0):
"""Set up for the stereochemistry analysis.
@keyword stage: Stage of analysis (see the module docstring above for the options).
@type stage: int
@keyword results_dir: The optional directory to place all results files into.
@type results_dir: None or str
@keyword num_ens: Number of ensembles.
@type num_ens: int
@keyword num_models: Ensemble size.
@type num_models: int
@keyword configs: All the configurations.
@type configs: list of str
@keyword snapshot_dir: Snapshot directories (corresponding to the configurations).
@type snapshot_dir: list of str
@keyword snapshot_min: The number of the first snapshots (corresponding to the configurations).
@type snapshot_min: list of int
@keyword snapshot_max: The number of the last snapshots (corresponding to the configurations).
@type snapshot_max: list of int
@keyword pseudo: The list of pseudo-atoms. Each element is a list of the pseudo-atom name and a list of all those atoms forming the pseudo-atom. For example, pseudo = [["Q7", ["@H16", "@H17", "@H18"]], ["Q9", ["@H20", "@H21", "@H22"]]].
@type pseudo: list of list of str and list of str
@keyword noe_file: The name of the NOE restraint file.
@type noe_file: str
@keyword noe_norm: The NOE normalisation factor (equal to the sum of all NOEs squared).
@type noe_norm: float
@keyword rdc_name: The label for this RDC data set.
@type rdc_name: str
@keyword rdc_file: The name of the RDC file.
@type rdc_file: str
@keyword rdc_spin_id1_col: The spin ID column of the first spin in the RDC file.
@type rdc_spin_id1_col: None or int
@keyword rdc_spin_id2_col: The spin ID column of the second spin in the RDC file.
@type rdc_spin_id2_col: None or int
@keyword rdc_data_col: The data column of the RDC file.
@type rdc_data_col: int
@keyword rdc_error_col: The error column of the RDC file.
@type rdc_error_col: int
@keyword bond_length: The bond length value in meters. This overrides the bond_length_file argument.
@type bond_length: float or None
@keyword bond_length_file: The file of bond lengths for each atom pair in meters. The first and second columns must be the spin ID strings and the third column must contain the data.
@type bond_length_file: float or None
@keyword log: Log file output flag (only for certain stages).
@type log: bool
@keyword bucket_num: Number of buckets for the distribution plots.
@type bucket_num: int
@keyword lower_lim_noe: Distribution plot limits.
@type lower_lim_noe: int
@keyword upper_lim_noe: Distribution plot limits.
@type upper_lim_noe: int
@keyword lower_lim_rdc: Distribution plot limits.
@type lower_lim_rdc: int
@keyword upper_lim_rdc: Distribution plot limits.
@type upper_lim_rdc: int
"""
# Execution lock.
status.exec_lock.acquire('auto stereochem analysis', mode='auto-analysis')
# Set up the analysis status object.
status.init_auto_analysis('stereochem', type='stereochem')
status.current_analysis = 'auto stereochem analysis'
# Store all the args.
self.stage = stage
self.results_dir = results_dir
self.num_ens = num_ens
self.num_models = num_models
self.configs = configs
self.snapshot_dir = snapshot_dir
self.snapshot_min = snapshot_min
self.snapshot_max = snapshot_max
self.pseudo = pseudo
self.noe_file = noe_file
self.noe_norm = noe_norm
self.rdc_name = rdc_name
self.rdc_file = rdc_file
self.rdc_spin_id1_col = rdc_spin_id1_col
self.rdc_spin_id2_col = rdc_spin_id2_col
self.rdc_data_col = rdc_data_col
self.rdc_error_col = rdc_error_col
self.bond_length = bond_length
self.bond_length_file = bond_length_file
self.log = log
self.bucket_num = bucket_num
self.lower_lim_noe = lower_lim_noe
self.upper_lim_noe = upper_lim_noe
self.lower_lim_rdc = lower_lim_rdc
self.upper_lim_rdc = upper_lim_rdc
# Load the interpreter.
self.interpreter = Interpreter(show_script=False, quit=False, raise_relax_error=True)
self.interpreter.populate_self()
self.interpreter.on(verbose=False)
# Create the results directory.
if self.results_dir:
mkdir_nofail(self.results_dir)
# Or use the current working directory.
else:
self.results_dir = getcwd()
# Create a directory for log files.
if self.log:
mkdir_nofail(self.results_dir + sep + "logs")
# Finish and unlock execution.
status.auto_analysis['stereochem'].fin = True
status.current_analysis = None
status.exec_lock.release()
def run(self):
"""Execute the given stage of the analysis."""
# Store the original STDOUT.
self.stdout_orig = sys.stdout
# Sampling of snapshots.
if self.stage == 1:
self.sample()
# NOE violation analysis.
elif self.stage == 2:
self.noe_viol()
# Ensemble superimposition.
elif self.stage == 3:
self.superimpose()
# RDC Q-factor analysis.
elif self.stage == 4:
self.rdc_analysis()
# Grace plot creation.
elif self.stage == 5:
self.grace_plots()
# Final combined Q ordering.
elif self.stage == 6:
self.combined_q()
# Unknown stage.
else:
raise RelaxError("The stage number %s is unknown." % self.stage)
# Restore STDOUT.
sys.stdout = self.stdout_orig
def combined_q(self):
"""Calculate the combined Q-factor.
The combined Q is defined as::
Q_total^2 = Q_NOE^2 + Q_RDC^2,
and the NOE Q-factor as::
Q^2 = U / sum(NOE_i^2),
where U is the quadratic flat bottom well potential - the NOE violation in Angstrom^2.
"""
# Checks.
if not access(self.results_dir+sep+"NOE_viol_" + self.configs[0] + "_sorted", F_OK):
raise RelaxError("The NOE analysis has not been performed, cannot find the file '%s'." % self.results_dir+sep+"NOE_viol_" + self.configs[0] + "_sorted")
if not access(self.results_dir+sep+"Q_factors_" + self.configs[0] + "_sorted", F_OK):
raise RelaxError("The RDC analysis has not been performed, cannot find the file '%s'." % self.results_dir+sep+"Q_factors_" + self.configs[0] + "_sorted")
# Loop over the configurations.
for i in range(len(self.configs)):
# Print out.
print("Creating the combined Q-factor file for configuration '%s'." % self.configs[i])
# Open the NOE results file and read the data.
file = open(self.results_dir+sep+"NOE_viol_" + self.configs[i])
noe_lines = file.readlines()
file.close()
# Open the RDC results file and read the data.
file = open(self.results_dir+sep+"Q_factors_" + self.configs[i])
rdc_lines = file.readlines()
file.close()
# The combined Q-factor file.
out = open(self.results_dir+sep+"Q_total_%s" % self.configs[i], 'w')
out_sorted = open(self.results_dir+sep+"Q_total_%s_sorted" % self.configs[i], 'w')
# Loop over the data (skipping the header line).
data = []
for j in range(1, len(noe_lines)):
# Split the lines.
ens = int(noe_lines[j].split()[0])
noe_viol = float(noe_lines[j].split()[1])
q_rdc = float(rdc_lines[j].split()[1])
# The NOE Q-factor.
q_noe = sqrt(noe_viol/self.noe_norm)
# Combined Q.
q = sqrt(q_noe**2 + q_rdc**2)
# Write out the unsorted list.
out.write("%-20i%20.15f\n" % (ens, q))
# Store the values.
data.append([q, ens])
# Sort the combined Q.
data.sort()
# Write the data.
for i in range(len(data)):
out_sorted.write("%-20i%20.15f\n" % (data[i][1], data[i][0]))
# Close the files.
out.close()
out_sorted.close()
def generate_distribution(self, values, lower=0.0, upper=200.0, inc=None):
"""Create the distribution data structure."""
# The bin width.
bin_width = (upper - lower)/float(inc)
# Init the dist object.
dist = []
for i in range(inc):
dist.append([bin_width*i+lower, 0])
# Loop over the values.
for val in values:
# The bin.
bin = int((val - lower)/bin_width)
# Outside of the limits.
if bin < 0 or bin >= inc:
print("Outside of the limits: '%s'" % val)
continue
# Increment the count.
dist[bin][1] = dist[bin][1] + 1
# Convert the counts to frequencies.
total_pr = 0.0
for i in range(inc):
dist[i][1] = dist[i][1] / float(len(values))
total_pr = total_pr + dist[i][1]
print("Total Pr: %s" % total_pr)
# Return the dist.
return dist
def grace_plots(self):
"""Generate grace plots of the results."""
# The number of configs.
n = len(self.configs)
# The colours for the different configs.
defaults = [4, 2] # Blue and red.
colours = []
for i in range(n):
# Default colours.
if i < len(defaults):
colours.append(defaults[i])
# Otherwise black!
else:
colours.append(0)
# The ensemble number text.
ens_text = ''
dividers = [1e15, 1e12, 1e9, 1e6, 1e3, 1]
num_ens = self.num_ens
for i in range(len(dividers)):
# The number.
num = int(num_ens / dividers[i])
# The text.
if num:
text = repr(num)
elif not num and ens_text:
text = '000'
else:
continue
# Update the text.
ens_text = ens_text + text
# A comma.
if i < len(dividers)-1:
ens_text = ens_text + ','
# Remove the front part of the number.
num_ens = num_ens - dividers[i]*num
# Subtitle for all graphs.
subtitle = '%s ensembles of %s' % (ens_text, self.num_models)
# NOE violations.
if access(self.results_dir+sep+"NOE_viol_" + self.configs[0] + "_sorted", F_OK):
# Print out.
print("Generating NOE violation Grace plots.")
# Open the output files.
grace_curve = open(self.results_dir+sep+"NOE_viol_curve.agr", 'w')
grace_dist = open(self.results_dir+sep+"NOE_viol_dist.agr", 'w')
# Loop over the configurations.
data = []
dist = []
for i in range(n):
# Open the results file and read the data.
file = open(self.results_dir+sep+"NOE_viol_" + self.configs[i] + "_sorted")
lines = file.readlines()
file.close()
# Add a new graph set.
data.append([])
# Loop over the ensembles and extract the NOE violation.
noe_viols = []
for j in range(1, len(lines)):
# Extract the violation.
viol = float(lines[j].split()[1])
noe_viols.append(viol)
# Add to the data structure.
data[i].append([j, viol])
# Calculate the R distribution.
dist.append(self.generate_distribution(noe_viols, inc=self.bucket_num, upper=self.upper_lim_noe, lower=self.lower_lim_noe))
# Headers.
write_xy_header(file=grace_curve, title='NOE violation comparison', subtitle=subtitle, sets=[n], set_names=[self.configs], set_colours=[colours], symbols=[[0]*n], axis_labels=[['Ensemble (sorted)', 'NOE violation (Angstrom\\S2\\N)']], legend_pos=[[0.3, 0.8]])
write_xy_header(file=grace_dist, title='NOE violation comparison', subtitle=subtitle, sets=[n], set_names=[self.configs], set_colours=[colours], symbols=[[1]*n], symbol_sizes=[[0.5]*n], linestyle=[[3]*n], axis_labels=[['NOE violation (Angstrom\\S2\\N)', 'Frequency']], legend_pos=[[1.1, 0.8]])
# Write the data.
write_xy_data([data], file=grace_curve, graph_type='xy')
write_xy_data([dist], file=grace_dist, graph_type='xy')
# Close the files.
grace_curve.close()
grace_dist.close()
# RDC Q-factors.
if access(self.results_dir+sep+"Q_factors_" + self.configs[0] + "_sorted", F_OK):
# Print out.
print("Generating RDC Q-factor Grace plots.")
# Open the Grace output files.
grace_curve = open(self.results_dir+sep+"RDC_%s_curve.agr" % self.rdc_name, 'w')
grace_dist = open(self.results_dir+sep+"RDC_%s_dist.agr" % self.rdc_name, 'w')
# Loop over the configurations.
data = []
dist = []
for i in range(n):
# Open the results file and read the data.
file = open(self.results_dir+sep+"Q_factors_" + self.configs[i] + "_sorted")
lines = file.readlines()
file.close()
# Add a new graph set.
data.append([])
# Loop over the Q-factors.
values = []
for j in range(1, len(lines)):
# Extract the violation.
value = float(lines[j].split()[1])
values.append(value)
# Add to the data structure.
data[i].append([j, value])
# Calculate the R distribution.
dist.append(self.generate_distribution(values, inc=self.bucket_num, upper=self.upper_lim_rdc, lower=self.lower_lim_rdc))
# Headers.
write_xy_header(file=grace_curve, title='%s RDC Q-factor comparison' % self.rdc_name, subtitle=subtitle, sets=[n], set_names=[self.configs], set_colours=[colours], symbols=[[0]*n], axis_labels=[['Ensemble (sorted)', '%s RDC Q-factor (pales format)' % self.rdc_name]], legend_pos=[[0.3, 0.8]])
write_xy_header(file=grace_dist, title='%s RDC Q-factor comparison' % self.rdc_name, subtitle=subtitle, sets=[n], set_names=[self.configs], set_colours=[colours], symbols=[[1]*n], symbol_sizes=[[0.5]*n], linestyle=[[3]*n], axis_labels=[['%s RDC Q-factor (pales format)' % self.rdc_name, 'Frequency']], legend_pos=[[1.1, 0.8]])
# Write the data.
write_xy_data([data], file=grace_curve, graph_type='xy')
write_xy_data([dist], file=grace_dist, graph_type='xy')
# Close the files.
grace_curve.close()
grace_dist.close()
# NOE-RDC correlation plots.
if access(self.results_dir+sep+"NOE_viol_" + self.configs[0] + "_sorted", F_OK) and access(self.results_dir+sep+"Q_factors_" + self.configs[0] + "_sorted", F_OK):
# Print out.
print("Generating NOE-RDC correlation Grace plots.")
# Open the Grace output files.
grace_file = open(self.results_dir+sep+"correlation_plot.agr", 'w')
grace_file_scaled = open(self.results_dir+sep+"correlation_plot_scaled.agr", 'w')
# Grace data.
data = []
data_scaled = []
for i in range(len(self.configs)):
# Open the NOE results file and read the data.
file = open(self.results_dir+sep+"NOE_viol_" + self.configs[i])
noe_lines = file.readlines()
file.close()
# Add a new graph set.
data.append([])
data_scaled.append([])
# Open the RDC results file and read the data.
file = open(self.results_dir+sep+"Q_factors_" + self.configs[i])
rdc_lines = file.readlines()
file.close()
# Loop over the data.
for j in range(1, len(noe_lines)):
# Split the lines.
noe_viol = float(noe_lines[j].split()[1])
q_factor = float(rdc_lines[j].split()[1])
# Add the xy pair.
data[i].append([noe_viol, q_factor])
data_scaled[i].append([sqrt(noe_viol/self.noe_norm), q_factor])
# Write the data.
write_xy_header(file=grace_file, title='Correlation plot - %s RDC vs. NOE' % self.rdc_name, subtitle=subtitle, sets=[n], set_names=[self.configs], set_colours=[colours], symbols=[[9]*n], symbol_sizes=[[0.24]*n], linetype=[[0]*n], axis_labels=[['NOE violation (Angstrom\\S2\\N)', '%s RDC Q-factor (pales format)' % self.rdc_name]], legend_pos=[[1.1, 0.8]])
write_xy_header(file=grace_file_scaled, title='Correlation plot - %s RDC vs. NOE Q-factor' % self.rdc_name, subtitle=subtitle, sets=[n], set_names=[self.configs], set_colours=[colours], symbols=[[9]*n], symbol_sizes=[[0.24]*n], linetype=[[0]*n], axis_labels=[['Normalised NOE violation (Q = sqrt(U / \\xS\\f{}NOE\\si\\N\\S2\\N))', '%s RDC Q-factor (pales format)' % self.rdc_name]], legend_pos=[[1.1, 0.8]])
write_xy_data([data], file=grace_file, graph_type='xy')
write_xy_data([data_scaled], file=grace_file_scaled, graph_type='xy')
def noe_viol(self):
"""NOE violation calculations."""
# Redirect STDOUT to a log file.
if self.log:
sys.stdout = open(self.results_dir+sep+"logs" + sep + "NOE_viol.log", 'w')
# Create a directory for the save files.
dir = self.results_dir + sep + "NOE_results"
mkdir_nofail(dir=dir)
# Loop over the configurations.
for config in self.configs:
# Print out.
print("\n"*10 + "# Set up for config " + config + " #" + "\n")
# Open the results file.
out = open(self.results_dir+sep+"NOE_viol_" + config, 'w')
out_sorted = open(self.results_dir+sep+"NOE_viol_" + config + "_sorted", 'w')
out.write("%-20s%20s\n" % ("# Ensemble", "NOE_volation"))
out_sorted.write("%-20s%20s\n" % ("# Ensemble", "NOE_volation"))
# Create the data pipe.
self.interpreter.pipe.create("noe_viol_%s" % config, "N-state")
# Read the first structure.
self.interpreter.structure.read_pdb("ensembles" + sep + config + "0.pdb", dir=self.results_dir, set_mol_name=config, set_model_num=list(range(1, self.num_models+1)))
# Load all protons as the sequence.
self.interpreter.structure.load_spins("@H*", ave_pos=False)
# Create the pseudo-atoms.
for i in range(len(self.pseudo)):
self.interpreter.spin.create_pseudo(spin_name=self.pseudo[i][0], members=self.pseudo[i][1], averaging="linear")
self.interpreter.sequence.display()
# Read the NOE list.
self.interpreter.noe.read_restraints(file=self.noe_file)
# Set up the N-state model.
self.interpreter.n_state_model.select_model(model="fixed")
# Print out.
print("\n"*2 + "# Set up complete #" + "\n"*10)
# Loop over each ensemble.
noe_viol = []
for ens in range(self.num_ens):
# Print out the ensemble to both the log and screen.
if self.log:
sys.stdout.write(config + repr(ens) + "\n")
sys.stderr.write(config + repr(ens) + "\n")
# Delete the old structures and rename the molecule.
self.interpreter.structure.delete()
# Read the ensemble.
self.interpreter.structure.read_pdb("ensembles" + sep + config + repr(ens) + ".pdb", dir=self.results_dir, set_mol_name=config, set_model_num=list(range(1, self.num_models+1)))
# Get the atomic positions.
self.interpreter.structure.get_pos(ave_pos=False)
# Calculate the average NOE potential.
self.interpreter.calc()
# Sum the violations.
cdp.sum_viol = 0.0
for i in range(len(cdp.ave_dist)):
if cdp.quad_pot[i][2]:
cdp.sum_viol = cdp.sum_viol + cdp.quad_pot[i][2]
# Write out the NOE violation.
noe_viol.append([cdp.sum_viol, ens])
out.write("%-20i%30.15f\n" % (ens, cdp.sum_viol))
# Save the state.
self.interpreter.results.write(file="%s_results_%s" % (config, ens), dir=dir, force=True)
# Sort the NOE violations.
noe_viol.sort()
# Write the data.
for i in range(len(noe_viol)):
out_sorted.write("%-20i%20.15f\n" % (noe_viol[i][1], noe_viol[i][0]))
def rdc_analysis(self):
"""Perform the RDC part of the analysis."""
# Redirect STDOUT to a log file.
if self.log:
sys.stdout = open(self.results_dir+sep+"logs" + sep + "RDC_%s_analysis.log" % self.rdc_name, 'w')
# The dipolar constant.
d = 0.0
if self.bond_length != None:
d = 3.0 / (2.0*pi) * dipolar_constant(g13C, g1H, self.bond_length)
# Create a directory for the save files.
dir = self.results_dir + sep + "RDC_%s_results" % self.rdc_name
mkdir_nofail(dir=dir)
# Loop over the configurations.
for config in self.configs:
# Print out.
print("\n"*10 + "# Set up for config " + config + " #" + "\n")
# Open the results files.
out = open(self.results_dir+sep+"Q_factors_" + config, 'w')
out_sorted = open(self.results_dir+sep+"Q_factors_" + config + "_sorted", 'w')
out.write("%-20s%20s%20s\n" % ("# Ensemble", "RDC_Q_factor(pales)", "RDC_Q_factor(standard)"))
out_sorted.write("%-20s%20s\n" % ("# Ensemble", "RDC_Q_factor(pales)"))
# Create the data pipe.
self.interpreter.pipe.create("rdc_analysis_%s" % config, "N-state")
# Read the first structure.
self.interpreter.structure.read_pdb("ensembles_superimposed" + sep + config + "0.pdb", dir=self.results_dir, set_mol_name=config, set_model_num=list(range(1, self.num_models+1)))
# Load all spins as the sequence.
self.interpreter.structure.load_spins(ave_pos=False)
# Create the pseudo-atoms.
for i in range(len(self.pseudo)):
self.interpreter.spin.create_pseudo(spin_name=self.pseudo[i][0], members=self.pseudo[i][1], averaging="linear")
self.interpreter.sequence.display()
# Read the RDC data.
self.interpreter.rdc.read(align_id=self.rdc_file, file=self.rdc_file, spin_id1_col=self.rdc_spin_id1_col, spin_id2_col=self.rdc_spin_id2_col, data_col=self.rdc_data_col, error_col=self.rdc_error_col)
# Define the magnetic dipole-dipole relaxation interaction.
if self.bond_length != None:
self.interpreter.interatom.set_dist(spin_id1='@C*', spin_id2='@H*', ave_dist=self.bond_length)
self.interpreter.interatom.set_dist(spin_id1='@C*', spin_id2='@Q*', ave_dist=self.bond_length)
else:
self.interpreter.interatom.read_dist(file=self.bond_length_file, spin_id1_col=1, spin_id2_col=2, data_col=3)
# Set the nuclear isotope.
self.interpreter.spin.isotope(isotope='13C', spin_id='@C*')
self.interpreter.spin.isotope(isotope='1H', spin_id='@H*')
self.interpreter.spin.isotope(isotope='1H', spin_id='@Q*')
# Set up the model.
self.interpreter.n_state_model.select_model(model="fixed")
# Print out.
print("\n"*2 + "# Set up complete #" + "\n"*10)
# Loop over each ensemble.
q_factors = []
for ens in range(self.num_ens):
# Print out the ensemble to both the log and screen.
if self.log:
sys.stdout.write(config + repr(ens) + "\n")
sys.stderr.write(config + repr(ens) + "\n")
# Delete the old structures.
self.interpreter.structure.delete()
# Read the ensemble.
self.interpreter.structure.read_pdb("ensembles_superimposed" + sep + config + repr(ens) + ".pdb", dir=self.results_dir, set_mol_name=config, set_model_num=list(range(1, self.num_models+1)))
# Get the positional information, then load the CH vectors.
self.interpreter.structure.get_pos(ave_pos=False)
if self.bond_length != None:
self.interpreter.interatom.set_dist(spin_id1='@C*', spin_id2='@H*', ave_dist=self.bond_length)
else:
self.interpreter.interatom.read_dist(file=self.bond_length_file, spin_id1_col=1, spin_id2_col=2, data_col=3)
self.interpreter.interatom.unit_vectors(ave=False)
# Minimisation.
#grid_search(inc=4)
self.interpreter.minimise("simplex", constraints=False)
# Store and write out the Q-factors.
q_factors.append([cdp.q_rdc, ens])
out.write("%-20i%20.15f%20.15f\n" % (ens, cdp.q_rdc, cdp.q_rdc_norm2))
# Calculate the alignment tensor in Hz, and store it for reference.
cdp.align_tensor_Hz = d * cdp.align_tensors[0].A
cdp.align_tensor_Hz_5D = d * cdp.align_tensors[0].A_5D
# Save the state.
self.interpreter.results.write(file="%s_results_%s" % (config, ens), dir=dir, force=True)
# Sort the NOE violations.
q_factors.sort()
# Write the data.
for i in range(len(q_factors)):
out_sorted.write("%-20i%20.15f\n" % (q_factors[i][1], q_factors[i][0]))
def sample(self):
"""Generate the ensembles by random sampling of the snapshots."""
# Create the directory for the ensembles, if needed.
mkdir_nofail(dir=self.results_dir + sep + "ensembles")
# Loop over the configurations.
for conf_index in range(len(self.configs)):
# Loop over each ensemble.
for ens in range(self.num_ens):
# Random sampling.
rand = []
for j in range(self.num_models):
rand.append(randint(self.snapshot_min[conf_index], self.snapshot_max[conf_index]))
# Print out.
print("Generating ensemble %s%s from structures %s." % (self.configs[conf_index], ens, rand))
# The file name.
file_name = "ensembles" + sep + self.configs[conf_index] + repr(ens) + ".pdb"
# Open the output file.
out = open(self.results_dir+sep+file_name, 'w')
# Header.
out.write("REM Structures: " + repr(rand) + "\n")
# Concatenation the files.
for j in range(self.num_models):
# The random file.
rand_name = self.snapshot_dir[conf_index] + sep + self.configs[conf_index] + repr(rand[j]) + ".pdb"
# Append the file.
out.write(open(rand_name).read())
# Close the file.
out.close()
def superimpose(self):
"""Superimpose the ensembles using fit to first in Molmol."""
# Create the output directory.
mkdir_nofail("ensembles_superimposed")
# Logging turned on.
if self.log:
log = open(self.results_dir+sep+"logs" + sep + "superimpose_molmol.stderr", 'w')
sys.stdout = open(self.results_dir+sep+"logs" + sep + "superimpose.log", 'w')
# Loop over S and R.
for config in ["R", "S"]:
# Loop over each ensemble.
for ens in range(self.num_ens):
# The file names.
file_in = "ensembles" + sep + config + repr(ens) + ".pdb"
file_out = "ensembles_superimposed" + sep + config + repr(ens) + ".pdb"
# Print out.
sys.stderr.write("Superimposing %s with Molmol, output to %s.\n" % (file_in, file_out))
if self.log:
log.write("\n\n\nSuperimposing %s with Molmol, output to %s.\n" % (file_in, file_out))
# Failure handling (if a failure occurred and this is rerun, skip all existing files).
if access(self.results_dir+sep+file_out, F_OK):
continue
# Open the Molmol pipe.
pipe = Popen("molmol -t -f -", shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, close_fds=False)
# Init all.
pipe.stdin.write("InitAll yes\n")
# Read the PDB.
pipe.stdin.write("ReadPdb " + self.results_dir+sep+file_in + "\n")
# Fitting to mean.
pipe.stdin.write("Fit to_first 'selected'\n")
pipe.stdin.write("Fit to_mean 'selected'\n")
# Write the result.
pipe.stdin.write("WritePdb " + self.results_dir+sep+file_out + "\n")
# End Molmol.
pipe.stdin.close()
# Get STDOUT and STDERR.
sys.stdout.write(pipe.stdout.read())
if self.log:
log.write(pipe.stderr.read())
# Close the pipe.
pipe.stdout.close()
pipe.stderr.close()
# Open the superimposed file in relax.
self.interpreter.reset()
self.interpreter.pipe.create('out', 'N-state')
self.interpreter.structure.read_pdb(file_out)
# Fix the retarded MOLMOL proton naming.
for model in cdp.structure.structural_data:
# Alias.
mol = model.mol[0]
# Loop over all atoms.
for i in range(len(mol.atom_name)):
# A proton.
if search('H', mol.atom_name[i]):
mol.atom_name[i] = mol.atom_name[i][1:] + mol.atom_name[i][0]
# Replace the superimposed file.
self.interpreter.structure.write_pdb(config + repr(ens) + ".pdb", dir=self.results_dir+sep+"ensembles_superimposed", force=True)
class Stereochem_analysis:
"""Class for performing the relative stereochemistry analysis."""
def __init__(self,
stage=1,
results_dir=None,
num_ens=10000,
num_models=10,
configs=None,
snapshot_dir='snapshots',
snapshot_min=None,
snapshot_max=None,
pseudo=None,
noe_file=None,
noe_norm=None,
rdc_name=None,
rdc_file=None,
rdc_spin_id1_col=None,
rdc_spin_id2_col=None,
rdc_data_col=None,
rdc_error_col=None,
bond_length=None,
bond_length_file=None,
log=None,
bucket_num=200,
lower_lim_noe=0.0,
upper_lim_noe=600.0,
lower_lim_rdc=0.0,
upper_lim_rdc=1.0):
"""Set up for the stereochemistry analysis.
@keyword stage: Stage of analysis (see the module docstring above for the options).
@type stage: int
@keyword results_dir: The optional directory to place all results files into.
@type results_dir: None or str
@keyword num_ens: Number of ensembles.
@type num_ens: int
@keyword num_models: Ensemble size.
@type num_models: int
@keyword configs: All the configurations.
@type configs: list of str
@keyword snapshot_dir: Snapshot directories (corresponding to the configurations).
@type snapshot_dir: list of str
@keyword snapshot_min: The number of the first snapshots (corresponding to the configurations).
@type snapshot_min: list of int
@keyword snapshot_max: The number of the last snapshots (corresponding to the configurations).
@type snapshot_max: list of int
@keyword pseudo: The list of pseudo-atoms. Each element is a list of the pseudo-atom name and a list of all those atoms forming the pseudo-atom. For example, pseudo = [["Q7", ["@H16", "@H17", "@H18"]], ["Q9", ["@H20", "@H21", "@H22"]]].
@type pseudo: list of list of str and list of str
@keyword noe_file: The name of the NOE restraint file.
@type noe_file: str
@keyword noe_norm: The NOE normalisation factor (equal to the sum of all NOEs squared).
@type noe_norm: float
@keyword rdc_name: The label for this RDC data set.
@type rdc_name: str
@keyword rdc_file: The name of the RDC file.
@type rdc_file: str
@keyword rdc_spin_id1_col: The spin ID column of the first spin in the RDC file.
@type rdc_spin_id1_col: None or int
@keyword rdc_spin_id2_col: The spin ID column of the second spin in the RDC file.
@type rdc_spin_id2_col: None or int
@keyword rdc_data_col: The data column of the RDC file.
@type rdc_data_col: int
@keyword rdc_error_col: The error column of the RDC file.
@type rdc_error_col: int
@keyword bond_length: The bond length value in meters. This overrides the bond_length_file argument.
@type bond_length: float or None
@keyword bond_length_file: The file of bond lengths for each atom pair in meters. The first and second columns must be the spin ID strings and the third column must contain the data.
@type bond_length_file: float or None
@keyword log: Log file output flag (only for certain stages).
@type log: bool
@keyword bucket_num: Number of buckets for the distribution plots.
@type bucket_num: int
@keyword lower_lim_noe: Distribution plot limits.
@type lower_lim_noe: int
@keyword upper_lim_noe: Distribution plot limits.
@type upper_lim_noe: int
@keyword lower_lim_rdc: Distribution plot limits.
@type lower_lim_rdc: int
@keyword upper_lim_rdc: Distribution plot limits.
@type upper_lim_rdc: int
"""
# Initial printout.
title(file=sys.stdout, text="Stereochemistry auto-analysis")
# Safely execute the full protocol.
try:
# Execution lock.
status.exec_lock.acquire('auto stereochem analysis',
mode='auto-analysis')
# Set up the analysis status object.
status.init_auto_analysis('stereochem', type='stereochem')
status.current_analysis = 'auto stereochem analysis'
# Store all the args.
self.stage = stage
self.results_dir = results_dir
self.num_ens = num_ens
self.num_models = num_models
self.configs = configs
self.snapshot_dir = snapshot_dir
self.snapshot_min = snapshot_min
self.snapshot_max = snapshot_max
self.pseudo = pseudo
self.noe_file = noe_file
self.noe_norm = noe_norm
self.rdc_name = rdc_name
self.rdc_file = rdc_file
self.rdc_spin_id1_col = rdc_spin_id1_col
self.rdc_spin_id2_col = rdc_spin_id2_col
self.rdc_data_col = rdc_data_col
self.rdc_error_col = rdc_error_col
self.bond_length = bond_length
self.bond_length_file = bond_length_file
self.log = log
self.bucket_num = bucket_num
self.lower_lim_noe = lower_lim_noe
self.upper_lim_noe = upper_lim_noe
self.lower_lim_rdc = lower_lim_rdc
self.upper_lim_rdc = upper_lim_rdc
# Load the interpreter.
self.interpreter = Interpreter(show_script=False,
raise_relax_error=True)
self.interpreter.populate_self()
self.interpreter.on(verbose=False)
# Create the results directory.
if self.results_dir:
mkdir_nofail(self.results_dir)
# Or use the current working directory.
else:
self.results_dir = getcwd()
# Create a directory for log files.
if self.log:
mkdir_nofail(self.results_dir + sep + "logs")
# Clean up.
finally:
# Final printout.
title(file=sys.stdout,
text="Completion of the stereochemistry auto-analysis")
print_elapsed_time(time() - status.start_time)
# Finish and unlock execution.
status.auto_analysis['stereochem'].fin = True
status.current_analysis = None
status.exec_lock.release()
def run(self):
"""Execute the given stage of the analysis."""
# Store the original STDOUT.
self.stdout_orig = sys.stdout
# Sampling of snapshots.
if self.stage == 1:
self.sample()
# NOE violation analysis.
elif self.stage == 2:
self.noe_viol()
# Ensemble superimposition.
elif self.stage == 3:
self.superimpose()
# RDC Q factor analysis.
elif self.stage == 4:
self.rdc_analysis()
# Grace plot creation.
elif self.stage == 5:
self.grace_plots()
# Final combined Q ordering.
elif self.stage == 6:
self.combined_q()
# Unknown stage.
else:
raise RelaxError("The stage number %s is unknown." % self.stage)
# Restore STDOUT.
sys.stdout = self.stdout_orig
def combined_q(self):
"""Calculate the combined Q factor.
The combined Q is defined as::
Q_total^2 = Q_NOE^2 + Q_RDC^2,
and the NOE Q factor as::
Q^2 = U / sum(NOE_i^2),
where U is the quadratic flat bottom well potential - the NOE violation in Angstrom^2.
"""
# Checks.
if not access(
self.results_dir + sep + "NOE_viol_" + self.configs[0] +
"_sorted", F_OK):
raise RelaxError(
"The NOE analysis has not been performed, cannot find the file '%s'."
% self.results_dir + sep + "NOE_viol_" + self.configs[0] +
"_sorted")
if not access(
self.results_dir + sep + "Q_factors_" + self.configs[0] +
"_sorted", F_OK):
raise RelaxError(
"The RDC analysis has not been performed, cannot find the file '%s'."
% self.results_dir + sep + "Q_factors_" + self.configs[0] +
"_sorted")
# Loop over the configurations.
for i in range(len(self.configs)):
# Print out.
print(
"Creating the combined Q factor file for configuration '%s'." %
self.configs[i])
# Open the NOE results file and read the data.
file = open(self.results_dir + sep + "NOE_viol_" + self.configs[i])
noe_lines = file.readlines()
file.close()
# Open the RDC results file and read the data.
file = open(self.results_dir + sep + "Q_factors_" +
self.configs[i])
rdc_lines = file.readlines()
file.close()
# The combined Q factor file.
out = open(self.results_dir + sep + "Q_total_%s" % self.configs[i],
'w')
out_sorted = open(
self.results_dir + sep + "Q_total_%s_sorted" % self.configs[i],
'w')
# Loop over the data (skipping the header line).
data = []
for j in range(1, len(noe_lines)):
# Split the lines.
ens = int(noe_lines[j].split()[0])
noe_viol = float(noe_lines[j].split()[1])
q_rdc = float(rdc_lines[j].split()[1])
# The NOE Q factor.
q_noe = sqrt(noe_viol / self.noe_norm)
# Combined Q.
q = sqrt(q_noe**2 + q_rdc**2)
# Write out the unsorted list.
out.write("%-20i%20.15f\n" % (ens, q))
# Store the values.
data.append([q, ens])
# Sort the combined Q.
data.sort()
# Write the data.
for i in range(len(data)):
out_sorted.write("%-20i%20.15f\n" % (data[i][1], data[i][0]))
# Close the files.
out.close()
out_sorted.close()
def generate_distribution(self, values, lower=0.0, upper=200.0, inc=None):
"""Create the distribution data structure."""
# The bin width.
bin_width = (upper - lower) / float(inc)
# Init the dist object.
dist = []
for i in range(inc):
dist.append([bin_width * i + lower, 0])
# Loop over the values.
for val in values:
# The bin.
bin = int((val - lower) / bin_width)
# Outside of the limits.
if bin < 0 or bin >= inc:
print("Outside of the limits: '%s'" % val)
continue
# Increment the count.
dist[bin][1] = dist[bin][1] + 1
# Convert the counts to frequencies.
total_pr = 0.0
for i in range(inc):
dist[i][1] = dist[i][1] / float(len(values))
total_pr = total_pr + dist[i][1]
print("Total Pr: %s" % total_pr)
# Return the dist.
return dist
def grace_plots(self):
"""Generate grace plots of the results."""
# The number of configs.
n = len(self.configs)
# The colours for the different configs.
defaults = [4, 2] # Blue and red.
colours = []
for i in range(n):
# Default colours.
if i < len(defaults):
colours.append(defaults[i])
# Otherwise black!
else:
colours.append(0)
# The ensemble number text.
ens_text = ''
dividers = [1e15, 1e12, 1e9, 1e6, 1e3, 1]
num_ens = self.num_ens
for i in range(len(dividers)):
# The number.
num = int(num_ens / dividers[i])
# The text.
if num:
text = repr(num)
elif not num and ens_text:
text = '000'
else:
continue
# Update the text.
ens_text = ens_text + text
# A comma.
if i < len(dividers) - 1:
ens_text = ens_text + ','
# Remove the front part of the number.
num_ens = num_ens - dividers[i] * num
# Subtitle for all graphs.
subtitle = '%s ensembles of %s' % (ens_text, self.num_models)
# NOE violations.
if access(
self.results_dir + sep + "NOE_viol_" + self.configs[0] +
"_sorted", F_OK):
# Print out.
print("Generating NOE violation Grace plots.")
# Open the output files.
grace_curve = open(self.results_dir + sep + "NOE_viol_curve.agr",
'w')
grace_dist = open(self.results_dir + sep + "NOE_viol_dist.agr",
'w')
# Loop over the configurations.
data = []
dist = []
for i in range(n):
# Open the results file and read the data.
file = open(self.results_dir + sep + "NOE_viol_" +
self.configs[i] + "_sorted")
lines = file.readlines()
file.close()
# Add a new graph set.
data.append([])
# Loop over the ensembles and extract the NOE violation.
noe_viols = []
for j in range(1, len(lines)):
# Extract the violation.
viol = float(lines[j].split()[1])
noe_viols.append(viol)
# Add to the data structure.
data[i].append([j, viol])
# Calculate the R distribution.
dist.append(
self.generate_distribution(noe_viols,
inc=self.bucket_num,
upper=self.upper_lim_noe,
lower=self.lower_lim_noe))
# Headers.
write_xy_header(format='grace',
file=grace_curve,
title='NOE violation comparison',
subtitle=subtitle,
sets=[n],
set_names=[self.configs],
set_colours=[colours],
symbols=[[0] * n],
axis_labels=[[
'Ensemble (sorted)',
'NOE violation (Angstrom\\S2\\N)'
]],
legend_pos=[[0.3, 0.8]])
write_xy_header(
format='grace',
file=grace_dist,
title='NOE violation comparison',
subtitle=subtitle,
sets=[n],
set_names=[self.configs],
set_colours=[colours],
symbols=[[1] * n],
symbol_sizes=[[0.5] * n],
linestyle=[[3] * n],
axis_labels=[['NOE violation (Angstrom\\S2\\N)', 'Frequency']],
legend_pos=[[1.1, 0.8]])
# Write the data.
write_xy_data(format='grace',
data=[data],
file=grace_curve,
graph_type='xy')
write_xy_data(format='grace',
data=[dist],
file=grace_dist,
graph_type='xy')
# Close the files.
grace_curve.close()
grace_dist.close()
# RDC Q factors.
if access(
self.results_dir + sep + "Q_factors_" + self.configs[0] +
"_sorted", F_OK):
# Print out.
print("Generating RDC Q factor Grace plots.")
# Open the Grace output files.
grace_curve = open(
self.results_dir + sep + "RDC_%s_curve.agr" % self.rdc_name,
'w')
grace_dist = open(
self.results_dir + sep + "RDC_%s_dist.agr" % self.rdc_name,
'w')
# Loop over the configurations.
data = []
dist = []
for i in range(n):
# Open the results file and read the data.
file = open(self.results_dir + sep + "Q_factors_" +
self.configs[i] + "_sorted")
lines = file.readlines()
file.close()
# Add a new graph set.
data.append([])
# Loop over the Q factors.
values = []
for j in range(1, len(lines)):
# Extract the violation.
value = float(lines[j].split()[1])
values.append(value)
# Add to the data structure.
data[i].append([j, value])
# Calculate the R distribution.
dist.append(
self.generate_distribution(values,
inc=self.bucket_num,
upper=self.upper_lim_rdc,
lower=self.lower_lim_rdc))
# Headers.
write_xy_header(format='grace',
file=grace_curve,
title='%s RDC Q factor comparison' % self.rdc_name,
subtitle=subtitle,
sets=[n],
set_names=[self.configs],
set_colours=[colours],
symbols=[[0] * n],
axis_labels=[[
'Ensemble (sorted)',
'%s RDC Q factor (pales format)' %
self.rdc_name
]],
legend_pos=[[0.3, 0.8]])
write_xy_header(format='grace',
file=grace_dist,
title='%s RDC Q factor comparison' % self.rdc_name,
subtitle=subtitle,
sets=[n],
set_names=[self.configs],
set_colours=[colours],
symbols=[[1] * n],
symbol_sizes=[[0.5] * n],
linestyle=[[3] * n],
axis_labels=[[
'%s RDC Q factor (pales format)' %
self.rdc_name, 'Frequency'
]],
legend_pos=[[1.1, 0.8]])
# Write the data.
write_xy_data(format='grace',
data=[data],
file=grace_curve,
graph_type='xy')
write_xy_data(format='grace',
data=[dist],
file=grace_dist,
graph_type='xy')
# Close the files.
grace_curve.close()
grace_dist.close()
# NOE-RDC correlation plots.
if access(
self.results_dir + sep + "NOE_viol_" + self.configs[0] +
"_sorted", F_OK) and access(
self.results_dir + sep + "Q_factors_" + self.configs[0] +
"_sorted", F_OK):
# Print out.
print("Generating NOE-RDC correlation Grace plots.")
# Open the Grace output files.
grace_file = open(self.results_dir + sep + "correlation_plot.agr",
'w')
grace_file_scaled = open(
self.results_dir + sep + "correlation_plot_scaled.agr", 'w')
# Grace data.
data = []
data_scaled = []
for i in range(len(self.configs)):
# Open the NOE results file and read the data.
file = open(self.results_dir + sep + "NOE_viol_" +
self.configs[i])
noe_lines = file.readlines()
file.close()
# Add a new graph set.
data.append([])
data_scaled.append([])
# Open the RDC results file and read the data.
file = open(self.results_dir + sep + "Q_factors_" +
self.configs[i])
rdc_lines = file.readlines()
file.close()
# Loop over the data.
for j in range(1, len(noe_lines)):
# Split the lines.
noe_viol = float(noe_lines[j].split()[1])
q_factor = float(rdc_lines[j].split()[1])
# Add the xy pair.
data[i].append([noe_viol, q_factor])
data_scaled[i].append(
[sqrt(noe_viol / self.noe_norm), q_factor])
# Write the data.
write_xy_header(
format='grace',
file=grace_file,
title='Correlation plot - %s RDC vs. NOE' % self.rdc_name,
subtitle=subtitle,
sets=[n],
set_names=[self.configs],
set_colours=[colours],
symbols=[[9] * n],
symbol_sizes=[[0.24] * n],
linetype=[[0] * n],
axis_labels=[[
'NOE violation (Angstrom\\S2\\N)',
'%s RDC Q factor (pales format)' % self.rdc_name
]],
legend_pos=[[1.1, 0.8]])
write_xy_header(
format='grace',
file=grace_file_scaled,
title='Correlation plot - %s RDC vs. NOE Q factor' %
self.rdc_name,
subtitle=subtitle,
sets=[n],
set_names=[self.configs],
set_colours=[colours],
symbols=[[9] * n],
symbol_sizes=[[0.24] * n],
linetype=[[0] * n],
axis_labels=[[
'Normalised NOE violation (Q = sqrt(U / \\xS\\f{}NOE\\si\\N\\S2\\N))',
'%s RDC Q factor (pales format)' % self.rdc_name
]],
legend_pos=[[1.1, 0.8]])
write_xy_data(format='grace',
data=[data],
file=grace_file,
graph_type='xy')
write_xy_data(format='grace',
data=[data_scaled],
file=grace_file_scaled,
graph_type='xy')
def noe_viol(self):
"""NOE violation calculations."""
# Redirect STDOUT to a log file.
if self.log:
sys.stdout = open(
self.results_dir + sep + "logs" + sep + "NOE_viol.log", 'w')
# Create a directory for the save files.
dir = self.results_dir + sep + "NOE_results"
mkdir_nofail(dir=dir)
# Loop over the configurations.
for config in self.configs:
# Print out.
print("\n" * 10 + "# Set up for config " + config + " #" + "\n")
# Open the results file.
out = open(self.results_dir + sep + "NOE_viol_" + config, 'w')
out_sorted = open(
self.results_dir + sep + "NOE_viol_" + config + "_sorted", 'w')
out.write("%-20s%20s\n" % ("# Ensemble", "NOE_volation"))
out_sorted.write("%-20s%20s\n" % ("# Ensemble", "NOE_volation"))
# Create the data pipe.
self.interpreter.pipe.create("noe_viol_%s" % config, "N-state")
# Read the first structure.
self.interpreter.structure.read_pdb(
"ensembles" + sep + config + "0.pdb",
dir=self.results_dir,
set_mol_name=config,
set_model_num=list(range(1, self.num_models + 1)))
# Load all protons as the sequence.
self.interpreter.structure.load_spins("@H*", ave_pos=False)
# Create the pseudo-atoms.
for i in range(len(self.pseudo)):
self.interpreter.spin.create_pseudo(
spin_name=self.pseudo[i][0],
members=self.pseudo[i][1],
averaging="linear")
self.interpreter.sequence.display()
# Read the NOE list.
self.interpreter.noe.read_restraints(file=self.noe_file)
# Set up the N-state model.
self.interpreter.n_state_model.select_model(model="fixed")
# Print out.
print("\n" * 2 + "# Set up complete #" + "\n" * 10)
# Loop over each ensemble.
noe_viol = []
for ens in range(self.num_ens):
# Print out the ensemble to both the log and screen.
if self.log:
sys.stdout.write(config + repr(ens) + "\n")
sys.stderr.write(config + repr(ens) + "\n")
# Delete the old structures and rename the molecule.
self.interpreter.structure.delete()
# Read the ensemble.
self.interpreter.structure.read_pdb(
"ensembles" + sep + config + repr(ens) + ".pdb",
dir=self.results_dir,
set_mol_name=config,
set_model_num=list(range(1, self.num_models + 1)))
# Get the atomic positions.
self.interpreter.structure.get_pos(ave_pos=False)
# Calculate the average NOE potential.
self.interpreter.minimise.calculate()
# Sum the violations.
cdp.sum_viol = 0.0
for i in range(len(cdp.ave_dist)):
if cdp.quad_pot[i][2]:
cdp.sum_viol = cdp.sum_viol + cdp.quad_pot[i][2]
# Write out the NOE violation.
noe_viol.append([cdp.sum_viol, ens])
out.write("%-20i%30.15f\n" % (ens, cdp.sum_viol))
# Save the state.
self.interpreter.results.write(file="%s_results_%s" %
(config, ens),
dir=dir,
force=True)
# Sort the NOE violations.
noe_viol.sort()
# Write the data.
for i in range(len(noe_viol)):
out_sorted.write("%-20i%20.15f\n" %
(noe_viol[i][1], noe_viol[i][0]))
def rdc_analysis(self):
"""Perform the RDC part of the analysis."""
# Redirect STDOUT to a log file.
if self.log:
sys.stdout = open(
self.results_dir + sep + "logs" + sep +
"RDC_%s_analysis.log" % self.rdc_name, 'w')
# The dipolar constant.
d = 0.0
if self.bond_length != None:
d = 3.0 / (2.0 * pi) * dipolar_constant(
periodic_table.gyromagnetic_ratio('13C'),
periodic_table.gyromagnetic_ratio('1H'), self.bond_length)
# Create a directory for the save files.
dir = self.results_dir + sep + "RDC_%s_results" % self.rdc_name
mkdir_nofail(dir=dir)
# Loop over the configurations.
for config in self.configs:
# Print out.
print("\n" * 10 + "# Set up for config " + config + " #" + "\n")
# Open the results files.
out = open(self.results_dir + sep + "Q_factors_" + config, 'w')
out_sorted = open(
self.results_dir + sep + "Q_factors_" + config + "_sorted",
'w')
out.write("%-20s%20s%20s\n" % ("# Ensemble", "RDC_Q_factor(pales)",
"RDC_Q_factor(standard)"))
out_sorted.write("%-20s%20s\n" %
("# Ensemble", "RDC_Q_factor(pales)"))
# Create the data pipe.
self.interpreter.pipe.create("rdc_analysis_%s" % config, "N-state")
# Read the first structure.
self.interpreter.structure.read_pdb(
"ensembles_superimposed" + sep + config + "0.pdb",
dir=self.results_dir,
set_mol_name=config,
set_model_num=list(range(1, self.num_models + 1)))
# Load all spins as the sequence.
self.interpreter.structure.load_spins(ave_pos=False)
# Create the pseudo-atoms.
for i in range(len(self.pseudo)):
self.interpreter.spin.create_pseudo(
spin_name=self.pseudo[i][0],
members=self.pseudo[i][1],
averaging="linear")
self.interpreter.sequence.display()
# Read the RDC data.
self.interpreter.rdc.read(align_id=self.rdc_file,
file=self.rdc_file,
spin_id1_col=self.rdc_spin_id1_col,
spin_id2_col=self.rdc_spin_id2_col,
data_col=self.rdc_data_col,
error_col=self.rdc_error_col)
# Define the magnetic dipole-dipole relaxation interaction.
if self.bond_length != None:
self.interpreter.interatom.set_dist(spin_id1='@C*',
spin_id2='@H*',
ave_dist=self.bond_length)
self.interpreter.interatom.set_dist(spin_id1='@C*',
spin_id2='@Q*',
ave_dist=self.bond_length)
else:
self.interpreter.interatom.read_dist(
file=self.bond_length_file,
spin_id1_col=1,
spin_id2_col=2,
data_col=3)
# Set the nuclear isotope.
self.interpreter.spin.isotope(isotope='13C', spin_id='@C*')
self.interpreter.spin.isotope(isotope='1H', spin_id='@H*')
self.interpreter.spin.isotope(isotope='1H', spin_id='@Q*')
# Set up the model.
self.interpreter.n_state_model.select_model(model="fixed")
# Print out.
print("\n" * 2 + "# Set up complete #" + "\n" * 10)
# Loop over each ensemble.
q_factors = []
for ens in range(self.num_ens):
# Print out the ensemble to both the log and screen.
if self.log:
sys.stdout.write(config + repr(ens) + "\n")
sys.stderr.write(config + repr(ens) + "\n")
# Delete the old structures.
self.interpreter.structure.delete()
# Read the ensemble.
self.interpreter.structure.read_pdb(
"ensembles_superimposed" + sep + config + repr(ens) +
".pdb",
dir=self.results_dir,
set_mol_name=config,
set_model_num=list(range(1, self.num_models + 1)))
# Get the positional information, then load the CH vectors.
self.interpreter.structure.get_pos(ave_pos=False)
if self.bond_length != None:
self.interpreter.interatom.set_dist(
spin_id1='@C*',
spin_id2='@H*',
ave_dist=self.bond_length)
else:
self.interpreter.interatom.read_dist(
file=self.bond_length_file,
spin_id1_col=1,
spin_id2_col=2,
data_col=3)
self.interpreter.interatom.unit_vectors(ave=False)
# Minimisation.
#minimise.grid_search(inc=4)
self.interpreter.minimise.execute("simplex", constraints=False)
# Store and write out the Q factors.
q_factors.append([cdp.q_rdc_norm_squared_sum, ens])
out.write("%-20i%20.15f%20.15f\n" %
(ens, cdp.q_rdc_norm_squared_sum,
cdp.q_rdc_norm_squared_sum))
# Calculate the alignment tensor in Hz, and store it for reference.
cdp.align_tensor_Hz = d * cdp.align_tensors[0].A
cdp.align_tensor_Hz_5D = d * cdp.align_tensors[0].A_5D
# Save the state.
self.interpreter.results.write(file="%s_results_%s" %
(config, ens),
dir=dir,
force=True)
# Sort the NOE violations.
q_factors.sort()
# Write the data.
for i in range(len(q_factors)):
out_sorted.write("%-20i%20.15f\n" %
(q_factors[i][1], q_factors[i][0]))
def sample(self):
"""Generate the ensembles by random sampling of the snapshots."""
# Create the directory for the ensembles, if needed.
mkdir_nofail(dir=self.results_dir + sep + "ensembles")
# Loop over the configurations.
for conf_index in range(len(self.configs)):
# Loop over each ensemble.
for ens in range(self.num_ens):
# Random sampling.
rand = []
for j in range(self.num_models):
rand.append(
randint(self.snapshot_min[conf_index],
self.snapshot_max[conf_index]))
# Print out.
print("Generating ensemble %s%s from structures %s." %
(self.configs[conf_index], ens, rand))
# The file name.
file_name = "ensembles" + sep + self.configs[
conf_index] + repr(ens) + ".pdb"
# Open the output file.
out = open(self.results_dir + sep + file_name, 'w')
# Header.
out.write("REM Structures: " + repr(rand) + "\n")
# Concatenation the files.
for j in range(self.num_models):
# The random file.
rand_name = self.snapshot_dir[
conf_index] + sep + self.configs[conf_index] + repr(
rand[j]) + ".pdb"
# Append the file.
out.write(open(rand_name).read())
# Close the file.
out.close()
def superimpose(self):
"""Superimpose the ensembles using fit to first in Molmol."""
# Create the output directory.
mkdir_nofail("ensembles_superimposed")
# Logging turned on.
if self.log:
log = open(
self.results_dir + sep + "logs" + sep +
"superimpose_molmol.stderr", 'w')
sys.stdout = open(
self.results_dir + sep + "logs" + sep + "superimpose.log", 'w')
# Loop over S and R.
for config in ["R", "S"]:
# Loop over each ensemble.
for ens in range(self.num_ens):
# The file names.
file_in = "ensembles" + sep + config + repr(ens) + ".pdb"
file_out = "ensembles_superimposed" + sep + config + repr(
ens) + ".pdb"
# Print out.
sys.stderr.write(
"Superimposing %s with Molmol, output to %s.\n" %
(file_in, file_out))
if self.log:
log.write(
"\n\n\nSuperimposing %s with Molmol, output to %s.\n" %
(file_in, file_out))
# Failure handling (if a failure occurred and this is rerun, skip all existing files).
if access(self.results_dir + sep + file_out, F_OK):
continue
# Open the Molmol pipe.
pipe = Popen("molmol -t -f -",
shell=True,
stdin=PIPE,
stdout=PIPE,
stderr=PIPE,
close_fds=False)
# Init all.
pipe.stdin.write("InitAll yes\n")
# Read the PDB.
pipe.stdin.write("ReadPdb " + self.results_dir + sep +
file_in + "\n")
# Fitting to mean.
pipe.stdin.write("Fit to_first 'selected'\n")
pipe.stdin.write("Fit to_mean 'selected'\n")
# Write the result.
pipe.stdin.write("WritePdb " + self.results_dir + sep +
file_out + "\n")
# End Molmol.
pipe.stdin.close()
# Get STDOUT and STDERR.
sys.stdout.write(pipe.stdout.read())
if self.log:
log.write(pipe.stderr.read())
# Close the pipe.
pipe.stdout.close()
pipe.stderr.close()
# Open the superimposed file in relax.
self.interpreter.reset()
self.interpreter.pipe.create('out', 'N-state')
self.interpreter.structure.read_pdb(file_out)
# Fix the retarded MOLMOL proton naming.
for model in cdp.structure.structural_data:
# Alias.
mol = model.mol[0]
# Loop over all atoms.
for i in range(len(mol.atom_name)):
# A proton.
if search('H', mol.atom_name[i]):
mol.atom_name[
i] = mol.atom_name[i][1:] + mol.atom_name[i][0]
# Replace the superimposed file.
self.interpreter.structure.write_pdb(
config + repr(ens) + ".pdb",
dir=self.results_dir + sep + "ensembles_superimposed",
force=True)
