def model_pipeline(project):
"""
Model pipeline for IMP project
"""
logging.info('model_pipeline title %s!' % project["title"])
#---------------------------
# Define Input Files
# The first section defines where input files are located.
# The topology file defines how the system components are structurally represented.
# target_gmm_file stores the EM map for the entire complex, which has already been converted into a Gaussian mixture model.
#---------------------------
datadirectory = project["data_directory"]
#"C:/dev/project/py_imp/py_imp/pmi_tut/rnapolii/data/"
# C:/Users/adminL/source/repos/py_imp/py_imp/pmi_tut/rnapolii/data/
logging.info('config data_directory %s!' % datadirectory)
print('config data_directory %s!' % datadirectory)
# Start by getting directory paths
#this_path = os.path.dirname(os.path.realpath(__file__)) + "/"
#cwd = os.getcwd()
this_path = project["data_directory"]
mkdir(this_path)
print('config this_path %s!' % this_path)
# these paths are relative to the topology file
pdb_dir = this_path + "data/xtal"
fasta_dir = this_path + "data/fasta"
gmm_dir = this_path + "data/em"
xl_dir = this_path + "data/xl"
topo_dir = this_path + "data/topo"
#pdb_dir = this_path + "../data/xtal"
#fasta_dir = this_path + "../data/fasta"
#gmm_dir = this_path + "../data/em"
#xl_dir = this_path + "../data/xl"
#topo_dir = this_path + "../data/topo"
logging.info('this_path %s!' % this_path)
print('this_path %s!' % this_path)
logging.info('pdb_dir %s!' % pdb_dir)
logging.info('fasta_dir %s!' % fasta_dir)
logging.info('gmm_dir %s!' % gmm_dir)
print('gmm_dir %s!' % gmm_dir)
logging.info('xl_dir %s!' % xl_dir)
logging.info('topo_dir %s!' % topo_dir)
#if not os.path.exists(pdb_dir):
# os.makedirs(pdb_dir)
#mkdir(pdb_dir)
#mkdir(fasta_dir)
#mkdir(gmm_dir)
#mkdir(xl_dir)
#mkdir(topo_dir)
topology_file = topo_dir + '/' + project["topology_file"]
target_gmm_file = gmm_dir + '/' + project["target_gmm_file"]
#logging.info('data_directory %s!' % datadirectory)
logging.info('model_pipeline topology_file %s!' % topology_file)
logging.info('target_gmm_file %s!' % target_gmm_file)
class MSStudioCrosslinks:
# Class that converts an MS Studio crosslink file
# into a csv file and corresponding IMP CrossLinkDataBase object
def __init__(self, infile):
self.infile = infile
self.xldbkc = self.get_xldbkc()
self.xldb = IMP.pmi.io.crosslink.CrossLinkDataBase(self.xldbkc)
self.xldb.create_set_from_file(self.infile, self.xldbkc)
def get_xldbkc(self):
# Creates the keyword converter database to translate MS Studio column names
# into IMP XL database keywords
xldbkc = IMP.pmi.io.crosslink.CrossLinkDataBaseKeywordsConverter(
IMP.pmi.io.crosslink.ResiduePairListParser("MSSTUDIO"))
xldbkc.set_site_pairs_key("Selected Sites")
xldbkc.set_protein1_key("Protein 1")
xldbkc.set_protein2_key("Protein 2")
xldbkc.set_unique_id_key("Peptide ID")
return xldbkc
def parse_infile(self):
# Returns a list of each crosslink's attributes as a dictionary.
import csv
return csv.DictReader(open(self.infile),
delimiter=',',
quotechar='"')
def get_database(self):
return self.xldb
# Topology file should be in the same directory as this script
#topology_file = this_path +"../topology/topology.txt"
logging.info('Initialize model')
# Initialize model
mdl = IMP.Model()
# Build the Model Representation Using a Topology File Using the topology file we define the overall topology: we introduce the molecules with their
# sequence and their known structure, and define the movers. Each line in the file is a user-defined molecular Domain,
# and each column contains the specifics needed to build the system. See the TopologyReader documentation for a full description of the topology file format.
#topology file example:
#|molecule_name |color |fasta_fn |fasta_id|pdb_fn |chain|residue_range|pdb_offset|bead_size|em_residues_per_gaussian|rigid_body|super_rigid_body|chain_of_super_rigid_bodies|
#|Rpb1 |blue |1WCM_new.fasta.txt|1WCM:A |1WCM_map_fitted.pdb|A |1,1140 |0 |20 |0 |1 | 1 | |
#|Rpb1 |blue |1WCM_new.fasta.txt|1WCM:A |1WCM_map_fitted.pdb|A |1141,1274 |0 |20 |0 |2 | 1 |
# https://integrativemodeling.org/2.10.1/doc/ref/classIMP_1_1pmi_1_1topology_1_1TopologyReader.html
#|molecule_name|color|fasta_fn|fasta_id|pdb_fn|chain|residue_range|pdb_offset|bead_size|em_residues_per_gaussian|rigid_body|super_rigid_body|chain_of_super_rigid_bodies|flags|
#|Rpb1 |blue |1WCM.fasta|1WCM:A|1WCM.pdb|A|1,1140 |0|10|0|1|1,3|1||
#|Rpb1 |blue |1WCM.fasta|1WCM:A|1WCM.pdb|A|1141,1274|0|10|0|2|1,3|1||
#|Rpb1 |blue |1WCM.fasta|1WCM:A|1WCM.pdb|A|1275,END |0|10|0|3|1,3|1||
# fasta.txt files are what is expected
# Read in the topology file. We must handle multiple topology files: meaning we need to handle either consolidate as one OR handle multiple sets of XL csv files
# Specify the directory where the PDB files, fasta files and GMM files are
logging.info(
'Specify the directory where the PDB files, fasta files and GMM files are'
)
toporeader = IMP.pmi.topology.TopologyReader(topology_file,
pdb_dir=pdb_dir,
fasta_dir=fasta_dir,
gmm_dir=gmm_dir)
# Use the BuildSystem macro to build states from the topology file
bldsys = IMP.pmi.macros.BuildSystem(mdl)
# Each state can be specified by a topology file.
logging.info('add_state(toporeader)')
bldsys.add_state(toporeader)
#Building the System Representation and Degrees of Freedom
#Here we can set the Degrees of Freedom parameters, which should be optimized according to MC acceptance ratios. There are three kind of movers: Rigid Body, Bead, and Super Rigid Body (super rigid bodies are sets of rigid bodies and beads that will move together in an additional Monte Carlo move).
#max_rb_trans and max_rb_rot are the maximum translation and rotation of the Rigid Body mover, max_srb_trans and max_srb_rot are the maximum translation and rotation of the Super Rigid Body mover and max_bead_trans is the maximum translation of the Bead Mover.
#The execution of the macro will return the root hierarchy (root_hier) and the degrees of freedom (dof) objects, both of which are used later on.
# Build the system representation and degrees of freedom
"""
root_hier, dof = bldsys.execute_macro(max_rb_trans=project.degree_of_freedom.max_rb_trans,
max_rb_rot=project.degree_of_freedom.max_rb_rot,
max_bead_trans=project.degree_of_freedom.max_bead_trans,
max_srb_trans=project.degree_of_freedom.max_srb_trans,
max_srb_rot=project.degree_of_freedom.max_srb_rot)
"""
logging.info('bldsys.execute_macro')
root_hier, dof = bldsys.execute_macro()
"""
fb = dof.create_flexible_beads(mol.get_non_atomic_residues(),
max_trans=bead_max_trans)
"""
#print(dof.get_rigid_bodies() )
#print(toporeader.get_rigid_bodies() )
outputobjects = []
# Stereochemistry restraints
ev = IMP.pmi.restraints.stereochemistry.ExcludedVolumeSphere(
included_objects=bldsys.get_molecules()[0].values(), resolution=20)
ev.add_to_model()
outputobjects.append(ev)
crs = []
for mol in bldsys.get_molecules()[0].values():
#dof.create_flexible_beads(mol.get_non_atomic_residues(),
# max_trans=bead_max_trans)
cr = IMP.pmi.restraints.stereochemistry.ConnectivityRestraint([mol])
cr.add_to_model()
crs.append(cr)
outputobjects.append(cr)
logging.info('IMP.pmi.tools.shuffle_configuration')
IMP.pmi.tools.shuffle_configuration(
root_hier,
max_translation=
100, # raise for larger systems if shuffling fails at niterations, want it ~1.5x size of system in angstrom
verbose=True,
cutoff=5.0,
niterations=100)
logging.info(ev.evaluate())
dof.optimize_flexible_beads(
500
) #if beads are not connecting at initial rmf, increase; number of steps to optimize connectivity
logging.info(ev.evaluate())
# Getting length of list
length = len(project["xl_groupB"])
i = 0
xlList = []
logging.info('Iterating using while loop')
# Iterating using while loop
while i < length:
logging.info(project["xl_groupB"][i])
#"refid","length","slope","resolution","label","weight","crosslink_distance"
logging.info(project["xl_groupB"][i]["refid"])
logging.info(project["xl_groupB"][i]["length"])
logging.info(project["xl_groupB"][i]["slope"])
logging.info(project["xl_groupB"][i]["resolution"])
logging.info(project["xl_groupB"][i]["sigma_kappa"])
logging.info(project["xl_groupB"][i]["sigma_theta"])
logging.info(project["xl_groupB"][i]["sigma_init"])
logging.info(project["xl_groupB"][i]["label"])
logging.info(project["xl_groupB"][i]["weight"])
logging.info(project["xl_groupB"][i]["crosslink_distance"])
# Set up crosslinking restraint
xlB = XLRestraint(
root_hier=root_hier,
CrossLinkDataBase=MSStudioCrosslinks(
xl_dir + "/" +
project["xl_groupB"][i]["refid"]).get_database(),
length=project["xl_groupB"][i]
["length"], #midpoint? Double check with Daniel and excel function thing
resolution=project["xl_groupB"][i]
["resolution"], #keep 1, lower limit
slope=project["xl_groupB"][i]
["slope"], # 0.01 for longer XL and 0.03 for shorter, range - check by making sure midpoint is less than 0.5 e.g 30 * 0.01
label=project["xl_groupB"][i]["label"],
sigma_form="Gamma",
sigma_kappa=project["xl_groupB"][i]["sigma_kappa"],
sigma_theta=project["xl_groupB"][i]["sigma_theta"],
sigma_init=xproject["xl_groupB"][i]["sigma_init"],
filelabel=project["xl_groupB"][i]["label"],
weight=project["xl_groupB"][i]
["weight"]) #ignore weight, calculated via IMP
logging.info(xlB)
# using slope of -1 to indicate exception ; this was previously 's', but that conflicts with the number field
if project["xl_groupB"][i]["slope"] != "-1":
xlB.set_psi_is_sampled(False)
xlB.set_sigma_is_sampled(True)
xlList.append(xlB)
xlB.add_to_model()
outputobjects.append(xlB)
#dof.get_nuisances_from_restraint(xlB)
dof.movers + xlB.get_movers()
i += 1
for i in range(len(xlList)):
logging.info(xlList[i])
# xl_files = [(xl_dir + 'PRC2_DSS_BS3_2020_vLoose.csv', 30, 0.01, 1.6, 12),
# (xl_dir + 'PRC2_DSS_BS3_2020_loose.csv', 30, 0.01, 1.45, 9),
# (xl_dir + 'PRC2_DSS_BS3_2020_mod.csv', 30, 0.01, 1.3, 6),
# (xl_dir + 'PRC2_DSS_BS3_2020_tight.csv', 30, 0.01, 1.15, 3),
# (xl_dir + 'PRC2_DSS_BS3_2020_vTight.csv', 30, "s", 1.0, 1)]
# xl_restraints = []
# for xlf in xl_files:
# xldb = MSStudioCrosslinks(xlf[0]).get_database()
# print xlf, xldb.unique_id_key
# label = xlf[0].split('/')[-1][0:-4]
# xlr = XLRestraint(root_hier=root_hier,
# CrossLinkDataBase=xldb,
# length=xlf[1], #midpoint? Double check with Daniel and excel function thing
# resolution=1, #keep 1, lower limit
# slope=0.01, # 0.01 for longer XL and 0.03 for shorter, range - check by making sure midpoint is less than 0.5 e.g 30 * 0.01
# label=label,
# sigma_form="Gamma",
# sigma_kappa=xlf[3],
# sigma_theta=2.0,
# sigma_init=xlf[4],
# filelabel=label+"_missing",
# weight=1.) #ignore weight, calculated via IMP
# if xlf[2]!="s":
# xlr.set_psi_is_sampled(False)
# xlr.set_sigma_is_sampled(True)
# xl_restraints.append(xlr)
# xlr.add_to_model()
# outputobjects.append(xlr)
# dof.movers + xlr.get_movers()
xl_rests = xlList + crs
logging.info('EM Restraint')
#EM Restraint
densities = IMP.atom.Selection(
root_hier,
representation_type=IMP.atom.DENSITIES).get_selected_particles()
'''
IMP.isd.gmm_tools.decorate_gmm_from_text(
"../data/em/Ciferri_PRC2.50.gmm.txt",
target_ps,
m,
radius_scale=3.0,
mass_scale=1.0)
'''
#coords=[IMP.core.XYZ(p) for p in target_ps]
#print coords
#TODO: add in the EM data file processing logic once we have the em data file
# https://github.com/salilab/imp/
# github\imp\modules\isd\pyext\src\create_gmm.py
# python.exe create_gmm.py ../data/em/Ciferri_CEM_PRC2.map.mrc 50 Ciferri_CEM_PRC2_map.gmm50.txt -m Ciferri_CEM_PRC2_map.gmm50.mrc
# Ciferri_CEM_PRC2_map.gmm50.txt
# "../data/em/Ciferri_CEM_PRC2_map.gmm50.txt",
# alias is gmm_file_ouput.txt
# TODO: skip this step if the gmm.txt is absent.
logging.info('EM filename check for %s!' % project["target_gmm_file"])
#print('EM filename check for %s!' % project["target_gmm_file"])
if os.path.isfile(target_gmm_file):
logging.info('EM file exists %s!' % target_gmm_file)
#print('EM file exists %s!' % target_gmm_file)
#print('EM file exists %s!' % project["target_gmm_file"])
gemt = IMP.pmi.restraints.em.GaussianEMRestraint(
densities,
#project["target_gmm_file"],
target_gmm_file,
scale_target_to_mass=True,
slope=0,
weight=200.0)
gemt.set_label("GaussianEMRestraint")
gemt.add_to_model()
outputobjects.append(gemt)
else:
logging.info('skip gemt addition: EM file does NOT exist %s!' %
target_gmm_file)
print('skip gemt addition: EM file does NOT exist %s!' %
target_gmm_file)
# Gaussian functions are widely used in statistics to describe the normal distributions, in signal processing to define Gaussian filters
# , in image processing where two-dimensional Gaussians are used for Gaussian blurs, and in mathematics to solve heat equations and diffusion equations
# and to define the Weierstrass transform.
# https://en.wikipedia.org/wiki/Gaussian_function
# Electron Microscopy Restraint
# The GaussianEMRestraint uses a density overlap function to compare model to data
# First the EM map is approximated with a Gaussian Mixture Model (done separately)
# Second, the components of the model are represented with Gaussians (forming the model GMM)
# Other options: scale_to_target_mass ensures the total mass of model and map are identical
# slope: nudge model closer to map when far away
# weight: experimental, needed becaues the EM restraint is quasi-Bayesian
#
#em_components = IMP.pmi.tools.get_densities(root_hier)
# substitute em_components with densities in the call given below
"""
gemt = IMP.pmi.restraints.em.GaussianEMRestraint(densities,
target_gmm_file,
scale_target_to_mass=True,
slope=0.000001,
weight=200.0)
#gemt.set_label("Ciferri_PRC2")
gemt.add_to_model()
outputobjects.append(gemt)
"""
#print("Monte-Carlo Sampling:")
logging.info("Monte-Carlo Sampling:")
#--------------------------
# Monte-Carlo Sampling
#--------------------------
#--------------------------
# Set MC Sampling Parameters
#--------------------------
#num_frames = 20000
#num_frames = 50
num_frames = project["sampling_frame"]
#if '--test' in sys.argv: num_frames=100
num_mc_steps = 10
logging.info('set states %s!' % project["states"])
logging.info('set sampling_frame %s!' % project["sampling_frame"])
logging.info('set num_frames %s!' % num_frames)
logging.info('set output_dir %s!' % project["output_dir"])
logging.info('set num_mc_steps %s!' % num_mc_steps)
#TODO: add config setup for these fixed values
logging.info('set monte_carlo_temperature=1.0')
logging.info('set simulated_annealing=True')
logging.info('set simulated_annealing_minimum_temperature=1.0')
logging.info('set simulated_annealing_maximum_temperature=2.5')
logging.info('set simulated_annealing_minimum_temperature_nframes=200')
logging.info('set simulated_annealing_maximum_temperature_nframes=20')
logging.info('set replica_exchange_minimum_temperature=1.0')
logging.info('set replica_exchange_maximum_temperature=2.5')
logging.info('set number_of_best_scoring_models=0')
logging.info('set monte_carlo_steps %s!' % num_mc_steps)
logging.info('set number_of_frames %s!' % num_frames)
logging.info('set global_output_directory %s!' % project["output_dir"])
# https://integrativemodeling.org/2.10.1/doc/ref/classIMP_1_1pmi_1_1macros_1_1ReplicaExchange0.html#a239c4009cc04c70236730479f9f79744
# This object defines all components to be sampled as well as the sampling protocol
mc1 = IMP.pmi.macros.ReplicaExchange0(
mdl,
root_hier=root_hier,
monte_carlo_sample_objects=dof.get_movers(),
output_objects=outputobjects,
crosslink_restraints=xl_rests, # allows XLs to be drawn in the RMF files
monte_carlo_temperature=1.0,
simulated_annealing=True,
simulated_annealing_minimum_temperature=1.0,
simulated_annealing_maximum_temperature=2.5,
simulated_annealing_minimum_temperature_nframes=200,
simulated_annealing_maximum_temperature_nframes=20,
replica_exchange_minimum_temperature=1.0,
replica_exchange_maximum_temperature=2.5,
number_of_best_scoring_models=0,
monte_carlo_steps=num_mc_steps, #keep at 10
number_of_frames=num_frames,
global_output_directory=project["output_dir"],
test_mode=False)
# start sampling
mc1.execute_macro()
#logging.info("GEMT", gemt.evaluate());
#logging.info("XL1", xl1.evaluate(), xl2.evaluate());
for i in range(len(xlList)):
logging.info(xlList[i].evaluate())
logging.info("EV", ev.evaluate())
logging.info("CR", cr.evaluate())
def model_pipeline(project):
"""
Model pipeline for IMP project
"""
logging.info('model_pipeline title %s!' % project["title"])
#---------------------------
# Define Input Files
# The first section defines where input files are located.
# The topology file defines how the system components are structurally represented.
# target_gmm_file stores the EM map for the entire complex, which has already been converted into a Gaussian mixture model.
#---------------------------
datadirectory = project["data_directory"]
logging.info('config data_directory %s!' % datadirectory)
print('config data_directory %s!' % datadirectory)
# Start by getting directory paths
this_path = project["data_directory"]
mkdir(this_path)
print('config this_path %s!' % this_path)
# these paths are relative to the topology file
pdb_dir = this_path + "data/xtal"
fasta_dir = this_path + "data/fasta"
gmm_dir = this_path + "data/em"
xl_dir = this_path + "data/xl"
topo_dir = this_path + "data/topo"
logging.info('this_path %s!' % this_path)
print('this_path %s!' % this_path)
logging.info('pdb_dir %s!' % pdb_dir)
logging.info('fasta_dir %s!' % fasta_dir)
logging.info('gmm_dir %s!' % gmm_dir)
print('gmm_dir %s!' % gmm_dir)
logging.info('xl_dir %s!' % xl_dir)
logging.info('topo_dir %s!' % topo_dir)
topology_file = topo_dir+'/'+project["topology_file"]
target_gmm_file = gmm_dir+'/'+project["target_gmm_file"]
logging.info('model_pipeline topology_file %s!' % topology_file)
logging.info('target_gmm_file %s!' % target_gmm_file)
class MSStudioCrosslinks:
# Class that converts an MS Studio crosslink file
# into a csv file and corresponding IMP CrossLinkDataBase object
def __init__(self, infile):
self.infile = infile
self.xldbkc = self.get_xldbkc()
self.xldb = IMP.pmi.io.crosslink.CrossLinkDataBase(self.xldbkc)
self.xldb.create_set_from_file(self.infile, self.xldbkc)
def get_xldbkc(self):
# Creates the keyword converter database to translate MS Studio column names
# into IMP XL database keywords
xldbkc = IMP.pmi.io.crosslink.CrossLinkDataBaseKeywordsConverter(IMP.pmi.io.crosslink.ResiduePairListParser("MSSTUDIO"))
xldbkc.set_site_pairs_key("Selected Sites")
xldbkc.set_protein1_key("Protein 1")
xldbkc.set_protein2_key("Protein 2")
xldbkc.set_unique_id_key("Peptide ID")
return xldbkc
def parse_infile(self):
# Returns a list of each crosslink's attributes as a dictionary.
import csv
return csv.DictReader(open(self.infile), delimiter=',', quotechar='"')
def get_database(self):
return self.xldb
# Topology file should be in the same directory as this script
logging.info('Initialize model')
# Initialize model
mdl = IMP.Model()
# Build the Model Representation Using a Topology File Using the topology file we define the overall topology: we introduce the molecules with their
# sequence and their known structure, and define the movers. Each line in the file is a user-defined molecular Domain,
# and each column contains the specifics needed to build the system. See the TopologyReader documentation for a full description of the topology file format.
# Read in the topology file. We must handle multiple topology files: meaning we need to handle either consolidate as one OR handle multiple sets of XL csv files
# Specify the directory where the PDB files, fasta files and GMM files are
logging.info('Specify the directory where the PDB files, fasta files and GMM files are')
toporeader = IMP.pmi.topology.TopologyReader(topology_file,
pdb_dir=pdb_dir,
fasta_dir=fasta_dir,
gmm_dir=gmm_dir)
# Use the BuildSystem macro to build states from the topology file
bldsys = IMP.pmi.macros.BuildSystem(mdl)
# Each state can be specified by a topology file.
logging.info('add_state(toporeader)')
bldsys.add_state(toporeader)
#Building the System Representation and Degrees of Freedom
#Here we can set the Degrees of Freedom parameters, which should be optimized according to MC acceptance ratios. There are three kind of movers: Rigid Body, Bead, and Super Rigid Body (super rigid bodies are sets of rigid bodies and beads that will move together in an additional Monte Carlo move).
#max_rb_trans and max_rb_rot are the maximum translation and rotation of the Rigid Body mover, max_srb_trans and max_srb_rot are the maximum translation and rotation of the Super Rigid Body mover and max_bead_trans is the maximum translation of the Bead Mover.
#The execution of the macro will return the root hierarchy (root_hier) and the degrees of freedom (dof) objects, both of which are used later on.
# Build the system representation and degrees of freedom
logging.info('bldsys.execute_macro')
root_hier, dof = bldsys.execute_macro()
outputobjects=[]
# Stereochemistry restraints
ev = IMP.pmi.restraints.stereochemistry.ExcludedVolumeSphere(included_objects=bldsys.get_molecules()[0].values(), resolution=20)
ev.add_to_model()
outputobjects.append(ev)
crs = []
for mol in bldsys.get_molecules()[0].values():
cr = IMP.pmi.restraints.stereochemistry.ConnectivityRestraint([mol])
cr.add_to_model()
crs.append(cr)
outputobjects.append(cr)
logging.info('IMP.pmi.tools.shuffle_configuration')
IMP.pmi.tools.shuffle_configuration(root_hier,
max_translation=100, # raise for larger systems if shuffling fails at niterations, want it ~1.5x size of system in angstrom
verbose=True,
cutoff=5.0,
niterations=100)
logging.info(ev.evaluate());
dof.optimize_flexible_beads(100) #if beads are not connecting at initial rmf, increase; number of steps to optimize connectivity
logging.info(ev.evaluate());
# Getting length of list
length = len(project["xl_groupA"])
i = 0
xlList=[]
logging.info('Iterating using while loop')
# Iterating using while loop
while i < length:
logging.info(project["xl_groupA"][i])
logging.info(project["xl_groupA"][i]["refid"])
logging.info(project["xl_groupA"][i]["length"])
logging.info(project["xl_groupA"][i]["slope"])
logging.info(project["xl_groupA"][i]["resolution"])
logging.info(project["xl_groupA"][i]["label"])
logging.info(project["xl_groupA"][i]["weight"])
logging.info(project["xl_groupA"][i]["crosslink_distance"])
# Set up crosslinking restraint
xlA = XLRestraint(root_hier=root_hier,
CrossLinkDataBase=MSStudioCrosslinks(xl_dir + "/" + project["xl_groupA"][i]["refid"]).get_database(),
length=project["xl_groupA"][i]["length"], #midpoint? Double check with Daniel and excel function thing
resolution=project["xl_groupA"][i]["resolution"], #keep 1, lower limit
slope=project["xl_groupA"][i]["slope"], # 0.01 for longer XL and 0.03 for shorter, range - check by making sure midpoint is less than 0.5 e.g 30 * 0.01
label=project["xl_groupA"][i]["label"],
filelabel=project["xl_groupA"][i]["label"],
weight=project["xl_groupA"][i]["weight"]) #ignore weight, calculated via IMP
logging.info(xlA)
xlList.append(xlA)
xlA.add_to_model()
outputobjects.append(xlA)
dof.get_nuisances_from_restraint(xlA)
i += 1
for i in range(len(xlList) ):
logging.info(xlList[i])
xl_rests = xlList + crs
logging.info('EM Restraint')
#EM Restraint
densities = IMP.atom.Selection(root_hier,representation_type=IMP.atom.DENSITIES).get_selected_particles()
# alias is gmm_file_ouput.txt
# TODO: skip this step if the gmm.txt is absent.
logging.info('EM filename check for %s!' % project["target_gmm_file"])
if os.path.isfile(target_gmm_file):
logging.info('EM file exists %s!' % target_gmm_file)
gemt = IMP.pmi.restraints.em.GaussianEMRestraint(densities,
#project["target_gmm_file"],
target_gmm_file,
scale_target_to_mass=True,
slope=0,
weight=200.0)
gemt.set_label("GaussianEMRestraint")
gemt.add_to_model()
outputobjects.append(gemt)
else:
logging.info('skip gemt addition: EM file does NOT exist %s!' % target_gmm_file)
print('skip gemt addition: EM file does NOT exist %s!' % target_gmm_file)
# Gaussian functions are widely used in statistics to describe the normal distributions, in signal processing to define Gaussian filters
# , in image processing where two-dimensional Gaussians are used for Gaussian blurs, and in mathematics to solve heat equations and diffusion equations
# and to define the Weierstrass transform.
# https://en.wikipedia.org/wiki/Gaussian_function
# Electron Microscopy Restraint
# The GaussianEMRestraint uses a density overlap function to compare model to data
# First the EM map is approximated with a Gaussian Mixture Model (done separately)
# Second, the components of the model are represented with Gaussians (forming the model GMM)
# Other options: scale_to_target_mass ensures the total mass of model and map are identical
# slope: nudge model closer to map when far away
# weight: experimental, needed becaues the EM restraint is quasi-Bayesian
#
#em_components = IMP.pmi.tools.get_densities(root_hier)
# substitute em_components with densities in the call given below
logging.info("Monte-Carlo Sampling:")
#--------------------------
# Monte-Carlo Sampling
#--------------------------
#--------------------------
# Set MC Sampling Parameters
#--------------------------
#num_frames = 20000
#num_frames = 50
num_frames = project["sampling_frame"]
#if '--test' in sys.argv: num_frames=100
num_mc_steps = 10
logging.info('set states %s!' % project["states"])
logging.info('set sampling_frame %s!' % project["sampling_frame"])
logging.info('set num_frames %s!' % num_frames)
logging.info('set output_dir %s!' % project["output_dir"])
logging.info('set num_mc_steps %s!' % num_mc_steps)
#TODO: add config setup for these fixed values
logging.info('set monte_carlo_temperature=1.0')
logging.info('set simulated_annealing=True')
logging.info('set simulated_annealing_minimum_temperature=1.0')
logging.info('set simulated_annealing_maximum_temperature=2.5')
logging.info('set simulated_annealing_minimum_temperature_nframes=200')
logging.info('set simulated_annealing_maximum_temperature_nframes=20')
logging.info('set replica_exchange_minimum_temperature=1.0')
logging.info('set replica_exchange_maximum_temperature=2.5')
logging.info('set number_of_best_scoring_models=0')
logging.info('set monte_carlo_steps %s!' % num_mc_steps)
logging.info('set number_of_frames %s!' % num_frames)
logging.info('set global_output_directory %s!' % project["output_dir"])
# This object defines all components to be sampled as well as the sampling protocol
mc1=IMP.pmi.macros.ReplicaExchange0(mdl,
root_hier=root_hier,
monte_carlo_sample_objects=dof.get_movers(),
output_objects=outputobjects,
crosslink_restraints=xl_rests, # allows XLs to be drawn in the RMF files
monte_carlo_temperature=1.0,
simulated_annealing=True,
simulated_annealing_minimum_temperature=1.0,
simulated_annealing_maximum_temperature=2.5,
simulated_annealing_minimum_temperature_nframes=200,
simulated_annealing_maximum_temperature_nframes=20,
replica_exchange_minimum_temperature=1.0,
replica_exchange_maximum_temperature=2.5,
number_of_best_scoring_models=0,
monte_carlo_steps=num_mc_steps, #keep at 10
number_of_frames=num_frames,
global_output_directory=project["output_dir"],
test_mode=False)
# start sampling
mc1.execute_macro()
for i in range(len(xlList) ):
logging.info(xlList[i].evaluate())
logging.info("EV", ev.evaluate());
logging.info("CR", cr.evaluate());
def model_pipeline(project):
"""
Model pipeline for IMP project
"""
logging.info('model_pipeline title_id %s!' % project.title_id)
#---------------------------
# Define Input Files
# The first section defines where input files are located.
# The topology file defines how the system components are structurally represented.
# target_gmm_file stores the EM map for the entire complex, which has already been converted into a Gaussian mixture model.
#---------------------------
datadirectory = project.data_directory
#"C:/dev/project/py_imp/py_imp/pmi_tut/rnapolii/data/"
# C:/Users/adminL/source/repos/py_imp/py_imp/pmi_tut/rnapolii/data/
logging.info('config data_directory %s!' % datadirectory)
# Start by getting directory paths
#this_path = os.path.dirname(os.path.realpath(__file__)) + "/"
#cwd = os.getcwd()
this_path = project.data_directory
mkdir(this_path)
# these paths are relative to the topology file
pdb_dir = this_path + "./data/xtal"
fasta_dir = this_path + "./data/fasta"
gmm_dir = this_path + "./data/em"
xl_dir = this_path + "./data/xl"
topo_dir = this_path + "./data/topo"
#pdb_dir = this_path + "../data/xtal"
#fasta_dir = this_path + "../data/fasta"
#gmm_dir = this_path + "../data/em"
#xl_dir = this_path + "../data/xl"
#topo_dir = this_path + "../data/topo"
logging.info('this_path %s!' % this_path)
logging.info('pdb_dir %s!' % pdb_dir)
logging.info('fasta_dir %s!' % fasta_dir)
logging.info('gmm_dir %s!' % gmm_dir)
logging.info('xl_dir %s!' % xl_dir)
logging.info('topo_dir %s!' % topo_dir)
#if not os.path.exists(pdb_dir):
# os.makedirs(pdb_dir)
#mkdir(pdb_dir)
#mkdir(fasta_dir)
#mkdir(gmm_dir)
#mkdir(xl_dir)
#mkdir(topo_dir)
topology_file = topo_dir + '/' + project.topology_file
target_gmm_file = gmm_dir + '/' + project.target_gmm_file
#logging.info('data_directory %s!' % datadirectory)
logging.info('model_pipeline topology_file %s!' % topology_file)
logging.info('target_gmm_file %s!' % target_gmm_file)
class MSStudioCrosslinks:
# Class that converts an MS Studio crosslink file
# into a csv file and corresponding IMP CrossLinkDataBase object
def __init__(self, infile):
self.infile = infile
self.xldbkc = self.get_xldbkc()
self.xldb = IMP.pmi.io.crosslink.CrossLinkDataBase(self.xldbkc)
self.xldb.create_set_from_file(self.infile, self.xldbkc)
def get_xldbkc(self):
# Creates the keyword converter database to translate MS Studio column names
# into IMP XL database keywords
xldbkc = IMP.pmi.io.crosslink.CrossLinkDataBaseKeywordsConverter(
IMP.pmi.io.crosslink.ResiduePairListParser("MSSTUDIO"))
xldbkc.set_site_pairs_key("Selected Sites")
xldbkc.set_protein1_key("Protein 1")
xldbkc.set_protein2_key("Protein 2")
xldbkc.set_unique_id_key("Peptide ID")
return xldbkc
def parse_infile(self):
# Returns a list of each crosslink's attributes as a dictionary.
import csv
return csv.DictReader(open(self.infile),
delimiter=',',
quotechar='"')
def get_database(self):
return self.xldb
# Topology file should be in the same directory as this script
#topology_file = this_path +"../topology/topology.txt"
# Initialize model
mdl = IMP.Model()
# Build the Model Representation Using a Topology File Using the topology file we define the overall topology: we introduce the molecules with their
# sequence and their known structure, and define the movers. Each line in the file is a user-defined molecular Domain,
# and each column contains the specifics needed to build the system. See the TopologyReader documentation for a full description of the topology file format.
#topology file example:
#|molecule_name |color |fasta_fn |fasta_id|pdb_fn |chain|residue_range|pdb_offset|bead_size|em_residues_per_gaussian|rigid_body|super_rigid_body|chain_of_super_rigid_bodies|
#|Rpb1 |blue |1WCM_new.fasta.txt|1WCM:A |1WCM_map_fitted.pdb|A |1,1140 |0 |20 |0 |1 | 1 | |
#|Rpb1 |blue |1WCM_new.fasta.txt|1WCM:A |1WCM_map_fitted.pdb|A |1141,1274 |0 |20 |0 |2 | 1 |
# https://integrativemodeling.org/2.10.1/doc/ref/classIMP_1_1pmi_1_1topology_1_1TopologyReader.html
#|molecule_name|color|fasta_fn|fasta_id|pdb_fn|chain|residue_range|pdb_offset|bead_size|em_residues_per_gaussian|rigid_body|super_rigid_body|chain_of_super_rigid_bodies|flags|
#|Rpb1 |blue |1WCM.fasta|1WCM:A|1WCM.pdb|A|1,1140 |0|10|0|1|1,3|1||
#|Rpb1 |blue |1WCM.fasta|1WCM:A|1WCM.pdb|A|1141,1274|0|10|0|2|1,3|1||
#|Rpb1 |blue |1WCM.fasta|1WCM:A|1WCM.pdb|A|1275,END |0|10|0|3|1,3|1||
# fasta.txt files are what is expected
# Read in the topology file. We must handle multiple topology files: meaning we need to handle either consolidate as one OR handle multiple sets of XL csv files
# Specify the directory where the PDB files, fasta files and GMM files are
toporeader = IMP.pmi.topology.TopologyReader(topology_file,
pdb_dir=pdb_dir,
fasta_dir=fasta_dir,
gmm_dir=gmm_dir)
# Use the BuildSystem macro to build states from the topology file
bldsys = IMP.pmi.macros.BuildSystem(mdl)
# Each state can be specified by a topology file.
bldsys.add_state(toporeader)
#Building the System Representation and Degrees of Freedom
#Here we can set the Degrees of Freedom parameters, which should be optimized according to MC acceptance ratios. There are three kind of movers: Rigid Body, Bead, and Super Rigid Body (super rigid bodies are sets of rigid bodies and beads that will move together in an additional Monte Carlo move).
#max_rb_trans and max_rb_rot are the maximum translation and rotation of the Rigid Body mover, max_srb_trans and max_srb_rot are the maximum translation and rotation of the Super Rigid Body mover and max_bead_trans is the maximum translation of the Bead Mover.
#The execution of the macro will return the root hierarchy (root_hier) and the degrees of freedom (dof) objects, both of which are used later on.
# Build the system representation and degrees of freedom
"""
root_hier, dof = bldsys.execute_macro(max_rb_trans=project.degree_of_freedom.max_rb_trans,
max_rb_rot=project.degree_of_freedom.max_rb_rot,
max_bead_trans=project.degree_of_freedom.max_bead_trans,
max_srb_trans=project.degree_of_freedom.max_srb_trans,
max_srb_rot=project.degree_of_freedom.max_srb_rot)
"""
root_hier, dof = bldsys.execute_macro()
"""
fb = dof.create_flexible_beads(mol.get_non_atomic_residues(),
max_trans=bead_max_trans)
"""
print(dof.get_rigid_bodies())
print(toporeader.get_rigid_bodies())
outputobjects = []
# Stereochemistry restraints
ev = IMP.pmi.restraints.stereochemistry.ExcludedVolumeSphere(
included_objects=bldsys.get_molecules()[0].values(), resolution=20)
ev.add_to_model()
outputobjects.append(ev)
crs = []
for mol in bldsys.get_molecules()[0].values():
#dof.create_flexible_beads(mol.get_non_atomic_residues(),
# max_trans=bead_max_trans)
cr = IMP.pmi.restraints.stereochemistry.ConnectivityRestraint([mol])
cr.add_to_model()
crs.append(cr)
outputobjects.append(cr)
IMP.pmi.tools.shuffle_configuration(
root_hier,
max_translation=
100, # raise for larger systems if shuffling fails at niterations, want it ~1.5x size of system in angstrom
verbose=True,
cutoff=5.0,
niterations=100)
logging.info(ev.evaluate())
dof.optimize_flexible_beads(
100
) #if beads are not connecting at initial rmf, increase; number of steps to optimize connectivity
logging.info(ev.evaluate())
#TODO: obtain XL filenames from yaml
# Convert crosslink file into IMP database
#xl_file1 = xl_dir + "/PRC2_BS3.csv"
#xl_file2 = xl_dir + "/PRC2_DSS.csv"
#xldb1 = MSStudioCrosslinks(xl_file1).get_database()
#xldb2 = MSStudioCrosslinks(xl_file2).get_database()
#for i in range(len(project.xl_dbA) ):
# logging.info(project.xl_dbA[i])
# Getting length of list
length = len(project.xl_groupA)
i = 0
xlList = []
# Iterating using while loop
while i < length:
logging.info(project.xl_groupA[i])
#"refid","length","slope","resolution","label","weight","crosslink_distance"
logging.info(project.xl_groupA[i].refid)
logging.info(project.xl_groupA[i].length)
logging.info(project.xl_groupA[i].slope)
logging.info(project.xl_groupA[i].resolution)
logging.info(project.xl_groupA[i].label)
logging.info(project.xl_groupA[i].weight)
logging.info(project.xl_groupA[i].crosslink_distance)
# Set up crosslinking restraint
xlA = XLRestraint(
root_hier=root_hier,
CrossLinkDataBase=MSStudioCrosslinks(
xl_dir + "/" + project.xl_groupA[i].refid).get_database(),
length=project.xl_groupA[i].
length, #midpoint? Double check with Daniel and excel function thing
resolution=project.xl_groupA[i].resolution, #keep 1, lower limit
slope=project.xl_groupA[i].
slope, # 0.01 for longer XL and 0.03 for shorter, range - check by making sure midpoint is less than 0.5 e.g 30 * 0.01
label=project.xl_groupA[i].label,
filelabel=project.xl_groupA[i].label,
weight=project.xl_groupA[i].weight
) #ignore weight, calculated via IMP
logging.info(xlA)
xlList.append(xlA)
xlA.add_to_model()
outputobjects.append(xlA)
dof.get_nuisances_from_restraint(xlA)
i += 1
for i in range(len(xlList)):
logging.info(xlList[i])
"""
# Set up crosslinking restraint
xl1 = XLRestraint(root_hier=root_hier,
CrossLinkDataBase=xldb1,
length=30.0, #midpoint? Double check with Daniel and excel function thing
resolution=1, #keep 1, lower limit
slope=0.01, # 0.01 for longer XL and 0.03 for shorter, range - check by making sure midpoint is less than 0.5 e.g 30 * 0.01
label="DSS",
filelabel="DSS_missing",
weight=1.) #ignore weight, calculated via IMP
xl1.add_to_model()
outputobjects.append(xl1)
dof.get_nuisances_from_restraint(xl1)
xl2 = XLRestraint(root_hier=root_hier,
CrossLinkDataBase=xldb2,
length=30.0,
resolution=1,
slope=0.01,
label="BS3",
filelabel="BS3_missing",
weight=1.)
xl2.add_to_model()
outputobjects.append(xl2)
dof.get_nuisances_from_restraint(xl2)
"""
#xl_rests = [xl1, xl2] + crs
xl_rests = xlList + crs
#EM Restraint
densities = IMP.atom.Selection(
root_hier,
representation_type=IMP.atom.DENSITIES).get_selected_particles()
'''
IMP.isd.gmm_tools.decorate_gmm_from_text(
"../data/em/Ciferri_PRC2.50.gmm.txt",
target_ps,
m,
radius_scale=3.0,
mass_scale=1.0)
'''
#coords=[IMP.core.XYZ(p) for p in target_ps]
#print coords
#TODO: add in the EM data file processing logic once we have the em data file
"""
gemt = IMP.pmi.restraints.em.GaussianEMRestraint(densities,
"../data/em/Ciferri_PRC2.50.gmm.txt",
scale_target_to_mass=True,
slope=0,
weight=200.0)
gemt.set_label("Ciferri_PRC2")
gemt.add_to_model()
outputobjects.append(gemt)
"""
#--------------------------
# Monte-Carlo Sampling
#--------------------------
#--------------------------
# Set MC Sampling Parameters
#--------------------------
#num_frames = 20000
num_frames = 50
#if '--test' in sys.argv: num_frames=100
num_mc_steps = 10
logging.info('set states %s!' % project.states)
logging.info('set sampling_frame %s!' % project.sampling_frame)
logging.info('set num_frames %s!' % num_frames)
logging.info('set output_dir %s!' % project.output_dir)
logging.info('set num_mc_steps %s!' % num_mc_steps)
#TODO: add config setup for these fixed values
logging.info('set monte_carlo_temperature=1.0')
logging.info('set simulated_annealing=True')
logging.info('set simulated_annealing_minimum_temperature=1.0')
logging.info('set simulated_annealing_maximum_temperature=2.5')
logging.info('set simulated_annealing_minimum_temperature_nframes=200')
logging.info('set simulated_annealing_maximum_temperature_nframes=20')
logging.info('set replica_exchange_minimum_temperature=1.0')
logging.info('set replica_exchange_maximum_temperature=2.5')
logging.info('set number_of_best_scoring_models=0')
logging.info('set monte_carlo_steps %s!' % num_mc_steps)
logging.info('set number_of_frames %s!' % num_frames)
logging.info('set global_output_directory %s!' % project.output_dir)
# https://integrativemodeling.org/2.10.1/doc/ref/classIMP_1_1pmi_1_1macros_1_1ReplicaExchange0.html#a239c4009cc04c70236730479f9f79744
# This object defines all components to be sampled as well as the sampling protocol
mc1 = IMP.pmi.macros.ReplicaExchange0(
mdl,
root_hier=root_hier,
monte_carlo_sample_objects=dof.get_movers(),
output_objects=outputobjects,
crosslink_restraints=xl_rests, # allows XLs to be drawn in the RMF files
monte_carlo_temperature=1.0,
simulated_annealing=True,
simulated_annealing_minimum_temperature=1.0,
simulated_annealing_maximum_temperature=2.5,
simulated_annealing_minimum_temperature_nframes=200,
simulated_annealing_maximum_temperature_nframes=20,
replica_exchange_minimum_temperature=1.0,
replica_exchange_maximum_temperature=2.5,
number_of_best_scoring_models=0,
monte_carlo_steps=num_mc_steps, #keep at 10
number_of_frames=num_frames,
global_output_directory=project.output_dir,
test_mode=False)
# start sampling
mc1.execute_macro()
#logging.info("GEMT", gemt.evaluate());
#logging.info("XL1", xl1.evaluate(), xl2.evaluate());
for i in range(len(xlList)):
logging.info(xlList[i].evaluate())
logging.info("EV", ev.evaluate())
logging.info("CR", cr.evaluate())
def model_pipeline(project):
"""
Model pipeline for IMP project
"""
logging.info('model_pipeline title_id %s!' % project.title_id)
#---------------------------
# Define Input Files
# The first section defines where input files are located.
# The topology file defines how the system components are structurally represented.
# target_gmm_file stores the EM map for the entire complex, which has already been converted into a Gaussian mixture model.
#---------------------------
datadirectory = project.data_directory
#"C:/dev/project/py_imp/py_imp/pmi_tut/rnapolii/data/"
# C:/Users/adminL/source/repos/py_imp/py_imp/pmi_tut/rnapolii/data/
logging.info('config data_directory %s!' % datadirectory)
# Start by getting directory paths
#this_path = os.path.dirname(os.path.realpath(__file__)) + "/"
#cwd = os.getcwd()
this_path = project.data_directory
mkdir(this_path)
# these paths are relative to the topology file
pdb_dir = this_path + "./data/xtal"
fasta_dir = this_path + "./data/fasta"
gmm_dir = this_path + "./data/em"
xl_dir = this_path + "./data/xl"
topo_dir = this_path + "./data/topo"
#pdb_dir = this_path + "../data/xtal"
#fasta_dir = this_path + "../data/fasta"
#gmm_dir = this_path + "../data/em"
#xl_dir = this_path + "../data/xl"
#topo_dir = this_path + "../data/topo"
logging.info('this_path %s!' % this_path)
logging.info('pdb_dir %s!' % pdb_dir)
logging.info('fasta_dir %s!' % fasta_dir)
logging.info('gmm_dir %s!' % gmm_dir)
logging.info('xl_dir %s!' % xl_dir)
logging.info('topo_dir %s!' % topo_dir)
#if not os.path.exists(pdb_dir):
# os.makedirs(pdb_dir)
#mkdir(pdb_dir)
#mkdir(fasta_dir)
#mkdir(gmm_dir)
#mkdir(xl_dir)
#mkdir(topo_dir)
topology_file = topo_dir + '/' + project.topology_file
target_gmm_file = gmm_dir + '/' + project.target_gmm_file
#logging.info('data_directory %s!' % datadirectory)
logging.info('model_pipeline topology_file %s!' % topology_file)
logging.info('target_gmm_file %s!' % target_gmm_file)
class MSStudioCrosslinks:
# Class that converts an MS Studio crosslink file
# into a csv file and corresponding IMP CrossLinkDataBase object
def __init__(self, infile):
self.infile = infile
self.xldbkc = self.get_xldbkc()
self.xldb = IMP.pmi.io.crosslink.CrossLinkDataBase(self.xldbkc)
self.xldb.create_set_from_file(self.infile, self.xldbkc)
def get_xldbkc(self):
# Creates the keyword converter database to translate MS Studio column names
# into IMP XL database keywords
xldbkc = IMP.pmi.io.crosslink.CrossLinkDataBaseKeywordsConverter(
IMP.pmi.io.crosslink.ResiduePairListParser("MSSTUDIO"))
xldbkc.set_site_pairs_key("Selected Sites")
xldbkc.set_protein1_key("Protein 1")
xldbkc.set_protein2_key("Protein 2")
xldbkc.set_unique_id_key("Peptide ID")
return xldbkc
def parse_infile(self):
# Returns a list of each crosslink's attributes as a dictionary.
import csv
return csv.DictReader(open(self.infile),
delimiter=',',
quotechar='"')
def get_database(self):
return self.xldb
class nonMSStudioCrosslinks:
# Class that converts an MS Studio crosslink file
# into a csv file and corresponding IMP CrossLinkDataBase object
def __init__(self, infile, indbA):
self.infile = infile
self.indbA = indbA
self.xldbkc = self.get_xldbkc()
self.xldb = IMP.pmi.io.crosslink.CrossLinkDataBase(self.xldbkc)
self.xldb.create_set_from_file(self.infile, self.xldbkc)
def get_xldbkc(self):
# Creates the keyword converter database to translate MS Studio column names
# into IMP XL database keywords
xldbkc = IMP.pmi.io.crosslink.CrossLinkDataBaseKeywordsConverter()
logging.info('set xl_dbA %s!' % self.indbA.refid)
logging.info('set xl_dbA set_protein1_key %s!' %
self.indbA.set_protein1_key)
logging.info('set xl_dbA set_protein2_key %s!' %
self.indbA.set_protein2_key)
logging.info('set xl_dbA set_residue1_key %s!' %
self.indbA.set_residue1_key)
logging.info('set xl_dbA set_residue2_key %s!' %
self.indbA.set_residue2_key)
xldbkc.set_protein1_key(self.indbA.set_protein1_key)
xldbkc.set_protein2_key(self.indbA.set_protein2_key)
xldbkc.set_residue1_key(self.indbA.set_residue1_key)
xldbkc.set_residue2_key(self.indbA.set_residue2_key)
return xldbkc
def parse_infile(self):
# Returns a list of each crosslink's attributes as a dictionary.
import csv
return csv.DictReader(open(self.infile),
delimiter=',',
quotechar='"')
def get_database(self):
return self.xldb
# Topology file should be in the same directory as this script
#topology_file = this_path +"../topology/topology.txt"
# Initialize model
mdl = IMP.Model()
# Build the Model Representation Using a Topology File Using the topology file we define the overall topology: we introduce the molecules with their
# sequence and their known structure, and define the movers. Each line in the file is a user-defined molecular Domain,
# and each column contains the specifics needed to build the system. See the TopologyReader documentation for a full description of the topology file format.
#topology file example:
#|molecule_name |color |fasta_fn |fasta_id|pdb_fn |chain|residue_range|pdb_offset|bead_size|em_residues_per_gaussian|rigid_body|super_rigid_body|chain_of_super_rigid_bodies|
#|Rpb1 |blue |1WCM_new.fasta.txt|1WCM:A |1WCM_map_fitted.pdb|A |1,1140 |0 |20 |0 |1 | 1 | |
#|Rpb1 |blue |1WCM_new.fasta.txt|1WCM:A |1WCM_map_fitted.pdb|A |1141,1274 |0 |20 |0 |2 | 1 |
# https://integrativemodeling.org/2.10.1/doc/ref/classIMP_1_1pmi_1_1topology_1_1TopologyReader.html
#|molecule_name|color|fasta_fn|fasta_id|pdb_fn|chain|residue_range|pdb_offset|bead_size|em_residues_per_gaussian|rigid_body|super_rigid_body|chain_of_super_rigid_bodies|flags|
#|Rpb1 |blue |1WCM.fasta|1WCM:A|1WCM.pdb|A|1,1140 |0|10|0|1|1,3|1||
#|Rpb1 |blue |1WCM.fasta|1WCM:A|1WCM.pdb|A|1141,1274|0|10|0|2|1,3|1||
#|Rpb1 |blue |1WCM.fasta|1WCM:A|1WCM.pdb|A|1275,END |0|10|0|3|1,3|1||
# fasta.txt files are what is expected
# Read in the topology file. We must handle multiple topology files: meaning we need to handle either consolidate as one OR handle multiple sets of XL csv files
# Specify the directory where the PDB files, fasta files and GMM files are
toporeader = IMP.pmi.topology.TopologyReader(topology_file,
pdb_dir=pdb_dir,
fasta_dir=fasta_dir,
gmm_dir=gmm_dir)
# Use the BuildSystem macro to build states from the topology file
bldsys = IMP.pmi.macros.BuildSystem(mdl)
# Each state can be specified by a topology file.
bldsys.add_state(toporeader)
#Building the System Representation and Degrees of Freedom
#Here we can set the Degrees of Freedom parameters, which should be optimized according to MC acceptance ratios. There are three kind of movers: Rigid Body, Bead, and Super Rigid Body (super rigid bodies are sets of rigid bodies and beads that will move together in an additional Monte Carlo move).
#max_rb_trans and max_rb_rot are the maximum translation and rotation of the Rigid Body mover, max_srb_trans and max_srb_rot are the maximum translation and rotation of the Super Rigid Body mover and max_bead_trans is the maximum translation of the Bead Mover.
#The execution of the macro will return the root hierarchy (root_hier) and the degrees of freedom (dof) objects, both of which are used later on.
# Build the system representation and degrees of freedom
root_hier, dof = bldsys.execute_macro(
max_rb_trans=project.degree_of_freedom.max_rb_trans,
max_rb_rot=project.degree_of_freedom.max_rb_rot,
max_bead_trans=project.degree_of_freedom.max_bead_trans,
max_srb_trans=project.degree_of_freedom.max_srb_trans,
max_srb_rot=project.degree_of_freedom.max_srb_rot)
#root_hier, dof = bldsys.execute_macro()
"""
fb = dof.create_flexible_beads(mol.get_non_atomic_residues(),
max_trans=bead_max_trans)
"""
print(dof.get_rigid_bodies())
print(toporeader.get_rigid_bodies())
#outputobjects=[]
# Fix all rigid bodies but not Rpb4 and Rpb7 (the stalk)
# First select and gather all particles to fix.
fixed_particles = []
for prot in [
"Rpb1", "Rpb2", "Rpb3", "Rpb5", "Rpb6", "Rpb8", "Rpb9", "Rpb10",
"Rpb11", "Rpb12"
]:
fixed_particles += IMP.atom.Selection(
root_hier, molecule=prot).get_selected_particles()
# Fix the Corresponding Rigid movers and Super Rigid Body movers using dof
# The flexible beads will still be flexible (fixed_beads is an empty list)!
fixed_beads, fixed_rbs = dof.disable_movers(
fixed_particles, [IMP.core.RigidBodyMover, IMP.pmi.TransformMover])
# Randomize the initial configuration before sampling, of only the molecules
# we are interested in (Rpb4 and Rpb7)
IMP.pmi.tools.shuffle_configuration(root_hier,
excluded_rigid_bodies=fixed_rbs,
max_translation=50,
verbose=False,
cutoff=5.0,
niterations=100)
outputobjects = [] # reporter objects (for stat files)
#-----------------------------------
# Define Scoring Function Components
#-----------------------------------
# Here we are defining a number of restraints on our system.
# For all of them we call add_to_model() so they are incorporated into scoring
# We also add them to the outputobjects list, so they are reported in stat files
# Connectivity keeps things connected along the backbone (ignores if inside
# same rigid body)
mols = IMP.pmi.tools.get_molecules(root_hier)
for mol in mols:
molname = mol.get_name()
IMP.pmi.tools.display_bonds(mol)
cr = IMP.pmi.restraints.stereochemistry.ConnectivityRestraint(
mol, scale=2.0)
cr.add_to_model()
cr.set_label(molname)
outputobjects.append(cr)
# Excluded Volume Restraint
# To speed up this expensive restraint, we operate it at resolution 20
ev = IMP.pmi.restraints.stereochemistry.ExcludedVolumeSphere(
included_objects=root_hier, resolution=10)
ev.add_to_model()
outputobjects.append(ev)
# Getting length of list
length = len(project.xl_groupA)
i = 0
xlList = []
# Iterating using while loop
while i < length:
logging.info(project.xl_groupA[i])
#"refid","length","slope","resolution","label","weight","crosslink_distance"
logging.info(project.xl_groupA[i].refid)
logging.info(project.xl_groupA[i].length)
logging.info(project.xl_groupA[i].slope)
logging.info(project.xl_groupA[i].resolution)
logging.info(project.xl_groupA[i].label)
logging.info(project.xl_groupA[i].weight)
logging.info(project.xl_groupA[i].crosslink_distance)
# Set up crosslinking restraint
xlA = XLRestraint(
root_hier=root_hier,
CrossLinkDataBase=nonMSStudioCrosslinks(
xl_dir + "/" + project.xl_groupA[i].refid,
project.xl_dbA[i]).get_database(),
length=project.xl_groupA[i].
length, #midpoint? Double check with Daniel and excel function thing
resolution=project.xl_groupA[i].resolution, #keep 1, lower limit
slope=project.xl_groupA[i].
slope, # 0.01 for longer XL and 0.03 for shorter, range - check by making sure midpoint is less than 0.5 e.g 30 * 0.01
label=project.xl_groupA[i].label,
filelabel=project.xl_groupA[i].label,
weight=project.xl_groupA[i].weight
) #ignore weight, calculated via IMP
logging.info(xlA)
xlList.append(xlA)
xlA.add_to_model()
outputobjects.append(xlA)
dof.get_nuisances_from_restraint(xlA)
i += 1
for i in range(len(xlList)):
logging.info(xlList[i])
xl_rests = xlList
# Gaussian functions are widely used in statistics to describe the normal distributions, in signal processing to define Gaussian filters
# , in image processing where two-dimensional Gaussians are used for Gaussian blurs, and in mathematics to solve heat equations and diffusion equations
# and to define the Weierstrass transform.
# https://en.wikipedia.org/wiki/Gaussian_function
# Electron Microscopy Restraint
# The GaussianEMRestraint uses a density overlap function to compare model to data
# First the EM map is approximated with a Gaussian Mixture Model (done separately)
# Second, the components of the model are represented with Gaussians (forming the model GMM)
# Other options: scale_to_target_mass ensures the total mass of model and map are identical
# slope: nudge model closer to map when far away
# weight: experimental, needed becaues the EM restraint is quasi-Bayesian
em_components = IMP.pmi.tools.get_densities(root_hier)
gemt = IMP.pmi.restraints.em.GaussianEMRestraint(em_components,
target_gmm_file,
scale_target_to_mass=True,
slope=0.000001,
weight=80.0)
gemt.add_to_model()
outputobjects.append(gemt)
#--------------------------
# Monte-Carlo Sampling
#--------------------------
#--------------------------
# Set MC Sampling Parameters
#--------------------------
#num_frames = 20000
num_frames = 50
#if '--test' in sys.argv: num_frames=100
num_mc_steps = 10
logging.info('set states %s!' % project.states)
logging.info('set sampling_frame %s!' % project.sampling_frame)
logging.info('set num_frames %s!' % num_frames)
logging.info('set output_dir %s!' % project.output_dir)
logging.info('set num_mc_steps %s!' % num_mc_steps)
#TODO: add config setup for these fixed values
logging.info('set monte_carlo_temperature=1.0')
logging.info('set simulated_annealing=True')
logging.info('set simulated_annealing_minimum_temperature=1.0')
logging.info('set simulated_annealing_maximum_temperature=2.5')
logging.info('set simulated_annealing_minimum_temperature_nframes=200')
logging.info('set simulated_annealing_maximum_temperature_nframes=20')
logging.info('set replica_exchange_minimum_temperature=1.0')
logging.info('set replica_exchange_maximum_temperature=2.5')
logging.info('set number_of_best_scoring_models=10')
logging.info('set monte_carlo_steps %s!' % num_mc_steps)
logging.info('set number_of_frames %s!' % num_frames)
logging.info('set global_output_directory %s!' % "output")
# https://integrativemodeling.org/2.10.1/doc/ref/classIMP_1_1pmi_1_1macros_1_1ReplicaExchange0.html#a239c4009cc04c70236730479f9f79744
# This object defines all components to be sampled as well as the sampling protocol
mc1 = IMP.pmi.macros.ReplicaExchange0(
mdl,
root_hier=root_hier,
monte_carlo_sample_objects=dof.get_movers(),
output_objects=outputobjects,
crosslink_restraints=
xl_rests, #[xl1,xl2], # allows XLs to be drawn in the RMF files
monte_carlo_temperature=1.0,
simulated_annealing=True,
simulated_annealing_minimum_temperature=1.0,
simulated_annealing_maximum_temperature=2.5,
simulated_annealing_minimum_temperature_nframes=200,
simulated_annealing_maximum_temperature_nframes=20,
replica_exchange_minimum_temperature=1.0,
replica_exchange_maximum_temperature=2.5,
number_of_best_scoring_models=10,
monte_carlo_steps=num_mc_steps,
number_of_frames=num_frames,
global_output_directory="output")
# Start Sampling
mc1.execute_macro()