nup84.py

## \example kernel/nup84.py
# Modify solve for a Nup84-like structure using a mix of rigid bodies
# and coarse grained models using crosslinking data. In
# addition, show how to visualize restraints and visualize the
# rejected conformations. Both are useful things to do when trying to
# figure out why optimization is not converging.

import IMP
import IMP.atom
import IMP.container
import IMP.display
import IMP.statistics
import IMP.example
import os
import sys

# not finished
IMP.base.add_bool_flag("run", "Whether to run the program")

# parse command line arguments so, eg profiling can be used
IMP.base.setup_from_argv(sys.argv, "Nup84 example")

if IMP.base.get_bool_flag("run") != "yes":
    exit(0)

# First we define some basic parameters for the modeling effort

# the spring constant to use, it doesn't really matter
k = 10
# the target resolution for the representation
resolution = 100
# the box to perform everything in, make it flat as it is a 2D structure
bb = IMP.algebra.BoundingBox3D(IMP.algebra.Vector3D(-300, -300, -50),
                               IMP.algebra.Vector3D(300, 300, 50))

# how many times to try to find a good solution
number_of_sampling_attempts = 1
number_of_mc_steps = 10000

# Create a coarse grained protein with a given name, adding it to universe


def add_protein_from_length(model, name, residues, parent, restraints,
                            excluded_volume_particles, optimized_particles):
    # Create a coarse grained protein with the passed residue information
    h = IMP.atom.create_protein(model, name, resolution, residues)

    parent.add_child(h)
    # Here, each of the constituent particles will be optimized independently
    leaves = IMP.atom.get_leaves(h)
    optimized_particles.extend(leaves)
    excluded_volume_particles.extend(leaves)

    # Ensure that the various particles of the protein stay connected
    r = IMP.atom.create_connectivity_restraint([IMP.atom.Selection(c)
                                                for c in h.get_children()], k,
                                               "connect " + name)

    if r:
        # make sure there is an actual restraint
        restraints.append(r)
        r.set_maximum_score(k)

# Create protein as a rigid body from a pdb file


def add_protein_from_pdb(model, name, file, parent, restraints,
                         excluded_volume_particles, optimized_particles):
    # we should keep the original particles around so they get written

    # create an atomic representation from the pdb file
    t = IMP.atom.read_pdb(
        IMP.get_example_path(os.path.join("data", file)), model,
        IMP.atom.ATOMPDBSelector())
    # extract the chain from the file
    c = IMP.atom.Chain(IMP.atom.get_by_type(t, IMP.atom.CHAIN_TYPE)[0])
    # there is no reason to use all atoms, just approximate the pdb shape
    # instead
    s = IMP.atom.create_simplified_along_backbone(c, resolution / 2.0, True)
    s.set_name(name)
    # tear down what is left
    IMP.atom.destroy(t)
    # make the simplified structure rigid
    rb = IMP.atom.create_rigid_body(s)
    rb.set_coordinates_are_optimized(True)
    optimized_particles.append(rb)
    excluded_volume_particles.extend(s.get_children())
    parent.add_child(s)


# Create protein as a rigid body from several pdb file
def add_protein_from_pdbs(model, name, files, parent, restraints,
                          excluded_volume_particles, optimized_particles):
    h = IMP.atom.Hierarchy.setup_particle(IMP.kernel.Particle(model, name))
    for i, f in enumerate(files):
        add_protein_from_pdb(model, name + str(i), f, h, restraints,
                             excluded_volume_particles, optimized_particles)
    r = IMP.atom.create_connectivity_restraint([IMP.atom.Selection(c, hierarchy_types=[IMP.atom.FRAGMENT_TYPE])
                                                for c in h.get_children()],
                                               k, "connect " + name)
    if r:
        restraints.append(r)
        r.set_maximum_score(k)

# Create all the needed representation using the above functions


def create_representation(model):
    restraints = []
    optimized_particles = []
    excluded_volume_particles = []
    universe = IMP.atom.Hierarchy.setup_particle(
        IMP.kernel.Particle(model, "the universe"))

    add_protein_from_length(model, "Nup85", 570, universe, restraints,
                            excluded_volume_particles, optimized_particles)

    # pin the c-terminus
    ct = IMP.atom.Selection(universe, molecule="Nup85",
                            hierarchy_types=[IMP.atom.FRAGMENT_TYPE],
                            terminus=IMP.atom.Selection.C)
    d = IMP.core.XYZ(ct.get_selected_particles()[0])
    d.set_coordinates(IMP.algebra.Vector3D(0, 0, 0))
    d.set_coordinates_are_optimized(False)

    add_protein_from_length(model, "Nup84", 460, universe, restraints,
                            excluded_volume_particles, optimized_particles)
    add_protein_from_length(model, "Nup145C", 442, universe, restraints,
                            excluded_volume_particles, optimized_particles)
    add_protein_from_length(
        model, "Nup120", [0, 500, 761], universe, restraints,
        excluded_volume_particles, optimized_particles)
    add_protein_from_length(
        model, "Nup133", [0, 450, 778, 1160], universe, restraints,
        excluded_volume_particles, optimized_particles)
    add_protein_from_pdb(model, "Seh1", "seh1.pdb", universe, restraints,
                         excluded_volume_particles, optimized_particles)
    add_protein_from_pdb(model, "Sec13", "sec13.pdb", universe, restraints,
                         excluded_volume_particles, optimized_particles)
    return universe, restraints, excluded_volume_particles, optimized_particles


def add_distance_restraint(selection0, selection1, name, restraints):
    r = IMP.atom.create_distance_restraint(selection0, selection1, 0, k, name)
    r.set_maximum_score(k)
    restraints.append(r)


def encode_data_as_restraints(universe, restraints):
    s0 = IMP.atom.Selection(
        hierarchy=universe, hierarchy_types=[IMP.atom.FRAGMENT_TYPE],
        molecule="Nup145C", residue_indexes=[(0, 423)])
    s1 = IMP.atom.Selection(
        hierarchy=universe, hierarchy_types=[IMP.atom.FRAGMENT_TYPE],
        molecule="Nup84")
    s2 = IMP.atom.Selection(
        hierarchy=universe, hierarchy_types=[IMP.atom.FRAGMENT_TYPE],
        molecule="Sec13")
    r = IMP.atom.create_connectivity_restraint(
        [s0, s1, s2], k, "Nup145C Nup84 Sec13")
    r.set_maximum_score(k)
    restraints.append(r)

    add_distance_restraint(
        IMP.atom.Selection(hierarchy=universe, molecule="Nup145C",
                           residue_indexes=[(0, 423)],
                           hierarchy_types=[
                               IMP.atom.FRAGMENT_TYPE]),
        IMP.atom.Selection(
            hierarchy=universe, molecule="Nup85",
            hierarchy_types=[
                IMP.atom.FRAGMENT_TYPE]),
        "Num145C, Nup85", restraints)
    add_distance_restraint(
        IMP.atom.Selection(hierarchy=universe, molecule="Nup145C",
                           residue_indexes=[(0, 423)],
                           hierarchy_types=[
                               IMP.atom.FRAGMENT_TYPE]),
        IMP.atom.Selection(
            hierarchy=universe, molecule="Nup120",
            residue_indexes=[(500, 762)],
            hierarchy_types=[
                IMP.atom.FRAGMENT_TYPE]),
        "Nup145C Nup120", restraints)
    add_distance_restraint(
        IMP.atom.Selection(hierarchy=universe, molecule="Nup84",
                           hierarchy_types=[
                               IMP.atom.FRAGMENT_TYPE]),
        IMP.atom.Selection(
            hierarchy=universe, molecule="Nup133",
            residue_indexes=[(778, 1160)],
            hierarchy_types=[
                IMP.atom.FRAGMENT_TYPE]),
        "Nup84 Nup133", restraints)
    add_distance_restraint(
        IMP.atom.Selection(hierarchy=universe, molecule="Nup85",
                           hierarchy_types=[
                               IMP.atom.FRAGMENT_TYPE]),
        IMP.atom.Selection(
            hierarchy=universe, molecule="Seh1",
            hierarchy_types=[
                IMP.atom.FRAGMENT_TYPE]),
        "Nup85 Seh1", restraints)
    add_distance_restraint(
        IMP.atom.Selection(hierarchy=universe, molecule="Nup145C",
                           residue_indexes=[(0, 423)],
                           hierarchy_types=[
                               IMP.atom.FRAGMENT_TYPE]),
        IMP.atom.Selection(
            hierarchy=universe, molecule="Sec13",
            hierarchy_types=[
                IMP.atom.FRAGMENT_TYPE]),
        "Nup145C Sec13", restraints)


# find acceptable conformations of the model
def get_configurations(
    model,
    restraints,
    excluded_volume_particles,
        optimized_particles):
    #cpc= IMP.container.ClosePairContainer(representation.get_particles(), 0, 10)
    # evr= IMP.container.PairRestraint(IMP.core.SoftSpherePairScore(k), cpc,
    #                                 "Excluded Volume")
    scale = .5
    mc = IMP.core.MonteCarlo(model)
    movers = []
    for p in optimized_particles:
        if IMP.core.RigidBody.get_is_setup(p):
            mover = IMP.core.RigidBodyMover(
                p, IMP.core.XYZR(p).get_radius() * scale,
                .2 * scale)
            movers.append(mover)
        else:
            mover = IMP.core.BallMover(
                [p], IMP.core.XYZR(p).get_radius() * scale)
            movers.append(mover)
    serial_mover = IMP.core.SerialMover(movers)
    mc.add_mover(serial_mover)
    scoring_function = IMP.core.IncrementalScoringFunction(
        optimized_particles, restraints)
    scoring_function.add_close_pair_score(IMP.core.SoftSpherePairScore(k), 0.0,
                                          excluded_volume_particles)

    configuration_set = IMP.ConfigurationSet(model)
    # must write our own sampler as IMP.core.MCCGSampler doesn't handle rigid
    # bodies
    for i in range(number_of_sampling_attempts):
        for p in optimized_particles:
            IMP.core.XYZ(p).set_coordinates(
                IMP.algebra.get_random_vector_in(bb))
        mc.optimize(number_of_mc_steps)
        if scoring_function.get_had_good_score():
            configuration_set.save()
    return configuration_set


model = IMP.kernel.Model()
universe, restraints, excluded_volume_particles, optimized_particles = create_representation(
    model)
encode_data_as_restraints(universe, restraints)

configuration_set = get_configurations(model, restraints,
                                       excluded_volume_particles,
                                       optimized_particles)

print "Found", configuration_set.get_number_of_configurations(), "good configurations"

# now lets save them all to a file
rmf_file_name = IMP.base.create_temporary_file_name("nup84", ".rmf")
rmf = RMF.create_rmf_file(rmf_file_name)

# we want to see the scores of the various restraints also
IMP.rmf.add_restraints(rmf, restraints)
# and the actual structures
IMP.rmf.add_hierarchy(rmf, universe)

for i in range(0, configuration_set.get_number_of_configurations()):
    configuration_set.load_configuration(i)
    # align all the configurations with the first so they display nicely
    # if we want to be fancy we can account for flips too
    if i == 0:
        base_coords = [IMP.core.XYZ(p).get_coordinates()
                       for p in optimized_particles]
    else:
        tr = IMP.algebra.get_transform_taking_first_to_second(
            optimized_particles, base_coords)
        IMP.core.transform(optimized_particles, tr)
    # update the restraint scores
    sf.evaluate(False)
    IMP.rmf.save_frame(rmf, i)

print "You can now open", rmf_file_name, "in chimera"