Hello again,

So I have the same overall problem as before - creating an ensemble of a 4-subunit complex using MSconnectivity restraints. Having visualised the output (via RMF - thanks Barak :¬) , it's clear that no matter how many steps of CG or MC I put, the models do not change from their initial random placement. I know that the restraints are present because the models are evaluated and scored appropriately.

So I saw on an old (2011) nup84 example that MCCG can't handle rigid bodies, is this still the case ? If so, should I switch to the DOMINO sampler ? or it does work and likely there's an error in my code ...

Many thanks !

Josh

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import IMP import IMP.atom import IMP.rmf import inspect import IMP.container import IMP.display import IMP.statistics #import IMP.example import sys, math, os, optparse import RMF

from optparse import OptionParser

# Convert the arguments into strings and number Firstpdb = str(sys.argv[1]) Secondpdb = str(sys.argv[2]) Thirdpdb = str(sys.argv[3]) Fourthpdb = str(sys.argv[4]) models = float(sys.argv[5])

#*****************************************

# the spring constant to use, it doesnt really matter k=100 # the target resolution for the representation, this is used to specify how detailed # the representation used should be resolution=300 # the box to perform everything bb=IMP.algebra.BoundingBox3D(IMP.algebra.Vector3D(0,0,0), IMP.algebra.Vector3D(100, 100, 100))

# this function creates the molecular hierarchies for the various involved proteins def create_representation(): m= IMP.Model() all=IMP.atom.Hierarchy.setup_particle(IMP.Particle(m)) all.set_name("the universe") # create a protein, represented as a set of connected balls of appropriate # radii and number, chose by the resolution parameter and the number of # amino acids.

def create_protein_from_pdbs(name, files):

def create_from_pdb(file): sls=IMP.SetLogState(IMP.NONE) datadir = os.getcwd() print datadir t=IMP.atom.read_pdb( datadir+'/' + file, m, IMP.atom.ATOMPDBSelector()) del sls #IMP.atom.show_molecular_hierarchy(t) c=IMP.atom.Chain(IMP.atom.get_by_type(t, IMP.atom.CHAIN_TYPE)[0]) if c.get_number_of_children()==0: IMP.atom.show_molecular_hierarchy(t) # there is no reason to use all atoms, just approximate the pdb shape instead s=IMP.atom.create_simplified_along_backbone(c, 1) #IMP.atom.destroy(t) # make the simplified structure rigid rb=IMP.atom.create_rigid_body(s) rb=IMP.atom.create_rigid_body(c) rb.set_coordinates_are_optimized(True) return s # <------- swapping c with s will give a coarse grain representation - much faster ! # return c

h= create_from_pdb(files[0]) h.set_name(name) all.add_child(h)

create_protein_from_pdbs("A", [Firstpdb]) create_protein_from_pdbs("B", [Secondpdb]) create_protein_from_pdbs("C", [Thirdpdb]) create_protein_from_pdbs("D", [Fourthpdb]) #create_protein_from_pdbs("C", ["rpt3_imp.pdb"]) return (m, all)

# create the needed restraints and add them to the model

def create_restraints(m, all): def add_connectivity_restraint(s):

tr= IMP.core.TableRefiner() rps=[] for sc in s: ps= sc.get_selected_particles() rps.append(ps[0]) tr.add_particle(ps[0], ps)

# duplicate the IMP.atom.create_connectivity_restraint functionality

score= IMP.core.KClosePairsPairScore(IMP.core.HarmonicSphereDistancePairScore(0,1),tr)

#ub = IMP.core.HarmonicUpperBound(1.0, 0.1) #ss = IMP.core.DistancePairScore(ub)

r= IMP.core.MSConnectivityRestraint(m,score)

iA = r.add_type([rps[0]]) iB = r.add_type([rps[1]]) iC = r.add_type([rps[2]]) iD = r.add_type([rps[3]]) #n1 = r.add_composite([iA, iB]) n1 = r.add_composite([iA, iB, iC, iD]) n2 = r.add_composite([iA, iB],n1) n3 = r.add_composite([iB, iD],n1) n4 = r.add_composite([iA, iB, iC],n1) n5 = r.add_composite([iB, iC, iD],n1)

m.add_restraint(r)

evr=IMP.atom.create_excluded_volume_restraint([all]) m.add_restraint(evr) # a Selection allows for natural specification of what the restraints act on S= IMP.atom.Selection sA=S(hierarchy=all, molecule="A") sB=S(hierarchy=all, molecule="B") sC=S(hierarchy=all, molecule="C") sD=S(hierarchy=all, molecule="D") add_connectivity_restraint([sA, sB, sC, sD])

nbl = IMP.container.ClosePairContainer([all], 0, 2) h = IMP.core.HarmonicLowerBound(0, 1) sd = IMP.core.SphereDistancePairScore(h) # use the lower bound on the inter-sphere distance to push the spheres apart nbr = IMP.container.PairsRestraint(sd, nbl) m.add_restraint(nbr)

# r1 = IMP.core.ExcludedVolumeRestraint(all) # m.add_restraint(r1)

# find acceptable conformations of the model def get_conformations(m): sampler= IMP.core.MCCGSampler(m) sampler.set_bounding_box(bb) # magic numbers, experiment with them and make them large enough for things to work sampler.set_number_of_conjugate_gradient_steps(200) sampler.set_number_of_monte_carlo_steps(40) sampler.set_number_of_attempts(models) # We don't care to see the output from the sampler #sampler.set_log_level(IMP.SILENT) # return the IMP.ConfigurationSet storing all the found configurations that # meet the various restraint maximum scores. cs= sampler.create_sample() return cs

# cluster the conformations and write them to a file def analyze_conformations(cs, all, gs): # we want to cluster the configurations to make them easier to understand # in this case, the clustering is pretty meaningless embed= IMP.statistics.ConfigurationSetXYZEmbedding(cs,

IMP.container.ListSingletonContainer(IMP.atom.get_leaves(all)), True) cluster= IMP.statistics.create_lloyds_kmeans(embed, 10, 10000) # dump each cluster center to a file so it can be viewed. for i in range(cluster.get_number_of_clusters()): center= cluster.get_cluster_center(i) cs.load_configuration(i) h = IMP.atom.Hierarchy.get_children(all) #tfn = IMP.create_temporary_file_name("cluster%d"%i, ".rmf") huh = "./models/CLUSTER%d"%i huh = huh +".rmf" #print "file is", tfn print "file is", huh rh = RMF.create_rmf_file(huh)

IMP.rmf.add_hierarchies(rh, h)

# add the current configuration to the file as frame 0 IMP.rmf.save_frame(rh)

#for g in gs: # rh.add_geometry(g)

#****************************************************************************************** # now do the actual work

(m,all)= create_representation() #IMP.atom.show_molecular_hierarchy(all) create_restraints(m, all)

# in order to display the results, we need something that maps the particles onto # geometric objets. The IMP.display.Geometry objects do this mapping. # IMP.display.XYZRGeometry map an IMP.core.XYZR particle onto a sphere gs=[] for i in range(all.get_number_of_children()): color= IMP.display.get_display_color(i) n= all.get_child(i) name= n.get_name() g= IMP.atom.HierarchyGeometry(n) g.set_color(color) gs.append(g)

cs= get_conformations(m)

print "found", cs.get_number_of_configurations(), "solutions"

ListScores = [] for i in range(0, cs.get_number_of_configurations()): cs.load_configuration(i) # print the configuration print "solution number: ",i,"scored :", m.evaluate(False) ListScores.append(m.evaluate(False))

# for each of the configuration, dump it to a file to view in pymol for i in range(0, cs.get_number_of_configurations()): cs.load_configuration(i) h = IMP.atom.Hierarchy.get_children(all) #tfn = IMP.create_temporary_file_name("josh%d"%i, ".rmf") #print "file is", tfn huh = "./models/IMP%d"%i huh = huh +".rmf" print "file is", huh rh = RMF.create_rmf_file(huh)

# add the hierarchy to the file IMP.rmf.add_hierarchies(rh, h)

# add the current configuration to the file as frame 0 IMP.rmf.save_frame(rh)

#for g in gs: # w.add_geometry(g)

analyze_conformations(cs, all, gs)