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Today's Topics:
 Â1. Re: Sampling and writing to pym/rmf (Barak Raveh)
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Message: 1
Date: Tue, 1 Jul 2014 09:31:33 -0700
From: Barak Raveh <">>
To: Help and discussion for users of IMP <">>
Subject: Re: [IMP-users] Sampling and writing to pym/rmf
Message-ID:
    <CAHp+_UowiBwJozbwOfi8yFEVt7Z8o2tEZ=">>
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Hi Josh, from a very superficial look, your code to write the RMF files
seems fine - do you get an output RMF file? Could you load it in Chimera?
On Tue, Jul 1, 2014 at 2:40 AM, Josh Bullock <">> wrote:
> Hello,
>
> I'm relatively new to all this so please let me know if i'm making any
> obvious errors ...
>
> Essentially all i'm trying to do is generate an ensemble of models made
> from four subunits - constrained by MS connectivity restraints. The models
> get scored but nothing seems to write to the pymol file. Ideally i'd like
> to write to an .rmf but i haven't worked that one out either ...
>
> Is this a reasonable way to go about my problem ?
>
> Many thanks,
>
> Josh
>
> -------------------------------------------
>
> 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),
> Â Â Â Â Â Â Â Â Â Â Â Â Â Â ÂIMP.algebra.Vector3D(300, 300, 300))
>
>
> # this function creates the molecular hierarchies for the various involved
> proteins
> def create_representation():
> Â Â m= IMP.Model()
> Â Â all=IMP.atom.Hierarchy.setup_particle(IMP.Particle(m))
> Â Â all.set_name("the universe")
> Â Â # create a protein, represented as a set of connected balls of
> appropriate
> Â Â # radii and number, chose by the resolution parameter and the number of
> Â Â # amino acids.
>
> Â Â def create_protein_from_pdbs(name, files):
>
> Â Â Â Â def create_from_pdb(file):
> Â Â Â Â Â Â sls=IMP.SetLogState(IMP.NONE)
> Â Â Â Â Â Â datadir = os.getcwd()
> Â Â Â Â Â Â print datadir
> Â Â t=IMP.atom.read_pdb( datadir+'/' + file, m,
> Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â ÂIMP.atom.ATOMPDBSelector())
> Â Â Â Â Â Â del sls
> Â Â Â Â Â Â #IMP.atom.show_molecular_hierarchy(t)
> Â Â Â Â Â Â c=IMP.atom.Chain(IMP.atom.get_by_type(t,
> IMP.atom.CHAIN_TYPE)[0])
> Â Â Â Â Â Â if c.get_number_of_children()==0:
> Â Â Â Â Â Â Â Â IMP.atom.show_molecular_hierarchy(t)
> Â Â Â Â Â Â # there is no reason to use all atoms, just approximate the
> pdb shape instead
> Â Â Â Â Â Â s=IMP.atom.create_simplified_along_backbone(c,
> Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â resolution/300.0)
> Â Â Â Â Â Â IMP.atom.destroy(t)
> Â Â Â Â Â Â # make the simplified structure rigid
> Â Â Â Â Â Â rb=IMP.atom.create_rigid_body(s)
> # Â Â Â Â Â Ârb=IMP.atom.create_rigid_body(c)
> Â Â Â Â Â Â rb.set_coordinates_are_optimized(True)
> Â Â Â Â Â Â return s
> # Â Â Â Â Â Âreturn c
>
> Â Â Â Â h= create_from_pdb(files[0])
> Â Â Â Â h.set_name(name)
> Â Â Â Â all.add_child(h)
>
> Â Â create_protein_from_pdbs("A", [Firstpdb])
> Â Â create_protein_from_pdbs("B", [Secondpdb])
> Â Â create_protein_from_pdbs("C", [Thirdpdb])
> Â Â create_protein_from_pdbs("D", [Fourthpdb])
> Â Â #create_protein_from_pdbs("C", ["rpt3_imp.pdb"])
> Â Â return (m, all)
>
> # create the needed restraints and add them to the model
>
> def create_restraints(m, all):
> Â Â def add_connectivity_restraint(s):
>
> Â Â Â Â tr= IMP.core.TableRefiner()
> Â Â Â Â rps=[]
> Â Â Â Â for sc in s:
> Â Â Â Â Â Â ps= sc.get_selected_particles()
> Â Â Â Â Â Â rps.append(ps[0])
> Â Â Â Â Â Â tr.add_particle(ps[0], ps)
>
> Â Â Â Â # duplicate the IMP.atom.create_connectivity_restraint
> functionality
>
> Â Â Â Â score=
> IMP.core.KClosePairsPairScore(IMP.core.HarmonicSphereDistancePairScore(0,1),tr)
>
> Â Â Â Â r= IMP.core.MSConnectivityRestraint(m,score)
>
> Â Â Â Â iA = r.add_type([rps[0]])
> Â Â Â Â iB = r.add_type([rps[1]])
> Â Â Â Â iC = r.add_type([rps[2]])
> Â Â Â Â iD = r.add_type([rps[3]])
> Â Â Â Â n1 = r.add_composite([iA, iB, iC, iD])
> Â Â Â Â n2 = r.add_composite([iA, iB], n1)
> Â Â Â Â n3 = r.add_composite([iC, iD], n1)
> Â Â Â Â n4 = r.add_composite([iB, iC, iD], n1)
>
> Â Â Â Â m.add_restraint(r)
>
> Â Â evr=IMP.atom.create_excluded_volume_restraint([all])
> Â Â m.add_restraint(evr)
> Â Â # a Selection allows for natural specification of what the restraints
> act on
> Â Â S= IMP.atom.Selection
> Â Â sA=S(hierarchy=all, molecule="A")
> Â Â sB=S(hierarchy=all, molecule="B")
> Â Â sC=S(hierarchy=all, molecule="C")
> Â Â sD=S(hierarchy=all, molecule="D")
> Â Â add_connectivity_restraint([sA, sB, sC, sD])
>
>
> # find acceptable conformations of the model
> def get_conformations(m):
> Â Â sampler= IMP.core.MCCGSampler(m)
> Â Â sampler.set_bounding_box(bb)
> Â Â # magic numbers, experiment with them and make them large enough for
> things to work
> Â Â sampler.set_number_of_conjugate_gradient_steps(100)
> Â Â sampler.set_number_of_monte_carlo_steps(20)
> Â Â sampler.set_number_of_attempts(models)
> Â Â # We don't care to see the output from the sampler
> Â Â sampler.set_log_level(IMP.SILENT)
> Â Â # return the IMP.ConfigurationSet storing all the found configurations
> that
> Â Â # meet the various restraint maximum scores.
> Â Â cs= sampler.create_sample()
> Â Â return cs
>
>
> # cluster the conformations and write them to a file
> def analyze_conformations(cs, all, gs):
> Â Â # we want to cluster the configurations to make them easier to
> understand
> Â Â # in the case, the clustering is pretty meaningless
> Â Â embed= IMP.statistics.ConfigurationSetXYZEmbedding(cs,
>
> ÂIMP.container.ListSingletonContainer(IMP.atom.get_leaves(all)), True)
> Â Â cluster= IMP.statistics.create_lloyds_kmeans(embed, 10, 10000)
> Â Â # dump each cluster center to a file so it can be viewed.
> Â Â for i in range(cluster.get_number_of_clusters()):
> Â Â Â Â center= cluster.get_cluster_center(i)
> Â Â Â Â cs.load_configuration(i)
> Â Â Â Â w= IMP.display.PymolWriter("cluster.%d.pym"%i)
> Â Â Â Â for g in gs:
> Â Â Â Â Â Â w.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()):
> Â Â color= IMP.display.get_display_color(i)
> Â Â n= all.get_child(i)
> Â Â name= n.get_name()
> Â Â g= IMP.atom.HierarchyGeometry(n)
> Â Â g.set_color(color)
> Â Â 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()):
> Â Â Â Â cs.load_configuration(i)
> Â Â Â Â # print the configuration
> Â Â Â Â print "solution number: ",i,"scored :", m.evaluate(False)
> Â Â Â Â ListScores.append(m.evaluate(False))
>
> f1 = open("out_scores.csv", "w")
> f1.write("\n".join(map(lambda x: str(x), ListScores)))
> f1.close()
>
> # for each of the configuration, dump it to a file to view in pymol
> for i in range(0, cs.get_number_of_configurations()):
> Â Â JOSH = cs.load_configuration(i)
> Â Â S= IMP.atom.Selection
> Â Â h= IMP.atom.Hierarchy.get_children(cs)
> Â Â tfn = IMP.create_temporary_file_name("josh%d"%i, ".rmf")
> Â Â rh = RMF.create_rmf_file(tfn)
>
> Â Â # add the hierarchy to the file
> Â Â IMP.rmf.add_hierarchies(rh, h)
>
> Â Â # add the current configuration to the file as frame 0
> Â Â IMP.rmf.save_frame(rh)
>
> Â Â for g in gs:
> Â Â Â Â w.add_geometry(g)
>
> analyze_conformations(cs, all, gs)
>
>
> _______________________________________________
> IMP-users mailing list
> ">
> https://salilab.org/mailman/listinfo/imp-users
>
>
--
Barak
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