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How to use IMP
by 张春雷 29 Sep '11

29 Sep '11

Dear all, I am trying to use IMP as a C++ library for my modeling work: using some functions of IMP in my own code. But I get no idea how to use it. One way I tried is to put my own code in the "applications" directory and put a Sconscript file along with it. But how to compile my code? Could anyone tell my some details? Thanks a lot! -Justin

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IMP.restrainer examples
by Jascha Casadio 20 Sep '11

20 Sep '11

Hi there. I was checking the IMP.restrained examples and I have a couple of questions about it. The rigid_body_and_excluded_volume_restrain.py example shows how to get proteins from multiple PDBs and enforce upon them connectivity restraints, but doesn't give any graphical representation of the whole assembly (before/after). The basic_display.py example does show how to create Chimera logs to store optimization states, but the rigid_body_and_excluded_volume_restrain.py example doesn't do any optimization. So, how do I simply see the assembly produced in rigid_body_and_excluded_volume_restrain.py before and after the restraints have been applied (the five proteins connected without crashing into each other). I've actually tried to play with it myself, but the results are far from what I've desired. The representation and restraints XML files are exaclty those coming with the IMP examples: five proteins loaded from PDB files, not allowed to crash into each other and connected (1,2,3;4,5). I've created a display file so that each particle has its own color.  <Display> �� <Protein id="Protein1"><Color r="0.15" g="0.58" b="0.66"/></Protein> �� <Protein id="Protein2"><Color r="0.38" g="0.04" b="0.37"/></Protein> �� <Protein id="Protein3"><Color r="0.84" g="0.78" b="0.24"/></Protein> �� <Protein id="Protein4"><Color r="0.62" g="0.47" b="0.45"/></Protein> �� <Protein id="Protein5"><Color r="0.00" g="0.71" b="0.01"/></Protein> </Display> I've also modified the file rigid_body_and_excluded_volume_restraint.py so that first the display information is loaded then, once restraints are enforced, the model is optimized (I've removed the lines showing how to manipulate/rotate a rigid body). #-- File: rigid_body_and_excluded_volume_restraint.py --# import IMP import IMP.restrainer import IMP.display # Load representation rep_parser = IMP.restrainer.XMLRepresentation('input/eg2_representation.xml') representation = rep_parser.run() # Load restraints restraint_parser = IMP.restrainer.XMLRestraint('input/eg2_restraint.xml') restraint = restraint_parser.run() # Load display display_parser = IMP.restrainer.XMLDisplay('input/eg2_display.xml') display = display_parser.run() model = representation.to_model() # Write initial display in Chimera format log = display.create_log(representation, 'pdb_log_%03d.py') log.write('rigid_display_initial.py') # Place restraint into IMP model restraint.add_to_representation(representation) ###=======================================================================### #� At this point all data from XML files have been placed into the model. #� Now it is possible to perform various operations on the IMP model. ###=======================================================================### # Optimize opt = IMP.core.ConjugateGradients() opt.set_model(model) opt.add_optimizer_state(log) opt.optimize(10) # Write final display in Chimera format log.write('rigid_display_optimized.py') First, I've noticed that in the initial representation, I can only see one of the molecules. I assume this is because they don't have an initial position (unlike when I use spheres), so they can be anywhere. Using the ConjugateGradients optimization makes it crash: _IMP.UsageException: Attempting to construct a rotation from a� non-quaternion value. The coefficient vector must have a length of 1. Got: 0.377264 0.0876134 0.185363 1.48248 gives 2.38212. Using MonteCarlo or SteepestDescent optimizations (opt = IMP.core.MonteCarlo()) doesn't seem to change the model score. In the end, I end up with an optimized display which looks exactly the same as the initial one, that is to say, I can only see one (the first of the five) proteins. I guess this is caused by the fact that optimizing doesn't change the score of the final model. Nevertheless, I was simply trying to diplay the five proteins before and after the connectivity and excluded volume restraints were applied... Hope you can help. Thanks

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coarse models with MCCG sampler : biased ?
by Benjamin SCHWARZ 20 Sep '11

20 Sep '11

Hi guys, I am currently interested in the coarse modeling of a small (10 subunits) system for which I have nothing but a few interactomics data as well as some (parts of) structures to help me to assess the size and number of beads to represent some of the subunits (details on my OS as well as on the modeled system can be found below.). Thanks to the nup84 example, I was rapidly able to generate a large number of configurations and to visualize it (thanks for the MCCG sampler and the IMP.display.PymolWriter). I am however puzzled by the look of the obtained configurations : having represented my subunits as a succession of beads (I explicitly defined my restraints between successive balls), I expected my subunits to accommodate various types of shapes whereas they seem to have a tendency to adopt preferentially a straight aligned rod-like shape ( An example of a pymol file containing 1000 configurations can be downloaded for visualization at alnitak.u-strasbg.fr/~schwarz/Perso/1000-configs.pym). I am now wondering if these configurations are really random generated, or if -as I fear- I have a bias in my sampling scheme, and how I can circumvent the trouble. Here is one explanations I could find to this apparent behavior : I don't know how the excluded volume is handled by the MCCG sampler, but my guess is that the CG step helps a lot in "locally" optimizing this particular restraint. Now, if all balls are considered in the computation of derivatives (and not only those that really present a common intersection), it is intuitively quite probable that the system will ultimately push successive balls the furthest away from each other, hence favoring rod-like shapes upon epileptic-snake-like ones. Does this assumption makes any sense ? I am running IMP kernel SVN 10177 with Boost.FileSystem on Mac OS My system is composed of - 10 subunits (divided in 30 beads to represent specific domains) - 29 restraints to enforce pair connectivity between domains of a same subunits, or domains of different subunits for which a connectivity is known - 2 ambiguous connections between several domains or subunits - 1 excluded volume restraint between every 30 particles in the system. --Ben (S)

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get_connected_pairs
by Pia Unverdorben 15 Sep '11

15 Sep '11

Dear all, I try to access the connected pairs of a connectivity restraint via get_connected_pairs(). Directly after defining the restraint, everything works fine. Now my problem is, that after adding it to a RestraintSet and retrieving it, the same function always gives an attribute error. Show() tells me in both cases that it is a connectivity restraint, so get_connected_pairs should work the same way? My test case is below (modified from the connectivity restraint example). What is wrong? Is there any possibility to get this connected pairs out of the restraint set? Any help is greatly appreciated! Thanks, Pia import IMP import IMP.core import IMP.misc import IMP.algebra import IMP.atom m=IMP.Model() hs=[] # create the molecules, with 5 particles for each of 10 molecules for i in range(0,10): p=IMP.Particle(m) d= IMP.atom.Hierarchy.setup_particle(p) for j in range(0,5): p=IMP.Particle(m) cd= IMP.atom.Fragment.setup_particle(p) d.add_child(cd) xd= IMP.core.XYZR.setup_particle(p, IMP.algebra.Sphere3D(IMP.algebra.Vector3D(3*i,j,0), 1)) hs.append(p) ps= IMP.core.SphereDistancePairScore(IMP.core.HarmonicUpperBound(0,1)) cps= IMP.core.ChildrenRefiner(IMP.atom.Hierarchy.get_traits()) # score based on the one closest particle from each set of balls lrps = IMP.misc.LowestRefinedPairScore(cps, ps) #lrps = IMP.core.KClosePairsPairScore(ps, cps, 1) # connect all 10 molecules together cr = IMP.core.ConnectivityRestraint(lrps) cr.set_particles(hs) rs_set=IMP.RestraintSet('connectivity') m.add_restraint(rs_set) rs_set.add_restraint(cr) cr.show() print len(cr.get_connected_pairs()) print m.evaluate(False) for i in range(0,rs_set.get_number_of_restraints()): res=rs_set.get_restraint(i) res.show() if res.get_is_part_of_model(): print "is part of model" pairs=res.get_connected_pairs() print len(pairs)

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MCCGsampler.set_number_of_attempts
by Benjamin SCHWARZ 09 Aug '11

09 Aug '11

Hi List, Thanks for the MCCGsampler, I am currently using it and its really useful. Just to be sure of that, am I correct to think that running 50 times MCCGsampler.get_sampler() with a preliminary set_number_of_attempts(1000) would be similar to running it just once with a preliminary set_number_of_attempts(50000) ? --Ben.S

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Trying to use MultiFit but I have problems with installing/running IMP
by Tolun, Gökhan 12 Jul '11

12 Jul '11

Dear IMP users, I followed the directions on http://salilab.org/imp/1.0/doc/html/installation.html to install IMP (I even installed the optional dependencies/Prerequisites installed. I got MultiFit from http://modbase.compbio.ucsf.edu/multifit/download.cgi I am trying to go through the tutorial for MultiFit on http://www.integrativemodeling.org/1.0/tutorial/multifit.html but when I enter the first command /opt/multifit/utils/run_anchor_points_detection.py assembly.input 700 It returns Traceback (most recent call last): File "/opt/multifit/utils/run_anchor_points_detection.py", line 7, in <module> import IMP ImportError: No module named IMP I looked at http://salilab.org/imp/archives/imp-users/msg00075.html but trying the suggestions there did not solve this problem. I would appreciate any suggestions. Though I can (barely) get by, I am not by any means a Linux guru, nor I am a coder, so please keep this in mind in your replies. I am using Ubuntu 10.04 LTS - the Lucid Lynx with all updates to date installed. Thank you. Gökhan

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[Coarse grain modeling] a bunch of questions and comments regarding the nup84 cg example
by Benjamin SCHWARZ 24 Jun '11

24 Jun '11

Hi list, I am currently considering IMP functionalities for coarse grain modeling, and the nup84 cg example is really helpful, thanks a lot for that ! Nonetheless, it raises some questions and remarks that I will detail hereafter. I know the mail is quite big, but I hope it can help with improving not only my comprehension, but also maybe the IMP documentation. I. Concerning the representation I discovered the IMP.atom helper functions, that seem to help quite a lot to get rid of many technical details. The exact behavior of each of these function is nevertheless still a bit elusive to me IMP.atom.create_protein, IMP.atom.create_connectivity_restraint, IMP.atom.create_distance_restraint. 1. I think there is a minor bug in the documentation for IMP.atom.create_protein which states that a hierarchy of balls is created, and "The balls are held together by a ConnectivityRestraint with the given spring constant.". The second assertion seems erroneous to me. I would probably also add some details on the hierarchy returned by the first occurrence of IMP.atom.create_protein, since it differs from the second one (another second hierarchical level seems indeed to be inserted). 2. In the nup84 cg example, the connection between the balls of a same protein is made in h=IMP.atom.create_protein(m, name, resolution, ds) with a call to r=IMP.atom.create_connectivity_restraint([IMP.atom.Selection(c ) for c in h.get_children()],k) …And after dissection of the function's code I am still wondering what is exactly done. In my understanding, when applied to a hierarchy returned by IMP.atom.create_protein(), the generated restraint is always created through a ConnectingPairContainer, which connects balls in a tree like structure, and I cannot see how and when this tree is built. Plus, I basically was expecting something pretty much simple, such as a distance restraint applied between the successive fragments in the molecule. 3. Nothing very important, just a bit noisy/confusing : in create_protein() sub-function, the leaves variable leaves= IMP.atom.get_leaves(h) is never used… So why not just stripping it ? 4. minor bug in the documentation : some occurrences of create_connectivity_restraint() have no mentioned return type. 5. When it comes to inserting inter-molecules restraints, I think I understand the meaning of the two functions : add_connectivity_restraint and add_distance_restraint, but I'd like to be sure of that : the first one enforces the specified molecules to be somehow connected (technically : consider each molecule as a node in a complete graph, weight each edge (A,B) with the smallest distance computed for a pair of particles belonging to AxB (thanks to KClosePairsPairScore), then computing MST and deriving score (thanks to ConnectivityRestraint).) the second one merely favors the two molecular hierarchies to be in exact contact (given the two molecules A and B test the closest pair of particles in AxB, and return the score for that pair of particles). II. Concerning the sampling part I am not sure to understand how the MCCG sampler works. In my understanding, the sampler uses an optimizer to improve a set of initial random solutions, hence generating several putative solutions, or at least not so bad ones (a sample). In this context, the two lines : sampler.set_number_of_conjugate_gradient_steps(100) sampler.set_number_of_monte_carlo_steps(50) Merely control each optimization step, whereas sampler.set_number_of_attempts(40) controls the number of initial (or final ?) retained solutions. Am I correct or at least near enough ? III. Concerning the analysis part of the example : 1. The last argument of : embed= IMP.statistics.ConfigurationSetXYZEmbedding(cs, IMP.container.ListSingletonContainer(IMP.atom.get_leaves(all)), True) is not documented. Even though the name of the variable "bool align=false" is quite suggestive, I have an issue to guess the type of alignment that is considered here. Maybe a simple question can help to leverage my problem : Let's say I have two configurations that can be derived from one another through a simple rotation°translation; does setting this parameter to true help me to have the same embedding for each conformations, and hence classify both in the same cluster ? 2. In the analyze_conformations() function, I think the line cs.load_configuration(i) ought to be replaced by cs.load_configuration( cluster.get_cluster_representative(i) ) Thanks a lot for all your work, and efforts. --Ben

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IMP.hdf5 changes
by Daniel Russel 22 Apr '11

22 Apr '11

As my initial naming scheme for the IMP methods in the hdf5 module seems to have confused everyone, I've changed it to a new, hopefully clearer, one. Sorry about the change, but I think it will make the model much easier to figure out. The new methods treat the files as objects rather than IO in light of the persistent state that is associated with them. Changes are - write_X -> add_X - save_configuration -> save_frame - load_configuration -> load_frame - read_all_X -> create_X To be more consistent with similarly named IMP methods, all methods take the RootHandle as the first argument. If they take a frame, it is the second argument. Hope this helps things. The module overview docs have been improved a bit too.

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Strange Pair Restraint behaviour
by Iain Moal 10 Apr '11

10 Apr '11

Hello IMP users, My pair restraints aren’t behaving as they should. I have a system with multiple domains which I want to move as rigid bodies while having a pair restraint between the end of the first domain and the beginning of the next. My input .pdb files are coarse-grained using IMP.atom.create_simplified_along_backbone. My approach was to take the coordinates of the alpha carbons I want to restrain, add the two points into my coarse-grained RigidBody objects, and add a pair restraint between them, see the following code. The funny thing is, as my initial .pdb files satisfy this restraint, an initial energy evaluation comes out as zero, as it should do. However, if I perturb the system and minimise with the MC mover, the energy starts high and reduces down to almost zero, as it should, but the distances between adjacent domains of the final structure are much higher than they should be – often over 20A away from the HarmonicUpperBound. I have set my_MC_optimiser.set_return_best(True), so this isn’t the reason. Does anyone have any idea what is wrong? Many thanks, Iain Moal Hingedata = [[9,0,34.885,-57.443,9.494,23.464,-48.297,6.885],… and so on] rigbods=[] for abcd in IMP.atom.get_by_type(all, IMP.atom.CHAIN_TYPE): rigbods.append(IMP.core.RigidBody(IMP.Decorator.get_particle(IMP.atom.Hierarchy.get_as_chain(abcd)))) for x in hingedata: p1 = IMP.Particle(m) p2 = IMP.Particle(m) XYZ_a= IMP.core.XYZR.setup_particle(p1,IMP.algebra.Sphere3D(IMP.algebra.Vector3D(x[2],x[3],x[4]),0.01)) XYZ_b= IMP.core.XYZR.setup_particle(p2,IMP.algebra.Sphere3D(IMP.algebra.Vector3D(x[5],x[6],x[7]),0.01)) #NB) THIS DOESN’T WORK HERE: XYZ_a= IMP.core.XYZ.setup_particle(p1,IMP.algebra.Vector3D(x[2],x[3],x[4])) # NB) THIS DOESN’T WORK HERE:XYZ_b= IMP.core.XYZ.setup_particle(p2,IMP.algebra.Vector3D(x[5],x[6],x[7])) rigbods[x[0]].add_member(XYZ_a) rigbods[x[1]].add_member(XYZ_b) r= IMP.core.PairRestraint(IMP.core.DistancePairScore(IMP.core.HarmonicUpperBound(20,5.0)),IMP.ParticlePair(p1,p2)) m.add_restraint(r) I read my .pdb files using this: all.add_child(create_from_pdb(“filename”)) def create_from_pdb(file): t=IMP.atom.read_pdb(file, m, IMP.atom.ATOMPDBSelector()) IMP.atom.add_radii(t) c=IMP.atom.Chain( IMP.Decorator.get_particle(IMP.atom.Hierarchy.get_as_chain(IMP.atom.get_by_type(t, IMP.atom.CHAIN_TYPE)[0] ))) s=IMP.atom.create_simplified_along_backbone(c,4) IMP.atom.destroy(t) IMP.Decorator.get_particle(IMP.atom.Hierarchy.get_as_chain(IMP.atom.get_by_type(s, IMP.atom.CHAIN_TYPE)[0] )) rb=IMP.atom.setup_as_rigid_body(s) rb.set_coordinates_are_optimized(True) return s This communication is from Cancer Research UK. Our website is at www.cancerresearchuk.org. We are a registered charity in England and Wales (1089464) and in Scotland (SC041666) and a company limited by guarantee registered in England and Wales under number 4325234. Our registered address is Angel Building, 407 St John Street, London, EC1V 4AD. Our central telephone number is 020 7242 0200. This communication and any attachments contain information which is confidential and may also be privileged. It is for the exclusive use of the intended recipient(s). If you are not the intended recipient(s) please note that any form of disclosure, distribution, copying or use of this communication or the information in it or in any attachments is strictly prohibited and may be unlawful. If you have received this communication in error, please notify the sender and delete the email and destroy any copies of it. E-mail communications cannot be guaranteed to be secure or error free, as information could be intercepted, corrupted, amended, lost, destroyed, arrive late or incomplete, or contain viruses. We do not accept liability for any such matters or their consequences. Anyone who communicates with us by e-mail is taken to accept the risks in doing so.

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Question about conditional restraints
by Davide Bau 04 Apr '11

04 Apr '11

Hi everyone, I need to implement a conditional restraint to account for multiple copies of the same particles in my data set, ie to account for interaction ambiguity. Are conditional restraints implemented in IMP? Thanks! Davide -- Davide Baù Structural Genomics Unit Bioinformatics & Genomics Department, Prince Felipe Research Center Avda. Autopista del Saler 16, 46012 Valencia, Spain Tel: +34 96 328 96 80 (ext. 1004) Fax: +34 96 328 97 01 email: dbau(a)cipf.es web: http://bioinfo.cipf.es/dbau/

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