- modeller_usage - salilab.org

resolution
by Marc De Maeyer 30 Apr '09

30 Apr '09

After executing the modeller compare.py command I noticed that the resolution is incorrectly listed in the log file. as an example the resolution for the 1GV0.pdb this is not 2.0 but 2.5 REMARK 2 RESOLUTION. 2.5 ANGSTROMS. Do I interprete this value incorrectly? .------------------------------------------ 1a5zA @2.0 62.0000 | .------------------------------------------------ 1ez4B @2.0 69.3750 | | .----------------------------------------- 2a92A @2.0 61.0000 | | | .------------------------------------------- 2ewdA @2.0 62.7500 | | | | .--- 1guzA @2.0 13.0000 | | | .-------------------------------------------------------- 1gv0A @2.0 78.4375 | | .---------------------------- 2cmdA @1.0 44.0000 | | .---------------------------------------------------------- 2dfdA @1.0 81.8516 | | .----------- 1b8pA @1.9 23.0000 | | | .------------------------------------- 3d5tA @2.0 55.5000 | | | .-------------------------------------------------------- 1civA @2.0 79.0000 | | .------------------------------------------------------------ 3fi9A @1.0 many thanks, Marc De Maeyer Leuven Belgium

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Fwd: Multiple Alignment Error
by •·.·´¯`·.·• Iƒƒ¥ •·.·´¯`·.·• 27 Apr '09

27 Apr '09

Hello, I m having problems in multiple alignment through modeller. I have protien sequence in fasta format and am doing alignment from the start. For this purpose i have used model-full.py file but it gives the following error. I am unable to understand the error. What I m doing wrong? Files are attached Regards, Thanks

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A problem for using loop models: the metal ions came together too close
by wayj86 wayj86 22 Apr '09

22 Apr '09

Hi, Yesterday I wanted to build a metalloprotease which had 4 metal ions (my modeller version is 9v4). Due to the low similarity with the templates, some loops emerged in the resulting models. Althought GA341 scores ==1 and DOPE score are ≈-30119，when I checked them with Procheck and Errat, the stereochemical quality and overall quality factor were somewhat poor. So I decided to try the loop modelling and loop optimization to improve the models since no better templates could be found. The script was below: # Homology modeling by the automodel class from modeller import * # Load standard Modeller classes from modeller.automodel import * # Load the automodel class log.verbose() # request verbose output env = environ() # create a new MODELLER environment to build this model in # directories for input atom files env.io.atom_files_directory = './:../atom_files' # Read in HETATM records from template PDBs env.io.hetatm = True a = loopmodel(env, alnfile = 'MPD.ali', # alignment filename knowns = ('a','b','c','d','e'), # sequence = 'MPD', assess_methods=(assess.DOPE, assess.GA341)) a.starting_model= 1 # index of the first model a.ending_model = 10 # index of the last model # Very thorough VTFM optimization: a.library_schedule = autosched.slow a.max_var_iterations = 300 # Thorough MD optimization: a.md_level = refine.slow a.loop.starting_model = 1 # First loop model a.loop.ending_model = 4 # Last loop model a.loop.md_level = refine.fast # Loop model refinement level # Repeat the whole cycle 2 times and do not stop unless obj.func. > 1E6 a.repeat_optimization = 3 a.max_molpdf = 1e6 a.make() # Get a list of all successfully built models from a.outputs ok_models = filter(lambda x: x['failure'] is None, a.outputs) # Rank the models by DOPE score key = 'DOPE score' ok_models.sort(lambda a,b: cmp(a[key], b[key])) # Get top model m = ok_models[0] print "Top model: %s (DOPE score %.3f)" % (m['name'], m[key]) After running again I found that althought in some models the overall quality factor did increase, in all of them the metal ions were almost merged. Their coordinates were taken too close together. So I was wondering was it because the resid for ions were numbered after the protein residues (in my model the resid for ions were 312, 313, 314 and 315) and loop modelling tended to put them together? If so how should I modify the script and multiple alignment file ( in multiple alignment file for the target the initial and final residues must be designated, like "sequence:MPD:1 : :311 : ::: :", if I hope to redefine the resids for ions, some change may also be required in this file) ? Thank you very much for any help. Best wishes, Stanley -- The future is now!

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Modelling a multiheme cytochrome
by Stephen.Hearnshaw＠uea.ac.uk 07 Apr '09

07 Apr '09

Dear all, Many thanks for your previous help with my copper binding proteins and I will apologies in advance for the length of this query! I require some guidance with a modelling problem I am having. I am attempting to model the structure of a 258 residue segment of a decaheme cytochrome, I have template models covering the positions of the 10 hemes and the first 223 residues. I have also implemented several distance and bond angle restraints on the heme binding residues in an attempt to correctly orientate them around the hemes, however when I run modeller some of the restraints dont seem to take affect. A specific example is residue 258 that (if I have implemented the script correctly) should be within 2.0A of the Fe atom from heme 268, however, in the final model it is 50A away from the heme and has been pulled 50A apart. The following is the complete script I have used for my modelling attempts: --------------------------------------------------------------------------- # Addition of restraints to the default ones from modeller import * from modeller.automodel import * # Load the automodel class log.verbose() class MyModel(automodel): def select_atoms(self): return selection(self.residue_range('1:', '258:')) def special_restraints(self, aln): rsr = self.restraints at = self.atoms # Restrict secondary structure, Residues that should be an alpha helix: rsr.add(secondary_structure.alpha(self.residue_range('16:', '19:'))) rsr.add(secondary_structure.alpha(self.residue_range('42:', '43:'))) rsr.add(secondary_structure.alpha(self.residue_range('48:', '49:'))) rsr.add(secondary_structure.alpha(self.residue_range('70:', '73:'))) rsr.add(secondary_structure.alpha(self.residue_range('78:', '80:'))) rsr.add(secondary_structure.alpha(self.residue_range('84:', '87:'))) rsr.add(secondary_structure.alpha(self.residue_range('132:', '134:'))) rsr.add(secondary_structure.alpha(self.residue_range('150:', '151:'))) rsr.add(secondary_structure.alpha(self.residue_range('162:', '164:'))) rsr.add(secondary_structure.alpha(self.residue_range('170:', '178:'))) rsr.add(secondary_structure.alpha(self.residue_range('216:', '222:'))) rsr.add(secondary_structure.alpha(self.residue_range('254:', '256:'))) # Add distance restraints to heme binding CXXCH residues: rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:41'], at['CAB:260']), mean=1.825, stdev=0.01)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:44'], at['CAC:260']), mean=1.825, stdev=0.01)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:45'], at['FE:260']), mean=2.0, stdev=0.1)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:77'], at['CAB:261']), mean=1.825, stdev=0.01)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:80'], at['CAC:261']), mean=1.825, stdev=0.01)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:81'], at['FE:261']), mean=2.0, stdev=0.1)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:101'], at['CAB:262']), mean=1.825, stdev=0.01)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:104'], at['CAC:262']), mean=1.825, stdev=0.01)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:105'], at['FE:262']), mean=2.0, stdev=0.1)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:124'], at['CAB:263']), mean=1.825, stdev=0.01)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:127'], at['CAC:263']), mean=1.825, stdev=0.01)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:128'], at['FE:263']), mean=2.0, stdev=0.1)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:150'], at['CAB:264']), mean=1.825, stdev=0.01)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:153'], at['CAC:264']), mean=1.825, stdev=0.01)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:154'], at['FE:264']), mean=2.0, stdev=0.1)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:174'], at['CAB:265']), mean=1.825, stdev=0.01)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:177'], at['CAC:265']), mean=1.825, stdev=0.01)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:178'], at['FE:265']), mean=2.0, stdev=0.1)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:196'], at['CAB:266']), mean=1.825, stdev=0.01)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:199'], at['CAC:266']), mean=1.825, stdev=0.01)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:200'], at['FE:266']), mean=2.0, stdev=0.1)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:219'], at['CAB:267']), mean=1.825, stdev=0.01)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:222'], at['CAC:267']), mean=1.825, stdev=0.01)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:223'], at['FE:267']), mean=2.0, stdev=0.1)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:8'], at['CAB:259']), mean=1.825, stdev=0.01)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:11'], at['CAC:259']), mean=1.825, stdev=0.01)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:12'], at['FE:259']), mean=2.0, stdev=0.1)) rsr.add(forms.upper_bound(group=physical.upper_distance, feature=features.distance(at['SG:254'], at['CAB:268']), mean=1.825, stdev=0.1)) rsr.add(forms.upper_bound(group=physical.upper_distance, feature=features.distance(at['SG:257'], at['CAC:268']), mean=1.825, stdev=0.1)) rsr.add(forms.upper_bound(group=physical.upper_distance, feature=features.distance(at['NE2:258'], at['FE:268']), mean=2.0, stdev=0.1)) # Add distance restraints to the other heme binding His residues: rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:51'], at['FE:259']), mean=2.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:108'], at['FE:260']), mean=2.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:26'], at['FE:261']), mean=2.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:94'], at['FE:262']), mean=2.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:93'], at['FE:263']), mean=2.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:203'], at['FE:264']), mean=2.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:141'], at['FE:265']), mean=2.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:189'], at['FE:267']), mean=2.0, stdev=0.1)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['NE2:228'], at['FE:268']), mean=2.0, stdev=0.1)) # Set distance restraints for Heme binding His-His residues at 4.0A rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:12'], at['NE2:51']), mean=4.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:45'], at['NE2:108']), mean=4.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:81'], at['NE2:26']), mean=4.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:105'], at['NE2:94']), mean=4.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:128'], at['NE2:93']), mean=4.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:154'], at['NE2:203']), mean=4.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:178'], at['NE2:141']), mean=4.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:223'], at['NE2:189']), mean=4.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:258'], at['NE2:228']), mean=4.0, stdev=0.1)) # Set bond angles for iron coordinating HIS residues of CXXCH motifs rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:12'], at['FE:259'], at['CHA:259']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:45'], at['FE:260'], at['CHA:260']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:81'], at['FE:261'], at['CHA:261']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:105'], at['FE:262'], at['CHA:262']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:128'], at['FE:263'], at['CHA:263']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:154'], at['FE:264'], at['CHA:264']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:178'], at['FE:265'], at['CHA:265']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:200'], at['FE:266'], at['CHA:266']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:223'], at['FE:267'], at['CHA:267']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:258'], at['FE:268'], at['CHA:268']), mean=1.63, stdev=0.1)) # Set bond angles for other iron coordinating HIS residues rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:51'], at['FE:259'], at['CHA:259']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:108'], at['FE:260'], at['CHA:260']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:26'], at['FE:261'], at['CHA:261']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:94'], at['FE:262'], at['CHA:262']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:93'], at['FE:263'], at['CHA:263']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:203'], at['FE:264'], at['CHA:264']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:141'], at['FE:265'], at['CHA:265']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:189'], at['FE:267'], at['CHA:267']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:228'], at['FE:268'], at['CHA:268']), mean=1.63, stdev=0.1)) # Restrain heme binding His-Fe-His bond angles rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['NE2:12'], at['FE:259'], at['NE2:51']), mean=3.07, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['NE2:45'], at['FE:260'], at['NE2:108']), mean=3.07, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['NE2:81'], at['FE:261'], at['NE2:26']), mean=3.07, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['NE2:105'], at['FE:262'], at['NE2:94']), mean=3.07, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['NE2:128'], at['FE:263'], at['NE2:93']), mean=3.07, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['NE2:154'], at['FE:264'], at['NE2:203']), mean=3.07, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['NE2:178'], at['FE:265'], at['NE2:141']), mean=3.07, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['NE2:223'], at['FE:267'], at['NE2:189']), mean=3.07, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['NE2:258'], at['FE:268'], at['NE2:228']), mean=3.07, stdev=0.1)) env = environ() env.io.hetatm = True # directories for input atom files env.io.atom_files_directory = ['.', '../atom_files'] a = MyModel(env, alnfile = 'alignment.ali', # alignment filename knowns = ('2OZY','MtrAst'), # codes of the templates sequence = 'MtrA', # code of the target assess_methods=(assess.DOPE, assess.GA341)) # To keep the hemes from being moved def select_atoms(self): self.pick_atoms(selection_segment=('1:', '258:'), selection_search='segment', pick_atoms_set=1, res_types='all', atom_types='all', selection_from='all', selection_status='initialize') a.starting_model= 1 # index of the first model a.ending_model = 1 # index of the last model # (determines how many models to calculate) a.make() # do homology modeling --------------------------------------------------------------------------- Many thanks for any help you can provide, and once again apologies for the length of this post. Stephen Hearnshaw

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Re: [modeller_usage] Filling in Missing atoms for a docking complex
by Jack Shultz 01 Apr '09

01 Apr '09

On Wed, Apr 1, 2009 at 10:55 AM, Jack Shultz <jshultz(a)hydrogenathome.org> wrote: > Hello, > > I am new to modeller and I have not gone through the tutorial yet. I > have submitted a couple of FASTA format files to mod web. I am would > like to fix a problem with my docking complexes I get from autodock. > Some of them are missing atoms in a given residue. It was suggested I > either use Deep-View or Modeller to insert missing atoms. Can modeller > indeed achieve this? Are there any existing scripts to perform this? > > -- > Jack > > http://www.facebook.com/home.php#/profile.php?id=832713248 > http://hydrogenathome.org >

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question about salt bridge
by Anna Marabotti 27 Mar '09

27 Mar '09

Dear Ben, I have a question about the possibility to introduce a restraint into a model. I obtained a model of a protein, and looking at the structure I saw that an Arg residue lies in proximity of an Asp residue, but their charged moieties are not positioned properly to infer a ion pair interaction between them. Since from other experimental data I strongly suspect that this ion pair is present (but I am not 100% certain), I would like to know if I can force somehow these two residues to interact each others, without disrupting the rest of the model. The problem is not only on the side chain, because I cannot simply rotate it or use some programs like SCWRL or so on to change the position of the side chains: also the backbone would be affected if such an ion pair would be present. Therefore I would like to know if I can add some "soft" restraints, for example to "ask" MODELLER: keep the charged moieties of Arg and Asp not farther than X A only if the backbone angles are changing not more than Y degrees to do it. Is there a way to do it? Many thanks and best regards Anna ______________________________________________ Anna Marabotti, Ph.D. Laboratory of Bioinformatics and Computational Biology Institute of Food Science, CNR Via Roma 64 83100 Avellino (Italy) Phone: +39 0825 299651 Fax: +39 0825 781585 Skype: annam1972 E-mail: amarabotti(a)isa.cnr.it Web page: http://bioinformatica.isa.cnr.it/anna/anna.htm ____________________________________________________ "If you think you are too small to make a difference, try sleeping with a mosquito"

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Rosetta Academic Training Webinar
by Nir London 25 Mar '09

25 Mar '09

The Rosetta Design Group is proud to present the first webinar in the Rosetta Academic Workshop Series. For the first webinar, we have selected to focus on Protein-Protein Docking based on the answers to the interest poll. We hope this will be the first in a line of helpful and inspiring webinars to kick-off our Rosetta Academic Workshop Series. What: Protein-Protein Docking When: May 4th 2009, 0800-1000 AM EST Where: Your office! Click here for more details and registration (For non html emails: http://rosettadesigngroup.com/RDGLS/index.php?sid=54479&lang=en ) Pleas note: This is not a promotional webinar. Rosetta is open-source and freeware for academic and non-profit organizations and can be downloaded here from University of Washington's TechTransfer Digital Ventures. The majority of the webinar is concerned with Rosetta 2.3.0. Rosetta 3.0 is still a beta version. Hope to see you there, Nir London. Rosetta Design Group | http://rosettadesigngroup.com/

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side-chain chi1 different from the templates
by Aline Thomas 23 Mar '09

23 Mar '09

Dear Modeller's I have built a 3D model on 4 structures aligned from Clustalw. In the model built, there is a threonine that bears a dihedral angle different from all equivalent threonines of the templates. Locally I cannot see why this threonine should be different. Would you help me to explain this unprecedented side-chain orientation ? Thank you Aline -- Dr. Aline Thomas Institut de Biologie Structurale Laboratoire de Dynamique Moleculaire 41 rue Jules Horowitz 38027 Grenoble cedex France tel : (+33) (0)4 38 78 95 72 fax : (+33) (0)4 38 78 54 94

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Subject: refinement of protein-DNA complex
by Mauricio Carrillo Tripp 19 Mar '09

19 Mar '09

From: Jan Kosinski <jan.kosinski(a)gmail.com> Subject: [modeller_usage] refinement of protein-DNA complex I don't think modeller can deal with dna molecules (yet). it only models proteins, and leave dna/rna as is... but you can search the mailing list, i recall reading about this subject some time ago. -- 0 | Mauricio Carrillo Tripp, PhD / | Department of Molecular Biology, TPC6 0 | The Scripps Research Institute \ | 10550 North Torrey Pines Road 0 | La Jolla, California 92037 / | trippm(a)scripps.edu 0 | http://www.scripps.edu/~trippm ** Aut tace aut loquere meliora silentio **

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refinement of protein-DNA complex
by Jan Kosinski 19 Mar '09

19 Mar '09

Hi, I use Modeller 9v5 for modeling protein-DNA complex. I include DNA during modeling. I wanted to try with optimization of protein-DNA interface using a.md_level = refine.slow_large and noticed that although the protein part changes significantly comparing to the template, the DNA part stays very similar to the template. Should I use another method for turning on the optimization of DNA itself? Or better I should not try with optimization of DNA using Modeller ;-) ? Cheers, Janek Kosinski

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