hi Parisa Farrokh,

U can do multi-template modeling using modeller. Like first u take the two template sequence along with target sequence in fasta format then do alignment in multalin interface server (http://multalin.toulouse.inra.fr/multalin/). Then download the "Results as a fasta file" link will be there. use this alignment for your alignment input. Download the templates pdb files also and keep in one folder. Now we need two files to run the modeller. one is "align-multiple.ali" and "model-multiple.py". First copy the sequence format from the downloaded fasta file format to the align-multiple.ali file. Mention the name and starting number and ending number of pdb templates should be mentioned properly in the alignment file. Then edit the model-multiple.py file and give the input file correctly and save then run. If u installed python 2.5 above version installed then no need run in command prompt. Just right click on the model-multiple.py file then click the "edit with IDLE" then what ever edition u want u can then run the program by pressing the F5 key.

I given general procedure to do multiple template modelling. If u have further query ask me. All the best for your good research work.............

Dear Kannan Muthu

Thank you for your attention. I used three command lines introduced for multiple alignment in Modeller tutorial (see below), and I cannot understand all the steps you mentioned. By the way, I have problem for introduce some limitations to the command lines in order to make a chimeric model from two templates as I mentioned earlier.

1)

# Illustrates the SALIGN multiple structure/sequence alignment

from modeller import *

log.verbose()

env = environ()

env.io.atom_files_directory = './:../atom_files/'

aln = alignment(env)

for (code, chain) in (('2mdh', 'A'), ('1bdm', 'A'), ('1b8p', 'A')):

mdl = model(env, file=code, model_segment=('FIRST:'+chain, 'LAST:'+chain))

aln.append_model(mdl, atom_files=code, align_codes=code+chain)

for (weights, write_fit, whole) in (((1., 0., 0., 0., 1., 0.), False, True),

((1., 0.5, 1., 1., 1., 0.), False, True),

((1., 1., 1., 1., 1., 0.), True, False)):

aln.salign(rms_cutoff=3.5, normalize_pp_scores=False,

rr_file='$(LIB)/as1.sim.mat', overhang=30,

gap_penalties_1d=(-450, -50),

gap_penalties_3d=(0, 3), gap_gap_score=0, gap_residue_score=0,

dendrogram_file='fm00495.tree',

alignment_type='tree', # If 'progresive', the tree is not

# computed and all structues will be

# aligned sequentially to the first

feature_weights=weights, # For a multiple sequence alignment only

# the first feature needs to be non-zero

improve_alignment=True, fit=True, write_fit=write_fit,

write_whole_pdb=whole, output='ALIGNMENT QUALITY')

aln.write(file='fm00495.pap', alignment_format='PAP')

aln.write(file='fm00495.ali', alignment_format='PIR')

aln.salign(rms_cutoff=1.0, normalize_pp_scores=False,

rr_file='$(LIB)/as1.sim.mat', overhang=30,

gap_penalties_1d=(-450, -50), gap_penalties_3d=(0, 3),

gap_gap_score=0, gap_residue_score=0, dendrogram_file='1is3A.tree',

alignment_type='progressive', feature_weights=[0]*6,

improve_alignment=False, fit=False, write_fit=True,

write_whole_pdb=False, output='QUALITY')

2)

from modeller import *

log.verbose()

env = environ()

env.libs.topology.read(file='$(LIB)/top_heav.lib')

# Read aligned structure(s):

aln = alignment(env)

aln.append(file='fm00495.ali', align_codes='all')

aln_block = len(aln)

# Read aligned sequence(s):

aln.append(file='TvLDH.ali', align_codes='TvLDH')

# Structure sensitive variable gap penalty sequence-sequence alignment:

aln.salign(output='', max_gap_length=20,

gap_function=True, # to use structure-dependent gap penalty

alignment_type='PAIRWISE', align_block=aln_block,

feature_weights=(1., 0., 0., 0., 0., 0.), overhang=0,

gap_penalties_1d=(-450, 0),

gap_penalties_2d=(0.35, 1.2, 0.9, 1.2, 0.6, 8.6, 1.2, 0., 0.),

similarity_flag=True)

aln.write(file='TvLDH-mult.ali', alignment_format='PIR')

aln.write(file='TvLDH-mult.pap', alignment_format='PAP')

3)

from modeller import *

from modeller.automodel import *

env = environ()

a = automodel(env, alnfile='TvLDH-mult.ali',

knowns=('1bdmA','2mdhA','1b8pA'), sequence='TvLDH')

a.starting_model = 1

a.ending_model = 5

a.make()

Hi there

can you do your chimeric protein with modeller? I have the same question. the answer of Kannan is build a modell with multiple templates, but the aligment maybe is not the best (for example the align of two templates is not the best to build the chimeric protein)

cheers

سلام. من هم مشکلی مشابه دارم. اگه شما تونستین راه حلی پیدا کنید ممنون میشم راهنماییم کنید.

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