{"id":4063,"date":"2025-03-26T14:48:43","date_gmt":"2025-03-26T05:48:43","guid":{"rendered":"https:\/\/bdsl.jbnu.ac.kr\/blog\/?p=4063"},"modified":"2025-03-26T15:23:00","modified_gmt":"2025-03-26T06:23:00","slug":"protein-structure-modeling-with-modeller","status":"publish","type":"post","link":"https:\/\/bdsl.jbnu.ac.kr\/blog\/protein-structure-modeling-with-modeller\/","title":{"rendered":"Protein Structure Modeling with Modeller"},"content":{"rendered":"\n

Introduction<\/h2>\n\n\n\n

Modeller\ub294 Andrej Sali \uc5f0\uad6c\uc2e4\uc5d0\uc11c \uac1c\ubc1c\ub41c \ub300\ud45c\uc801\uc778 \uc0c1\ub3d9 \ubaa8\ub378\ub9c1(homology modeling) \ub3c4\uad6c\uc774\ub2e4. \ubcf8 \uc2e4\uc2b5\uc5d0\uc11c\ub294 \ud574\ub2f9 \uc5f0\uad6c\uc2e4\uc5d0\uc11c \uc81c\uacf5\ud558\ub294 \ud29c\ud1a0\ub9ac\uc5bc\uc744 \ud1b5\ud574 \ub2e8\ubc31\uc9c8 \uad6c\uc870 \ubaa8\ub378\ub9c1\uc758 \uc804\ubc18\uc801\uc778 \uacfc\uc815\uc744 \ud559\uc2b5\ud55c\ub2e4.<\/p>\n\n\n\n

Tutorial \uc694\uc57d<\/h2>\n\n\n\n

\uc5ec\uae30\uc11c \uc5f0\uc2b5\ud558\ub294 \uacfc\uc7a5\uc740 \uc544\ub798\uc640 \uac19\uc774 \uad6c\uc131\ub418\uc5b4 \uc788\ub2e4. <\/p>\n\n\n\n

    \n
  1. Search for structures related to TvLDH<\/li>\n\n\n\n
  2. Selecting a template<\/li>\n\n\n\n
  3. Aligning TvLDF with the template<\/li>\n\n\n\n
  4. Model building<\/li>\n\n\n\n
  5. Model evaluation<\/li>\n\n\n\n
  6. Visualization <\/li>\n<\/ol>\n\n\n\n

    Tutorial \uac01 \uacfc\uc815\uc5d0 \ub300\ud55c \uc790\uc138\ud55c \uc124\uba85\uc740 \uc544\ub798 \ub9c1\ud06c\ub97c \ucc38\uace0\ud55c\ub2e4. <\/p>\n\n\n\n

    https:\/\/salilab.org\/modeller\/tutorial\/basic.html<\/a><\/p>\n\n\n\n

    <\/p>\n\n\n\n

    \uc5ec\uae30\uc11c\ub294 \ud3b8\uc758\uc0c1 \ud574\ub2f9 tutorial \uc758 \uc608\uc81c\uc5d0 \uae30\ubc18\ud558\uc5ec, directory \uad6c\uc870\ub9cc \uc57d\uac04 \ubcc0\ud615\ud55c \uc608\uc81c\ub97c \uc774\uc6a9\ud55c\ub2e4. <\/p>\n\n\n\n

    \uc544\ub798 \ud30c\uc77c\uc744 \ub2e4\uc6b4 \ubc1b\uace0, tutorial\uc744 \uc9c4\ud589\ud560 directory\uc5d0\uc11c \uc555\ucd95\uc744 \ud480\uace0 tutorial\uc744 \uc9c4\ud589\ud55c\ub2e4.<\/p>\n\n\n\n

    basic-example-mod.tar<\/a>Download<\/a><\/div>\n\n\n\n

    1. Search for structures related to TvLDH<\/h2>\n\n\n\n

    \ud2b9\uc815 \ub2e8\ubc31\uc9c8\uc758 \uc11c\uc5f4\uc744 \uc774\uc6a9\ud574 3\ucc28\uc6d0 \uad6c\uc870\ub97c \ubaa8\ub378\ub9c1\ud558\uae30 \uc704\ud574\uc11c\ub294, \ud574\ub2f9 \uc11c\uc5f4\uacfc \uc720\uc0ac\ud55c \uad6c\uc870\ub97c \uac00\uc9c4 \ub2e8\ubc31\uc9c8\uc744 \ucc3e\uc544\uc57c \ud55c\ub2e4. \uc774\ub97c \uc704\ud574\uc11c\ub294 \uad6c\uc870\uac00 \ubc1d\ud600\uc9c4 \ub2e8\ubc31\uc9c8\ub85c \uad6c\uc131\ub41c \ub370\uc774\ud130\ubca0\uc774\uc2a4\uac00 \ud544\uc694\ud558\ub2e4. \ub300\ud45c\uc801\uc778 \uc608\ub85c\ub294 Protein Data Bank<\/em>\uac00 \uc788\ub2e4. \uad6c\uc870 \ub370\uc774\ud130\ubca0\uc774\uc2a4\uc5d0\uc11c \ubaa8\ub378\ub9c1\ud558\uace0\uc790 \ud558\ub294 \uc11c\uc5f4\uacfc \uc720\uc0ac\ud55c \ub2e8\ubc31\uc9c8\uc744 \ucc3e\uae30 \uc704\ud574\uc11c\ub294 \uc11c\uc5f4 \ube44\uad50\ub97c \uc218\ud589\ud574\uc57c \ud55c\ub2e4. \ubcf8 \uc608\uc81c\uc5d0\uc11c\ub294 TvLDH\uc758 \uc11c\uc5f4\uc744 \uad6c\uc870 \ub370\uc774\ud130\ubca0\uc774\uc2a4 \ub0b4 \ub2e8\ubc31\uc9c8\uc758 \uc11c\uc5f4\uacfc \ube44\uad50\ud558\ub294 \uacfc\uc815\uc774 \ud544\uc694\ud558\ub2e4.<\/p>\n\n\n\n

    \uc774\ub97c \uc704\ud574 \ubcf8 \uc608\uc81c\uc5d0\uc11c\ub294 \uc11c\uc5f4 \uc720\uc0ac\ub3c4\uac00 95% \uc774\ud558\uc778 \ub2e8\ubc31\uc9c8\ub9cc\uc744 \ud3ec\ud568\ud55c \ub370\uc774\ud130\ubca0\uc774\uc2a4(database\/pdb_95.pir)\ub97c \ud65c\uc6a9\ud55c\ub2e4. \uc989, \uc11c\uc5f4 \uc720\uc0ac\ub3c4\uac00 95% \uc774\uc0c1\uc778 \ub2e8\ubc31\uc9c8\ub4e4 \uc911\uc5d0\uc11c\ub294 \ud558\ub098\ub97c \ub300\ud45c \uad6c\uc870\ub85c \uc120\ud0dd\ud558\uc5ec \ub370\uc774\ud130\ubca0\uc774\uc2a4\ub97c \uad6c\uc131\ud55c \uac83\uc774\ub2e4.<\/p>\n\n\n\n

    Modeller \ub294 \uc11c\uc5f4 \uac80\uc0c9 \uae30\ub2a5\uc744 \uc81c\uacf5\ud55c\ub2e4. \uc544\ub798\ub294 Modeller\ub97c \uc774\uc6a9\ud574 \uc11c\uc5f4 \uac80\uc0c9\ud558\ub294 Python \ucf54\ub4dc\uc774\ub2e4. (\uc608\uc81c \ud30c\uc77c: 1_ build_profile.py<\/code>)<\/p>\n\n\n\n

    from modeller import *\n\nlog.verbose()\nenv = Environ()\n\n#-- Prepare the input files\n\n#-- Read in the sequence database\nsdb = SequenceDB(env)\nsdb.read(seq_database_file='database\/pdb_95.pir', seq_database_format='PIR',\n         chains_list='ALL', minmax_db_seq_len=(30, 4000), clean_sequences=True)\n\n#-- Write the sequence database in binary form\nsdb.write(seq_database_file='database\/pdb_95.bin', seq_database_format='BINARY',\n          chains_list='ALL')\n\n#-- Now, read in the binary database\nsdb.read(seq_database_file='database\/pdb_95.bin', seq_database_format='BINARY',\n         chains_list='ALL')\n\n#-- Read in the target sequence\/alignment\naln = Alignment(env)\naln.append(file='input\/TvLDH.ali', alignment_format='PIR', align_codes='ALL')\n\n#-- Convert the input sequence\/alignment into\n#   profile format\nprf = aln.to_profile()\n\n#-- Scan sequence database to pick up homologous sequences\nprf.build(sdb, matrix_offset=-450, rr_file='${LIB}\/blosum62.sim.mat',\n          gap_penalties_1d=(-500, -50), n_prof_iterations=1,\n          check_profile=False, max_aln_evalue=0.01)\n\n#-- Write out the profile in text format\nprf.write(file='results_aln\/build_profile.prf', profile_format='TEXT')\n\n#-- Convert the profile back to alignment format\naln = prf.to_alignment()\n\n#-- Write out the alignment file\naln.write(file='results_aln\/build_profile.ali', alignment_format='PIR')\n\n<\/code><\/pre>\n\n\n\n
    2. Select a template <\/h2>\n\n\n\n
    \ub370\uc774\ud130\ubca0\uc774\uc2a4 \uac80\uc0c9 \uacb0\uacfc\uc5d0\uc11c \uac00\uc7a5 \uc815\ub82c\uc774 \uc798 \ub41c \uc11c\uc5f4\uc744 \ucc3e\ub294\ub2e4. \uadf8 \uae30\uc900\uc740 \uad6c\uc870\uc5d0 \uc815\ub82c\ub41c \uc11c\uc5f4 \ubd80\uc704\uc758 \uae38\uc774\uc640 \uc815\ub82c\ub41c \ubd80\ubd84\uc758 \uc11c\uc5f4 \uc720\uc0ac\uc131\uc774\ub2e4. \uc11c\uc5f4\uc758 \ub300\ubd80\ubd84\uc774 \uc815\ub82c\ub418\uace0 \ub192\uc740 \uc11c\uc5f4 \uc720\uc0ac\uc131\uc744 \uac00\uc9c4 \ub2e8\ubc31\uc9c8\uc744 \ud15c\ud50c\ub9bf(template)\uc73c\ub85c \uc120\ud0dd\ud55c\ub2e4<\/p>\n\n\n\n
    \uc774\ub97c \uc704\ud574 Modeller\uc758 \uae30\ub2a5\uc744 \uc77c\ubd80 \ud65c\uc6a9\ud560 \uc218 \uc788\ub2e4. \uc608\uc81c\uc5d0\uc11c\ub294 2_compare.py<\/code>\ub97c \uc774\uc6a9\ud574 \uc815\ub82c\ub41c \uc11c\uc5f4\uc758 \uad00\uacc4\ub97c \uc11c\ub85c \ube44\uad50\ud560 \uc218\ub3c4 \uc788\ub2e4. <\/p>\n\n\n\n
    from modeller import *\n\nenv = Environ()\naln = Alignment(env)\nfor (pdb, chain) in (('1b8p', 'A'), ('1bdm', 'A'), ('1civ', 'A'),\n                     ('5mdh', 'A'), ('7mdh', 'A'), ('1smk', 'A')):\n    m = Model(env, file='pdb\/'+pdb, model_segment=('FIRST:'+chain, 'LAST:'+chain))\n    aln.append_model(m, atom_files='pdb\/'+pdb, align_codes=pdb+chain)\naln.malign()\naln.malign3d()\naln.compare_structures()\naln.id_table(matrix_file='results_aln\/family.mat')\nenv.dendrogram(matrix_file='results_aln\/family.mat', cluster_cut=-1.0)\n<\/code><\/pre>\n\n\n\n
    3. Aligning TvLDF with the template<\/h2>\n\n\n\n
    Template\uc744 \uc120\ud0dd\ud55c \ud6c4, \uc11c\uc5f4\uc758 \uc5b4\ub5a4 \ubd80\ubd84\uc774 \ub2e8\ubc31\uc9c8 \uad6c\uc870\uc5d0 \ud574\ub2f9\ud558\ub294\uc9c0\ub97c \ud655\uc778\ud558\uae30 \uc704\ud574 \ucd94\uac00\uc801\uc778 \uc815\ub82c\uc744 \uc218\ud589\ud55c\ub2e4. \uc774 \uacfc\uc815\uc740 \uc608\uc81c\uc758 3_align2d.py<\/code> \uba85\ub839\uc744 \ud1b5\ud574 \uc2e4\ud589\ub41c\ub2e4. \uc815\ub82c\uc774 \uc644\ub8cc\ub418\uba74, \uc815\ub82c\ub41c \uc11c\uc5f4 \uc815\ubcf4\uac00 \ub2f4\uae34 *.ali<\/code> \ubc0f *.pap<\/code> \ud30c\uc77c\uc774 \uc0dd\uc131\ub41c\ub2e4.<\/p>\n\n\n\n
    from modeller import *\n\nenv = Environ()\naln = Alignment(env)\nmdl = Model(env, file='pdb\/1bdm', model_segment=('FIRST:A','LAST:A'))\naln.append_model(mdl, align_codes='1bdmA', atom_files='pdb\/1bdm.pdb')\naln.append(file='input\/TvLDH.ali', align_codes='TvLDH')\naln.align2d(max_gap_length=50)\naln.write(file='results_aln\/TvLDH-1bdmA.ali', alignment_format='PIR')\naln.write(file='results_aln\/TvLDH-1bdmA.pap', alignment_format='PAP')\n<\/code><\/pre>\n\n\n\n
    4. Model building <\/h2>\n\n\n\n
    \uc11c\uc5f4\uc744 \ud15c\ud50c\ub9bf\uc5d0 \uc815\ub82c\ud55c \ud6c4, \ud574\ub2f9 \uc11c\uc5f4\uc758 3\ucc28\uc6d0 \uad6c\uc870\ub97c \ubaa8\ub378\ub9c1\ud55c\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c\ub294 \ud15c\ud50c\ub9bf\uc758 \uc544\ubbf8\ub178\uc0b0\uc744 \ub300\uc0c1 \uc11c\uc5f4\uc758 \uc544\ubbf8\ub178\uc0b0\uc73c\ub85c \uce58\ud658\ud558\uace0, \uad6c\uc870\uc801 \uc5d0\ub108\uc9c0 \uacc4\uc0b0\uc744 \ud1b5\ud574 \uc5d0\ub108\uc9c0\uac00 \ub0ae\uc740 \uad6c\uc870\ub97c \ud0d0\uc0c9\ud55c\ub2e4. \ubaa8\ub378\ub9c1\uc5d0\ub294 \ubb34\uc791\uc704\uc801\uc778 \uc694\uc18c\uac00 \ud3ec\ud568\ub418\ubbc0\ub85c, \uc591\uc9c8\uc758 \uad6c\uc870\ub97c \uc5bb\uae30 \uc704\ud574 \uc5ec\ub7ec \ubc88\uc758 \ubaa8\ub378\ub9c1\uc744 \uc218\ud589\ud558\ub294 \uac83\uc774 \uc77c\ubc18\uc801\uc774\ub2e4. \ubcf8 \uc608\uc81c\uc5d0\uc11c\ub294 5\ud68c\uc758 \uad6c\uc870 \ubaa8\ub378\ub9c1\uc744 \uc9c4\ud589\ud55c\ub2e4.<\/p>\n\n\n\n
    from modeller import *\nfrom modeller.automodel import *\n\nenv = Environ()\na = AutoModel(env, alnfile='results_aln\/TvLDH-1bdmA.ali',\n              knowns='1bdmA', sequence='TvLDH',\n              assess_methods=(assess.DOPE,\n                              #soap_protein_od.Scorer(),\n                              assess.GA341))\na.starting_model = 1\na.ending_model = 5\na.make()\n\nimport os\nos.system('mv TvLDH* results_models')\n<\/code><\/pre>\n\n\n\n
    5. Model evaluation <\/h2>\n\n\n\n
    \ubaa8\ub378\ub9c1\ub41c \uad6c\uc870\uc758 \uc815\ud655\uc131\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574, \ubaa8\ub378\uc758 \uc5d0\ub108\uc9c0 \ud2b9\uc131\uc744 \ubd84\uc11d\ud55c\ub2e4. \uac01 \uc794\uae30(residue)\uc758 \uc548\uc815\uc131\uc744 \ud655\uc778\ud558\uae30 \uc704\ud574 DOPE(Distance-scaled, Optimized Protein Energy) \uc2a4\ucf54\uc5b4\ub97c \uacc4\uc0b0\ud558\uba70, \uc774\ub97c \ud15c\ud50c\ub9bf \uad6c\uc870\uc758 DOPE \uc2a4\ucf54\uc5b4\uc640 \ube44\uad50\ud558\uc5ec \ubaa8\ub378\uc758 \ud488\uc9c8\uc744 \ud310\ub2e8\ud55c\ub2e4.<\/p>\n\n\n\n
    from modeller import *\nfrom modeller.scripts import complete_pdb\n\nlog.verbose()    # request verbose output\nenv = Environ()\nenv.libs.topology.read(file='$(LIB)\/top_heav.lib') # read topology\nenv.libs.parameters.read(file='$(LIB)\/par.lib') # read parameters\n\n# read model file\nmdl = complete_pdb(env, 'results_models\/TvLDH.B99990001.pdb')\n\n# Assess with DOPE:\ns = Selection(mdl)   # all atom selection\ns.assess_dope(output='ENERGY_PROFILE NO_REPORT', file='results_models\/TvLDH.profile',\n              normalize_profile=True, smoothing_window=15)\n\n# read model file\nmdl = complete_pdb(env, 'pdb\/1bdm.pdb')\n<\/code><\/pre>\n\n\n\n
    <\/p>\n\n\n\n
    from modeller import *\nfrom modeller.scripts import complete_pdb\n\nlog.verbose()    # request verbose output\nenv = Environ()\nenv.libs.topology.read(file='$(LIB)\/top_heav.lib') # read topology\nenv.libs.parameters.read(file='$(LIB)\/par.lib') # read parameters\n\n# directories for input atom files\nenv.io.atom_files_directory = '.\/:..\/atom_files'\n\n# read model file\nmdl = complete_pdb(env, 'pdb\/1bdm.pdb', model_segment=('FIRST:A', 'LAST:A'))\n\ns = Selection(mdl)\ns.assess_dope(output='ENERGY_PROFILE NO_REPORT', file='results_models\/1bdmA.profile',\n              normalize_profile=True, smoothing_window=15)\n<\/code><\/pre>\n\n\n\n
    <\/p>\n\n\n\n
    import matplotlib.pyplot as plt\nimport modeller\n\ndef r_enumerate(seq):\n    \"\"\"Enumerate a sequence in reverse order\"\"\"\n    # Note that we don't use reversed() since Python 2.3 doesn't have it\n    num = len(seq) - 1\n    while num >= 0:\n        yield num, seq[num]\n        num -= 1\n\ndef get_profile(profile_file, seq):\n    \"\"\"Read `profile_file` into a Python array, and add gaps corresponding to\n       the alignment sequence `seq`.\"\"\"\n    # Read all non-comment and non-blank lines from the file:\n    f = open(profile_file)\n    vals = []\n    for line in f:\n        if not line.startswith('#') and len(line) > 10:\n            spl = line.split()\n            vals.append(float(spl[-1]))\n    # Insert gaps into the profile corresponding to those in seq:\n    for n, res in r_enumerate(seq.residues):\n        for gap in range(res.get_leading_gaps()):\n            vals.insert(n, None)\n    # Add a gap at position '0', so that we effectively count from 1:\n    vals.insert(0, None)\n    return vals\n\ne = modeller.Environ()\na = modeller.Alignment(e, file='results_aln\/TvLDH-1bdmA.ali')\n\ntemplate = get_profile('results_models\/1bdmA.profile', a['1bdmA'])\nmodel = get_profile('results_models\/TvLDH.profile', a['TvLDH'])\n\n# Plot the template and model profiles in the same plot for comparison:\nfig, ax = plt.subplots()\nax.set_xlabel('Alignment position')\nax.set_ylabel('DOPE per-residue score')\nax.plot(model, color='red', linewidth=2, label='Model')\nax.plot(template, color='green', linewidth=2, label='Template')\nfig.legend()\nfig.savefig('dope_profile.png', dpi=200)\n<\/code><\/pre>\n\n\n\n
    6. Visualization <\/h2>\n\n\n\n
    \ub9c8\uc9c0\ub9c9\uc73c\ub85c PyMOL\uc744 \uc774\uc6a9\ud574 \ud15c\ud50c\ub9bf \uad6c\uc870\uc640 \ubaa8\ub378\uc744 \uc2dc\uac01\ud654\ud558\uace0, \ub450 \uad6c\uc870\ub97c \ube44\uad50 \ubd84\uc11d\ud55c\ub2e4.<\/p>\n\n\n\n
    <\/p>\n","protected":false},"excerpt":{"rendered":"
    Introduction Modeller\ub294 Andrej Sali \uc5f0\uad6c\uc2e4\uc5d0\uc11c \uac1c\ubc1c\ub41c \ub300\ud45c\uc801\uc778 \uc0c1\ub3d9 \ubaa8\ub378\ub9c1(homology modeling) \ub3c4\uad6c\uc774\ub2e4. \ubcf8 \uc2e4\uc2b5\uc5d0\uc11c\ub294 \ud574\ub2f9 \uc5f0\uad6c\uc2e4\uc5d0\uc11c \uc81c\uacf5\ud558\ub294 \ud29c\ud1a0\ub9ac\uc5bc\uc744 \ud1b5\ud574 \ub2e8\ubc31\uc9c8 \uad6c\uc870 \ubaa8\ub378\ub9c1\uc758 \uc804\ubc18\uc801\uc778 \uacfc\uc815\uc744 \ud559\uc2b5\ud55c\ub2e4. Tutorial \uc694\uc57d \uc5ec\uae30\uc11c \uc5f0\uc2b5\ud558\ub294 \uacfc\uc7a5\uc740 \uc544\ub798\uc640 \uac19\uc774 \uad6c\uc131\ub418\uc5b4 \uc788\ub2e4. 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Tutorial \uc694\uc57d \uc5ec\uae30\uc11c \uc5f0\uc2b5\ud558\ub294 \uacfc\uc7a5\uc740 \uc544\ub798\uc640 \uac19\uc774 \uad6c\uc131\ub418\uc5b4 \uc788\ub2e4. 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