{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Building chimeras" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can build chimeras from two proteins that present a fragment in common. There are several ways to do this, but we will here build a chimera by aligning the domains (in the fragment they have in common) and finding a crossover point in this region that divides the two parents in two parts. The parts are then shuffled and recombined. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First import the protlego module." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/agh/projects/noelia/github/protlego/TMalign\n" ] } ], "source": [ "from protlego.all import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fetch for example by the HitID from the Fuzzle database, as explained more in detail in the \"Fetching from the Fuzzle database\" section. We are fetching a hit between the P-loop and the Rossmann folds in this case." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "myhit= fetch_id('4413706')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Chimera-Building using Protlego" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First create an instance of the Builder class." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2020-10-16 12:17:21,548 - protlego - INFO - Attempting to download domain d2dfda1 from the SCOP server\n", "2020-10-16 12:17:22,721 - protlego - INFO - File downloaded as /tmp/tmp723odzdf.pdb\n", "2020-10-16 12:17:22,721 - protlego - INFO - Attempting to download domain d1wa5a_ from the SCOP server\n", "2020-10-16 12:17:23,881 - protlego - INFO - File downloaded as /tmp/tmpz50rsvv3.pdb\n", "2020-10-16 12:17:23,883 - protlego - INFO - Loading /tmp/tmp723odzdf.pdb as a chimera object\n", "2020-10-16 12:17:23,960 - moleculekit.molecule - WARNING - Alternative atom locations detected. Only altloc A was kept. If you prefer to keep all use the keepaltloc=\"all\" option when reading the file.\n", "2020-10-16 12:17:23,964 - protlego - INFO - Loading /tmp/tmpz50rsvv3.pdb as a chimera object\n" ] } ], "source": [ "a=Builder(myhit)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see the query and the subject of the hit stored in the variables qPDB and sPDB as chimera objects" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "Protein with:\n", "Number of residues: 145\n", "Number of atoms: 1054" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a.qPDB" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As described in the previous tutorial, myhit includes information about the two proteins that share a fragment, like the scop IDs, length, and TM-align scores, among others." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hit between d2dfda1 and d1wa5a_ with probability 81.7 %\n", "\n", "c.2.1.5\n", "c.37.1.8\n", "101\n", "0.54938\n" ] } ], "source": [ "print(myhit)\n", "print(myhit.q_scop_id)\n", "print(myhit.s_scop_id)\n", "print(myhit.cols)\n", "print(myhit.score_tm)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Align the sequences of query and subject with HHPred using get_alignment function." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2020-10-16 12:17:24,046 - protlego - INFO - Attempting to download hhs file for d2dfda1 from the FUZZLE server\n", "2020-10-16 12:17:24,184 - protlego - INFO - File downloaded as /tmp/tmpg1t9myzv.hhs\n" ] } ], "source": [ "aln=a.get_alignment(myhit.query,myhit.no)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can have a look at the sequence alignment:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KVAVLGASGGIGQ-PLSLLLKNS------------PLVSRLTLYDIAHTPGVAADLSHIETKAAVKGYLGPEQLPDCLKGCDVVVIPAGVPRKPGMTRDDLFNTNATIVATLTAACAQHCPEAMICVIANPVN\n", "KLVLVG-DGGTGKTTFVKRHLTGEFEKKYVATLGVEVHPLVFHTNRGPIKFNVWDTAGQEKFGGLRDGY--------YIQAQCAIIMFDVTSRV----------TYKNVPNWHRDLVRVCENIPIVLCGNKVD\n" ] } ], "source": [ "print(aln.query)\n", "print(aln.subject)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The sequence alignment corresponds to the exact fragment that the two proteins have in common, so it does not include the full protein sequences. Now, we need to map these alignments to the protein structures, because ultimately we need to align the two protein structures in these fragment region. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can thus superimpose the structures with the superimpose_structures() function, and we choose to align the structures using partial alignment. This means that instead of only one, global alignment being produced, rather, several alignments comprising smaller regions of the fragment (10-20 residues) are undertaken in parallel. We do this to provide multiple regions where the two proteins are closer than 1 armstrong in distance, and thus obtain more fusion points for the chimeras." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2020-10-16 12:17:25,126 - protlego - INFO - Performing alignment 1 with TMalign\n", "/agh/projects/noelia/miniconda3/envs/release/lib/python3.6/site-packages/moleculekit/align.py:16: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(float32, 2d, A), array(float32, 2d, A))\n", " covariance = np.dot(P.T, Q)\n", "/agh/projects/noelia/miniconda3/envs/release/lib/python3.6/site-packages/moleculekit/align.py:54: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(float32, 2d, A), array(float32, 2d, A))\n", " all1 = np.dot(all1, rot.T)\n", "2020-10-16 12:17:28,332 - protlego - INFO - The RMSD between the fragments is 0.6881077885627747 over 6 alpha carbons\n", "2020-10-16 12:17:28,340 - protlego - INFO - Performing alignment 2 with TMalign\n", "2020-10-16 12:17:28,420 - protlego - INFO - The RMSD between the fragments is 2.323467254638672 over 6 alpha carbons\n", "2020-10-16 12:17:28,430 - protlego - INFO - Performing alignment 3 with TMalign\n", "2020-10-16 12:17:28,498 - protlego - INFO - The RMSD between the fragments is 1.180463433265686 over 9 alpha carbons\n", "2020-10-16 12:17:28,506 - protlego - INFO - Performing alignment 4 with TMalign\n", "2020-10-16 12:17:28,606 - protlego - INFO - The RMSD between the fragments is 10.348337173461914 over 34 alpha carbons\n", "2020-10-16 12:17:28,616 - protlego - INFO - Performing alignment 5 with TMalign\n", "2020-10-16 12:17:28,708 - protlego - INFO - The RMSD between the fragments is 4.646137714385986 over 17 alpha carbons\n", "2020-10-16 12:17:28,717 - protlego - INFO - Performing alignment 6 with TMalign\n", "2020-10-16 12:17:28,797 - protlego - INFO - The RMSD between the fragments is 3.5291929244995117 over 29 alpha carbons\n" ] } ], "source": [ "qPDB, sPDB = a.superimpose_structures(aln,partial_alignment=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The two variables produced are two dictionaries, which contain several molecules, each pair in a geometry that optimizes each of the alignments. We can visualize these structures easily, by simply calling the view() method. A new VMD window will open showing the structure. For example, less visualize the first alignment by slicing the first key in the dictionary:\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "qPDB[0].view()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The superimpose_structures() command also produces two lists for query and subject. One of them is global_qpairs, which simply contains the indices of the protein that correspond to the srquence aligment. This list is used when we superimpose the structures using a global alignment (all atoms taken into account at once). The other is qpairs, which correspond to the same residues, but they are clustered in smaller regions that will be aligned iteratively if we choose to perform a partial alignment. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[14, 23, 30, 35, 42, 50], [59, 65, 69, 73, 81, 85], [94, 101, 109, 115, 123, 131, 139, 148, 156], [162, 169, 177, 184, 190, 201, 209, 216, 224, 236, 244, 252, 257, 267, 274, 281, 285, 292, 297, 302, 310, 318, 324, 334, 342, 351, 358, 364, 369, 374, 381, 390, 394, 406], [472, 480, 489, 493, 499, 507, 514, 521, 528, 536, 543, 548, 552, 559, 566, 577, 586], [673, 681, 686, 693, 701, 708, 713, 720, 728, 735, 740, 745, 751, 756, 765, 775, 781, 788, 797, 802, 810, 818, 824, 831, 839, 844, 852, 859, 866]]\n", "[[14, 23, 30, 35, 42, 50, 59, 65, 69, 73, 81, 85, 94, 101, 109, 115, 123, 131, 139, 148, 156, 162, 169, 177, 184, 190, 201, 209, 216, 224, 236, 244, 252, 257, 267, 274, 281, 285, 292, 297, 302, 310, 318, 324, 334, 342, 351, 358, 364, 369, 374, 381, 390, 394, 406, 472, 480, 489, 493, 499, 507, 514, 521, 528, 536, 543, 548, 552, 559, 566, 577, 586, 673, 681, 686, 693, 701, 708, 713, 720, 728, 735, 740, 745, 751, 756, 765, 775, 781, 788, 797, 802, 810, 818, 824, 831, 839, 844, 852, 859, 866]]\n" ] } ], "source": [ "print(a.qpairs)\n", "print(a.global_qpairs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can build all the possible chimeras and store them in a dictionary." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "chimeras=a.build_chimeras(partial_alignment=True)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "{'comb1_72': \n", " Protein with:\n", " Number of residues: 163\n", " Number of atoms: 1278,\n", " 'comb1_73': \n", " Protein with:\n", " Number of residues: 163\n", " Number of atoms: 1279,\n", " 'comb1_74': \n", " Protein with:\n", " Number of residues: 163\n", " Number of atoms: 1274,\n", " 'comb2_75': \n", " Protein with:\n", " Number of residues: 152\n", " Number of atoms: 1166,\n", " 'comb1_76': \n", " Protein with:\n", " Number of residues: 163\n", " Number of atoms: 1274,\n", " 'comb2_76': \n", " Protein with:\n", " Number of residues: 152\n", " Number of atoms: 1167,\n", " 'comb1_77': \n", " Protein with:\n", " Number of residues: 163\n", " Number of atoms: 1275,\n", " 'comb1_78': \n", " Protein with:\n", " Number of residues: 163\n", " Number of atoms: 1277,\n", " 'comb1_79': \n", " Protein with:\n", " Number of residues: 163\n", " Number of atoms: 1276,\n", " 'comb2_80': \n", " Protein with:\n", " Number of residues: 152\n", " Number of atoms: 1165,\n", " 'comb1_81': \n", " Protein with:\n", " Number of residues: 163\n", " Number of atoms: 1275,\n", " 'comb2_81': \n", " Protein with:\n", " Number of residues: 152\n", " Number of atoms: 1166,\n", " 'comb2_103': \n", " Protein with:\n", " Number of residues: 142\n", " Number of atoms: 1101,\n", " 'comb2_104': \n", " Protein with:\n", " Number of residues: 142\n", " Number of atoms: 1103,\n", " 'comb2_105': \n", " Protein with:\n", " Number of residues: 142\n", " Number of atoms: 1104,\n", " 'comb2_106': \n", " Protein with:\n", " Number of residues: 142\n", " Number of atoms: 1110,\n", " 'comb2_107': \n", " Protein with:\n", " Number of residues: 142\n", " Number of atoms: 1113,\n", " 'comb2_109': \n", " Protein with:\n", " Number of residues: 142\n", " Number of atoms: 1122,\n", " 'comb2_112': \n", " Protein with:\n", " Number of residues: 142\n", " Number of atoms: 1128,\n", " 'comb2_113': \n", " Protein with:\n", " Number of residues: 142\n", " Number of atoms: 1125,\n", " 'comb2_118': \n", " Protein with:\n", " Number of residues: 142\n", " Number of atoms: 1128}" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chimeras" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the Builder class you can plot all the possible chimeras, showing the distance on the y-axis and the fragment of the query relative to the domain in the subject. There are five possible scenarios. For every two fussion point that is under the cutoff distanc (1 Armstrong in this case) we can build chimeras starting from the N-terminus and C-terminus. When the two resulting chimeras at this point present backbone clashes, the plot shows a black dot at this position. If the N-terminus or C-terminus is chimera is buildable, we show that position with a cross in yellow, and red, respectively. In some fusion points, like position 145 in this example, it is possible to build both N-terminus and C-terminus chimeras. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "a.plot_curves(myhit.query)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To access the chimeras we can retrieve them from the key value in the dictionary. Combinations starting with 1 come from the query, and combinations starting with 2 come from the subject." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To view or write the structures out you can store the chimera from the chimeras dictionary in a variable and use the view and the write function." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "mychimera=chimeras['comb1_72']" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "mychimera.view()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When we represent the structures we can see the part that comes from each parent is shown in different colors. We can also save the structure as a pdb anytime:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "mychimera.write('./mychimera.pdb')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can, in fact, visualize all of them at once by looping over the structures:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "for name, chimera in chimeras.items():\n", " chimera.view(name=name)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that VMD guesses the bonds between atoms by computing distances. As these structures have not been minimized they do not have idealized bond distances and sometimes VMD cannot guess properly the NewCartoon representation. Choose Licorice Method to represent those cases." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" }, "pycharm": { "stem_cell": { "cell_type": "raw", "metadata": { "collapsed": false }, "source": [] } } }, "nbformat": 4, "nbformat_minor": 2 }