{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "from CoDIAC import contactMap as cm\n",
    "from CoDIAC import PDBHelper \n",
    "from CoDIAC import pTyrLigand_helpers as pTyr_helpers\n",
    "import CoDIAC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#N and C terminal offsets for regions of interest (ROI)\n",
    "N_offset_ROI_1 = 0\n",
    "C_offset_ROI_1 = -1\n",
    "N_offset_ROI_2 = 0 \n",
    "C_offset_ROI_2 = -1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis using PDB structures \n",
    "## A. Domain-domain contact interface extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STEP 1: Fetching data from PDB reference file for a PDB examined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = './Data/Adjacency_files/' #this is where you can find the adjacency files\n",
    "ann_file = './Data/PDB_Reference/SH2_IPR000980_PDB_reference.csv' #this is the annotation file\n",
    "PDB_ID = '4JGH' #SOCS family protein\n",
    "\n",
    "ann = pd.read_csv(ann_file)\n",
    "entities = CoDIAC.PDBHelper.PDBEntitiesClass(ann, PDB_ID)\n",
    "entity =1 #there's only one entity in this example\n",
    "pdbClass = entities.pdb_dict[entity] #this holds information about the protein crystalized, such as domains\n",
    "chain_A = CoDIAC.contactMap.chainMap(PDB_ID, entity)\n",
    "chain_A.construct(PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "struct sequence: HMDPEFQAARLAKALRELGQTGWYWGSMTVNEAKEKLKEAPEGTFLIRDSSHSDYLLTISVKTSAGPTNLRIEYQDGKFRLDSIICVKSKLKQFDSVVHLIDYYVQMCKD----------GTVHLYLTKPLYTSAPSLQHLCRLTINKCTGAIWGLPLPTRLKDYLEEYKFQV\n",
      "minimum residue number: 26\n",
      "length of residues in seq: 173\n",
      "residue array size: (173, 173)\n",
      "unmodelled residues list: [136, 145]\n"
     ]
    }
   ],
   "source": [
    "print('struct sequence:', chain_A.structSeq)\n",
    "print('minimum residue number:', chain_A.return_min_residue())\n",
    "print('length of residues in seq:', len(chain_A.resNums))\n",
    "print('residue array size:', chain_A.arr.shape)\n",
    "print('unmodelled residues list:',chain_A.unmodeled_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QAARLAKALRELGQTGWYWGSMTVNEAKEKLKEAPEGTFLIRDSSHSDYLLTISVKTSAGPTNLRIEYQDGKFRLDSIICVKSKLKQFDSVVHLIDYYVQMCKDKRTGPEAPRNGTVHLYLTKPLYTSAPSLQHLCRLTINKCTGAIWGLPLPTRLKDYLEEYKFQV\n",
      "32\n",
      "167\n"
     ]
    }
   ],
   "source": [
    "print(pdbClass.ref_seq_mutated)\n",
    "print(pdbClass.ref_seq_positions[0])\n",
    "print(len(pdbClass.ref_seq_mutated))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STEP 2: Create an entity specific class to align structure to reference sequence positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleting 6 amino acids\n",
      "Adding 1 c-terminal gaps\n"
     ]
    }
   ],
   "source": [
    "chain_A_aligned = CoDIAC.contactMap.translate_chainMap_to_RefSeq(chain_A, pdbClass) #class created to align structure to reference sequence positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: {'SH2': [46, 156, 0, 0]}, 1: {'SOCS_box': [151, 197, 0, 0]}}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#These are the domains that are available in the structure for analysis\n",
    "pdbClass.domains"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STEP 3: Identify domain boundaries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ROI_1 = [46, 156] #SH2 domain \n",
    "ROI_2 = [151, 197] #socsbox domain\n",
    "fastaHeader = 'SH2|SOCS_box'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STEP 4: Generate fasta/feature files for jalview visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "chain_A_aligned.print_fasta_feature_files(ROI_1[0],N_offset_ROI_1, ROI_1[1], C_offset_ROI_1,ROI_2[0], N_offset_ROI_2,ROI_2[1],C_offset_ROI_2, 'O14508|SOCS2|1|46|156', 'SOCS_box', \n",
    "                                         './Data/Feature_Fasta_files/SH2_interdomain_4JGH', append=False, use_ref_seq_aligned=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#another visualization of contacts between regions of interest\n",
    "chain_A_aligned.generateAnnotatedHeatMap(ROI_2[0], ROI_2[1], ROI_1[0], ROI_1[1], remove_no_contacts=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## B. Ligand interface contact extraction using PDB structures "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STEP 1: Identity PTMS from PDB reference file for a specific PDB "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "main = pd.read_csv(ann_file)\n",
    "PDB_ID='3TL0'\n",
    "for name, group in main.groupby('PDB_ID'):\n",
    "\n",
    "    for index, row in group.iterrows():\n",
    "        if name == PDB_ID:\n",
    "            modification_from_PDB = (row['modifications'])\n",
    "            if isinstance(row['modifications'],str): #PTM dictionary\n",
    "                transDict = CoDIAC.PDBHelper.return_PTM_dict(modification_from_PDB) #dictionary with ptm information from PDB reference file\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{4: 'PTR'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transDict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STEP 2: Generate single dictionary of contact map for each entity in the structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "Adding 1 n_term positions\n",
      "Adding 2 c-terminal gaps\n",
      "6\n",
      "Adding 4 n_term positions\n",
      "Adding 3 c-terminal gaps\n"
     ]
    }
   ],
   "source": [
    "lig_entity = 2\n",
    "SH2_entity = 1\n",
    "dict_of_lig = CoDIAC.contactMap.return_single_chain_dict(main, PDB_ID, PATH, lig_entity)\n",
    "dict_of_SH2 = CoDIAC.contactMap.return_single_chain_dict(main, PDB_ID, PATH, SH2_entity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'entity': 2,\n",
       " 'PDB_ID': '3TL0',\n",
       " 'pdb_class': <CoDIAC.PDBHelper.PDBClass at 0x7fd119898310>,\n",
       " 'cm': <CoDIAC.contactMap.chainMap at 0x7fd10a013ee0>,\n",
       " 'cm_aligned': <CoDIAC.contactMap.chainMap at 0x7fd108c29e20>}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_of_lig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm_aligned = dict_of_lig['cm_aligned']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PDB_ID': '3TL0',\n",
       " 'entity': 2,\n",
       " 'aaRes_dict': {},\n",
       " 'arr': array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 1., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 1., 1., 0., 1., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 1., 0., 1., 1., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 1., 0., 1., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 1., 1., 0., 1., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]),\n",
       " 'transDict': {4: 'PTR'},\n",
       " 'structSeq': 'RLNYAQLWHR',\n",
       " 'resNums': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       " 'adjacencyDict': {2: {3: 1, 4: 1},\n",
       "  3: {4: 1},\n",
       "  4: {5: 1},\n",
       "  5: {6: 1, 7: 1},\n",
       "  6: {7: 1},\n",
       "  7: {8: 1}},\n",
       " 'unmodeled_list': [1, 1, 8, 10],\n",
       " 'match': True,\n",
       " 'offset': 198,\n",
       " 'ERROR_CODE': 0,\n",
       " 'refseq': 'RLNYAQLWHR'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_aligned.__dict__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STEP 3: Select the ligand length by specifying no of residues N and C terminal of PTM of interest ('y'-pTyr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "PTM='PTR'\n",
    "for res in cm_aligned.transDict:\n",
    "    if res in cm_aligned.resNums:\n",
    "        if PTM in cm_aligned.transDict[res]: \n",
    "            res_start, res_end, aligned_str, tick_labels = CoDIAC.pTyr_helpers.return_pos_of_interest(\n",
    "                cm_aligned.resNums, cm_aligned.structSeq, res, n_term_num=5, c_term_num=5, PTR_value = 'y')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1,\n",
       " 9,\n",
       " '--RLNyAQLWH',\n",
       " ['R-3', 'L-2', 'N-1', 'y0', 'A1', 'Q2', 'L3', 'W4', 'H5', 'R10'])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_start, res_end, aligned_str, tick_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STEP 4: Generate contacts between two single entity dictionaries with contactmaps (domains and ligands on different entities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "adjList, arr = CoDIAC.contactMap.return_interChain_adj(PATH, dict_of_lig, dict_of_SH2) #outer dict keys for ligand residues - ligand to domain contacts\n",
    "adjList_alt, arr_alt = CoDIAC.contactMap.return_interChain_adj(PATH, dict_of_SH2, dict_of_lig) #outer dict keys for SH2 domain - domain to ligand contacts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2: {14: 1, 17: 1},\n",
       " 4: {32: 1, 34: 1, 35: 1, 36: 1, 42: 1, 53: 1, 55: 1},\n",
       " 3: {52: 1, 53: 1},\n",
       " 5: {52: 1, 53: 1, 54: 1, 90: 1, 96: 1},\n",
       " 7: {54: 1, 65: 1, 88: 1, 89: 1, 90: 1, 96: 1},\n",
       " 8: {89: 1},\n",
       " 6: {90: 1, 91: 1}}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adjList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fetching start and end residue positions of domains and ligands\n",
    "domains = dict_of_SH2['pdb_class'].domains\n",
    "SH2_start = int(domains[0]['SH2'][0])\n",
    "SH2_stop = int(domains[0]['SH2'][1])\n",
    "arr_sub, list_aa_from_sub, list_to_aa_sub = CoDIAC.contactMap.return_arr_subset_by_ROI(arr, \n",
    "                                                                 res_start, res_end, dict_of_lig['cm_aligned'].return_min_residue(), \n",
    "                                                                 SH2_start, SH2_stop, dict_of_SH2['cm_aligned'].return_min_residue())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 102)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SH2_start, SH2_stop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STEP 5: Generate fasta/feature files for jalview visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#SH2 domain-centric contacts\n",
    "fasta_header = 'Q06124|PTPN11|1|4|102|lig_4'\n",
    "PTM_file = './Data/Feature_Fasta_files/SH2_ligand_3TL0_test'\n",
    "CoDIAC.contactMap.print_fasta_feature_files(arr_alt, dict_of_SH2['cm_aligned'].refseq, \n",
    "                                                            SH2_start,N_offset_ROI_1, SH2_stop, C_offset_ROI_1,\n",
    "                                                            dict_of_SH2['cm_aligned'].return_min_residue(), \n",
    "                                                            res_start,C_offset_ROI_1, res_end, C_offset_ROI_2,\n",
    "                                                            dict_of_lig['cm_aligned'].return_min_residue(),\n",
    "                                                            fasta_header,'pTyr', PTM_file, threshold=1, append=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pTyr ligand-centric contacts\n",
    "SH2_file = './Data/Feature_Fasta_files/pTyr_3TL0_test'\n",
    "CoDIAC.contactMap.print_fasta_feature_files(arr, dict_of_lig['cm_aligned'].structSeq, \n",
    "                                                                 res_start, N_offset_ROI_1,res_end, C_offset_ROI_1,\n",
    "                                                                dict_of_lig['cm_aligned'].return_min_residue(), \n",
    "                                                                 SH2_start,N_offset_ROI_2, SH2_stop, C_offset_ROI_2,\n",
    "                                                                dict_of_SH2['cm_aligned'].return_min_residue(),\n",
    "                                                                fasta_header,'SH2', SH2_file, threshold=1, append=True )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis using AlphaFold structures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STEP 1: Generate mmCIF files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from CoDIAC import AlphaFold\n",
    "refUniprot_file = './Data/Uniprot_Reference/SH2_IPR000980_uniprot_reference.csv'\n",
    "outputDir = './Data/AlphaFold_Reference/cifFiles/'\n",
    "CoDIAC.AlphaFold.get_cifFile(refUniprot_file, outputDir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STEP 2: Generate AlphaFold reference csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cifFile_path = './Data/AlphaFold_Reference/cifFiles/'\n",
    "outputFile = './Data/AlphaFold_Reference/SH2_AlphaFold_reference.csv'\n",
    "CoDIAC.AlphaFold.makeRefFile(refUniprot_file, cifFile_path, outputFile) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STEP 3: Generate json files using Arppegio \n",
    "\n",
    "Arpeggio was ran for input mmCIF files of PDBs of interest on High-performance computing system that can allocate more memory for running these jobs.\n",
    "\n",
    "<!-- .. code-block:: bash -->\n",
    "\n",
    "    #!/bin/bash\n",
    "    #SBATCH --nodes=1\n",
    "    #SBATCH --mem=750MB\n",
    "    #SBATCH --time=50:00:00\n",
    "    #SBATCH --partition=standard\n",
    "    #SBATCH -A g_bme-naeglelab\n",
    "    #SBATCH -o SH2_PDB.output\n",
    "    #SBATCH -e SH2_PDB.error\n",
    "\n",
    "    module load anaconda\n",
    "    conda create -n arp-env python=3.7\n",
    "    source activate arp-env\n",
    "    conda install -c openbabel openbabel\n",
    "    pip install biopython\n",
    "    pip install git+https://github.com/PDBeurope/arpeggio.git@master#egg=arpeggio\n",
    "    \n",
    "    for fname in *.cif\n",
    "    do\n",
    "    arpeggio $fname -sa\n",
    "    done\n",
    "        \n",
    "The refactored version of [Arpeggio](https://github.com/harryjubb/arpeggio) that is being made available by [PDBe](https://github.com/PDBeurope/arpeggio) has been used to generate .json files in this work. The pdbe-arpeggio (git release version 1.4.*) was used at the time of contactmap generation. \n",
    "\n",
    "*pip package installation ```pip install pdbe-arpeggio``` can be used for this purpose as well. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STEP 4: Generate binary Adjacency text files\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from CoDIAC import AdjacencyFiles \n",
    "cif_path = './Data/CIF_files'\n",
    "json_path = './Data/JSON_files'\n",
    "adj_path='./Data/Adjacency_files'\n",
    "cif_file = cif_path+'/'+PDB_ID+'.cif'\n",
    "json_file = json_path+'/'+PDB_ID+'.json'\n",
    "newpath = adj_path+'/'+PDB_ID \n",
    "os.mkdir(newpath)   #create a directory for PDB to store its corresponding adjacency files\n",
    "outfile = newpath\n",
    "CoDIAC.AdjacencyFiles.ProcessArpeggio(json_file, outfile, cif_file, small_molecule=False)    #generates adjacency file \n",
    "CoDIAC.AdjacencyFiles.BinaryFeatures(PDB_ID, outfile, translate_resid=True)    #generates binarized version\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STEP 5: Generate domain-domain contact maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from CoDIAC import contactMap \n",
    "from CoDIAC import PDBHelper \n",
    "\n",
    "PATH = './Data/Adjacency_files/' #path to adjacency files\n",
    "ann_file = './Data/AlphaFold_Reference/SH2_AlphaFold_reference.csv' #this is the reference structure annotation file\n",
    "PDB_ID = 'AF-O14508-F1' #SOCS family protein\n",
    "ann = pd.read_csv(ann_file)\n",
    "entities = CoDIAC.PDBHelper.PDBEntitiesClass(ann, PDB_ID)\n",
    "entity = 1 #there's only one entity in this example\n",
    "pdbClass = entities.pdb_dict[entity] #this holds information about the protein crystalized, such as domains\n",
    "chain_A = CoDIAC.contactMap.chainMap(PDB_ID, entity)\n",
    "chain_A.construct(PATH)\n",
    "chain_A_aligned = CoDIAC.contactMap.translate_chainMap_to_RefSeq(chain_A, pdbClass)\n",
    "ROI_1 = [46, 156] #SH2 domain \n",
    "ROI_2 = [151, 197] #socsbox domain\n",
    "fastaHeader = 'SH2|SOCS_box' "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Print fasta and feature files that contain contacts extracted across specified domain-domain interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chain_A_aligned.print_fasta_feature_files(ROI_1[0],N_offset_ROI_1, ROI_1[1], C_offset_ROI_1,ROI_2[0], N_offset_ROI_2,ROI_2[1],C_offset_ROI_2,\n",
    "                                          'O14508|SOCS2|1|46|156', 'SOCS_box', \n",
    "                                         './Data/Feature_Fasta_files/SH2_interdomain_Afold', \n",
    "                                          append=False, use_ref_seq_aligned=True)\n"
   ]
  }
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