{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RNA-Puzzle 20 - Twister sister ribozyme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import nglview\n",
    "from rna_tools.Seq import RNASequence\n",
    "from rna_tools.BlastPDB import BlastPDB\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq = RNASequence(\"ACCCGCAAGGCCGACGGCGCCGCCGCUGGUGCAAGUCCAGCCACGCUUCGGCGUGGGCGCUCAUGGGU\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ACCCGCAAGGCCGACGGCGCCGCCGCUGGUGCAAGUCCAGCCACGCUUCGGCGUGGGCGCUCAUGGGU"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Secondary structure prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">rna_seq\n",
      "ACCCGCAAGGCCGACGGCGCCGCCGCUGGUGCAAGUCCAGCCACGCUUCGGCGUGGGCGCUCAUGGGU\n",
      "(((((..(((((.(((.((..((.(((((.......)))))...))..)).))).))).))..))))) (-32.50)\n"
     ]
    }
   ],
   "source": [
    "print seq.predict_ss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">rna_seq [100]\n",
      "ACCCGCAAGGCCGACGGCGCCGCCGCUGGUGCAAGUCCAGCCACGCUUCGGCGUGGGCGCUCAUGGGU -32.50   1.00\n",
      "(((((..(((((.(((.((..((.(((((.......)))))...))..)).))).))).))..))))) -32.50\n",
      ".((((..(((((.(((.((..((.(((((.......)))))...))..)).))).))).))..)))). -31.90\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print seq.predict_ss(method='RNAsubopt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACCCGCAAGGCCGACGGCGCCGCCGCUGGUGCAAGUCCAGCCACGCUUCGGCGUGGGCGCUCAUGGGU\n",
      "..((....))(((..((((((...(((((.......)))))(((((....)))))))))))..)))..\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print seq.predict_ss(method='ipknot', verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(((((..........((((((...(((((.......)))))(((((....)))))))))))..)))))\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print seq.predict_ss(method='contextfold')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Search Rfam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('RFAM_DB_PATH', '/home/magnus/work/db/rfam/Rfam.cm')\n",
      "cmscan -E 1 /home/magnus/work/db/rfam/Rfam.cm /var/folders/yc/ssr9692s5fzf7k165grnhpk80000gp/T/tmpCfvTrK.fa  > /var/folders/yc/ssr9692s5fzf7k165grnhpk80000gp/T/tmphHDnj0\n",
      "# cmscan :: search sequence(s) against a CM database\n",
      "# INFERNAL 1.1.2 (July 2016)\n",
      "# Copyright (C) 2016 Howard Hughes Medical Institute.\n",
      "# Freely distributed under a BSD open source license.\n",
      "# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n",
      "# query sequence file:                   /var/folders/yc/ssr9692s5fzf7k165grnhpk80000gp/T/tmpCfvTrK.fa\n",
      "# target CM database:                    /home/magnus/work/db/rfam/Rfam.cm\n",
      "# sequence reporting threshold:          E-value <= 1\n",
      "# number of worker threads:              4\n",
      "# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n",
      "\n",
      "Query:       target  [L=50]\n",
      "Hit scores:\n",
      " rank     E-value  score  bias  modelname       start    end   mdl trunc   gc  description\n",
      " ----   --------- ------ -----  -------------- ------ ------   --- ----- ----  -----------\n",
      "  (1) !   1.7e-08   42.7   0.0  Twister-sister      1     50 +  cm 5'&3' 0.72  Twister_sister_ribozyme\n",
      "\n",
      "\n",
      "Hit alignments:\n",
      ">> Twister-sister  Twister_sister_ribozyme\n",
      " rank     E-value  score  bias mdl mdl from   mdl to       seq from      seq to       acc trunc   gc\n",
      " ----   --------- ------ ----- --- -------- --------    ----------- -----------      ---- ----- ----\n",
      "  (1) !   1.7e-08   42.7   0.0  cm       27       79 ~~           1          50 + ~~ 0.97 5'&3' 0.72\n",
      "\n",
      "                           ???                                    ???      ????      NC\n",
      "                    ~~~~~~_>>>,,<<<<_________>>>><<<<<<_____>>>>>>)))------))))~~~~~ CS\n",
      "  Twister-sister  1 <[26]*ccCGCCGCcGGUGCAAGCCCgGCCgCCCcagacagGGGcGGGCGCUCAUGGGU*[5]> 84\n",
      "                          +CCGCCGC:GGUGCAAG CC:GCC:C:C:    +:G:G:GGGCGCUCAUGGGU     \n",
      "          target  1 <[ 0]*GCCGCCGCUGGUGCAAGUCCAGCCACGCUU---CGGCGUGGGCGCUCAUGGGU*[0]> 50\n",
      "                    ......9***************************95...49******************..... PP\n",
      "\n",
      "\n",
      "\n",
      "Internal HMM-only pipeline statistics summary: (run for model(s) with zero basepairs)\n",
      "--------------------------------------------------------------------------------------\n",
      "Query sequence(s):                                               1  (100 residues searched)\n",
      "Target model(s):                                               347  (40911 consensus positions)\n",
      "Windows   passing  local HMM SSV           filter:               2  (0.002882); expected (0.02)\n",
      "Windows   passing  local HMM MSV      bias filter:               2  (0.002882); expected (0.02)\n",
      "Windows   passing  local HMM Viterbi       filter:               0  (0); expected (0.001)\n",
      "Windows   passing  local HMM Forward       filter:               0  (0); expected (1e-05)\n",
      "Total HMM hits reported:                                         0  (0)\n",
      "\n",
      "Internal CM pipeline statistics summary:\n",
      "----------------------------------------\n",
      "Query sequence(s):                                               1  (100 residues searched)\n",
      "Query sequences re-searched for truncated hits:                  1  (297.2 residues re-searched, avg per model)\n",
      "Target model(s):                                              2669  (345034 consensus positions)\n",
      "Windows   passing  local HMM SSV           filter:             798  (0.03589); expected (0.35)\n",
      "Windows   passing  local HMM Viterbi       filter:                  (off)\n",
      "Windows   passing  local HMM Viterbi  bias filter:                  (off)\n",
      "Windows   passing  local HMM Forward       filter:             290  (0.01307); expected (0.02)\n",
      "Windows   passing  local HMM Forward  bias filter:             251  (0.01136); expected (0.02)\n",
      "Windows   passing glocal HMM Forward       filter:              59  (0.002673); expected (0.02)\n",
      "Windows   passing glocal HMM Forward  bias filter:              55  (0.002484); expected (0.02)\n",
      "Envelopes passing glocal HMM envelope defn filter:              54  (0.002259); expected (0.02)\n",
      "Envelopes passing  local CM  CYK           filter:              14  (0.0005188); expected (0.0001)\n",
      "Total CM hits reported:                                          1  (9.432e-05); includes 1 truncated hit(s)\n",
      "\n",
      "Total CM and HMM hits reported:                                  1\n",
      "\n",
      "# CPU time: 0.99u 0.10s 00:00:01.09 Elapsed: 00:00:00.45\n",
      "//\n",
      "[ok]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import rna_tools.RfamSearch as RfamSearch\n",
    "rs = RfamSearch.RfamSearch()\n",
    "print rs.cmscan(seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SingleLetterAlphabet() alignment with 4 rows and 85 columns\n",
       "GCAACCCGCAAGGCCGACGCACAAC---GCGCCGCCGGUGCAAG...ACA ADJS01013948.1/250-330\n",
       "GAAACCCGCUAGGCCGACAGCCUCACCGCUGCCGCUGGUGCAAG...CAG BABG01005008.1/780-696\n",
       "ACGACCCGCAAGGCCGACGCAUAAC---GCGCCGCCGGUGCAAG...ACA ADJS01013948.1/577-657\n",
       "AUGACCCGCAAGGCCGACGGCAUCCCG-CCGCCGCUGGUGCAAG...ACA FP929046.1/2708602-2708521"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import rna_tools.tools.rna_alignment.rna_alignment as ra\n",
    "a = ra.RNAalignment(fetch=\"RF02681\")# '/Users/magnus/work/sister_twister/RAGATH-3-twister-sister.sto')\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rchie: plot saved to rchie.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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SYmXvKk4ijwITHSCB+0K2if6+qzjJvSNLSCgllGvIdyy7jBPYcO+EUW34TD/O88isjPRH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mkRppcx3yOBIbzwXTJZAMwDSGrbR6VXC9DxP0JrqvMgu7Mq+VxLRDQlQLzuQcIc4aZExErXe7g5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\nrmFcVPIN3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3ntY33SNOQyvpUkLS7uncKZFuX4egR1FaC/7nWvK/rk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DguItm61mFjdXr6GXGs1ps5lZvLJyBb1y9g1jnVZ3VNP+h581hsHTZb9448aNtG3bNmNal5CA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\nhIAQEAJC4C0S+P93UHIdysXHcAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print a.io.format(\"fasta\")\n",
    "ra.RChie().plot_cov(a.io.format(\"fasta\"), a.ss_cons_std, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SeqRecord(seq=Seq('AUGACCCGCAAGGCCGACGGCAUCCCG-CCGCCGCUGGUGCAAGCCCAGCCGCC...ACA', SingleLetterAlphabet()), id='FP929046.1/2708602-2708521', name='FP929046.1', description='FP929046.1/2708602-2708521', dbxrefs=[])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.find_seq(\"UGCAAGUC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('RFAM_DB_PATH', '/home/magnus/work/db/rfam/Rfam.cm')\n",
      "cmscan -E 1 /home/magnus/work/db/rfam/Rfam.cm /var/folders/yc/ssr9692s5fzf7k165grnhpk80000gp/T/tmp4qzzKI.fa  > /var/folders/yc/ssr9692s5fzf7k165grnhpk80000gp/T/tmpm7wPJ8\n",
      "# cmscan :: search sequence(s) against a CM database\n",
      "# INFERNAL 1.1.2 (July 2016)\n",
      "# Copyright (C) 2016 Howard Hughes Medical Institute.\n",
      "# Freely distributed under a BSD open source license.\n",
      "# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n",
      "# query sequence file:                   /var/folders/yc/ssr9692s5fzf7k165grnhpk80000gp/T/tmp4qzzKI.fa\n",
      "# target CM database:                    /home/magnus/work/db/rfam/Rfam.cm\n",
      "# sequence reporting threshold:          E-value <= 1\n",
      "# number of worker threads:              4\n",
      "# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n",
      "\n",
      "Query:       target  [L=50]\n",
      "Hit scores:\n",
      " rank     E-value  score  bias  modelname       start    end   mdl trunc   gc  description\n",
      " ----   --------- ------ -----  -------------- ------ ------   --- ----- ----  -----------\n",
      "  (1) !   1.7e-08   42.7   0.0  Twister-sister      1     50 +  cm 5'&3' 0.72  Twister_sister_ribozyme\n",
      "\n",
      "\n",
      "Hit alignments:\n",
      ">> Twister-sister  Twister_sister_ribozyme\n",
      " rank     E-value  score  bias mdl mdl from   mdl to       seq from      seq to       acc trunc   gc\n",
      " ----   --------- ------ ----- --- -------- --------    ----------- -----------      ---- ----- ----\n",
      "  (1) !   1.7e-08   42.7   0.0  cm       27       79 ~~           1          50 + ~~ 0.97 5'&3' 0.72\n",
      "\n",
      "                           ???                                    ???      ????      NC\n",
      "                    ~~~~~~_>>>,,<<<<_________>>>><<<<<<_____>>>>>>)))------))))~~~~~ CS\n",
      "  Twister-sister  1 <[26]*ccCGCCGCcGGUGCAAGCCCgGCCgCCCcagacagGGGcGGGCGCUCAUGGGU*[5]> 84\n",
      "                          +CCGCCGC:GGUGCAAG CC:GCC:C:C:    +:G:G:GGGCGCUCAUGGGU     \n",
      "          target  1 <[ 0]*GCCGCCGCUGGUGCAAGUCCAGCCACGCUU---CGGCGUGGGCGCUCAUGGGU*[0]> 50\n",
      "                    ......9***************************95...49******************..... PP\n",
      "\n",
      "\n",
      "\n",
      "Internal HMM-only pipeline statistics summary: (run for model(s) with zero basepairs)\n",
      "--------------------------------------------------------------------------------------\n",
      "Query sequence(s):                                               1  (100 residues searched)\n",
      "Target model(s):                                               347  (40911 consensus positions)\n",
      "Windows   passing  local HMM SSV           filter:               2  (0.002882); expected (0.02)\n",
      "Windows   passing  local HMM MSV      bias filter:               2  (0.002882); expected (0.02)\n",
      "Windows   passing  local HMM Viterbi       filter:               0  (0); expected (0.001)\n",
      "Windows   passing  local HMM Forward       filter:               0  (0); expected (1e-05)\n",
      "Total HMM hits reported:                                         0  (0)\n",
      "\n",
      "Internal CM pipeline statistics summary:\n",
      "----------------------------------------\n",
      "Query sequence(s):                                               1  (100 residues searched)\n",
      "Query sequences re-searched for truncated hits:                  1  (297.2 residues re-searched, avg per model)\n",
      "Target model(s):                                              2669  (345034 consensus positions)\n",
      "Windows   passing  local HMM SSV           filter:             798  (0.03589); expected (0.35)\n",
      "Windows   passing  local HMM Viterbi       filter:                  (off)\n",
      "Windows   passing  local HMM Viterbi  bias filter:                  (off)\n",
      "Windows   passing  local HMM Forward       filter:             290  (0.01307); expected (0.02)\n",
      "Windows   passing  local HMM Forward  bias filter:             251  (0.01136); expected (0.02)\n",
      "Windows   passing glocal HMM Forward       filter:              59  (0.002673); expected (0.02)\n",
      "Windows   passing glocal HMM Forward  bias filter:              55  (0.002484); expected (0.02)\n",
      "Envelopes passing glocal HMM envelope defn filter:              54  (0.002259); expected (0.02)\n",
      "Envelopes passing  local CM  CYK           filter:              14  (0.0005188); expected (0.0001)\n",
      "Total CM hits reported:                                          1  (9.432e-05); includes 1 truncated hit(s)\n",
      "\n",
      "Total CM and HMM hits reported:                                  1\n",
      "\n",
      "# CPU time: 0.95u 0.09s 00:00:01.04 Elapsed: 00:00:00.37\n",
      "//\n",
      "[ok]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "seq = RNASequence(\"GCCGCCGCUGGUGCAAGUCCAGCCACGCUUCGGCGUGGGCGCUCAUGGGU\")\n",
    "print rs.cmscan(seq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PDB Blast search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<HTML>\n",
      "<TITLE>BLAST Search Results</TITLE>\n",
      "<BODY BGCOLOR=\"#FFFFFF\" LINK=\"#0000FF\" VLINK=\"#660099\" ALINK=\"#660099\">\n",
      "<PRE>\n",
      "<b>BLASTN 2.2.18 [Mar-02-2008]</b>\n",
      "\n",
      "\n",
      "<b><a href=\"http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Retrieve&list_uids\n",
      "=9254694&dopt=Citation\">Reference</a>:</b>\n",
      "Altschul, Stephen F., Thomas L. Madden, Alejandro A. Sch&auml;ffer, \n",
      "Jinghui Zhang, Zheng Zhang, Webb Miller, and David J. Lipman (1997), \n",
      "\"Gapped BLAST and PSI-BLAST: a new generation of protein database search\n",
      "programs\",  Nucleic Acids Res. 25:3389-3402.\n",
      "\n",
      "<b>Query=</b> UNKNOWN_SEQUENCE\n",
      "         (50 letters)\n",
      "\n",
      "<b>Database:</b> pdb_nucleotide \n",
      "           20,601 sequences; 4,025,796 total letters\n",
      "\n",
      "Searching..................................................done\n",
      "\n",
      "<PRE>\n",
      "\n",
      "\n",
      "                                                                 Score    E\n",
      "Sequences producing significant alignments:                      (bits) Value\n",
      "\n",
      "5Y87:2:B,D|pdbid|entity|chain(s)|sequence                             <a href = #17652>100</a>   1e-22\n",
      "5Y85:2:B,D|pdbid|entity|chain(s)|sequence                             <a href = #17650>100</a>   1e-22\n",
      "2AU4:1:A|pdbid|entity|chain(s)|sequence                               <a href = #3796> 24</a>   6.6  \n",
      "</PRE>\n",
      "<PRE>\n",
      "><a name = 17652></a>5Y87:2:B,D|pdbid|entity|chain(s)|sequence\n",
      "          Length = 50\n",
      "\n",
      " Score = 99.6 bits (50), Expect = 1e-22\n",
      " Identities = 50/50 (100%)\n",
      " Strand = Plus / Plus\n",
      "\n",
      "                                                            \n",
      "Query: 1  gccgccgctggtgcaagtccagccacgcttcggcgtgggcgctcatgggt 50\n",
      "          ||||||||||||||||||||||||||||||||||||||||||||||||||\n",
      "Sbjct: 1  gccgccgctggtgcaagtccagccacgcttcggcgtgggcgctcatgggt 50\n",
      "</PRE>\n",
      "\n",
      "\n",
      "<PRE>\n",
      "><a name = 17650></a>5Y85:2:B,D|pdbid|entity|chain(s)|sequence\n",
      "          Length = 50\n",
      "\n",
      " Score = 99.6 bits (50), Expect = 1e-22\n",
      " Identities = 50/50 (100%)\n",
      " Strand = Plus / Plus\n",
      "\n",
      "                                                            \n",
      "Query: 1  gccgccgctggtgcaagtccagccacgcttcggcgtgggcgctcatgggt 50\n",
      "          ||||||||||||||||||||||||||||||||||||||||||||||||||\n",
      "Sbjct: 1  gccgccgctggtgcaagtccagccacgcttcggcgtgggcgctcatgggt 50\n",
      "</PRE>\n",
      "\n",
      "\n",
      "<PRE>\n",
      "><a name = 3796></a>2AU4:1:A|pdbid|entity|chain(s)|sequence\n",
      "          Length = 41\n",
      "\n",
      " Score = 24.3 bits (12), Expect = 6.6\n",
      " Identities = 12/12 (100%)\n",
      " Strand = Plus / Plus\n",
      "\n",
      "                      \n",
      "Query: 26 cgcttcggcgtg 37\n",
      "          ||||||||||||\n",
      "Sbjct: 18 cgcttcggcgtg 29\n",
      "</PRE>\n",
      "\n",
      "\n",
      "<PRE>\n",
      "  Database: pdb_nucleotide\n",
      "    Posted date:  May 31, 2019  2:22 PM\n",
      "  Number of letters in database: 4,025,796\n",
      "  Number of sequences in database:  20,601\n",
      "  \n",
      "Lambda     K      H\n",
      "    1.37    0.711     1.31 \n",
      "\n",
      "Gapped\n",
      "Lambda     K      H\n",
      "    1.37    0.711     1.31 \n",
      "\n",
      "\n",
      "Matrix: blastn matrix:1 -3\n",
      "Gap Penalties: Existence: 5, Extension: 2\n",
      "Number of Sequences: 20601\n",
      "Number of Hits to DB: 2112\n",
      "Number of extensions: 17\n",
      "Number of successful extensions: 16\n",
      "Number of sequences better than 10.0: 3\n",
      "Number of HSP's gapped: 16\n",
      "Number of HSP's successfully gapped: 3\n",
      "Length of query: 50\n",
      "Length of database: 4,025,796\n",
      "Length adjustment: 14\n",
      "Effective length of query: 36\n",
      "Effective length of database: 3,737,382\n",
      "Effective search space: 134545752\n",
      "Effective search space used: 134545752\n",
      "X1: 10 (19.8 bits)\n",
      "X2: 15 (29.7 bits)\n",
      "X3: 50 (99.1 bits)\n",
      "S1: 10 (20.3 bits)\n",
      "S2: 12 (24.3 bits)\n",
      "</PRE>\n",
      "</BODY>\n",
      "</HTML>\n"
     ]
    }
   ],
   "source": [
    "p = BlastPDB(seq.seq)\n",
    "p.search()\n",
    "print p.result"
   ]
  }
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