imagerb_docking_strand_screen.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code for screening docking strand to match imager B (AAGAAGTAAAGGGAG) while avoiding other imagers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import itertools\n",
    "import pandas as pd\n",
    "\n",
    "import Bio\n",
    "from Bio import Seq, SeqUtils, Align\n",
    "from Bio.SeqUtils import MeltingTemp\n",
    "mpl.rcParams['figure.facecolor'] = 'white'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_lookup = {\"A\":\"#5050FF\", \"C\": \"#E00000\", \"G\":\"#00C000\", \"T\":\"#E6E600\", \"U\":\"#CC9900\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up Aligner object\n",
    "\n",
    "aligner = Align.PairwiseAligner()\n",
    "aligner.mode = \"local\"\n",
    "aligner.open_gap_score = -0.5\n",
    "aligner.extend_gap_score = -0.1\n",
    "aligner.mismatch = -0.0\n",
    "\n",
    "# print (aligner)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# functions for checking alignement and melting temperature between 2 sequences\n",
    "\n",
    "def compare_dict_seq(seq1_dict, seq1_key, seq2_dict, seq2_key, aligner=aligner):\n",
    "    seq1 = seq1_dict[seq1_key]\n",
    "    seq2 = seq2_dict[seq2_key]\n",
    "    compare(seq1, seq2, aligner, seq1_key, seq2_key)\n",
    "    \n",
    "def compare(seq1, seq2, aligner=aligner, seq1_key=None, seq2_key=None):\n",
    "    alignments = aligner.align(seq1, seq2.reverse_complement())\n",
    "    if len(alignments) > 1:\n",
    "        print(\"More than 1 successful alignment\")\n",
    "    shift = max(alignments[0].path[0]) * (-1 if alignments[0].path[0][0] > 0 else 1)\n",
    "#     print shift\n",
    "    melt = MeltingTemp.Tm_NN(seq1, strict=False, c_seq=seq2[::-1], shift=shift)\n",
    "    print(\"{} vs {}, {} matches, {:.2f} C melt. temp.\".format(seq1 if seq1_key is None else seq1_key,\n",
    "                                                              seq2 if seq2_key is None else seq2_key,\n",
    "                                                              alignments[0].score, melt))\n",
    "    print(alignments[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# function plotting a summary of the filtered sequences\n",
    "\n",
    "def breakdown(raw_data, masks=None):\n",
    "    if not masks is None:\n",
    "        masks_array = np.asarray(masks)\n",
    "        mask_cum = np.cumprod(masks_array, axis=0)\n",
    "        mask_all = np.prod(masks_array, axis=0, dtype=bool)\n",
    "        data = raw_data[mask_all]\n",
    "    else:\n",
    "        data = raw_data\n",
    "    \n",
    "    print(\"seqs remaining: {}\".format(len(data)))\n",
    "    \n",
    "    sequences = data['seq']\n",
    "\n",
    "    # Displays the relative frequency of bases at each position (after masking if supplied)\n",
    "    summary = dict()\n",
    "    for i in range(sequences.shape[1]):\n",
    "        bases = sequences[:, i]\n",
    "        unique, counts = numpy.unique(bases, return_counts=True)\n",
    "        for uni, ct in zip(unique, 1.0*counts/counts.sum()):\n",
    "            if not uni in summary:\n",
    "                summary[uni] = np.zeros(sequences.shape[1])\n",
    "            summary[uni][i] = ct\n",
    "\n",
    "    fig, ax = subplots(1,1, figsize=(5,4))\n",
    "    bottom = np.zeros(sequences.shape[1])\n",
    "    for key in sorted(summary.keys()):\n",
    "        val = summary[key]\n",
    "        ax.bar(range(val.shape[0]), val, bottom=bottom, color=color_lookup[key], label=key)\n",
    "        bottom += val\n",
    "    ax.legend(bbox_to_anchor=(0.5, 1.2), loc=9, ncol=len(summary))\n",
    "    ax.set_xlabel(\"nucleotide #\")\n",
    "    ax.set_ylabel(\"proportion\")\n",
    "    \n",
    "    # Plots the parameters provided\n",
    "    # Each parameter is plotted in a historgram if no mask is provide, otherwise 2 historgrams are displayed for pre/post mask\n",
    "    keys_to_plot = list(data.dtype.names)\n",
    "    keys_to_plot.remove('seq')\n",
    "    for key in keys_to_plot:\n",
    "        \n",
    "        if masks is None:\n",
    "            fig, axes = subplots(1, 1, figsize=(5,4))\n",
    "            axes = [axes]\n",
    "        else:\n",
    "            fig, axes = subplots(1, 2, figsize=(10,4))            \n",
    "            data_to_plot = raw_data[key]\n",
    "            if np.issubdtype(data_to_plot.dtype, np.int32):\n",
    "                range_x = np.arange(data_to_plot.min()-0.5, data_to_plot.max()+1.5, 1)\n",
    "            else:\n",
    "                range_x = np.linspace(np.percentile(data_to_plot, 1)-0.5, np.percentile(data_to_plot, 99)+0.5, 20)\n",
    "                \n",
    "            axes[0].hist(data_to_plot, range_x, label='all')\n",
    "#             print(mask_cum.shape)\n",
    "            for i in np.arange(mask_cum.shape[0]):\n",
    "                axes[0].hist(data_to_plot[mask_cum[i].astype(bool)], range_x, label='mask {}'.format(i), alpha=1)\n",
    "        \n",
    "        data_to_plot = data[key]\n",
    "#         print(data_to_plot.min(), data_to_plot.max())\n",
    "        if np.issubdtype(data_to_plot.dtype, np.int32):\n",
    "            range_x = np.arange(data_to_plot.min()-0.5, data_to_plot.max()+1.5, 1)\n",
    "        else:\n",
    "            range_x = np.linspace(np.percentile(data_to_plot, 1)-0.5, np.percentile(data_to_plot, 99)+0.5, 20)\n",
    "        axes[-1].hist(data_to_plot, range_x, label='final')        \n",
    "        \n",
    "        for ax in axes:\n",
    "            ax.set_xlabel(key)\n",
    "            ax.set_ylabel(\"frequency\")\n",
    "            ax.legend(loc=0)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# functions to help with filtering by position\n",
    "\n",
    "def match_start(lookup_array, ref, num, exact=True):\n",
    "    return match(lookup_array, ref, 0, num, exact)\n",
    "\n",
    "def match_end(lookup_array, ref, num, exact=True):\n",
    "    return match(lookup_array, ref, len(ref)-num, len(ref), exact)\n",
    "\n",
    "def match(lookup_array, ref, start, end, exact=True):\n",
    "    \"\"\"\n",
    "    if exact, match subsequence at specified positions, bases bordering sequence should specifically not match\n",
    "    \"\"\"\n",
    "    mask = np.ones(lookup_array.shape[0], dtype=bool)\n",
    "    for i in range(start, end):\n",
    "        mask &= lookup_array[:, i] == ref[i]\n",
    "    if exact:\n",
    "        if start > 0:\n",
    "            i = start - 1\n",
    "            mask &= (lookup_array[:, i] != ref[i])\n",
    "        if end < len(ref):\n",
    "            i = end\n",
    "            mask &= (lookup_array[:, i] != ref[i])\n",
    "    return mask\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "imager_dict = dict()\n",
    "imager_dict[\"Imager A\"] = Seq.Seq(\"AGAAGTAATGTGGAA\", )\n",
    "imager_dict[\"Imager B\"] = Seq.Seq(\"AAGAAGTAAAGGGAG\", )\n",
    "imager_dict[\"Imager C\"] = Seq.Seq(\"TAAATAAAGCAAAGT\", )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def generate_seq_data(imager_dict, imager_names, start_fixed=2, end_fixed=2):\n",
    "    \"\"\"\n",
    "    Generate all possible sequences with the same length as the first imager provided.\n",
    "    Takes 10+ minutes. Fixing the end bases will significantly improve the speed.\n",
    "    \n",
    "    Calculates various parameters with the aim of designing a docking strand for the first imager.\n",
    "    \"\"\"\n",
    "    \n",
    "    imager_1, imager_2, imager_3 = [imager_dict[name] for name in imager_names]\n",
    "    \n",
    "    full_len = len(imager_1)\n",
    "    \n",
    "    cache_name = \"cache({})({})({})_{}-{}.npz\".format(imager_names[0], imager_names[1], imager_names[2], start_fixed, end_fixed)\n",
    "\n",
    "    try:        \n",
    "        docking_results = np.load(cache_name)[\"arr_0\"]\n",
    "    except:\n",
    "        print(\"failed to load from cache.\")\n",
    "\n",
    "        imager_1_rc = imager_1.reverse_complement()\n",
    "\n",
    "        dtype = [\n",
    "            ('seq', 'U{}'.format(full_len)),\n",
    "    #         ('seq', '{}U1'.format(len(base_imager))),\n",
    "            ('melt temp match', np.float),\n",
    "            ('align count match', np.int),\n",
    "            ('align score match', np.float),\n",
    "            ('align path count match', np.int),\n",
    "            ('melt temp self', np.float),\n",
    "            ('align count self', np.float),\n",
    "            ('align score self', np.float),\n",
    "            ('melt temp mismatch 1', np.float),\n",
    "            ('align count mismatch 1', np.int),\n",
    "            ('align score mismatch 1', np.float),\n",
    "            ('align path count mismatch 1', np.int),\n",
    "            ('melt temp mismatch 2', np.float),\n",
    "            ('align count mismatch 2', np.int),\n",
    "            ('align score mismatch 2', np.float),\n",
    "            ('align path count mismatch 2', np.int),\n",
    "        ]\n",
    "\n",
    "        docking_results = np.zeros(np.power(4, full_len-start_fixed-end_fixed),\n",
    "                                  dtype = dtype)\n",
    "\n",
    "        for i, itr in enumerate(itertools.product(\"ATCG\", repeat=full_len-start_fixed-end_fixed)):\n",
    "        #     print itr\n",
    "            seq = \"\".join((str(imager_1_rc[:start_fixed]),) + itr + (str(imager_1_rc[-end_fixed:]),))\n",
    "            docking_results['seq'][i] = str(Seq.Seq(seq))\n",
    "\n",
    "        for i in np.arange(docking_results.shape[0]):\n",
    "            seq = Seq.Seq(docking_results['seq'][i])\n",
    "\n",
    "            # high priority\n",
    "            # matching imager/docking needs to behave well.\n",
    "            # unaligned strong binding not considered at this step            \n",
    "            docking_results['melt temp match'][i] = MeltingTemp.Tm_NN(imager_1, strict=False, c_seq=seq[::-1],)\n",
    "            alignments = aligner.align(imager_1, seq.reverse_complement())\n",
    "            docking_results['align count match'][i] = len(alignments)\n",
    "            docking_results['align score match'][i] = max([a.score for a in alignments])\n",
    "            docking_results['align path count match'][i] = min([len(a.path)/2 for a in alignments])\n",
    "        \n",
    "            # self\n",
    "            docking_results['melt temp self'][i] = MeltingTemp.Tm_NN(seq, strict=False, c_seq=seq[::-1],)\n",
    "            alignments = aligner.align(seq, seq.reverse_complement())\n",
    "            scores = [a.score for a in alignments]\n",
    "            docking_results['align count self'][i] = len(scores)\n",
    "            if len(scores) > 0:\n",
    "                docking_results['align score self'][i] = max(scores)\n",
    "            \n",
    "            # second imager mismatch\n",
    "            docking_results['melt temp mismatch 1'][i] = MeltingTemp.Tm_NN(imager_2, strict=False, c_seq=seq[::-1],)\n",
    "            alignments = aligner.align(imager_2, seq.reverse_complement())\n",
    "            scores = [a.score for a in alignments]\n",
    "            docking_results['align count mismatch 1'][i] = len(scores)\n",
    "            if len(scores) > 0:\n",
    "                docking_results['align score mismatch 1'][i] = max(scores)\n",
    "                docking_results['align path count mismatch 1'][i] = min([len(a.path)/2 for a in alignments])\n",
    "            \n",
    "            # third imager mismatch\n",
    "            docking_results['melt temp mismatch 2'][i] = MeltingTemp.Tm_NN(imager_3, strict=False, c_seq=seq[::-1],)\n",
    "            alignments = aligner.align(imager_3, seq.reverse_complement())\n",
    "            scores = [a.score for a in alignments]\n",
    "            docking_results['align count mismatch 2'][i] = len(scores)\n",
    "            if len(scores) > 0:\n",
    "                docking_results['align score mismatch 2'][i] = max(scores)\n",
    "                docking_results['align path count mismatch 2'][i] = min([len(a.path)/2 for a in alignments])\n",
    "\n",
    "\n",
    "        dtype[0] = ('seq', '{}U1'.format(full_len))\n",
    "        docking_results = docking_results.view(dtype)\n",
    "        \n",
    "        np.savez_compressed(cache_name, arr_0=docking_results)\n",
    "\n",
    "    return docking_results\n",
    "    \n",
    "imager_names=['Imager B', 'Imager C', 'Imager A']\n",
    "docking_results = generate_seq_data(imager_dict, imager_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# breakdown(docking_results,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seqs remaining: 88\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# screening for fast docking\n",
    "masks = list()\n",
    "\n",
    "# fix bases at specific positions\n",
    "mask_seq = match_start(docking_results['seq'], imager_dict[imager_names[0]].reverse_complement(), 2, False)\n",
    "mask_seq &= match_end(docking_results['seq'], imager_dict[imager_names[0]].reverse_complement(), 2, False)\n",
    "# mask_seq &= (docking_results['seq'][:, 9] == \"C\")\n",
    "masks.append(mask_seq)\n",
    "\n",
    "# filter by melting temperature for a aligned fit against imager\n",
    "mask_temp = np.ones_like(mask_seq) & (docking_results['melt temp match'] > -35) & (docking_results['melt temp match'] < -25)\n",
    "masks.append(mask_temp)\n",
    "\n",
    "# filter by how many possible alignments found (weak aligner)\n",
    "mask_aligns = np.ones_like(mask_seq)\n",
    "mask_aligns &= docking_results['align count match'] <= 1\n",
    "masks.append(mask_aligns)\n",
    "\n",
    "# filter by best alignments score (weak aligner)\n",
    "mask_aligns = np.ones_like(mask_seq)\n",
    "mask_aligns &= docking_results['align score match'] >= 10\n",
    "masks.append(mask_aligns)\n",
    "\n",
    "# filter by number of fragments in alignments (weak aligner)\n",
    "mask_aligns = np.ones_like(mask_seq)\n",
    "mask_aligns &= docking_results['align path count match'] <= 1\n",
    "masks.append(mask_aligns)\n",
    "\n",
    "# filter by best alignments score against its own sequence (weak aligner)\n",
    "mask_aligns = np.ones_like(mask_seq)\n",
    "mask_aligns &= docking_results['align score self'] <= 7\n",
    "masks.append(mask_aligns)\n",
    "\n",
    "# filter by difference in melting temperature between aligned fit against imger vs aligned fit against itself\n",
    "mask_aligns = np.ones_like(mask_seq)\n",
    "mask_aligns &= docking_results['melt temp self']+15 < docking_results['melt temp match']\n",
    "masks.append(mask_aligns)\n",
    "\n",
    "mask_aligns = np.ones_like(mask_seq)\n",
    "mask_aligns &= docking_results['melt temp mismatch 1']+15 < docking_results['melt temp match']\n",
    "masks.append(mask_aligns)\n",
    "\n",
    "mask_aligns = np.ones_like(mask_seq)\n",
    "mask_aligns &= docking_results['align score mismatch 1'] <= 7\n",
    "masks.append(mask_aligns)\n",
    "\n",
    "mask_aligns = np.ones_like(mask_seq)\n",
    "mask_aligns &= docking_results['melt temp mismatch 2']+15 < docking_results['melt temp match']\n",
    "masks.append(mask_aligns)\n",
    "\n",
    "mask_aligns = np.ones_like(mask_seq)\n",
    "mask_aligns &= docking_results['align score mismatch 2'] <= 7\n",
    "masks.append(mask_aligns)\n",
    "\n",
    "breakdown(docking_results, masks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0   CTCTCTTCGCCGCTT\n",
      "1   CTCTCTTGGCGCCTT\n",
      "2   CTCTCTCGACGGCTT\n",
      "3   CTCTCTCGGCTGCTT\n",
      "4   CTCTCTGAACGGCTT\n",
      "5   CTCTCTGTGCGGCTT\n",
      "6   CTCTCTGGACCGCTT\n",
      "7   CTCTCTGGGCCTCTT\n",
      "8   CTCCTTTTCCGGGTT\n",
      "9   CTCCCTAAAAGTGTT\n",
      "10   CTCCCTAAAGATGTT\n",
      "11   CTCCCTAGAAATGTT\n",
      "12   CTCCCTAGAAGTGTT\n",
      "13   CTCCCTCTGCAGGTT\n",
      "14   CTCCCTCTGCCGGTT\n",
      "15   CTCCCTCCCCTGGTT\n",
      "16   CTCCCTCCCCCGCTT\n",
      "17   CTCCCTCCCCGCCTT\n",
      "18   CTCCCTCCGCCTGTT\n",
      "19   CTCCCTCGAAGTGTT\n",
      "20   CTCCCTCGCCCGCTT\n",
      "21   CTCCCTCGCCGCCTT\n",
      "22   CTCCCTCGGCATGTT\n",
      "23   CTCCCTCGGCCTGTT\n",
      "24   CTCCCTGAAATGGTT\n",
      "25   CTCCCTGAAAGTGTT\n",
      "26   CTCCCTGAAGATGTT\n",
      "27   CTCCCTGAAGTGGTT\n",
      "28   CTCCCTGTCCGAATT\n",
      "29   CTCCCTGTCCGAGTT\n",
      "30   CTCCCTGTCCGGATT\n",
      "31   CTCCCTGCAAGTGTT\n",
      "32   CTCCCTGCCCACCTT\n",
      "33   CTCCCTGCCCCCCTT\n",
      "34   CTCCCTGCCCGTGTT\n",
      "35   CTCCCTGCGCCTGTT\n",
      "36   CTCCCTGGAAATGTT\n",
      "37   CTCCCTGGAATGGTT\n",
      "38   CTCCCTGGCCACCTT\n",
      "39   CTCCCTGGCCCCCTT\n",
      "40   CTCCCTGGGCCTGTT\n",
      "41   CTCCGTTTCCCGGTT\n",
      "42   CTCCGTTCCCGGCTT\n",
      "43   CTCCGTTCGCCCCTT\n",
      "44   CTCCGTCTGCGGCTT\n",
      "45   CTCCGTCGGCCTCTT\n",
      "46   CTCCGTGAACGTGTT\n",
      "47   CTCCGTGAACGCCTT\n",
      "48   CTCCGTGAAGTTGTT\n",
      "49   CTCCGTGTGCACCTT\n",
      "50   CTCCGTGTGCCGCTT\n",
      "51   CTCCGTGTGCGCCTT\n",
      "52   CTCCGTGGACCCCTT\n",
      "53   CTCCGTGGACGTGTT\n",
      "54   CTCCGTGGGCTCCTT\n",
      "55   CTCGTTTTCCGGCTT\n",
      "56   CTCGTTTTGCCCCTT\n",
      "57   CTCGCTAAACCCCTT\n",
      "58   CTCGCTATGCCGCTT\n",
      "59   CTCGCTATGCGCCTT\n",
      "60   CTCGCTAGACCCCTT\n",
      "61   CTCGCTTCGCCCCTT\n",
      "62   CTCGCTCTGCGGCTT\n",
      "63   CTCGCTCGACCCCTT\n",
      "64   CTCGCTCGACGCCTT\n",
      "65   CTCGCTGAACCCCTT\n",
      "66   CTCGCTGTCCGGCTT\n",
      "67   CTCGCTGTGCCCCTT\n",
      "68   CTCGCTGCACCCCTT\n",
      "69   CTCGCTGCCCCTCTT\n",
      "70   CTCGCTGGACCCCTT\n",
      "71   CTCGCTGGGCTCCTT\n",
      "72   CTCGGTTAACGCCTT\n",
      "73   CTCGGTTTCCCGCTT\n",
      "74   CTCGGTTCACGGCTT\n",
      "75   CTCGGTTGACACCTT\n",
      "76   CTCGGTTGACCCCTT\n",
      "77   CTCGGTTGACGCCTT\n",
      "78   CTCGGTCTACGGCTT\n",
      "79   CTGCCTAGACGTGTT\n",
      "80   CTGCCTTCGCCCCTT\n",
      "81   CTGCCTTCGCGCCTT\n",
      "82   CTGCCTTGGCCCCTT\n",
      "83   CTGCCTGTGCCGCTT\n",
      "84   CTGCCTGTGCGCCTT\n",
      "85   CTGCCTGGACCCCTT\n",
      "86   CTGCCTGGACGTGTT\n",
      "87   CTGCCTGGACGCCTT\n"
     ]
    }
   ],
   "source": [
    "for i, seq in enumerate(docking_results[np.product(masks, axis=0).astype(bool)]['seq']):\n",
    "    print(\"{}   {}\".format(i, \"\".join(seq)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AAGAAGTAAAGGGAG vs CTCCCTTTACTTCTT, 15.0 matches, 35.69 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||||||||||||||\n",
      "AAGAAGTAAAGGGAG\n",
      "\n",
      "0 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCTCTTCGCCGCTT, 10.0 matches, -31.90 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|..|||.|||\n",
      "AAGCGGCGAAGAGAG\n",
      "\n",
      "1 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCTCTTGGCGCCTT, 10.0 matches, -26.72 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|..|||.|||\n",
      "AAGGCGCCAAGAGAG\n",
      "\n",
      "2 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCTCTCGACGGCTT, 10.0 matches, -31.62 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..||..||.|||\n",
      "AAGCCGTCGAGAGAG\n",
      "\n",
      "3 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCTCTCGGCTGCTT, 10.0 matches, -33.63 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||.||...||.|||\n",
      "AAGCAGCCGAGAGAG\n",
      "\n",
      "4 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCTCTGAACGGCTT, 10.0 matches, -28.97 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..||..||.|||\n",
      "AAGCCGTTCAGAGAG\n",
      "\n",
      "5 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCTCTGTGCGGCTT, 10.0 matches, -30.43 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|.|.||.|||\n",
      "AAGCCGCACAGAGAG\n",
      "\n",
      "6 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCTCTGGACCGCTT, 10.0 matches, -31.90 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..||..||.|||\n",
      "AAGCGGTCCAGAGAG\n",
      "\n",
      "7 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCTCTGGGCCTCTT, 10.0 matches, -30.43 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||||.|...||.|||\n",
      "AAGAGGCCCAGAGAG\n",
      "\n",
      "8 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCTTTTCCGGGTT, 10.0 matches, -28.76 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||...|.|||.||||\n",
      "AACCCGGAAAAGGAG\n",
      "\n",
      "9 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTAAAAGTGTT, 10.0 matches, -33.68 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||.|..|..||||||\n",
      "AACACTTTTAGGGAG\n",
      "\n",
      "10 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTAAAGATGTT, 10.0 matches, -34.09 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||.|..|..||||||\n",
      "AACATCTTTAGGGAG\n",
      "\n",
      "11 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTAGAAATGTT, 10.0 matches, -32.29 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||.|..|..||||||\n",
      "AACATTTCTAGGGAG\n",
      "\n",
      "12 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTAGAAGTGTT, 10.0 matches, -27.46 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||.|..|..||||||\n",
      "AACACTTCTAGGGAG\n",
      "\n",
      "13 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTCTGCAGGTT, 10.0 matches, -28.44 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||...|.|.||||||\n",
      "AACCTGCAGAGGGAG\n",
      "\n",
      "14 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTCTGCCGGTT, 10.0 matches, -31.96 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||...|.|.||||||\n",
      "AACCGGCAGAGGGAG\n",
      "\n",
      "15 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTCCCCTGGTT, 10.0 matches, -29.62 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||..||...||||||\n",
      "AACCAGGGGAGGGAG\n",
      "\n",
      "16 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTCCCCCGCTT, 10.0 matches, -29.49 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|...||||||\n",
      "AAGCGGGGGAGGGAG\n",
      "\n",
      "17 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTCCCCGCCTT, 10.0 matches, -32.97 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|...||||||\n",
      "AAGGCGGGGAGGGAG\n",
      "\n",
      "18 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTCCGCCTGTT, 10.0 matches, -34.43 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||.|.|...||||||\n",
      "AACAGGCGGAGGGAG\n",
      "\n",
      "19 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTCGAAGTGTT, 10.0 matches, -34.13 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||.|..|..||||||\n",
      "AACACTTCGAGGGAG\n",
      "\n",
      "20 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTCGCCCGCTT, 10.0 matches, -29.49 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|...||||||\n",
      "AAGCGGGCGAGGGAG\n",
      "\n",
      "21 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTCGCCGCCTT, 10.0 matches, -32.97 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|...||||||\n",
      "AAGGCGGCGAGGGAG\n",
      "\n",
      "22 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTCGGCATGTT, 10.0 matches, -28.51 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||.|.|...||||||\n",
      "AACATGCCGAGGGAG\n",
      "\n",
      "23 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTCGGCCTGTT, 10.0 matches, -34.43 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||.|.|...||||||\n",
      "AACAGGCCGAGGGAG\n",
      "\n",
      "24 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTGAAATGGTT, 10.0 matches, -33.73 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||..|.|..||||||\n",
      "AACCATTTCAGGGAG\n",
      "\n",
      "25 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTGAAAGTGTT, 10.0 matches, -31.39 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||.|..|..||||||\n",
      "AACACTTTCAGGGAG\n",
      "\n",
      "26 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTGAAGATGTT, 10.0 matches, -31.82 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||.|..|..||||||\n",
      "AACATCTTCAGGGAG\n",
      "\n",
      "27 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTGAAGTGGTT, 10.0 matches, -25.54 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||..|.|..||||||\n",
      "AACCACTTCAGGGAG\n",
      "\n",
      "28 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTGTCCGAATT, 10.0 matches, -34.58 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||...|.|.||||||\n",
      "AATTCGGACAGGGAG\n",
      "\n",
      "29 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTGTCCGAGTT, 10.0 matches, -30.78 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||...|.|.||||||\n",
      "AACTCGGACAGGGAG\n",
      "\n",
      "30 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTGTCCGGATT, 10.0 matches, -34.58 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||...|.|.||||||\n",
      "AATCCGGACAGGGAG\n",
      "\n",
      "31 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTGCAAGTGTT, 10.0 matches, -30.72 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||.|..|..||||||\n",
      "AACACTTGCAGGGAG\n",
      "\n",
      "32 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTGCCCACCTT, 10.0 matches, -29.44 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|...||||||\n",
      "AAGGTGGGCAGGGAG\n",
      "\n",
      "33 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTGCCCCCCTT, 10.0 matches, -33.26 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|...||||||\n",
      "AAGGGGGGCAGGGAG\n",
      "\n",
      "34 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTGCCCGTGTT, 10.0 matches, -29.07 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||.|.|...||||||\n",
      "AACACGGGCAGGGAG\n",
      "\n",
      "35 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTGCGCCTGTT, 10.0 matches, -26.00 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||.|.|...||||||\n",
      "AACAGGCGCAGGGAG\n",
      "\n",
      "36 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTGGAAATGTT, 10.0 matches, -30.14 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||.|..|..||||||\n",
      "AACATTTCCAGGGAG\n",
      "\n",
      "37 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTGGAATGGTT, 10.0 matches, -27.57 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||..|.|..||||||\n",
      "AACCATTCCAGGGAG\n",
      "\n",
      "38 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTGGCCACCTT, 10.0 matches, -29.44 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|...||||||\n",
      "AAGGTGGCCAGGGAG\n",
      "\n",
      "39 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTGGCCCCCTT, 10.0 matches, -33.26 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|...||||||\n",
      "AAGGGGGCCAGGGAG\n",
      "\n",
      "40 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCCTGGGCCTGTT, 10.0 matches, -26.00 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||.|.|...||||||\n",
      "AACAGGCCCAGGGAG\n",
      "\n",
      "41 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCGTTTCCCGGTT, 10.0 matches, -27.00 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||...|.|||.||||\n",
      "AACCGGGAAACGGAG\n",
      "\n",
      "42 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCGTTCCCGGCTT, 10.0 matches, -28.71 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|..||.||||\n",
      "AAGCCGGGAACGGAG\n",
      "\n",
      "43 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCGTTCGCCCCTT, 10.0 matches, -30.42 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|..||.||||\n",
      "AAGGGGCGAACGGAG\n",
      "\n",
      "44 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCGTCTGCGGCTT, 10.0 matches, -32.34 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|.|.|.||||\n",
      "AAGCCGCAGACGGAG\n",
      "\n",
      "45 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCGTCGGCCTCTT, 10.0 matches, -25.88 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||||.|...|.||||\n",
      "AAGAGGCCGACGGAG\n",
      "\n",
      "46 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCGTGAACGTGTT, 10.0 matches, -32.25 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||.|.||..|.||||\n",
      "AACACGTTCACGGAG\n",
      "\n",
      "47 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCGTGAACGCCTT, 10.0 matches, -27.66 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..||..|.||||\n",
      "AAGGCGTTCACGGAG\n",
      "\n",
      "48 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCGTGAAGTTGTT, 10.0 matches, -30.62 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||.||.|..|.||||\n",
      "AACAACTTCACGGAG\n",
      "\n",
      "49 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCGTGTGCACCTT, 10.0 matches, -34.09 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|.|.|.||||\n",
      "AAGGTGCACACGGAG\n",
      "\n",
      "50 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCGTGTGCCGCTT, 10.0 matches, -26.14 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|.|.|.||||\n",
      "AAGCGGCACACGGAG\n",
      "\n",
      "51 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCGTGTGCGCCTT, 10.0 matches, -29.08 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|.|.|.||||\n",
      "AAGGCGCACACGGAG\n",
      "\n",
      "52 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCGTGGACCCCTT, 10.0 matches, -30.42 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..||..|.||||\n",
      "AAGGGGTCCACGGAG\n",
      "\n",
      "53 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCGTGGACGTGTT, 10.0 matches, -26.84 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||.|.||..|.||||\n",
      "AACACGTCCACGGAG\n",
      "\n",
      "54 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCCGTGGGCTCCTT, 10.0 matches, -31.96 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||.||...|.||||\n",
      "AAGGAGCCCACGGAG\n",
      "\n",
      "55 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCGTTTTCCGGCTT, 10.0 matches, -31.71 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|.|||..|||\n",
      "AAGCCGGAAAACGAG\n",
      "\n",
      "56 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCGTTTTGCCCCTT, 10.0 matches, -29.65 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|.|||..|||\n",
      "AAGGGGCAAAACGAG\n",
      "\n",
      "57 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCGCTAAACCCCTT, 10.0 matches, -33.33 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..||..||.|||\n",
      "AAGGGGTTTAGCGAG\n",
      "\n",
      "58 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCGCTATGCCGCTT, 10.0 matches, -27.62 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|.|.||.|||\n",
      "AAGCGGCATAGCGAG\n",
      "\n",
      "59 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCGCTATGCGCCTT, 10.0 matches, -30.62 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|.|.||.|||\n",
      "AAGGCGCATAGCGAG\n",
      "\n",
      "60 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCGCTAGACCCCTT, 10.0 matches, -27.49 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..||..||.|||\n",
      "AAGGGGTCTAGCGAG\n",
      "\n",
      "61 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCGCTTCGCCCCTT, 10.0 matches, -25.65 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|..|||.|||\n",
      "AAGGGGCGAAGCGAG\n",
      "\n",
      "62 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCGCTCTGCGGCTT, 10.0 matches, -27.56 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|.|.||.|||\n",
      "AAGCCGCAGAGCGAG\n",
      "\n",
      "63 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCGCTCGACCCCTT, 10.0 matches, -33.75 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..||..||.|||\n",
      "AAGGGGTCGAGCGAG\n",
      "\n",
      "64 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCGCTCGACGCCTT, 10.0 matches, -25.39 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..||..||.|||\n",
      "AAGGCGTCGAGCGAG\n",
      "\n",
      "65 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCGCTGAACCCCTT, 10.0 matches, -31.18 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..||..||.|||\n",
      "AAGGGGTTCAGCGAG\n",
      "\n",
      "66 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCGCTGTCCGGCTT, 10.0 matches, -34.71 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|.|.||.|||\n",
      "AAGCCGGACAGCGAG\n",
      "\n",
      "67 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCGCTGTGCCCCTT, 10.0 matches, -32.51 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|.|.||.|||\n",
      "AAGGGGCACAGCGAG\n",
      "\n",
      "68 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCGCTGCACCCCTT, 10.0 matches, -30.55 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..||..||.|||\n",
      "AAGGGGTGCAGCGAG\n",
      "\n",
      "69 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCGCTGCCCCTCTT, 10.0 matches, -30.80 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||||.|...||.|||\n",
      "AAGAGGGGCAGCGAG\n",
      "\n",
      "70 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCGCTGGACCCCTT, 10.0 matches, -25.65 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..||..||.|||\n",
      "AAGGGGTCCAGCGAG\n",
      "\n",
      "71 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCGCTGGGCTCCTT, 10.0 matches, -26.88 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||.||...||.|||\n",
      "AAGGAGCCCAGCGAG\n",
      "\n",
      "72 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCGGTTAACGCCTT, 10.0 matches, -33.32 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..||.||..|||\n",
      "AAGGCGTTAACCGAG\n",
      "\n",
      "73 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCGGTTTCCCGCTT, 10.0 matches, -31.55 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|.|||..|||\n",
      "AAGCGGGAAACCGAG\n",
      "\n",
      "74 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCGGTTCACGGCTT, 10.0 matches, -27.44 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..||.||..|||\n",
      "AAGCCGTGAACCGAG\n",
      "\n",
      "75 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCGGTTGACACCTT, 10.0 matches, -30.59 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..||.||..|||\n",
      "AAGGTGTCAACCGAG\n",
      "\n",
      "76 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCGGTTGACCCCTT, 10.0 matches, -33.99 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..||.||..|||\n",
      "AAGGGGTCAACCGAG\n",
      "\n",
      "77 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCGGTTGACGCCTT, 10.0 matches, -25.93 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..||.||..|||\n",
      "AAGGCGTCAACCGAG\n",
      "\n",
      "78 / 88\n",
      "AAGAAGTAAAGGGAG vs CTCGGTCTACGGCTT, 10.0 matches, -28.54 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|||.|..|||\n",
      "AAGCCGTAGACCGAG\n",
      "\n",
      "79 / 88\n",
      "AAGAAGTAAAGGGAG vs CTGCCTAGACGTGTT, 10.0 matches, -32.19 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||.|.||..|||.||\n",
      "AACACGTCTAGGCAG\n",
      "\n",
      "80 / 88\n",
      "AAGAAGTAAAGGGAG vs CTGCCTTCGCCCCTT, 10.0 matches, -34.28 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|..||||.||\n",
      "AAGGGGCGAAGGCAG\n",
      "\n",
      "81 / 88\n",
      "AAGAAGTAAAGGGAG vs CTGCCTTCGCGCCTT, 10.0 matches, -25.49 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|..||||.||\n",
      "AAGGCGCGAAGGCAG\n",
      "\n",
      "82 / 88\n",
      "AAGAAGTAAAGGGAG vs CTGCCTTGGCCCCTT, 10.0 matches, -25.76 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|..||||.||\n",
      "AAGGGGCCAAGGCAG\n",
      "\n",
      "83 / 88\n",
      "AAGAAGTAAAGGGAG vs CTGCCTGTGCCGCTT, 10.0 matches, -29.46 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|.|.|||.||\n",
      "AAGCGGCACAGGCAG\n",
      "\n",
      "84 / 88\n",
      "AAGAAGTAAAGGGAG vs CTGCCTGTGCGCCTT, 10.0 matches, -32.68 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..|.|.|||.||\n",
      "AAGGCGCACAGGCAG\n",
      "\n",
      "85 / 88\n",
      "AAGAAGTAAAGGGAG vs CTGCCTGGACCCCTT, 10.0 matches, -34.28 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..||..|||.||\n",
      "AAGGGGTCCAGGCAG\n",
      "\n",
      "86 / 88\n",
      "AAGAAGTAAAGGGAG vs CTGCCTGGACGTGTT, 10.0 matches, -30.13 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "||.|.||..|||.||\n",
      "AACACGTCCAGGCAG\n",
      "\n",
      "87 / 88\n",
      "AAGAAGTAAAGGGAG vs CTGCCTGGACGCCTT, 10.0 matches, -25.49 C melt. temp.\n",
      "AAGAAGTAAAGGGAG\n",
      "|||..||..|||.||\n",
      "AAGGCGTCCAGGCAG\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Kenny\\anaconda3\\envs\\pyme_py3\\lib\\site-packages\\Bio\\SeqUtils\\MeltingTemp.py:841: BiopythonWarning: no themodynamic data for neighbors 'AA/GC' available. Calculation will be wrong\n",
      "  BiopythonWarning,\n",
      "C:\\Users\\Kenny\\anaconda3\\envs\\pyme_py3\\lib\\site-packages\\Bio\\SeqUtils\\MeltingTemp.py:841: BiopythonWarning: no themodynamic data for neighbors 'TA/GC' available. Calculation will be wrong\n",
      "  BiopythonWarning,\n",
      "C:\\Users\\Kenny\\anaconda3\\envs\\pyme_py3\\lib\\site-packages\\Bio\\SeqUtils\\MeltingTemp.py:841: BiopythonWarning: no themodynamic data for neighbors 'AA/CG' available. Calculation will be wrong\n",
      "  BiopythonWarning,\n",
      "C:\\Users\\Kenny\\anaconda3\\envs\\pyme_py3\\lib\\site-packages\\Bio\\SeqUtils\\MeltingTemp.py:841: BiopythonWarning: no themodynamic data for neighbors 'TA/GG' available. Calculation will be wrong\n",
      "  BiopythonWarning,\n",
      "C:\\Users\\Kenny\\anaconda3\\envs\\pyme_py3\\lib\\site-packages\\Bio\\SeqUtils\\MeltingTemp.py:841: BiopythonWarning: no themodynamic data for neighbors 'AA/GG' available. Calculation will be wrong\n",
      "  BiopythonWarning,\n",
      "C:\\Users\\Kenny\\anaconda3\\envs\\pyme_py3\\lib\\site-packages\\Bio\\SeqUtils\\MeltingTemp.py:841: BiopythonWarning: no themodynamic data for neighbors 'AA/AG' available. Calculation will be wrong\n",
      "  BiopythonWarning,\n",
      "C:\\Users\\Kenny\\anaconda3\\envs\\pyme_py3\\lib\\site-packages\\Bio\\SeqUtils\\MeltingTemp.py:841: BiopythonWarning: no themodynamic data for neighbors 'GA/GG' available. Calculation will be wrong\n",
      "  BiopythonWarning,\n",
      "C:\\Users\\Kenny\\anaconda3\\envs\\pyme_py3\\lib\\site-packages\\Bio\\SeqUtils\\MeltingTemp.py:841: BiopythonWarning: no themodynamic data for neighbors 'AG/GA' available. Calculation will be wrong\n",
      "  BiopythonWarning,\n",
      "C:\\Users\\Kenny\\anaconda3\\envs\\pyme_py3\\lib\\site-packages\\Bio\\SeqUtils\\MeltingTemp.py:841: BiopythonWarning: no themodynamic data for neighbors 'AA/AA' available. Calculation will be wrong\n",
      "  BiopythonWarning,\n",
      "C:\\Users\\Kenny\\anaconda3\\envs\\pyme_py3\\lib\\site-packages\\Bio\\SeqUtils\\MeltingTemp.py:841: BiopythonWarning: no themodynamic data for neighbors 'AG/AG' available. Calculation will be wrong\n",
      "  BiopythonWarning,\n",
      "C:\\Users\\Kenny\\anaconda3\\envs\\pyme_py3\\lib\\site-packages\\Bio\\SeqUtils\\MeltingTemp.py:841: BiopythonWarning: no themodynamic data for neighbors 'AG/AA' available. Calculation will be wrong\n",
      "  BiopythonWarning,\n",
      "C:\\Users\\Kenny\\anaconda3\\envs\\pyme_py3\\lib\\site-packages\\Bio\\SeqUtils\\MeltingTemp.py:841: BiopythonWarning: no themodynamic data for neighbors 'AA/GA' available. Calculation will be wrong\n",
      "  BiopythonWarning,\n",
      "C:\\Users\\Kenny\\anaconda3\\envs\\pyme_py3\\lib\\site-packages\\Bio\\SeqUtils\\MeltingTemp.py:841: BiopythonWarning: no themodynamic data for neighbors 'TA/CC' available. Calculation will be wrong\n",
      "  BiopythonWarning,\n",
      "C:\\Users\\Kenny\\anaconda3\\envs\\pyme_py3\\lib\\site-packages\\Bio\\SeqUtils\\MeltingTemp.py:841: BiopythonWarning: no themodynamic data for neighbors 'AA/CC' available. Calculation will be wrong\n",
      "  BiopythonWarning,\n",
      "C:\\Users\\Kenny\\anaconda3\\envs\\pyme_py3\\lib\\site-packages\\Bio\\SeqUtils\\MeltingTemp.py:841: BiopythonWarning: no themodynamic data for neighbors 'TA/CG' available. Calculation will be wrong\n",
      "  BiopythonWarning,\n",
      "C:\\Users\\Kenny\\anaconda3\\envs\\pyme_py3\\lib\\site-packages\\Bio\\SeqUtils\\MeltingTemp.py:841: BiopythonWarning: no themodynamic data for neighbors 'GA/AA' available. Calculation will be wrong\n",
      "  BiopythonWarning,\n",
      "C:\\Users\\Kenny\\anaconda3\\envs\\pyme_py3\\lib\\site-packages\\Bio\\SeqUtils\\MeltingTemp.py:841: BiopythonWarning: no themodynamic data for neighbors 'GA/GA' available. Calculation will be wrong\n",
      "  BiopythonWarning,\n",
      "C:\\Users\\Kenny\\anaconda3\\envs\\pyme_py3\\lib\\site-packages\\Bio\\SeqUtils\\MeltingTemp.py:841: BiopythonWarning: no themodynamic data for neighbors 'GA/AG' available. Calculation will be wrong\n",
      "  BiopythonWarning,\n",
      "C:\\Users\\Kenny\\anaconda3\\envs\\pyme_py3\\lib\\site-packages\\Bio\\SeqUtils\\MeltingTemp.py:841: BiopythonWarning: no themodynamic data for neighbors 'AA/CA' available. Calculation will be wrong\n",
      "  BiopythonWarning,\n",
      "C:\\Users\\Kenny\\anaconda3\\envs\\pyme_py3\\lib\\site-packages\\Bio\\SeqUtils\\MeltingTemp.py:841: BiopythonWarning: no themodynamic data for neighbors 'GG/TG' available. Calculation will be wrong\n",
      "  BiopythonWarning,\n",
      "C:\\Users\\Kenny\\anaconda3\\envs\\pyme_py3\\lib\\site-packages\\Bio\\SeqUtils\\MeltingTemp.py:841: BiopythonWarning: no themodynamic data for neighbors 'GG/GG' available. Calculation will be wrong\n",
      "  BiopythonWarning,\n"
     ]
    }
   ],
   "source": [
    "def compare_list(ref_strand, sequences, masks):    \n",
    "    compare(ref_strand, ref_strand.reverse_complement())\n",
    "    sequences_masked = sequences[np.prod(np.asarray(masks), axis=0, dtype=bool)]\n",
    "#     print (len(sequences_masked))\n",
    "    for i, seq in enumerate(sequences_masked):\n",
    "        print(\"{} / {}\".format(i, len(sequences_masked)))\n",
    "        seq = Seq.Seq(\"\".join(seq))\n",
    "        compare(ref_strand, seq, aligner)\n",
    "        \n",
    "compare_list(imager_dict[\"Imager B\"], docking_results['seq'], masks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>base_1</th>\n",
       "      <th>base_2</th>\n",
       "      <th>base_3</th>\n",
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       "      <th>base_10</th>\n",
       "      <th>base_11</th>\n",
       "      <th>base_12</th>\n",
       "      <th>base_13</th>\n",
       "      <th>base_14</th>\n",
       "      <th>base_15</th>\n",
       "      <th>melt temp match</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>C</td>\n",
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       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>-26.723079</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>G</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>-31.618966</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>G</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>-33.632767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>G</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>-28.966861</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>-29.456440</td>\n",
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       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>-32.683909</td>\n",
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       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>-34.280202</td>\n",
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       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>-30.133994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>-25.489123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   base_1 base_2 base_3 base_4 base_5 base_6 base_7 base_8 base_9 base_10  \\\n",
       "0       T      T      C      G      C      C      G      C      T       T   \n",
       "1       T      T      C      C      G      C      G      G      T       T   \n",
       "2       T      T      C      G      G      C      A      G      C       T   \n",
       "3       T      T      C      G      T      C      G      G      C       T   \n",
       "4       T      T      C      G      G      C      A      A      G       T   \n",
       "..    ...    ...    ...    ...    ...    ...    ...    ...    ...     ...   \n",
       "83      T      T      C      G      C      C      G      T      G       T   \n",
       "84      T      T      C      C      G      C      G      T      G       T   \n",
       "85      T      T      C      C      C      C      A      G      G       T   \n",
       "86      T      T      G      T      G      C      A      G      G       T   \n",
       "87      T      T      C      C      G      C      A      G      G       T   \n",
       "\n",
       "   base_11 base_12 base_13 base_14 base_15  melt temp match  \n",
       "0        C       T       C       T       C       -31.901140  \n",
       "1        C       T       C       T       C       -26.723079  \n",
       "2        C       T       C       T       C       -31.618966  \n",
       "3        C       T       C       T       C       -33.632767  \n",
       "4        C       T       C       T       C       -28.966861  \n",
       "..     ...     ...     ...     ...     ...              ...  \n",
       "83       C       C       G       T       C       -29.456440  \n",
       "84       C       C       G       T       C       -32.683909  \n",
       "85       C       C       G       T       C       -34.280202  \n",
       "86       C       C       G       T       C       -30.133994  \n",
       "87       C       C       G       T       C       -25.489123  \n",
       "\n",
       "[88 rows x 16 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def copy_to_clipboard(seq, data_dict, masks=None):\n",
    "    if not masks is None:\n",
    "        mask_all = np.prod(np.asarray(masks), axis=0, dtype=bool)\n",
    "        data_dict_tmp = dict()\n",
    "        for key, val in data_dict.items():\n",
    "            data_dict_tmp[key] = val[mask_all]\n",
    "        data_dict = data_dict_tmp\n",
    "        seq = seq[mask_all]\n",
    "    \n",
    "    dframe_data = dict()\n",
    "    for i in range(seq.shape[1]):\n",
    "        dframe_data['base_{}'.format(i+1)] = seq[:,-1-i]\n",
    "    for key, val in data_dict.items():\n",
    "        dframe_data[key] = val\n",
    "    dataFrame = pd.DataFrame.from_dict(dframe_data, )\n",
    "    dataFrame.to_clipboard()\n",
    "    display(dataFrame)\n",
    "\n",
    "copy_to_clipboard(docking_results['seq'], {\"melt temp match\": docking_results['melt temp match']}, masks)"
   ]
  }
 ],
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