Peptide_Clusters-3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c2cdeb43",
   "metadata": {},
   "outputs": [],
   "source": [
    "#this version of the program is using presets for the cyclic peptide library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f692fb7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UserWarning: Distributing <class 'list'> object. This may take some time.\n",
      "UserWarning: `DataFrame.to_excel` defaulting to pandas implementation.\n",
      "To request implementation, send an email to feature_requests@modin.org.\n",
      "UserWarning: Distributing <class 'pandas.core.frame.DataFrame'> object. This may take some time.\n"
     ]
    }
   ],
   "source": [
    "#This program is used for unpaired read illumina sequencing fastq files.  The goal here is to take in your\n",
    "#sequencing file, and to cut out reads that match your defined template.  In this case of a synthetic \n",
    "#peptide library we know the exact sequences that should be constant before and after our randomized positions.\n",
    "#Using the user specified sequences we cut out those sequences and then translate them to AA sequences. \n",
    "# please note that the user specified sequences need to be in-frame (all of our mutants are in frame)\n",
    "from datetime import datetime\n",
    "from Bio.Seq import Seq\n",
    "from Bio import SeqIO\n",
    "import modin.pandas as pd #multithreading version of pandas\n",
    "from distributed import Client\n",
    "import os\n",
    "client = Client()\n",
    "\n",
    "# Directory containing the .fa files\n",
    "Output_directory = \"Output_Files\"\n",
    "\n",
    "# Create the directory folder if it doesn't exist\n",
    "if not os.path.exists(Output_directory):\n",
    "    os.makedirs(Output_directory)\n",
    "# Directory containing the .fa files\n",
    "Scatterplot_directory = \"Scatterplots\"\n",
    "\n",
    "# Create the directory folder if it doesn't exist\n",
    "if not os.path.exists(Scatterplot_directory):\n",
    "    os.makedirs(Scatterplot_directory)\n",
    "\n",
    "def reverse_complement(seq):\n",
    "    \"\"\"Return the reverse complement of a DNA sequence\"\"\"\n",
    "    return str(Seq(seq).reverse_complement())\n",
    "\n",
    "\n",
    "# User-defined sequences in 5' to 3' direction - you can also add other startX and stopXs to the lists\n",
    "seqs_5to3 = {\n",
    "    \"start1\": \"GAAGGGGTACAATTAGAT\",\n",
    "    \"stop1\": \"GAATGGCATGAAGGT\",\n",
    "}\n",
    "\n",
    "# User-defined sequences in 3' to 5' direction - this is the same sequence you put above, just in reverse\n",
    "seqs_3to5 = {\n",
    "    \"start3\": \"ACCTTCATGCCATTC\", #this should be the reverse complement of stop 1\n",
    "    \"stop3\": \"ATCTAATTGTACCCCTTC\",#this should be the reverse complement of start1\n",
    "}\n",
    "\n",
    "# Convert 3' to 5' sequences to 5' to 3'\n",
    "seqs_5to3.update(\n",
    "    {f\"{key}_rc\": reverse_complement(val) for key, val in seqs_3to5.items()}\n",
    ")\n",
    "\n",
    "# Initialize an empty list to store results\n",
    "results = []\n",
    "\n",
    "# Iterate over each record in the FASTQ file\n",
    "for record in SeqIO.parse(\"Illumina_test_peptide.fastq\", \"fastq\"):\n",
    "    # Extract the DNA sequence from the record\n",
    "    seq = str(record.seq)\n",
    "\n",
    "    # Iterate over the user-defined sequences\n",
    "    for seq_key, seq_val in seqs_5to3.items():\n",
    "        # Find the start and end indices of the sequence of interest\n",
    "        start_index = seq.find(seq_val)\n",
    "        end_index = seq.find(seqs_5to3[f\"{seq_key.replace('start', 'stop')}\"], start_index + len(seq_val))\n",
    "\n",
    "        # Check if the sequence of interest was found\n",
    "        if start_index != -1 and end_index != -1:\n",
    "            # Extract the DNA sequence between the start and stop sequences\n",
    "            dna_seq = seq[start_index + len(seq_val): end_index]\n",
    "\n",
    "            # Reverse complement the DNA sequence if the user-defined sequence was in the 3' to 5' direction\n",
    "            if seq_key.endswith(\"_rc\"):\n",
    "                dna_seq = reverse_complement(dna_seq)\n",
    "\n",
    "            # Translate the DNA sequence to amino acids\n",
    "            aa_seq = Seq(dna_seq).translate()\n",
    "\n",
    "            # Add the result to the list of results\n",
    "            results.append({\"Sequence\": aa_seq, \"KD\": \"1\"})\n",
    "\n",
    "# Convert the results to a pandas DataFrame and write to an Excel file\n",
    "\n",
    "df = pd.DataFrame(results)\n",
    "df.to_excel(\"Output_Files/output-peptide-clusters.xlsx\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d1d09489",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                Sequence  KD\n",
      "0  KREAEAGPHMADCAWHLGELVWCTGGGLNDIFEAQKI   1\n",
      "1  KREAEAGPHMADCAWHLGGLVWCTGGGLNDIFEAQKI   1\n",
      "2  KREAEAGPHMADCAWHLGELVWCTGGGLNDIFEAQKI   1\n",
      "3  KREAEAGPHMADCARQLGQSVSCTGGGLNDIFEAQKI   1\n",
      "Number of clusters found: 1\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkAAAAHFCAYAAAAaD0bAAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAABFv0lEQVR4nO3de3zO9f/H8ee12clsa4yxjImccsqIOcspZfgWITGU75eUc0TkUKxIqZ+QckjfQuWYkKUccohhCfvOaUxltIoNmdnevz98d31dtrHV2oXP4367Xbdb1/vz/rw/r8/nc+36PH0OVzZjjBEAAICFuDi7AAAAgIJGAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIKwIIFC2Sz2eyvQoUKqXTp0urdu7d++umnv225Fy9e1Pjx47Vx48Ycazp+/PhNx2nWrJmaNWuWb3X16tXLYXt4e3srJCRE7du31/z585WampovNRw8eFDjx4/P1Toid3r16qUiRYpkad+1a5cCAgJUsWJFnThxQtLVfZa5j11cXOTj46MKFSqoc+fO+uyzz5SRkVHQ5QN2hZxdAGAl8+fPV+XKlfXHH39o8+bNioyM1KZNm/TDDz/I29s735d38eJFTZgwQZKyhIdHHnlE27dvV6lSpfJ9ubnh5eWlr7/+WpL0xx9/6OTJk1q7dq369u2radOmad26dSpdurS9/8yZM/O8jIMHD2rChAlq1qyZQkJC8qt0XOebb75Rhw4dVL58eX355ZcqUaKEfdo999yjjz76SJJ04cIFxcfHa8WKFercubMaN26szz//XH5+fs4qHRZGAAIKULVq1VSnTh1JUvPmzZWenq6XX35ZK1asUPfu3Qu0luLFi6t48eIFusxrubi4qH79+g5tPXv2VO/evdWuXTt16tRJO3bssE+rWrVqQZeIXFi5cqW6dOmiunXravXq1VnCjJeXV5b9/PTTT2v+/Pnq06eP/vnPf2rJkiUFWTIgiUtggFNlHhgyLxkYYzRz5kzVqlVLXl5e8vf3V6dOnXTs2DGH+Zo1a6Zq1appy5Ytql+/vry8vHT33Xdr7NixSk9PlyQdP37cHnAmTJhgvxTRq1cvSdlfAjPGaMqUKSpbtqw8PT1Vu3ZtrV27Ntvak5OTNXz4cJUrV07u7u66++67NXjwYF24cOEvbZPWrVurb9+++u6777R582aHdb7+LNasWbNUs2ZNFSlSRD4+PqpcubJGjx5tX7/OnTtLuho2M9d/wYIFkqSoqCh16NBBpUuXlqenpypUqKB//etfSkpKcljG+PHjZbPZdODAAXXr1k1+fn4KDAxUnz59dO7cOYe+GRkZ+r//+z/7/rvrrrtUv359rVq1yqHfkiVLFBYWJm9vbxUpUkRt2rTR3r17HfocO3ZMXbt2VVBQkDw8PBQYGKgWLVooJiYmx203ffp02Ww2HTlyJMu0kSNHyt3d3b5+e/fuVbt27VSiRAl5eHgoKChIjzzyiH788cccx7/ehx9+qE6dOunBBx/U+vXr83Qmp3fv3nr44Yf16aef2j//QEEiAAFOlHmgygwq//rXvzR48GC1bNlSK1as0MyZM3XgwAE1aNBAp0+fdpg3MTFRXbt2Vffu3bVy5Up16tRJr7zyigYNGiRJKlWqlNatWydJeuqpp7R9+3Zt375dY8eOzbGeCRMmaOTIkWrVqpVWrFih/v37q2/fvoqLi3Pod/HiRTVt2lQffPCBBg4cqLVr12rkyJFasGCB2rdvL2PMX9ou7du3lySHAHS9xYsX65lnnlHTpk21fPlyrVixQkOGDLEHsEceeUSTJ0+WJL3zzjv29X/kkUckSUePHlVYWJhmzZql9evX66WXXtJ3332nRo0aKS0tLcvyHnvsMVWsWFFLly7VCy+8oI8//lhDhgxx6NOrVy8NGjRIdevW1ZIlS7R48WK1b9/eIWROnjxZ3bp1U9WqVfXJJ5/oww8/VEpKiho3bqyDBw/a+z388MPavXu3pkyZoqioKM2aNUv333+/zp49m+M2efLJJ+Xu7m4PeZnS09P173//W+Hh4QoICNCFCxfUqlUrnT59Wu+8846ioqI0ffp0lSlTRikpKTmOf623335bERER6tSpk1auXCkvL69czXetzM/Kli1b8jwv8JcZAH+7+fPnG0lmx44dJi0tzaSkpJjVq1eb4sWLGx8fH5OYmGi2b99uJJlp06Y5zHvy5Enj5eVlRowYYW9r2rSpkWRWrlzp0Ldv377GxcXFnDhxwhhjzC+//GIkmXHjxuVYU3x8vDHGmN9//914enqaf/zjHw79tm7daiSZpk2b2tsiIyONi4uL2bVrl0Pfzz77zEgya9asueH2iIiIMN7e3jlOj42NNZJM//79Hdb52hqeffZZc9ddd91wOZ9++qmRZL755psb9svIyDBpaWnmxIkTWbbruHHjjCQzZcoUh3meeeYZ4+npaTIyMowxxmzevNlIMi+++GKOy0lISDCFChUyzz33nEN7SkqKKVmypHn88ceNMcYkJSUZSWb69Ok3rDs7jz76qCldurRJT0+3t61Zs8ZIMp9//rkxxpjo6GgjyaxYsSLP40dERBhJRpJp1KiRw3Ku17RpU3PfffflOH3t2rVGknnttdfyXAfwV3EGCChA9evXl5ubm3x8fNSuXTuVLFlSa9euVWBgoFavXi2bzaYnn3xSV65csb9KliypmjVrZnmSy8fHx36mJNMTTzyhjIyMG545ycn27dt16dKlLPciNWjQQGXLlnVoW716tapVq6ZatWo51NqmTRvZbLZsnzrLC5OLM0gPPPCAzp49q27dumnlypVZLl3dzJkzZ9SvXz8FBwerUKFCcnNzs69nbGxslv7Xb+saNWro0qVLOnPmjCTZLxUOGDAgx2V++eWXunLlinr27Omw3Tw9PdW0aVP7ditatKjKly+vqVOn6o033tDevXtz/cRU79699eOPP+qrr76yt82fP18lS5ZU27ZtJUkVKlSQv7+/Ro4cqdmzZzucecoNLy8vtWrVSlu3btXs2bPzNO+1crOfgb8LAQgoQAsXLtSuXbu0d+9e/fzzz9q3b58aNmwoSTp9+rSMMQoMDJSbm5vDa8eOHVkO8IGBgVnGL1mypCTp119/zXNtmfNkjpHduJlOnz6tffv2ZanTx8dHxpg8h5HrZd4TEhQUlGOfHj16aN68eTpx4oQee+wxlShRQvXq1VNUVNRNx8/IyFDr1q21bNkyjRgxQhs2bNDOnTvtN13/8ccfWeYpVqyYw3sPDw+Hvr/88otcXV2z3X6ZMi9j1q1bN8u2W7JkiX272Ww2bdiwQW3atNGUKVNUu3ZtFS9eXAMHDrzpJaq2bduqVKlSmj9/viTp999/16pVq9SzZ0+5urpKkvz8/LRp0ybVqlVLo0eP1n333aegoCCNGzcu28t/13NxcdGqVavUqlUrDRgwQO+8885N58lObvYz8HfhKTCgAFWpUsX+FNj1AgICZLPZtGXLFvvB9VrXt11/T5B09b4gKevBOjcy58kc4/pxr32MPCAgQF5eXpo3b162YwUEBOR5+dfKvGn4Zr/707t3b/Xu3VsXLlzQ5s2bNW7cOLVr106HDh3KctbqWvv379f333+vBQsWKCIiwt6e3c3DuVW8eHGlp6crMTExx58WyNwun3322Q3rk6SyZctq7ty5kqRDhw7pk08+0fjx43X58uUbnnVxdXVVjx499Pbbb+vs2bP6+OOPlZqaqt69ezv0q169uhYvXixjjPbt26cFCxZo4sSJ8vLy0gsvvHDT9fX09NTKlSv1j3/8Q88++6wyMjL03HPP3XS+a61atUo2m01NmjTJ03xAfuAMEHCLaNeunYwx+umnn1SnTp0sr+rVqzv0T0lJyfJ00ccffywXFxf7AeX6sxQ3Ur9+fXl6etp/syXTtm3bsjyl065dOx09elTFihXLtta/8ps7UVFRev/999WgQQM1atQoV/N4e3urbdu2evHFF3X58mUdOHBAUs7rb7PZHKZnevfdd/903ZmXl2bNmpVjnzZt2qhQoUI6evRottstp3BcsWJFjRkzRtWrV9eePXtuWkvv3r116dIlLVq0SAsWLFBYWJgqV66cbV+bzaaaNWvqzTff1F133ZWr8TN5enpqxYoVatu2rQYOHKi33nor1/POnz9fa9euVbdu3VSmTJlczwfkF84AAbeIhg0b6p///Kd69+6t6OhoNWnSRN7e3jp16pS+/fZbVa9eXf3797f3L1asmPr376+EhARVrFhRa9as0Xvvvaf+/fvbDyg+Pj4qW7asVq5cqRYtWqho0aIKCAjINqD4+/tr+PDheuWVV/T000+rc+fOOnnypMaPH5/lss7gwYO1dOlSNWnSREOGDFGNGjWUkZGhhIQErV+/XsOGDVO9evVuuL4ZGRn2S06pqalKSEjQ2rVr9cknn6hKlSr65JNPbjh/37595eXlpYYNG6pUqVJKTExUZGSk/Pz8VLduXUlXf3dJkubMmSMfHx95enqqXLlyqly5ssqXL68XXnhBxhgVLVpUn3/+ea4un+WkcePG6tGjh1555RWdPn1a7dq1k4eHh/bu3avChQvrueeeU0hIiCZOnKgXX3xRx44d00MPPSR/f3+dPn1aO3fulLe3tyZMmKB9+/bp2WefVefOnXXvvffK3d1dX3/9tfbt25erszOVK1dWWFiYIiMjdfLkSc2ZM8dh+urVqzVz5kx17NhR99xzj4wxWrZsmc6ePatWrVrlab09PDy0fPlyPfbYYxo8eLAyMjIcno77448/HC4tHjt2TCtWrNDq1avVtGnTv3QPEfCXOO/+a8A6Mp+4uv6pqezMmzfP1KtXz3h7exsvLy9Tvnx507NnTxMdHW3vk/l0zcaNG02dOnWMh4eHKVWqlBk9erRJS0tzGO+rr74y999/v/Hw8DCSTEREhENNmU+BGXP1aajIyEgTHBxs3N3dTY0aNcznn3+e5QksY4w5f/68GTNmjKlUqZJxd3c3fn5+pnr16mbIkCEmMTHxhut47ZNEkoyXl5cpU6aMCQ8PN/PmzTOpqalZ5rm+hg8++MA0b97cBAYGGnd3dxMUFGQef/xxs2/fPof5pk+fbsqVK2dcXV2NJDN//nxjjDEHDx40rVq1Mj4+Psbf39907tzZJCQkZHlqLvMpsF9++cVh3Oy2X3p6unnzzTdNtWrV7NskLCzM/vRVphUrVpjmzZsbX19f4+HhYcqWLWs6depkvvrqK2OMMadPnza9evUylStXNt7e3qZIkSKmRo0a5s033zRXrly54bbNNGfOHPu2PXfunMO0//znP6Zbt26mfPnyxsvLy/j5+ZkHHnjALFiw4Kbj5vQEX2pqqgkPDzeSzOuvv26M+d/Tipkvb29vc88995hOnTqZTz/99IZPkAF/N5sx3IYP3G6aNWumpKQk7d+/39mlAMBtiXuAAACA5RCAAACA5XAJDAAAWA5ngAAAgOUQgAAAgOUQgAAAgOXwQ4jZyMjI0M8//ywfHx/7L8YCAIBbmzFGKSkpCgoKkovLjc/xEICy8fPPPys4ONjZZQAAgD/h5MmTKl269A37EICy4ePjI+nqBvT19XVyNQAAIDeSk5MVHBxsP47fCAEoG5mXvXx9fQlAAADcZnJz+wo3QQMAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMtxagDavHmzwsPDFRQUJJvNphUrVtx0nk2bNik0NFSenp665557NHv27Bz7Ll68WDabTR07dsy/ogEAwG3PqQHowoULqlmzpmbMmJGr/vHx8Xr44YfVuHFj7d27V6NHj9bAgQO1dOnSLH1PnDih4cOHq3HjxvldNgAAuM0VcubC27Ztq7Zt2+a6/+zZs1WmTBlNnz5dklSlShVFR0fr9ddf12OPPWbvl56eru7du2vChAnasmWLzp49m8+VAwCA29ltdQ/Q9u3b1bp1a4e2Nm3aKDo6Wmlpafa2iRMnqnjx4nrqqacKukQAAHAbcOoZoLxKTExUYGCgQ1tgYKCuXLmipKQklSpVSlu3btXcuXMVExOT63FTU1OVmppqf5+cnJxfJQMAgFvQbXUGSJJsNpvDe2OMvT0lJUVPPvmk3nvvPQUEBOR6zMjISPn5+dlfwcHB+VozAAC4tdxWZ4BKliypxMREh7YzZ86oUKFCKlasmA4cOKDjx48rPDzcPj0jI0OSVKhQIcXFxal8+fJZxh01apSGDh1qf5+cnEwIAgDgDnZbBaCwsDB9/vnnDm3r169XnTp15ObmpsqVK+uHH35wmD5mzBilpKTorbfeyjHUeHh4yMPD42+rGwAA3FqcGoDOnz+vI0eO2N/Hx8crJiZGRYsWVZkyZTRq1Cj99NNPWrhwoSSpX79+mjFjhoYOHaq+fftq+/btmjt3rhYtWiRJ8vT0VLVq1RyWcdddd0lSlnYAAGBdTg1A0dHRat68uf195mWoiIgILViwQKdOnVJCQoJ9erly5bRmzRoNGTJE77zzjoKCgvT22287PAIPAABwMzaTeRcx7JKTk+Xn56dz587J19fX2eUAAIBcyMvx+7Z7CgwAAOCvIgABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLcWoA2rx5s8LDwxUUFCSbzaYVK1bcdJ5NmzYpNDRUnp6euueeezR79myH6e+9954aN24sf39/+fv7q2XLltq5c+fftAYAAOB25NQAdOHCBdWsWVMzZszIVf/4+Hg9/PDDaty4sfbu3avRo0dr4MCBWrp0qb3Pxo0b1a1bN33zzTfavn27ypQpo9atW+unn376u1YDAADcZmzGGOPsIiTJZrNp+fLl6tixY459Ro4cqVWrVik2Ntbe1q9fP33//ffavn17tvOkp6fL399fM2bMUM+ePXNVS3Jysvz8/HTu3Dn5+vrmaT0AAIBz5OX4fVvdA7R9+3a1bt3aoa1NmzaKjo5WWlpatvNcvHhRaWlpKlq0aEGUCAAAbgOFnF1AXiQmJiowMNChLTAwUFeuXFFSUpJKlSqVZZ4XXnhBd999t1q2bJnjuKmpqUpNTbW/T05Ozr+iAQDALee2OgMkXb1Udq3MK3jXt0vSlClTtGjRIi1btkyenp45jhkZGSk/Pz/7Kzg4OH+LBgAAt5TbKgCVLFlSiYmJDm1nzpxRoUKFVKxYMYf2119/XZMnT9b69etVo0aNG447atQonTt3zv46efJkvtcOAABuHbfVJbCwsDB9/vnnDm3r169XnTp15ObmZm+bOnWqXnnlFX355ZeqU6fOTcf18PCQh4dHvtcLAABuTU49A3T+/HnFxMQoJiZG0tXH3GNiYpSQkCDp6pmZa5/c6tevn06cOKGhQ4cqNjZW8+bN09y5czV8+HB7nylTpmjMmDGaN2+eQkJClJiYqMTERJ0/f75A1w0AANy6nPoY/MaNG9W8efMs7REREVqwYIF69eql48ePa+PGjfZpmzZt0pAhQ3TgwAEFBQVp5MiR6tevn316SEiITpw4kWXMcePGafz48bmqi8fgAQC4/eTl+H3L/A7QrYQABADA7eeO/R0gAACA/EAAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlpPnAHThwgW99NJLqlatmooUKSIfHx/VqFFDEydO1MWLF/+OGgEAAPJVobx0vnz5spo2bar9+/erbdu2Cg8PlzFGsbGxmjRpktauXavNmzfLzc3t76r3tmSMkVKjZC58KF05KNncJY/Wsnn3lK1QeWeXd0cxl/fIXFggXd4hyUju9WXzjpDNvY6zSwMAS0tPT9fGxdu08p11Soj9UZ7eHmrWpaE6PtdWJUNKFHg9eToDNGvWLP3444/6/vvvtXz5ckVGRurVV1/VypUr9f333ys+Pl6zZ8/O9XibN29WeHi4goKCZLPZtGLFipvOs2nTJoWGhsrT01P33HNPtstbunSpqlatKg8PD1WtWlXLly/Py2rmK2OMTPJYmbPPSmnRkkmRMn6V/vhEJqm9TOpmp9V2pzEXFsr81lVKjZLMWcmck1K/kvntCZkLHzi7PACwrPQr6ZrYaZpe7fG24nYe1oVzF/Xrz79r+dtr9M8aw3Rwx6ECrylPAWjZsmUaO3asKlWqlGVa5cqV9eKLL+qzzz7L9XgXLlxQzZo1NWPGjFz1j4+P18MPP6zGjRtr7969Gj16tAYOHKilS5fa+2zfvl1dunRRjx499P3336tHjx56/PHH9d133+W6rnx1aYX0xyf/fZN+zYR0SVdkfn9WJuNcwdd1hzFpB2VSXvnvu+u3s2RSJsmk7S/wugAA0tI3V2v7qmhJUkaGsbdnpGco9Y/LGtfhNaVdTivQmmzGGHPzblcVL15cGzdu1H333Zft9P3796t58+b65Zdf8l6Izably5erY8eOOfYZOXKkVq1apdjYWHtbv3799P3332v79u2SpC5duig5OVlr166193nooYfk7++vRYsW5aqW5ORk+fn56dy5c/L19c3zulwrI6mDdCVOUkYOPWyy+YySzbvXX1qO1WWcHXU1bDqEn2u5Sp4d5HLXqwVYFQAgIyND3cv2V9JPv92w3+iPB6t514Z/aVl5OX7n6QzQ2bNnVaxYsRynFytWTOfO/X1nM7Zv367WrVs7tLVp00bR0dFKS0u7YZ9t27blOG5qaqqSk5MdXvnBmCvSlVjlHH4kySaTtjdflmdpadHKOfzo6rS0XQVVDQDgv3479ftNw4+rm6sObo8roIquylMAysjIkKura86DubgoPf1GB6G/JjExUYGBgQ5tgYGBunLlipKSkm7YJzExMcdxIyMj5efnZ38FBwfnU8W2/75uJk/3oiNbOX8u/4ftDAAFzcU1d1HDNZf98kuejgjGGLVo0UKFCmU/25UrV/KlqBux2RwDReYVvGvbs+tzfdu1Ro0apaFDh9rfJycn50sIstlcZdzrSZd3KuezQBmyuf+1U36Q5NFUunhCN7wE5tG0ICsCAEjyD7xLZarcrZP/+Uk53XSTnpau2q1qFmhdeQpA48aNu2mfli1b/ulibqZkyZJZzuScOXNGhQoVsl+ay6nP9WeFruXh4SEPD4/8L1iSzftpmcs7cpjqKrn4S14P/y3LthJb4e4yF/+tq0Hz+r+wq2fibIW7F3xhAGBxNptNXUZ01NTe72Q73bWQi0qVL6k6bW7hAOTt7a3hw4fnOD05OTnL/Tf5KSwsTJ9//rlD2/r161WnTh37bw+FhYUpKipKQ4YMcejToEGDv62uG7F5NJF8XpBJeVVXL9Oky35ZzOYrm/9c2WyeTqntTmIrVEa6a4bM2eckXdH/zri5SHKV7a63ZStU1nkFAoCFterZVAmxP2rJlJVyLeSi9CsZstlsMsaoWFBRTf5itFxcbuFLYGPHjlWxYsXUu3fvLNPOnz+vhx56KE83EJ8/f15Hjhyxv4+Pj1dMTIyKFi2qMmXKaNSoUfrpp5+0cOFCSVef+JoxY4aGDh2qvn37avv27Zo7d67D012DBg1SkyZN9Nprr6lDhw5auXKlvvrqK3377bd5WdV8ZfPuI7k3lvljkZS2X7J5yubRUvLqKJvLX3vKDP9j82wuFf9K5uKS//4Qoq7+EGLhLrK5lnRucQBgYTabTU+/+qQadwrTF++uV/z+kyrs66WmncLU/IlG8vJ2wokAkweffvqp8fDwMMuXL3doT0lJMWFhYaZixYrm1KlTuR7vm2++Mbp6vcLhFRERYYwxJiIiwjRt2tRhno0bN5r777/fuLu7m5CQEDNr1qxs66xUqZJxc3MzlStXNkuXLs3Lappz584ZSebcuXN5mg8AADhPXo7fefodIEl6//33NXDgQH3xxRdq3ry5/czPmTNntGnTJpUqVSrfQ1pBy8/fAQIAAAUjL8fvPD8X/PTTT+u3335Tx44dtXLlSo0dO1aJiYl3TPgBAAB3vj/1wygjRozQ77//rhYtWigkJESbNm3S3Xffnd+1AQAA/C3yFIAeffRRh/dubm4KCAjQwIEDHdqXLVv21ysDAAD4m+QpAPn5+Tm879atW74WAwAAUBDyFIDmz5//d9UBAABQYAr2V4cAAABuAQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOU4PQDNnzlS5cuXk6emp0NBQbdmy5Yb933nnHVWpUkVeXl6qVKmSFi5cmKXP9OnTValSJXl5eSk4OFhDhgzRpUuX/q5VAAAAt5lCzlz4kiVLNHjwYM2cOVMNGzbUu+++q7Zt2+rgwYMqU6ZMlv6zZs3SqFGj9N5776lu3brauXOn+vbtK39/f4WHh0uSPvroI73wwguaN2+eGjRooEOHDqlXr16SpDfffLMgVw8AANyibMYY46yF16tXT7Vr19asWbPsbVWqVFHHjh0VGRmZpX+DBg3UsGFDTZ061d42ePBgRUdH69tvv5UkPfvss4qNjdWGDRvsfYYNG6adO3fe9OxSpuTkZPn5+encuXPy9fX9s6sHAAAKUF6O3067BHb58mXt3r1brVu3dmhv3bq1tm3blu08qamp8vT0dGjz8vLSzp07lZaWJklq1KiRdu/erZ07d0qSjh07pjVr1uiRRx7JsZbU1FQlJyc7vAAAwJ3LaQEoKSlJ6enpCgwMdGgPDAxUYmJitvO0adNG77//vnbv3i1jjKKjozVv3jylpaUpKSlJktS1a1e9/PLLatSokdzc3FS+fHk1b95cL7zwQo61REZGys/Pz/4KDg7OvxUFAAC3HKffBG2z2RzeG2OytGUaO3as2rZtq/r168vNzU0dOnSw39/j6uoqSdq4caMmTZqkmTNnas+ePVq2bJlWr16tl19+OccaRo0apXPnztlfJ0+ezJ+VAwAAtySnBaCAgAC5urpmOdtz5syZLGeFMnl5eWnevHm6ePGijh8/roSEBIWEhMjHx0cBAQGSroakHj166Omnn1b16tX1j3/8Q5MnT1ZkZKQyMjKyHdfDw0O+vr4OLwAAcOdyWgByd3dXaGiooqKiHNqjoqLUoEGDG87r5uam0qVLy9XVVYsXL1a7du3k4nJ1VS5evGj/70yurq4yxsiJ93sDAIBbiFMfgx86dKh69OihOnXqKCwsTHPmzFFCQoL69esn6eqlqZ9++sn+Wz+HDh3Szp07Va9ePf3+++964403tH//fn3wwQf2McPDw/XGG2/o/vvvV7169XTkyBGNHTtW7du3t18mAwAA1ubUANSlSxf9+uuvmjhxok6dOqVq1appzZo1Klu2rCTp1KlTSkhIsPdPT0/XtGnTFBcXJzc3NzVv3lzbtm1TSEiIvc+YMWNks9k0ZswY/fTTTypevLjCw8M1adKkgl49AABwi3Lq7wDdqvgdIAAAbj+3xe8AAQAAOAsBCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWI7TA9DMmTNVrlw5eXp6KjQ0VFu2bLlh/3feeUdVqlSRl5eXKlWqpIULF2bpc/bsWQ0YMEClSpWSp6enqlSpojVr1vxdqwAAAG4zhZy58CVLlmjw4MGaOXOmGjZsqHfffVdt27bVwYMHVaZMmSz9Z82apVGjRum9995T3bp1tXPnTvXt21f+/v4KDw+XJF2+fFmtWrVSiRIl9Nlnn6l06dI6efKkfHx8Cnr1AADALcpmjDHOWni9evVUu3ZtzZo1y95WpUoVdezYUZGRkVn6N2jQQA0bNtTUqVPtbYMHD1Z0dLS+/fZbSdLs2bM1depU/ec//5Gbm9ufqis5OVl+fn46d+6cfH19/9QYAACgYOXl+O20S2CXL1/W7t271bp1a4f21q1ba9u2bdnOk5qaKk9PT4c2Ly8v7dy5U2lpaZKkVatWKSwsTAMGDFBgYKCqVaumyZMnKz09PcdaUlNTlZyc7PACAAB3LqcFoKSkJKWnpyswMNChPTAwUImJidnO06ZNG73//vvavXu3jDGKjo7WvHnzlJaWpqSkJEnSsWPH9Nlnnyk9PV1r1qzRmDFjNG3aNE2aNCnHWiIjI+Xn52d/BQcH59+KAgCAW47Tb4K22WwO740xWdoyjR07Vm3btlX9+vXl5uamDh06qFevXpIkV1dXSVJGRoZKlCihOXPmKDQ0VF27dtWLL77ocJnteqNGjdK5c+fsr5MnT+bPygEAgFuS0wJQQECAXF1ds5ztOXPmTJazQpm8vLw0b948Xbx4UcePH1dCQoJCQkLk4+OjgIAASVKpUqVUsWJFeyCSrt5XlJiYqMuXL2c7roeHh3x9fR1eAADgzuW0AOTu7q7Q0FBFRUU5tEdFRalBgwY3nNfNzU2lS5eWq6urFi9erHbt2snF5eqqNGzYUEeOHFFGRoa9/6FDh1SqVCm5u7vn/4oAAIDbjlMvgQ0dOlTvv/++5s2bp9jYWA0ZMkQJCQnq16+fpKuXpnr27Gnvf+jQIf373//W4cOHtXPnTnXt2lX79+/X5MmT7X369++vX3/9VYMGDdKhQ4f0xRdfaPLkyRowYECBrx8AALg1OfV3gLp06aJff/1VEydO1KlTp1StWjWtWbNGZcuWlSSdOnVKCQkJ9v7p6emaNm2a4uLi5ObmpubNm2vbtm0KCQmx9wkODtb69es1ZMgQ1ahRQ3fffbcGDRqkkSNHFvTqAQCAW5RTfwfoVsXvAAEAcPu5LX4HCAAAwFkIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIKObuAW5ExRpKUnJzs5EoAAEBuZR63M4/jN0IAykZKSookKTg42MmVAACAvEpJSZGfn98N+9hMbmKSxWRkZOjnn3+Wj4+PbDZbvo6dnJys4OBgnTx5Ur6+vvk6Nv6H7Vww2M4Fg+1ccNjWBePv2s7GGKWkpCgoKEguLje+y4czQNlwcXFR6dKl/9Zl+Pr68sdVANjOBYPtXDDYzgWHbV0w/o7tfLMzP5m4CRoAAFgOAQgAAFgOAaiAeXh4aNy4cfLw8HB2KXc0tnPBYDsXDLZzwWFbF4xbYTtzEzQAALAczgABAADLIQABAADLIQABAADLIQABAADLIQAVoJkzZ6pcuXLy9PRUaGiotmzZ4uyS7jibN29WeHi4goKCZLPZtGLFCmeXdEeKjIxU3bp15ePjoxIlSqhjx46Ki4tzdll3nFmzZqlGjRr2H4sLCwvT2rVrnV3WHS8yMlI2m02DBw92dil3lPHjx8tmszm8SpYs6bR6CEAFZMmSJRo8eLBefPFF7d27V40bN1bbtm2VkJDg7NLuKBcuXFDNmjU1Y8YMZ5dyR9u0aZMGDBigHTt2KCoqSleuXFHr1q114cIFZ5d2RyldurReffVVRUdHKzo6Wg8++KA6dOigAwcOOLu0O9auXbs0Z84c1ahRw9ml3JHuu+8+nTp1yv764YcfnFYLj8EXkHr16ql27dqaNWuWva1KlSrq2LGjIiMjnVjZnctms2n58uXq2LGjs0u54/3yyy8qUaKENm3apCZNmji7nDta0aJFNXXqVD311FPOLuWOc/78edWuXVszZ87UK6+8olq1amn69OnOLuuOMX78eK1YsUIxMTHOLkUSZ4AKxOXLl7V79261bt3aob1169batm2bk6oC8s+5c+ckXT044++Rnp6uxYsX68KFCwoLC3N2OXekAQMG6JFHHlHLli2dXcod6/DhwwoKClK5cuXUtWtXHTt2zGm18D9DLQBJSUlKT09XYGCgQ3tgYKASExOdVBWQP4wxGjp0qBo1aqRq1ao5u5w7zg8//KCwsDBdunRJRYoU0fLly1W1alVnl3XHWbx4sXbv3q3o6Ghnl3LHqlevnhYuXKiKFSvq9OnTeuWVV9SgQQMdOHBAxYoVK/B6CEAFyGazObw3xmRpA243zz77rPbt26dvv/3W2aXckSpVqqSYmBidPXtWS5cuVUREhDZt2kQIykcnT57UoEGDtH79enl6ejq7nDtW27Zt7f9dvXp1hYWFqXz58vrggw80dOjQAq+HAFQAAgIC5OrqmuVsz5kzZ7KcFQJuJ88995xWrVqlzZs3q3Tp0s4u547k7u6uChUqSJLq1KmjXbt26a233tK7777r5MruHLt379aZM2cUGhpqb0tPT9fmzZs1Y8YMpaamytXV1YkV3pm8vb1VvXp1HT582CnL5x6gAuDu7q7Q0FBFRUU5tEdFRalBgwZOqgr484wxevbZZ7Vs2TJ9/fXXKleunLNLsgxjjFJTU51dxh2lRYsW+uGHHxQTE2N/1alTR927d1dMTAzh52+Smpqq2NhYlSpVyinL5wxQARk6dKh69OihOnXqKCwsTHPmzFFCQoL69evn7NLuKOfPn9eRI0fs7+Pj4xUTE6OiRYuqTJkyTqzszjJgwAB9/PHHWrlypXx8fOxnN/38/OTl5eXk6u4co0ePVtu2bRUcHKyUlBQtXrxYGzdu1Lp165xd2h3Fx8cny/1r3t7eKlasGPe15aPhw4crPDxcZcqU0ZkzZ/TKK68oOTlZERERTqmHAFRAunTpol9//VUTJ07UqVOnVK1aNa1Zs0Zly5Z1dml3lOjoaDVv3tz+PvO6ckREhBYsWOCkqu48mT/n0KxZM4f2+fPnq1evXgVf0B3q9OnT6tGjh06dOiU/Pz/VqFFD69atU6tWrZxdGpBnP/74o7p166akpCQVL15c9evX144dO5x2HOR3gAAAgOVwDxAAALAcAhAAALAcAhAAALAcAhAAALAcAhAAALAcAhAAALAcAhAAALAcAhAs4fjx47LZbIqJicn3sRcsWKC77ror38fNSa9evdSxY8cCW15B2rp1q6pXry43N7c7dh0B3BoIQMiVO/mgm+nPBpkuXbro0KFDf2nZGzdulM1mk81mk4uLi/z8/HT//fdrxIgROnXqlEPft956K9e/an277behQ4eqVq1aio+PvyV+uTtzv/j7++vSpUsO03bu3GnfZ/mlWbNmGjx4cK76HjlyRL1791bp0qXl4eGhcuXKqVu3boqOjs63em4He/fuVbt27VSiRAl5enoqJCREXbp0UVJSkrNLwy2OAAT8RV5eXipRokS+jBUXF6eff/5Zu3bt0siRI/XVV1+pWrVq+uGHH+x9/Pz8CvSMU0E6evSoHnzwQZUuXTrbdTTG6MqVKwVel4+Pj5YvX+7QNm/ePKf9/+Wio6MVGhqqQ4cO6d1339XBgwe1fPlyVa5cWcOGDXNKTc5w5swZtWzZUgEBAfryyy8VGxurefPmqVSpUrp48aKzy8OtzgC5EBERYTp06JDj9AMHDpi2bdsab29vU6JECfPkk0+aX375xRhjzOzZs01QUJBJT093mCc8PNz07NnT/n7VqlWmdu3axsPDw5QrV86MHz/epKWl2adLMu+9957p2LGj8fLyMhUqVDArV660T//tt9/ME088YQICAoynp6epUKGCmTdvnjHGmPj4eCPJLF261DRr1sx4eXmZGjVqmG3bthljjPnmm2+MJIfXuHHjjDHGpKammueff94EBQWZwoULmwceeMB888039uXOnz/f+Pn52d+PGzfO1KxZ0yxcuNCULVvW+Pr6mi5dupjk5OQct1/m8n///XeH9osXL5pKlSqZhg0b5rgvPv30U1OtWjXj6elpihYtalq0aGHOnz9vxo0bl2WdMuseMWKEuffee42Xl5cpV66cGTNmjLl8+XKe1iE9Pd28+uqrpnz58sbd3d0EBwebV155xT79xx9/NI8//ri56667TNGiRU379u1NfHx8tuufuX+ufc2fP9++XdatW2dCQ0ONm5ub+frrr82lS5fMc889Z4oXL248PDxMw4YNzc6dO7Nsz3Xr1platWoZT09P07x5c3P69GmzZs0aU7lyZePj42O6du1qLly4cNP9MmbMGNOyZUuH/eLn52fGjh1rrv8a/eyzz0zVqlWNu7u7KVu2rHn99dcdpr/zzjumQoUKxsPDw5QoUcI89thj9v16/TbIbntlZGSY++67z4SGhmb5mzLGOHyG9u3bZ5o3b27/bPTt29ekpKTYp2d+liZNmmRKlChh/Pz87H93w4cPN/7+/ubuu+82c+fOzbKvlixZYho1amQ8PT1NnTp1TFxcnNm5c6cJDQ013t7epk2bNubMmTP2+dLT082ECRPM3Xffbdzd3U3NmjXN2rVrs4yb099odpYvX24KFSrk8D2RnRt9PxljzPnz502PHj2Mt7e3KVmypHn99ddN06ZNzaBBg+x9JJnly5c7jOvn52fmz59vf3+zz3zm9p46daopWbKkKVq0qHnmmWcc/vYuXbpknn/+eVO6dGnj7u5uKlSoYN5///1crwtyjwCEXLlRAPr5559NQECAGTVqlImNjTV79uwxrVq1Ms2bNzfGGPPrr78ad3d389VXX9nn+e2334y7u7v58ssvjTHGrFu3zvj6+poFCxaYo0ePmvXr15uQkBAzfvx4+zySTOnSpc3HH39sDh8+bAYOHGiKFClifv31V2OMMQMGDDC1atUyu3btMvHx8SYqKsqsWrXKGPO/L9fKlSub1atXm7i4ONOpUydTtmxZk5aWZlJTU8306dONr6+vOXXqlDl16pT9QPHEE0+YBg0amM2bN5sjR46YqVOnGg8PD3Po0CFjTPYBqEiRIubRRx81P/zwg9m8ebMpWbKkGT16dI7bN6cAZIwxb775ppFkTp8+nWVf/Pzzz6ZQoULmjTfeMPHx8Wbfvn3mnXfeMSkpKSYlJcU8/vjj5qGHHrKvU2pqqjHGmJdfftls3brVxMfHm1WrVpnAwEDz2muv5WkdRowYYfz9/c2CBQvMkSNHzJYtW8x7771njDHmwoUL5t577zV9+vQx+/btMwcPHjRPPPGEqVSpkr2Ga125csWcOnXK+Pr6munTp5tTp06Zixcv2rdLjRo1zPr1682RI0dMUlKSGThwoAkKCjJr1qwxBw4cMBEREcbf39/+Wcicr379+ubbb781e/bsMRUqVDBNmzY1rVu3Nnv27DGbN282xYoVM6+++upN90tcXJzx8PAwJ06cMMYY8+GHH5qaNWua5cuXOwSg6Oho4+LiYiZOnGji4uLM/PnzjZeXl/0guWvXLuPq6mo+/vhjc/z4cbNnzx7z1ltvGWOMOXv2rAkLCzN9+/a1768rV65kqWnPnj1Gkvn4449zrDtzHwQFBdn34YYNG0y5cuVMRESEvU9ERITx8fExAwYMMP/5z3/M3LlzjSTTpk0bM2nSJHPo0CHz8ssvGzc3N5OQkGCMcfxbWrdunTl48KCpX7++qV27tmnWrJnD9u7Xr599WW+88Ybx9fU1ixYtMv/5z3/MiBEjjJubm/3v6GZ/o9nZvn27kWQ++eQTk5GRkW2fm30/GWNM//79TenSpc369evNvn37TLt27UyRIkXyFIBy85mPiIgwvr6+pl+/fiY2NtZ8/vnnpnDhwmbOnDn2MR9//HETHBxsli1bZo4ePWq++uors3jx4lyvC3KPAIRcuVEAGjt2rGndurVD28mTJ+0HDmOMad++venTp499+rvvvmtKlixp/4Jv3LixmTx5ssMYH374oSlVqpT9fea/xDOdP3/e2Gw2+78iw8PDTe/evbOtMfPL9fp/SUkysbGxxpisQcYYY44cOWJsNpv56aefHNpbtGhhRo0ale1848aNM4ULF3Y4W/L888+bevXqZVubMTcOQGvXrjWSzHfffWeMcdwXu3fvNpLM8ePHsx33ZmfuMk2ZMsWEhobmeh2Sk5ONh4eHPfBcb+7cuaZSpUoOB6XU1FTj5eVlD73Zuf5f1JnbZcWKFfa28+fPGzc3N/PRRx/Z2y5fvmyCgoLMlClTHOa7NnRHRkYaSebo0aP2tn/961+mTZs2OdZz7X7p2LGjmTBhgjHGmObNm5u33norSwB64oknTKtWrRzGeP75503VqlWNMcYsXbrU+Pr65ng28PqzDtlZsmSJkWT27Nlzw35z5swx/v7+5vz58/a2L774wri4uJjExERjzNXPR9myZR3OJFWqVMk0btzY/v7KlSvG29vbLFq0yBiT/d/SokWLjCSzYcMGe1tkZKSpVKmS/X1QUJCZNGmSQ41169Y1zzzzTI7jXv83mp3Ro0ebQoUKmaJFi5qHHnrITJkyxb5+xtz8+yklJcW4u7vbQ4YxV//R5uXllacAlJvPfOb2vjbYdu7c2XTp0sUYY0xcXJyRZKKiorJd19x81yL3uAcIf9nu3bv1zTffqEiRIvZX5cqVJV29p0OSunfvrqVLlyo1NVWS9NFHH6lr165ydXW1jzFx4kSHMfr27atTp045XMuvUaOG/b+9vb3l4+OjM2fOSJL69++vxYsXq1atWhoxYoS2bduWpdZr5y9VqpQk2efPzp49e2SMUcWKFR1q27Rpk33dshMSEiIfHx+HZd1oOTdijJGkbG+2rVmzplq0aKHq1aurc+fOeu+99/T777/fdMzPPvtMjRo1UsmSJVWkSBGNHTtWCQkJuV6H2NhYpaamqkWLFtmOv3v3bh05ckQ+Pj72bVa0aFFdunTphtstJ3Xq1LH/99GjR5WWlqaGDRva29zc3PTAAw8oNjbWYb5r93dgYKAKFy6se+65x6Ett/ulT58+WrBggY4dO6bt27ere/fuWfrExsY61CVJDRs21OHDh5Wenq5WrVqpbNmyuueee9SjRw999NFHeb5X5Uafh+trqVmzpry9vR1qycjIUFxcnL3tvvvuk4vL/w4FgYGBql69uv29q6urihUrlmU7Xb9tJTnMd+22TU5O1s8//5zttrnRPsvN3+ikSZOUmJio2bNnq2rVqpo9e7YqV65sv2/uZt9PR48e1eXLlxUWFmYfs2jRoqpUqVKOy8xObj/z9913n/17L3MdM9cvJiZGrq6uatq0aY7LuNl3LXKvkLMLwO0vIyND4eHheu2117JMy/wCCw8PV0ZGhr744gvVrVtXW7Zs0RtvvOEwxoQJE/Too49mGcPT09P+325ubg7TbDabMjIyJElt27bViRMn9MUXX+irr75SixYtNGDAAL3++uvZzp95AMmcP6d1c3V11e7dux2+tCSpSJEiOc53ozrzKvMAERISkmWaq6uroqKitG3bNq1fv17/93//pxdffFHfffedypUrl+14O3bsUNeuXTVhwgS1adNGfn5+Wrx4saZNm5brdfDy8rphzRkZGQoNDdVHH32UZVrx4sVvOG92rj2I5xQAjDFZ2q7f339lvzz88MP617/+paeeekrh4eEqVqxYlj7Z1ZBZr3T1Zuo9e/Zo48aNWr9+vV566SWNHz9eu3btyvWN7RUrVpR09XNRq1atHPtlV0uma9uz2ya52U7Z/S1d33b9PH9mn0k3/huVpGLFiqlz587q3LmzIiMjdf/99+v111/XBx98cNPvp8OHD99w7GtruXZfSlJaWpr9v3P7mf+rf1c3+65F7nEGCH9Z7dq1deDAAYWEhKhChQoOr8wDl5eXlx599FF99NFHWrRokSpWrKjQ0FCHMeLi4rLMX6FCBYd/nd5M8eLF1atXL/373//W9OnTNWfOnFzP6+7urvT0dIe2+++/X+np6Tpz5kyWukqWLJnrsf+sP/74Q3PmzFGTJk1yDA42m00NGzbUhAkTtHfvXrm7u9ufWMpunbZu3aqyZcvqxRdfVJ06dXTvvffqxIkTearr3nvvlZeXlzZs2JDt9Nq1a+vw4cMqUaJElu3m5+eXp2Vdr0KFCnJ3d9e3335rb0tLS1N0dLSqVKnyl8a+EVdXV/Xo0UMbN25Unz59su1TtWpVh7okadu2bapYsaI9QBcqVEgtW7bUlClTtG/fPh0/flxff/21pOz31/Vq1aqlqlWratq0adkGg7Nnz9priYmJ0YULF+zTtm7dKhcXF3uIKii+vr4KCgrKdtvk9z5zd3dX+fLl7et9s++nChUqyM3NTTt27LCP8fvvv2f5aYvixYs7/CTF4cOHHc7e5cdnvnr16srIyNCmTZuynZ6b71rkHgEIuXbu3DnFxMQ4vBISEjRgwAD99ttv6tatm3bu3Kljx45p/fr16tOnj8OXeffu3fXFF19o3rx5evLJJx3Gfumll7Rw4UKNHz9eBw4cUGxsrJYsWaIxY8bkur6XXnpJK1eu1JEjR3TgwAGtXr06T1+uISEhOn/+vDZs2KCkpCRdvHhRFStWVPfu3dWzZ08tW7ZM8fHx2rVrl1577TWtWbMm12Pn1pkzZ5SYmKjDhw9r8eLFatiwoZKSkjRr1qxs+3/33XeaPHmyoqOjlZCQoGXLlumXX36xr3dISIj27dunuLg4JSUlKS0tTRUqVFBCQoIWL16so0eP6u23387yiPfNeHp6auTIkRoxYoQWLlyoo0ePaseOHZo7d66kq/s6ICBAHTp00JYtWxQfH69NmzZp0KBB+vHHH//SNvL29lb//v31/PPPa926dTp48KD69u2rixcv6qmnnvpLY9/Myy+/rF9++UVt2rTJdvqwYcO0YcMGvfzyyzp06JA++OADzZgxQ8OHD5ckrV69Wm+//bZiYmJ04sQJLVy4UBkZGfbLLSEhIfruu+90/PhxJSUlZRtwbDab5s+fr0OHDqlJkyZas2aNjh07pn379mnSpEnq0KGDpKv7wNPTUxEREdq/f7+++eYbPffcc+rRo4f9klVBev755/Xaa69pyZIliouL0wsvvKCYmBgNGjToT4+5evVqPfnkk1q9erUOHTqkuLg4vf7661qzZo19O9zs+6lIkSJ66qmn9Pzzz2vDhg3av3+/evXqleUfXg8++KBmzJihPXv2KDo6Wv369XM4m5Mfn/mQkBBFRESoT58+WrFiheLj47Vx40Z98sknuVoX5A0BCLm2ceNG3X///Q6vl156SUFBQdq6davS09PVpk0bVatWTYMGDZKfn5/Dl8iDDz6ookWLKi4uTk888YTD2G3atNHq1asVFRWlunXrqn79+nrjjTdUtmzZXNfn7u6uUaNGqUaNGmrSpIlcXV21ePHiXM/foEED9evXT126dFHx4sU1ZcoUSdL8+fPVs2dPDRs2TJUqVVL79u313XffKTg4ONdj51alSpUUFBSk0NBQvfrqq2rZsqX279+vqlWrZtvf19dXmzdv1sMPP6yKFStqzJgxmjZtmtq2bStJ6tu3rypVqqQ6deqoePHi2rp1qzp06KAhQ4bo2WefVa1atbRt2zaNHTs2z7WOHTtWw4YN00svvaQqVaqoS5cu9nsZChcurM2bN6tMmTJ69NFHVaVKFfXp00d//PGHfH19//wG+q9XX31Vjz32mHr06KHatWvryJEj+vLLL+Xv7/+Xx74Rd3d3BQQE5HhpqXbt2vrkk0+0ePFiVatWTS+99JImTpyoXr16SZLuuusuLVu2TA8++KCqVKmi2bNna9GiRbrvvvskScOHD5erq6uqVq2q4sWLZ7kvK9MDDzyg6OholS9fXn379lWVKlXUvn17HThwQNOnT5d0dR98+eWX+u2331S3bl116tRJLVq00IwZM/J9u+TGwIEDNWzYMA0bNkzVq1fXunXrtGrVKt17771/esyqVauqcOHCGjZsmGrVqqX69evrk08+0fvvv68ePXpIUq6+n6ZOnaomTZqoffv2atmypRo1auRwhlqSpk2bpuDgYDVp0kRPPPGEhg8frsKFC9un59dnftasWerUqZOeeeYZVa5cWX379rWfzcrtdy1yx2auv6gJAIDFNWvWTLVq1bIHStx5iIwAAMByCEAAAMByuAQGAAAshzNAAADAcghAAADAcghAAADAcghAAADAcghAAADAcghAAADAcghAAADAcghAAADAcghAAADAcv4f4I6oTplAigEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#this works, just has the future warning\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import Levenshtein as lev\n",
    "from matplotlib.pyplot import figure\n",
    "from sklearn.cluster import DBSCAN\n",
    "from Levenshtein import distance\n",
    "import numpy as np\n",
    "\n",
    "# Define the framework sequences\n",
    "frameworks = {\n",
    "    'Framework1': 'KREAEAGPHMADC',\n",
    "    'Framework2': 'CTGGGLNDIFEAQKI',\n",
    "}\n",
    "\n",
    "# Define the CDR loop positions relative to the framework sequences\n",
    "cdr_positions = {\n",
    "    'CDR1': ['Framework1', 'Framework2']\n",
    "    #'CDR2': ['Framework2', 'Framework3'], #we don't need these because we only have one \"framework\"\n",
    "    #'CDR3': ['Framework3', 'Framework4']\n",
    "}\n",
    "\n",
    "# Load the data from the Excel file\n",
    "df = pd.read_excel('Output_Files/output-peptide-clusters.xlsx')\n",
    "print(df)\n",
    "# Extract the CDR loop sequences\n",
    "cdr_seqs = {}\n",
    "for cdr, positions in cdr_positions.items():\n",
    "    start, end = positions\n",
    "    cdr_seqs[cdr] = [seq[seq.find(frameworks[start])+len(frameworks[start]):seq.find(frameworks[end])] for seq in df['Sequence']]\n",
    "\n",
    "# Calculate the frequency percentage of each CDR sequence as a percentage\n",
    "cdr_freqs = {}\n",
    "for cdr in cdr_seqs:\n",
    "    cdr_count = len(cdr_seqs[cdr])\n",
    "    cdr_freqs[cdr] = [cdr_seqs[cdr].count(seq)/cdr_count*100 for seq in cdr_seqs[cdr]]\n",
    "\n",
    "\n",
    "# Find the most common CDR sequence for each loop\n",
    "cdr_consensus_seqs = {}\n",
    "for cdr in cdr_seqs:\n",
    "    max_freq_idx = cdr_freqs[cdr].index(max(cdr_freqs[cdr]))\n",
    "    cdr_consensus_seqs[cdr] = cdr_seqs[cdr][max_freq_idx]\n",
    "\n",
    "# Calculate the Levenshtein distance between each CDR sequence and the consensus sequence\n",
    "cdr_distances = {}\n",
    "for cdr in cdr_seqs:\n",
    "    cdr_distances[cdr] = [lev.distance(seq, cdr_consensus_seqs[cdr]) for seq in cdr_seqs[cdr]]\n",
    "\n",
    "# Create a new dataframe with the results, removing duplicate sequences\n",
    "cdr_df = pd.DataFrame()\n",
    "for i, (sequence, kd) in df[['Sequence', 'KD']].iterrows():\n",
    "    cdr_df = pd.concat([cdr_df, pd.DataFrame({'Sequence': sequence,\n",
    "                                           'KD': kd,\n",
    "                                           'CDR1 Seq': cdr_seqs['CDR1'][i],\n",
    "                                           'CDR1 Freq': cdr_freqs['CDR1'][i],\n",
    "                                           'CDR1 Distance': cdr_distances['CDR1'][i]}, index=[i])],\n",
    "                    ignore_index=True)\n",
    "\n",
    "cdr_df = cdr_df.drop_duplicates(subset=['Sequence', 'CDR1 Seq'])\n",
    "cdr_df[['CDR1 Freq']] = cdr_df[['CDR1 Freq']].apply(lambda x: round(x, 2))\n",
    "\n",
    "\n",
    "# Define a function to compute the pairwise distance matrix between all sequences\n",
    "def pairwise_distance_matrix(sequences):\n",
    "    n = len(sequences)\n",
    "    dist_matrix = np.zeros((n, n))\n",
    "    for i in range(n):\n",
    "        for j in range(i+1, n):\n",
    "            dist_matrix[i, j] = distance(sequences[i], sequences[j])\n",
    "            dist_matrix[j, i] = dist_matrix[i, j]\n",
    "    return dist_matrix\n",
    "\n",
    "# Compute the pairwise distance matrix\n",
    "dist_matrix = pairwise_distance_matrix(cdr_df['CDR1 Seq'].values)\n",
    "\n",
    "# Run DBSCAN on the distance matrix\n",
    "dbscan = DBSCAN(metric='precomputed', eps=1.5, min_samples=2)\n",
    "dbscan.fit(dist_matrix)\n",
    "\n",
    "# Extract the cluster labels\n",
    "cluster_labels = dbscan.labels_\n",
    "\n",
    "# Print the number of clusters found\n",
    "n_clusters = len(set(cluster_labels)) - (1 if -1 in cluster_labels else 0)\n",
    "print('Number of clusters found:', n_clusters)\n",
    "\n",
    "# Add the cluster labels to the dataframe\n",
    "cdr_df['Cluster'] = cluster_labels\n",
    "\n",
    "from Bio.SeqUtils.ProtParam import ProteinAnalysis\n",
    "import pandas as pd\n",
    "\n",
    "# Define a function to calculate GRAVY and pI\n",
    "def calculate_gravy_and_pi(seq):\n",
    "    # Create a ProteinAnalysis object for the amino acid sequence\n",
    "    protein = ProteinAnalysis(seq)\n",
    "    \n",
    "    # Calculate GRAVY\n",
    "    gravy = protein.gravy()\n",
    "    \n",
    "    # Calculate pI\n",
    "    pi = protein.isoelectric_point()\n",
    "    \n",
    "    return gravy, pi\n",
    "\n",
    "# Apply the function to the 'CDR1 Seq' column and store the results in new columns\n",
    "cdr_df[['GRAVY_Index', 'pI']] = pd.DataFrame(cdr_df['CDR1 Seq'].apply(calculate_gravy_and_pi).tolist(), index=cdr_df.index)\n",
    "cdr_df = cdr_df.reset_index(drop=True)\n",
    "# Save the results to a new Excel file\n",
    "cdr_df.to_excel('Output_Files/peptide_distances.xlsx', index=False)\n",
    "\n",
    "# Create scatterplots for each CDR loop\n",
    "\n",
    "for cdr in cdr_distances:\n",
    "    plt.scatter(cdr_df['CDR1 Distance'], cdr_df['KD'], c=cluster_labels)\n",
    "    plt.xlabel('Levenshtein Distance from Most Common Sequence')\n",
    "    plt.ylabel('KD')\n",
    "    plt.title(\"Peptide Distances vs KD\")\n",
    "    for i, seq in enumerate(cdr_seqs[cdr]):\n",
    "        plt.annotate(seq, (cdr_distances[cdr][i], df['KD'][i]))\n",
    "    plt.savefig('Scatterplots/Scatter_with_sequences.png', dpi=300)\n",
    "    plt.show()\n",
    "    \n",
    "for cdr in cdr_distances:\n",
    "    plt.scatter(cdr_df['CDR1 Distance'], cdr_df['KD'], c=cluster_labels)\n",
    "    plt.xlabel('Levenshtein Distance from Most Common Sequence')\n",
    "    plt.ylabel('KD')\n",
    "    plt.title(\"Peptide Distances vs KD\")\n",
    "    plt.savefig('Scatterplots/Scatter_only_KD_distance.png', dpi=300)\n",
    "    plt.show()\n",
    "for cdr in cdr_distances:\n",
    "    plt.scatter(cdr_df['CDR1 Distance'], cdr_df['GRAVY_Index'], c=cluster_labels)\n",
    "    plt.xlabel('Levenshtein Distance from Most Common Sequence')\n",
    "    plt.ylabel('GRAVY Index')\n",
    "    plt.title(\"Peptide Distances vs GRAVY Index\")\n",
    "    plt.savefig('Scatterplots/Scatter_only_GRAVY_distance.png', dpi=300)\n",
    "    plt.show()\n",
    "for cdr in cdr_distances:\n",
    "    plt.scatter(cdr_df['CDR1 Distance'], cdr_df['pI'], c=cluster_labels)\n",
    "    plt.xlabel('Levenshtein Distance from Most Common Sequence')\n",
    "    plt.ylabel('pI')\n",
    "    plt.title(\"Peptide Distances vs pI\")\n",
    "    plt.savefig('Scatterplots/Scatter_only_pI_distance.png', dpi=300)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "96bf147c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sequence</th>\n",
       "      <th>KD</th>\n",
       "      <th>CDR1 Seq</th>\n",
       "      <th>CDR1 Freq</th>\n",
       "      <th>CDR1 Distance</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>GRAVY_Index</th>\n",
       "      <th>pI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KREAEAGPHMADCAWHLGELVWCTGGGLNDIFEAQKI</td>\n",
       "      <td>1</td>\n",
       "      <td>AWHLGELVW</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.522222</td>\n",
       "      <td>5.241828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KREAEAGPHMADCAWHLGGLVWCTGGGLNDIFEAQKI</td>\n",
       "      <td>1</td>\n",
       "      <td>AWHLGGLVW</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>6.786087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KREAEAGPHMADCARQLGQSVSCTGGGLNDIFEAQKI</td>\n",
       "      <td>1</td>\n",
       "      <td>ARQLGQSVS</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.411111</td>\n",
       "      <td>9.795020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Sequence  KD   CDR1 Seq  CDR1 Freq  \\\n",
       "0  KREAEAGPHMADCAWHLGELVWCTGGGLNDIFEAQKI   1  AWHLGELVW       50.0   \n",
       "1  KREAEAGPHMADCAWHLGGLVWCTGGGLNDIFEAQKI   1  AWHLGGLVW       25.0   \n",
       "2  KREAEAGPHMADCARQLGQSVSCTGGGLNDIFEAQKI   1  ARQLGQSVS       25.0   \n",
       "\n",
       "   CDR1 Distance  Cluster  GRAVY_Index        pI  \n",
       "0              0        0     0.522222  5.241828  \n",
       "1              1        0     0.866667  6.786087  \n",
       "2              5       -1    -0.411111  9.795020  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdr_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f29e29cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global alignment completed\n"
     ]
    }
   ],
   "source": [
    "from Bio.Seq import Seq\n",
    "from Bio.SeqRecord import SeqRecord\n",
    "from Bio.Align import MultipleSeqAlignment\n",
    "from Bio import AlignIO\n",
    "import pandas as pd\n",
    "\n",
    "# Read the amino acid sequences from the CDR1 Seq column of the dataframe\n",
    "sequences = [SeqRecord(Seq(seq), id=str(i+1)) for i, seq in enumerate(cdr_df['CDR1 Seq'])]\n",
    "\n",
    "# Align the sequences\n",
    "alignment = MultipleSeqAlignment(sequences)\n",
    "\n",
    "# Write the aligned sequences to a file in FASTA format\n",
    "with open('Clusters/cdr1_global_alignment.fa', 'w') as f:\n",
    "    AlignIO.write(alignment, f, 'fasta')\n",
    "print(\"Global alignment completed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "14c9c569",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster sequence alignment completed\n"
     ]
    }
   ],
   "source": [
    "from Bio import SeqIO\n",
    "from Bio.Seq import Seq\n",
    "from Bio.SeqRecord import SeqRecord\n",
    "from Bio.Align import MultipleSeqAlignment\n",
    "\n",
    "# Read the sequences from the DataFrame\n",
    "cdr_sequences = cdr_df['CDR1 Seq'].tolist()\n",
    "\n",
    "# Group sequences by cluster\n",
    "groups = cdr_df.groupby('Cluster')\n",
    "for cluster, group in groups:\n",
    "    # Create a list of SeqRecord objects\n",
    "    seq_records = []\n",
    "    for i, seq in enumerate(group['CDR1 Seq']):\n",
    "        seq_record = SeqRecord(Seq(seq), id=f'seq{i+1}')\n",
    "        seq_records.append(seq_record)\n",
    "    \n",
    "    # Align the sequences\n",
    "    alignment = MultipleSeqAlignment(seq_records)\n",
    "    \n",
    "    # Write the aligned sequences to a file in FASTA format\n",
    "    with open(f'Clusters/cdr1_alignment_{cluster}.fa', 'w') as f:\n",
    "        SeqIO.write(alignment, f, 'fasta')\n",
    "print(\"Cluster sequence alignment completed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4ff43a01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import logomaker\n",
    "from Bio import AlignIO\n",
    "\n",
    "# Read in the alignment file in FASTA format\n",
    "alignment = AlignIO.read(\"Clusters/cdr1_alignment_0.fa\", \"fasta\")\n",
    "\n",
    "# Convert the alignment to a list of sequences\n",
    "sequences = [str(record.seq) for record in alignment]\n",
    "\n",
    "# Convert the sequences to a pandas dataframe\n",
    "alignment_df = logomaker.alignment_to_matrix(sequences)\n",
    "\n",
    "# Create the sequence logo\n",
    "logo = logomaker.Logo(alignment_df, color_scheme='dmslogo_funcgroup')\n",
    "# Customize the logo appearance\n",
    "logo.style_spines(visible=False)\n",
    "logo.ax.xaxis.set_tick_params(labelsize=8, which='major', pad=0)\n",
    "logo.ax.xaxis.set_tick_params(labelsize=0, which='minor', pad=0)\n",
    "logo.ax.xaxis.set_major_locator(plt.MultipleLocator(1))\n",
    "\n",
    "# Save the figure as a PDF file\n",
    "logo_fig = logo.fig\n",
    "logo_fig.savefig('Clusters/Logos/peptide_logo.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3b060afe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for outputting every logo in the folder\n",
    "import os\n",
    "import logomaker\n",
    "from Bio import AlignIO\n",
    "\n",
    "# Directory containing the .fa files\n",
    "directory = \"Clusters\"\n",
    "\n",
    "# Create the directory folder if it doesn't exist\n",
    "if not os.path.exists(directory):\n",
    "    os.makedirs(directory)\n",
    "\n",
    "# Get a list of .fa files in the directory\n",
    "fa_files = [file for file in os.listdir(directory) if file.startswith(\"cdr1_alignment_\") and file.endswith(\".fa\")]\n",
    "\n",
    "# Process each .fa file\n",
    "for fa_file in fa_files:\n",
    "    # Extract the varying part of the file name\n",
    "    varying_part = fa_file[len(\"cdr1_alignment_\"):-len(\".fa\")]\n",
    "\n",
    "    # Read in the alignment file in FASTA format\n",
    "    alignment = AlignIO.read(os.path.join(directory, fa_file), \"fasta\")\n",
    "\n",
    "    # Convert the alignment to a list of sequences\n",
    "    sequences = [str(record.seq) for record in alignment]\n",
    "\n",
    "    # Convert the sequences to a pandas dataframe\n",
    "    alignment_df = logomaker.alignment_to_matrix(sequences)\n",
    "\n",
    "    # Create the sequence logo\n",
    "    logo = logomaker.Logo(alignment_df, color_scheme='dmslogo_funcgroup')\n",
    "    # Customize the logo appearance\n",
    "    logo.style_spines(visible=False)\n",
    "    logo.ax.xaxis.set_tick_params(labelsize=8, which='major', pad=0)\n",
    "    logo.ax.xaxis.set_tick_params(labelsize=0, which='minor', pad=0)\n",
    "    logo.ax.xaxis.set_major_locator(plt.MultipleLocator(1))\n",
    "\n",
    "    # Create the \"Logos\" folder if it doesn't exist\n",
    "    logos_folder = os.path.join(directory, \"Logos\")\n",
    "    if not os.path.exists(logos_folder):\n",
    "        os.makedirs(logos_folder)\n",
    "\n",
    "    # Generate the output file name based on the varying part of the input file name\n",
    "    output_file = os.path.join(logos_folder, f\"peptide_logo_{varying_part}.png\")\n",
    "\n",
    "    # Save the figure as a PNG file\n",
    "    logo_fig = logo.fig\n",
    "    logo_fig.savefig(output_file, dpi=300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "34256b55",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                Sequence  KD   CDR1 Seq  CDR1 Freq  \\\n",
      "0  KREAEAGPHMADCAWHLGELVWCTGGGLNDIFEAQKI   1  AWHLGELVW       50.0   \n",
      "1  KREAEAGPHMADCAWHLGGLVWCTGGGLNDIFEAQKI   1  AWHLGGLVW       25.0   \n",
      "2  KREAEAGPHMADCARQLGQSVSCTGGGLNDIFEAQKI   1  ARQLGQSVS       25.0   \n",
      "\n",
      "   CDR1 Distance  Cluster  GRAVY_Index        pI  \n",
      "0              0        0     0.522222  5.241828  \n",
      "1              1        0     0.866667  6.786087  \n",
      "2              5       -1    -0.411111  9.795020  \n"
     ]
    }
   ],
   "source": [
    "print(cdr_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d8c4803e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0   1   2   3  4   5   6   7   8\n",
      "0  1  19   7  10  6   4  10  18  19\n",
      "1  1  19   7  10  6   6  10  18  19\n",
      "2  1  15  14  10  6  14  16  18  16\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = cdr_df\n",
    "\n",
    "parallel_df = pd.DataFrame({'CDR1 Seq': df['CDR1 Seq'], 'KD': df['KD']})\n",
    "\n",
    "split_seqs = parallel_df['CDR1 Seq'].apply(list).apply(pd.Series)\n",
    "split_seqs = split_seqs.fillna('Del')\n",
    "\n",
    "# Define a dictionary to map amino acids to numeric codes\n",
    "amino_acid_codes = {\n",
    "    'A': 1,\n",
    "    'C': 2,\n",
    "    'D': 3,\n",
    "    'E': 4,\n",
    "    'F': 5,\n",
    "    'G': 6,\n",
    "    'H': 7,\n",
    "    'I': 8,\n",
    "    'K': 9,\n",
    "    'L': 10,\n",
    "    'M': 11,\n",
    "    'N': 12,\n",
    "    'P': 13,\n",
    "    'Q': 14,\n",
    "    'R': 15,\n",
    "    'S': 16,\n",
    "    'T': 17,\n",
    "    'V': 18,\n",
    "    'W': 19,\n",
    "    'Y': 20,\n",
    "    'Del': 21\n",
    "}\n",
    "\n",
    "# Convert amino acids to numeric codes\n",
    "split_seqs = split_seqs.replace(amino_acid_codes)\n",
    "print(split_seqs)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "683e768a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Splitting CDR Loop residues\n",
      "   CDR1.1  CDR1.2  CDR1.3  CDR1.4  CDR1.5  CDR1.6  CDR1.7  CDR1.8  CDR1.9  KD\n",
      "0       5      12       2       8      18      20       8       6      12   1\n",
      "1       5      12       2       8      18      18       8       6      12   1\n",
      "2       5       1      16       8      18      16      13       6      13   1\n",
      "Minimum KD \n",
      "1\n",
      "Maximum KD \n",
      "1\n",
      "Number of Clusters Found\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = cdr_df\n",
    "\n",
    "parallel_df = pd.DataFrame({'CDR1 Seq': df['CDR1 Seq'], 'KD': df['KD']})\n",
    "\n",
    "split_seqs = parallel_df['CDR1 Seq'].apply(list).apply(pd.Series)\n",
    "split_seqs = split_seqs.fillna('Del')\n",
    "\n",
    "# Define a dictionary to map amino acids to numeric codes\n",
    "amino_acid_codes = {\n",
    "    'R': 1,\n",
    "    'H': 2,\n",
    "    'K': 3,\n",
    "    'Del': 4,\n",
    "    'A': 5,\n",
    "    'V': 6,\n",
    "    'I': 7,\n",
    "    'L': 8,\n",
    "    'M': 9,\n",
    "    'F': 10,\n",
    "    'Y': 11,\n",
    "    'W': 12,\n",
    "    'S': 13,\n",
    "    'T': 14,\n",
    "    'N': 15,\n",
    "    'Q': 16,\n",
    "    'C': 17,\n",
    "    'G': 18,\n",
    "    'P': 19,\n",
    "    'E': 20,\n",
    "    'D': 21\n",
    "}\n",
    "\n",
    "# Convert amino acids to numeric codes\n",
    "split_seqs = split_seqs.replace(amino_acid_codes)\n",
    "\n",
    "# Rename the columns in split_seqs to 'CDR1.X'\n",
    "split_seqs.columns = [f'CDR1.{i+1}' for i in range(split_seqs.shape[1])]\n",
    "\n",
    "split_seqs = split_seqs.assign(KD=cdr_df['KD'])\n",
    "print(\"Splitting CDR Loop residues\")\n",
    "print(split_seqs)\n",
    "MinKD= min(cdr_df['KD'])\n",
    "MaxKD= max(cdr_df['KD'])\n",
    "print(\"Minimum KD \")\n",
    "print(MinKD)\n",
    "print(\"Maximum KD \")\n",
    "print(MaxKD)\n",
    "split_seqs = split_seqs.assign(Cluster=cdr_df['Cluster'])\n",
    "MaxClusters= max(cdr_df['Cluster'])\n",
    "MinClusters= min(cdr_df['Cluster'])\n",
    "print(\"Number of Clusters Found\")\n",
    "print(abs(MaxClusters-MinClusters)+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "98cf0b89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.12.1.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "dimensions": [
          {
           "label": "CDR1.1",
           "range": [
            1,
            21
           ],
           "ticktext": [
            "R",
            "H",
            "K",
            "DEL",
            "A",
            "V",
            "I",
            "L",
            "M",
            "F",
            "Y",
            "W",
            "S",
            "T",
            "N",
            "Q",
            "C",
            "G",
            "P",
            "E",
            "D"
           ],
           "tickvals": [
            1,
            2,
            3,
            4,
            5,
            6,
            7,
            8,
            9,
            10,
            11,
            12,
            13,
            14,
            15,
            16,
            17,
            18,
            19,
            20,
            21
           ],
           "values": [
            5,
            5,
            5
           ]
          },
          {
           "label": "CDR1.2",
           "range": [
            1,
            21
           ],
           "ticktext": [
            "R",
            "H",
            "K",
            "DEL",
            "A",
            "V",
            "I",
            "L",
            "M",
            "F",
            "Y",
            "W",
            "S",
            "T",
            "N",
            "Q",
            "C",
            "G",
            "P",
            "E",
            "D"
           ],
           "tickvals": [
            1,
            2,
            3,
            4,
            5,
            6,
            7,
            8,
            9,
            10,
            11,
            12,
            13,
            14,
            15,
            16,
            17,
            18,
            19,
            20,
            21
           ],
           "values": [
            12,
            12,
            1
           ]
          },
          {
           "label": "CDR1.3",
           "range": [
            1,
            21
           ],
           "ticktext": [
            "R",
            "H",
            "K",
            "DEL",
            "A",
            "V",
            "I",
            "L",
            "M",
            "F",
            "Y",
            "W",
            "S",
            "T",
            "N",
            "Q",
            "C",
            "G",
            "P",
            "E",
            "D"
           ],
           "tickvals": [
            1,
            2,
            3,
            4,
            5,
            6,
            7,
            8,
            9,
            10,
            11,
            12,
            13,
            14,
            15,
            16,
            17,
            18,
            19,
            20,
            21
           ],
           "values": [
            2,
            2,
            16
           ]
          },
          {
           "label": "CDR1.4",
           "range": [
            1,
            21
           ],
           "ticktext": [
            "R",
            "H",
            "K",
            "DEL",
            "A",
            "V",
            "I",
            "L",
            "M",
            "F",
            "Y",
            "W",
            "S",
            "T",
            "N",
            "Q",
            "C",
            "G",
            "P",
            "E",
            "D"
           ],
           "tickvals": [
            1,
            2,
            3,
            4,
            5,
            6,
            7,
            8,
            9,
            10,
            11,
            12,
            13,
            14,
            15,
            16,
            17,
            18,
            19,
            20,
            21
           ],
           "values": [
            8,
            8,
            8
           ]
          },
          {
           "label": "CDR1.5",
           "range": [
            1,
            21
           ],
           "ticktext": [
            "R",
            "H",
            "K",
            "DEL",
            "A",
            "V",
            "I",
            "L",
            "M",
            "F",
            "Y",
            "W",
            "S",
            "T",
            "N",
            "Q",
            "C",
            "G",
            "P",
            "E",
            "D"
           ],
           "tickvals": [
            1,
            2,
            3,
            4,
            5,
            6,
            7,
            8,
            9,
            10,
            11,
            12,
            13,
            14,
            15,
            16,
            17,
            18,
            19,
            20,
            21
           ],
           "values": [
            18,
            18,
            18
           ]
          },
          {
           "label": "CDR1.6",
           "range": [
            1,
            21
           ],
           "ticktext": [
            "R",
            "H",
            "K",
            "DEL",
            "A",
            "V",
            "I",
            "L",
            "M",
            "F",
            "Y",
            "W",
            "S",
            "T",
            "N",
            "Q",
            "C",
            "G",
            "P",
            "E",
            "D"
           ],
           "tickvals": [
            1,
            2,
            3,
            4,
            5,
            6,
            7,
            8,
            9,
            10,
            11,
            12,
            13,
            14,
            15,
            16,
            17,
            18,
            19,
            20,
            21
           ],
           "values": [
            20,
            18,
            16
           ]
          },
          {
           "label": "CDR1.7",
           "range": [
            1,
            21
           ],
           "ticktext": [
            "R",
            "H",
            "K",
            "DEL",
            "A",
            "V",
            "I",
            "L",
            "M",
            "F",
            "Y",
            "W",
            "S",
            "T",
            "N",
            "Q",
            "C",
            "G",
            "P",
            "E",
            "D"
           ],
           "tickvals": [
            1,
            2,
            3,
            4,
            5,
            6,
            7,
            8,
            9,
            10,
            11,
            12,
            13,
            14,
            15,
            16,
            17,
            18,
            19,
            20,
            21
           ],
           "values": [
            8,
            8,
            13
           ]
          },
          {
           "label": "CDR1.8",
           "range": [
            1,
            21
           ],
           "ticktext": [
            "R",
            "H",
            "K",
            "DEL",
            "A",
            "V",
            "I",
            "L",
            "M",
            "F",
            "Y",
            "W",
            "S",
            "T",
            "N",
            "Q",
            "C",
            "G",
            "P",
            "E",
            "D"
           ],
           "tickvals": [
            1,
            2,
            3,
            4,
            5,
            6,
            7,
            8,
            9,
            10,
            11,
            12,
            13,
            14,
            15,
            16,
            17,
            18,
            19,
            20,
            21
           ],
           "values": [
            6,
            6,
            6
           ]
          },
          {
           "label": "CDR1.9",
           "range": [
            1,
            21
           ],
           "ticktext": [
            "R",
            "H",
            "K",
            "DEL",
            "A",
            "V",
            "I",
            "L",
            "M",
            "F",
            "Y",
            "W",
            "S",
            "T",
            "N",
            "Q",
            "C",
            "G",
            "P",
            "E",
            "D"
           ],
           "tickvals": [
            1,
            2,
            3,
            4,
            5,
            6,
            7,
            8,
            9,
            10,
            11,
            12,
            13,
            14,
            15,
            16,
            17,
            18,
            19,
            20,
            21
           ],
           "values": [
            12,
            12,
            13
           ]
          },
          {
           "label": "KD",
           "range": [
            1,
            1
           ],
           "values": [
            1,
            1,
            1
           ]
          },
          {
           "label": "Cluster",
           "range": [
            -1,
            0
           ],
           "values": [
            0,
            0,
            -1
           ]
          }
         ],
         "line": {
          "cmax": 21,
          "cmin": 1,
          "color": [
           5,
           5,
           5
          ],
          "colorscale": [
           [
            0,
            "rgb(0,0,131)"
           ],
           [
            0.2,
            "rgb(0,60,170)"
           ],
           [
            0.4,
            "rgb(5,255,255)"
           ],
           [
            0.6,
            "rgb(255,255,0)"
           ],
           [
            0.8,
            "rgb(250,0,0)"
           ],
           [
            1,
            "rgb(128,0,0)"
           ]
          ],
          "showscale": false
         },
         "type": "parcoords"
        }
       ],
       "layout": {
        "height": 600,
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Hit Peptide Sequences"
        },
        "width": 800
       }
      },
      "text/html": [
       "<div>                            <div id=\"e0048598-8744-4ca1-be3c-2cf04b961884\" class=\"plotly-graph-div\" style=\"height:600px; width:800px;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"e0048598-8744-4ca1-be3c-2cf04b961884\")) {                    Plotly.newPlot(                        \"e0048598-8744-4ca1-be3c-2cf04b961884\",                        [{\"dimensions\":[{\"label\":\"CDR1.1\",\"range\":[1,21],\"ticktext\":[\"R\",\"H\",\"K\",\"DEL\",\"A\",\"V\",\"I\",\"L\",\"M\",\"F\",\"Y\",\"W\",\"S\",\"T\",\"N\",\"Q\",\"C\",\"G\",\"P\",\"E\",\"D\"],\"tickvals\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21],\"values\":[5,5,5]},{\"label\":\"CDR1.2\",\"range\":[1,21],\"ticktext\":[\"R\",\"H\",\"K\",\"DEL\",\"A\",\"V\",\"I\",\"L\",\"M\",\"F\",\"Y\",\"W\",\"S\",\"T\",\"N\",\"Q\",\"C\",\"G\",\"P\",\"E\",\"D\"],\"tickvals\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21],\"values\":[12,12,1]},{\"label\":\"CDR1.3\",\"range\":[1,21],\"ticktext\":[\"R\",\"H\",\"K\",\"DEL\",\"A\",\"V\",\"I\",\"L\",\"M\",\"F\",\"Y\",\"W\",\"S\",\"T\",\"N\",\"Q\",\"C\",\"G\",\"P\",\"E\",\"D\"],\"tickvals\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21],\"values\":[2,2,16]},{\"label\":\"CDR1.4\",\"range\":[1,21],\"ticktext\":[\"R\",\"H\",\"K\",\"DEL\",\"A\",\"V\",\"I\",\"L\",\"M\",\"F\",\"Y\",\"W\",\"S\",\"T\",\"N\",\"Q\",\"C\",\"G\",\"P\",\"E\",\"D\"],\"tickvals\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21],\"values\":[8,8,8]},{\"label\":\"CDR1.5\",\"range\":[1,21],\"ticktext\":[\"R\",\"H\",\"K\",\"DEL\",\"A\",\"V\",\"I\",\"L\",\"M\",\"F\",\"Y\",\"W\",\"S\",\"T\",\"N\",\"Q\",\"C\",\"G\",\"P\",\"E\",\"D\"],\"tickvals\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21],\"values\":[18,18,18]},{\"label\":\"CDR1.6\",\"range\":[1,21],\"ticktext\":[\"R\",\"H\",\"K\",\"DEL\",\"A\",\"V\",\"I\",\"L\",\"M\",\"F\",\"Y\",\"W\",\"S\",\"T\",\"N\",\"Q\",\"C\",\"G\",\"P\",\"E\",\"D\"],\"tickvals\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21],\"values\":[20,18,16]},{\"label\":\"CDR1.7\",\"range\":[1,21],\"ticktext\":[\"R\",\"H\",\"K\",\"DEL\",\"A\",\"V\",\"I\",\"L\",\"M\",\"F\",\"Y\",\"W\",\"S\",\"T\",\"N\",\"Q\",\"C\",\"G\",\"P\",\"E\",\"D\"],\"tickvals\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21],\"values\":[8,8,13]},{\"label\":\"CDR1.8\",\"range\":[1,21],\"ticktext\":[\"R\",\"H\",\"K\",\"DEL\",\"A\",\"V\",\"I\",\"L\",\"M\",\"F\",\"Y\",\"W\",\"S\",\"T\",\"N\",\"Q\",\"C\",\"G\",\"P\",\"E\",\"D\"],\"tickvals\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21],\"values\":[6,6,6]},{\"label\":\"CDR1.9\",\"range\":[1,21],\"ticktext\":[\"R\",\"H\",\"K\",\"DEL\",\"A\",\"V\",\"I\",\"L\",\"M\",\"F\",\"Y\",\"W\",\"S\",\"T\",\"N\",\"Q\",\"C\",\"G\",\"P\",\"E\",\"D\"],\"tickvals\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21],\"values\":[12,12,13]},{\"label\":\"KD\",\"range\":[1,1],\"values\":[1,1,1]},{\"label\":\"Cluster\",\"range\":[-1,0],\"values\":[0,0,-1]}],\"line\":{\"cmax\":21,\"cmin\":1,\"color\":[5,5,5],\"colorscale\":[[0.0,\"rgb(0,0,131)\"],[0.2,\"rgb(0,60,170)\"],[0.4,\"rgb(5,255,255)\"],[0.6,\"rgb(255,255,0)\"],[0.8,\"rgb(250,0,0)\"],[1.0,\"rgb(128,0,0)\"]],\"showscale\":false},\"type\":\"parcoords\"}],                        {\"height\":600,\"template\":{\"data\":{\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"title\":{\"text\":\"Hit Peptide Sequences\"},\"width\":800},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('e0048598-8744-4ca1-be3c-2cf04b961884');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import plotly.offline as pyo\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import init_notebook_mode, iplot\n",
    "\n",
    "init_notebook_mode(connected=True)\n",
    "\n",
    "amino_acids = ['R', 'H', 'K', 'DEL', 'A', 'V', 'I', 'L', 'M', 'F', 'Y', 'W', 'S', 'T', 'N', 'Q', 'C', 'G', 'P', 'E', 'D']\n",
    "\n",
    "dimensions = []\n",
    "\n",
    "for col in split_seqs.columns:  #this works with any number of CDR1 residues (aka both 9 and 11 length peptides)\n",
    "    if col.startswith('CDR'):\n",
    "        dimension = dict(range=[1, 21],\n",
    "                         label=col,\n",
    "                         values=split_seqs[col],\n",
    "                         tickvals=list(range(1, 22)),\n",
    "                         ticktext=amino_acids)\n",
    "        dimensions.append(dimension)\n",
    "    if col.startswith('KD'):\n",
    "        KD_dimension = dict(range=[MinKD,MaxKD],\n",
    "                        label=col,\n",
    "                         values=split_seqs[col])\n",
    "        dimensions.append(KD_dimension)\n",
    "    if col.startswith('Cluster'):\n",
    "        KD_dimension = dict(range=[MinClusters,MaxClusters],\n",
    "                        label=col,\n",
    "                         values=split_seqs[col])\n",
    "        dimensions.append(KD_dimension)\n",
    "fig = go.Figure(data=go.Parcoords(\n",
    "    line=dict(color=split_seqs['CDR1.1'],\n",
    "              colorscale='Jet',\n",
    "              showscale=False,\n",
    "              cmin=1,\n",
    "              cmax=21),\n",
    "    dimensions=dimensions\n",
    "))\n",
    "\n",
    "fig.update_layout(\n",
    "    title=\"Hit Peptide Sequences\",\n",
    "    width=800,\n",
    "    height=600\n",
    ")\n",
    "\n",
    "pyo.iplot(fig, filename='Output_Files/parallel_coordinates_plot.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "27c7f45e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sequence</th>\n",
       "      <th>KD</th>\n",
       "      <th>CDR1 Seq</th>\n",
       "      <th>CDR1 Freq</th>\n",
       "      <th>CDR1 Distance</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>GRAVY_Index</th>\n",
       "      <th>pI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KREAEAGPHMADCAWHLGELVWCTGGGLNDIFEAQKI</td>\n",
       "      <td>1</td>\n",
       "      <td>AWHLGELVW</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.522222</td>\n",
       "      <td>5.241828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KREAEAGPHMADCAWHLGGLVWCTGGGLNDIFEAQKI</td>\n",
       "      <td>1</td>\n",
       "      <td>AWHLGGLVW</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>6.786087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KREAEAGPHMADCARQLGQSVSCTGGGLNDIFEAQKI</td>\n",
       "      <td>1</td>\n",
       "      <td>ARQLGQSVS</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.411111</td>\n",
       "      <td>9.795020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Sequence  KD   CDR1 Seq  CDR1 Freq  \\\n",
       "0  KREAEAGPHMADCAWHLGELVWCTGGGLNDIFEAQKI   1  AWHLGELVW       50.0   \n",
       "1  KREAEAGPHMADCAWHLGGLVWCTGGGLNDIFEAQKI   1  AWHLGGLVW       25.0   \n",
       "2  KREAEAGPHMADCARQLGQSVSCTGGGLNDIFEAQKI   1  ARQLGQSVS       25.0   \n",
       "\n",
       "   CDR1 Distance  Cluster  GRAVY_Index        pI  \n",
       "0              0        0     0.522222  5.241828  \n",
       "1              1        0     0.866667  6.786087  \n",
       "2              5       -1    -0.411111  9.795020  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdr_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de29a017",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9b446f1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}