Peptide_Clusters-1.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` is not currently supported by PandasOnDask, defaulting to pandas implementation.\n",
      "Please refer to https://modin.readthedocs.io/en/stable/supported_apis/defaulting_to_pandas.html for explanation.\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",
    "\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",
    "client = Client()\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",
    "df = pd.DataFrame(results)\n",
    "df.to_excel(\"output-peptide.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  0.50\n",
      "1  KREAEAGPHMADCAWDLGELVWCTGGGLNDIFEAQKI  1.00\n",
      "2  KREAEAGPHMADCAWDLGELVWCTGGGLNDIFEAQKI  1.00\n",
      "3  KREAEAGPHMADCAWRLGELVWCTGGGLNDIFEAQKI  0.10\n",
      "4  KREAEAGPHMADCAWKLGELVWCTGGGLNDIFEAQKI  0.01\n",
      "5  KREAEAGPHMADCLYFLGELVWCTGGGLNDIFEAQKI  0.25\n",
      "6  KREAEAGPHMADCLLFLGELVWCTGGGLNDIFEAQKI  0.35\n",
      "Number of clusters found: 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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l8FjVMhOBqWlUa8YA+0taHRFXFFiXmZlVKDIIZgLbSdoG+BtwGOkSl/8UEdt03pZ0AXCVQ8DMbP0qLAgiYrWk40lnAzUD50XEfEnH5vk9fi9gZmbrR5FHBETENGBa1bSaARARRxVZi5mZ1VZoEJiZ2bqLVfdAx62g0dC6H2rq34tHOgjMzAaoiDXEsyfCiunAC0ALPH8ybPxTNHRiv23HYw2ZmQ1UK6fDimuAFcAqYBnEUuKZ44hY3W+bcRCYmQ1QsewSYHmNOR2wam6/bcdBYGY2YHX3W1sBa/ptKw4CM7MBSsMPBobXmNMELbv223YcBGZmA1XrATDsDaDOMBgGtKLRpyG19NtmfNaQmdkAJTXD6B/DqpnEyltQ02hoPQA1j+nX7TgIzMwGMEkwdE80dM/CtuGuITOzknMQmJmVnIPAzKzkHARmZiXnIDAzKzkHgZlZyTkIzMxKzkFgZlZyDgIzs5JzEJiZlZyDwMys5DzWkJnZABYRsGpOumZx00bQOgU1bdKv23AQmJkNUBFriMUnwMqbgJXAUHj+2zD6J2jYXv22HXcNmZkNVCumQcdNpMtVrgFWQCwnFn+SiFX9thkHgZnZABXLL4Wodc3iF1J3UT9xEJiZNZwgXbe4fzgIzMwGKA0/pOIylZWG+JrFZmal0Lo/DH1bDoNmYDhoBBp9JlL/nevjs4bMzAYoqQltfBrRMRc6boGmjaF1crp2cT9yEJiZDXAaugsM3aWw9btryMys5BwEZmYlV2gQSJos6X5JCySdVGP+ByTdlf/dIqm4Yx8zM6upsCCQ1Az8CJgCTAAOlzSharGHgEkRsTPwdeCcouoxM7Paijwi2BNYEBELI6IDmAocWLlARNwSEc/ku7cBWxZYj5mZ1VBkEIwDHq24vyhP685HgKtrzZB0jKQ2SW3t7e39WKKZmRUZBLV+/xw1F5TeRgqCE2vNj4hzImJiREwcO3ZsP5ZoZmZF/o5gETC+4v6WwGPVC0naGTgXmBIRTxVYj5mZ1VDkEcFMYDtJ20gaChwGXFm5gKRXAJcBH4yIBwqsxczMulHYEUFErJZ0PDCdNEjGeRExX9Kxef7ZwJeBlwFnSQJYHRETi6rJzMy6UkTNbvsBa+LEidHW1lbvMszMGoqkWd190PYvi83MSs5BYGZWcg4CM7OScxCYmZWcg8DMrOQcBGZmJecgMDMrOQeBmVnJOQjMzErOQWBmVnJFjj5q1q2IYP7N93HLlW0MH9XKPv/+FrbY9uX1LsuslBwEtt5FBN8+6kz+fNntrFi2kiFDmpn6rSv41Fkf5Z0felu9yzMrHXcN2XrXds3cFAJLV0LA6lUv0LG8gzP+41yWLF5a7/LMSsdBYOvdjKk3pxCoMqSlmVnXzK1DRWbl5iCw9a5l2BDy9Se6aG5pXs/VmJmDwNa7d3xwEkOHD+0yfc2aNbxuv13qUJFZuTkIbL3b6U07cMh/vouhrS0MGz6U1pGtDBsxjC//9jMM36C13uWZlY6vUGZ18/jCf3DH1bNp3WAYbzpoT0aO3qDeJZkNWj1docynj1rdbP7KzTjwuMn1LsOs9Nw1ZGZWcg4CM7OScxCYmZWcg8DMrOQcBGZmJecgMDMrOQeBmVnJOQjMzErOQWBmVnL+ZbHVRUQw+7p53PK7O2gdOZx9j5zEVq/Zst5lmZWSg8DWu4jgG4f/gNv/cCcrlq6keUgTl58xjU+cdhTv+ti+9S7PrHTcNWTr3R1Xz/5nCAC8sHoNHcs7OOuE83nu6efrXJ1Z+RQaBJImS7pf0gJJJ9WYL0ln5Pl3Sdq9yHpsYLjx4ltqXqGsuaWZO6+9qw4VmZVbYUEgqRn4ETAFmAAcLmlC1WJTgO3yv2OAHxdVjw0cQ1uHoqauVyiTRMuwljpUZFZuRR4R7AksiIiFEdEBTAUOrFrmQOAXkdwGjJa0eYE12QCw31F7M7S16xXKYk34CmVmdVBkEIwDHq24vyhPW9tlkHSMpDZJbe3t7f1eqK1fE/bansO/cDAtrS20bjCM4aNaGT6yla9e8XlaRwyrd3lmpVPkWUO1rk5efTm0vixDRJwDnAPpCmXrXprV2we+eAj7HjmJWdfMpXWDVvY6YHeGjxxe77LMSqnIIFgEjK+4vyXw2EtYxgapTcePYcpH9ql3GWalV2TX0ExgO0nbSBoKHAZcWbXMlcCR+eyhvYBnI+LxAmsyM7MqhR0RRMRqSccD04Fm4LyImC/p2Dz/bGAasD+wAFgGHF1UPWZmVluhvyyOiGmkN/vKaWdX3A7guCJrMDOznvmXxWZmJecgMDMrOaXemcYhqR3460t8+BjgyX4sp57cloFpsLRlsLQD3JZOW0XE2FozGi4I1oWktoiYWO86+oPbMjANlrYMlnaA29IX7hoyMys5B4GZWcmVLQjOqXcB/chtGZgGS1sGSzvAbelVqb4jMDOzrsp2RGBmZlUcBGZmJTcog2AwXSKzD23ZW9Kzkubkf1+uR529kXSepCck3d3N/EbaJ721pVH2yXhJN0i6V9J8SSfUWKYh9ksf29Io+6VV0h2S5ua2fLXGMv27XyJiUP0jDXD3F+CVwFBgLjChapn9gatJ10PYC7i93nWvQ1v2Bq6qd619aMtbgd2Bu7uZ3xD7pI9taZR9sjmwe749Cniggf9W+tKWRtkvAkbm2y3A7cBeRe6XwXhEMJgukdmXtjSEiLgJeLqHRRpln/SlLQ0hIh6PiDvz7eeBe+l6hcCG2C99bEtDyM/1kny3Jf+rPqunX/fLYAyCfrtE5gDQ1zrfkA8jr5a04/oprd81yj7pq4baJ5K2BnYjffqs1HD7pYe2QIPsF0nNkuYATwDXRkSh+6XQYajrpN8ukTkA9KXOO0ljiCyRtD9wBbBd0YUVoFH2SV801D6RNBK4FPh0RDxXPbvGQwbsfumlLQ2zXyLiBWBXSaOByyXtFBGV30n1634ZjEcEg+kSmb3WGRHPdR5GRrr+Q4ukMeuvxH7TKPukV420TyS1kN44L4yIy2os0jD7pbe2NNJ+6RQRi4EZwOSqWf26XwZjEAymS2T22hZJL5ekfHtP0j59ar1Xuu4aZZ/0qlH2Sa7xZ8C9EfH9bhZriP3Sl7Y00H4Zm48EkDQceAdwX9Vi/bpfBl3XUAyiS2T2sS3vBf5D0mpgOXBY5NMKBhJJF5HO2hgjaRHwv6QvwRpqn0Cf2tIQ+wR4E/BBYF7ujwb4b+AV0HD7pS9taZT9sjnwc0nNpLC6OCKuKvI9zENMmJmV3GDsGjIzs7XgIDAzKzkHgZlZyTkIzMxKzkFgZlZyDoJ+ImlJ70utH93VIukgSRP68PhjJR25FtvbWtJySbPz6I93SPpQxfx/U42RUyvm75p/6VkXkr6TR3n8TsHbeVjSn6qmzVE3o5j2sq7Rkj7Rw/yXS5oq6S+S7pE0TdL2L6Xu9U3SZpKuykNB3CNpWr1rGuwG3e8IrEcHAVcB9/S0UD5PeW39JSJ2A5D0SuAySU0RcX5EXEnXH/VV2hWYSDo3uh4+DoyNiJWVEyUNiYjV/bytUZLGR8Sjkl6zDusZDXwCOKt6Rv7R1OXAzyPisDxtV2Az0qicA93XSOPrnA4gaec61zP4rcvQpf73omFhl9SYti3wf8As4E/ADsBGwMNAU15mBGnwqJZay+dlLgDOAG4BFgLvzdM3B24C5gB3A2/prAX4BmnY6ttIbwBvJI2Y+VBeftsetvcV4LP59gzgVOAO0pvIW2q0c2uqhmQG3g7MzrePAs7Mtw/Ntc7NtQ8FHgHac13vJ426egswO///6or1XJZrfhD4dsX2JpPGkpkLXJenbQCcR/qF9mzgwBq1Xwm8ULHtC4DvAzcA3yOF1G3AXaQ3140rnpcf5DbcC+yRa3sQOLmb18jDpB85dT63XwNO7HzugFbgfGBervdtefqO+fmfk+vYjjQS7fI87Ts1nvubuqlBwHfyPpgHvD9P3xu4Ebg47+dvAR/I250HbFvxWvxxfn4WApPyc3wvcEHFdg7Pj7sbOLXy74Sq12Y3++SQbur/XN6fdwFfrZj+ReB+4I/ARbz49Tsx3x4DPJxvN+fnoXNdH694HmYAl5B+zXsh//q91R6k1+Pc/LyM6m49jfav7gUMln/UDoLrgO3y7dcD1+fbv6v4I38/cG4vy18A/JbUlTeBNDQ1wGeAL+bbzcCofDuAd+fb3wa+VLGe9/ahvq9U/SF9L9/eH/hjjXZuTdcgGA0sz7eP4l9BMA8Y17lM9fx8f0NgSL79DuDSiuUWksK0FfgrabyVsaQw3SYvt0n+/5vAERX1PABs0NO+y8/RVUBzvn8XMCnf/hpwWsXzcmq+fQJpnJfNgWGkcWBeVmM7DwPbA7fk+7Pz/uwMgs8A5+fbO5ACshX4IfCBPH0oMLzWc16xnU8BP+hm3iHAtaTXy2Z5G5uT3gAXV7Thb+Q32ty+0yqen6mkQDkQeA54Lem1OYsUnFvk9Y4l9TpcDxzU02uzqsZ35lpuIL3Bb5Gn70e6eLvy9q4iXRvidaTX1QjSa2cBvQfBMfzr72IY0AZsk5+HZ0lj9zQBtwJvzs/7QmCPytdod+up9/vR2v5z11BB8iiIbwR+m4c3gfRCAfgNKQBuII0fdFYvywNcERFrgHskbZanzQTOy4NtXRERc/L0DtIfCaQ/zn3Xsr5qnQN4zSK9AfVFrdERAW4GLpB0ccV6q21E+on9dqQ3jpaKeddFxLMAku4BtgI2Jn0CfgggIjqvFbAf8G+SPpvvt5KGHLi3l9p/GxEvSNqIFFY35uk/JwVyp87urnnA/MhjvUhaSAqoWuPYPA08I+mwXMeyinlvJr3pExH3SforKThuBb4oaUvgsoh4sGKfra03AxdFGt3yH5JuJH3SfQ6YWdGGvwDXVLTvbRXr+H1EhKR5wD8iYl5+zHzS62MrYEZEtOfpF5LesK+gD6/NiJieuxcnA1OA2ZJ2Iu3P/UgBCjCSdHQ0Crg8Ipbl7fXUDdlpP2BnSe/N9zfK6+oA7oiIRXldc3KbngUej4iZucbn8vzu1vNQH2oYMBwExWkCFkfErjXmXQmcImkT0qeZ60ndGN0tD1DZfy1IF0iR9FbgXcAvJX0nIn4BrIr8EYXU7VFrP/dUX3fb7m5dtexGjTfciDhW0utzzXNy33W1rwM3RMTBSmPLz6hRS2U9ovYQvCJ1Mdzfx5o7Le3jcp21rKmqaw09P0+/AX5EOsKpVPPdPSJ+Lel20nM2XdJHSZ9OuzOfNK5OLT0lSHUbKts3pMZy3bW7p+9V+vLa7AzzXwO/ltT5yV/AKRHxk8plJX2a7odgXs2/TopprXwY8MmImF61rr1Z+9dYl/U0Gp81VJD8ieEhSYfCP68xukuet4TUx3g66dJ5L/S0fHckbQU8ERE/JY282Nt1S58nfXrqsb51ld+8v0v+dFs1b9uIuD0ivgw8Sfrk/M+6so1IXRPQ9c2ylluBSZK2ydvYJE+fDnwyf3mKpN3Wph35yOMZSW/Jkz5I6kdfV5eTukWq3zxuIvXLk8/weQVwf/50vDAiziB9iNiZrs9ZpeuBYZI+1jlB0h6SJuVtvF/pwidjSW+wd/RDmyrdTtofY/LAaYezFs+bpLdLGpFvjyJ9l/UI6fn6cD6aRdI4SZuS2nSwpOF5+XdXrO5h0octeHE4TicNQNeS17W9pA16KOs+YAtJe3TWJWnIS1jPgOQjgv4zQmkkyk7fJ/1R/1jSl0jdG1NJXzRB+lT4W1KfZKeelq9lb+BzklaRvoTr7ZTPqcBPJX2K9EexttvrybaSZpM+dT0P/DAizq+x3Hdyl49I31HMJf2Rn5QPw08hvUn+XNJ/kd7UehQR7ZKOIZ+pRLqq076kI4vTgLtyGDwMHLCW7foQcHZ+Y1pIP4y+GelSiqcCVHXxnJW3NY/0SfaoiFgp6f3AEXk//x34WkQ8LelmpVNPr46Iz1WsPyQdDJymdNruClLbP01603wD6XkP4PMR8XdJO6xruyq2/7ikL5C6PgVMi4jfrcUqXgecqTRKaBPpO7SZAEpnWt2an7clpO+A7pT0G9IX538lnfjQ6bvAxZI+yItfS+eSunzuzK+NdtJZdd21qSPvhx8qDQ29nPT91VqtZ6Dy6KNmNqhI+grpBIDv1ruWRuGuITOzkvMRgZlZyfmIwMys5BwEZmYl5yAwMys5B4GZWck5CMzMSu7/AQeg8nU4nitTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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-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=2, 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('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\")\n",
    "    for i, seq in enumerate(cdr_seqs[cdr]):\n",
    "        plt.annotate(seq, (cdr_distances[cdr][i], df['KD'][i]))\n",
    "    plt.savefig('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\")\n",
    "    plt.savefig('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('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('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>0.50</td>\n",
       "      <td>AWHLGELVW</td>\n",
       "      <td>14.29</td>\n",
       "      <td>1</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>KREAEAGPHMADCAWDLGELVWCTGGGLNDIFEAQKI</td>\n",
       "      <td>1.00</td>\n",
       "      <td>AWDLGELVW</td>\n",
       "      <td>28.57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.488889</td>\n",
       "      <td>4.050028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KREAEAGPHMADCAWRLGELVWCTGGGLNDIFEAQKI</td>\n",
       "      <td>0.10</td>\n",
       "      <td>AWRLGELVW</td>\n",
       "      <td>14.29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.377778</td>\n",
       "      <td>6.047009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KREAEAGPHMADCAWKLGELVWCTGGGLNDIFEAQKI</td>\n",
       "      <td>0.01</td>\n",
       "      <td>AWKLGELVW</td>\n",
       "      <td>14.29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>6.046100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KREAEAGPHMADCLYFLGELVWCTGGGLNDIFEAQKI</td>\n",
       "      <td>0.25</td>\n",
       "      <td>LYFLGELVW</td>\n",
       "      <td>14.29</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.366667</td>\n",
       "      <td>4.050028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KREAEAGPHMADCLLFLGELVWCTGGGLNDIFEAQKI</td>\n",
       "      <td>0.35</td>\n",
       "      <td>LLFLGELVW</td>\n",
       "      <td>14.29</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.933333</td>\n",
       "      <td>4.050028</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Sequence    KD   CDR1 Seq  CDR1 Freq  \\\n",
       "0  KREAEAGPHMADCAWHLGELVWCTGGGLNDIFEAQKI  0.50  AWHLGELVW      14.29   \n",
       "1  KREAEAGPHMADCAWDLGELVWCTGGGLNDIFEAQKI  1.00  AWDLGELVW      28.57   \n",
       "2  KREAEAGPHMADCAWRLGELVWCTGGGLNDIFEAQKI  0.10  AWRLGELVW      14.29   \n",
       "3  KREAEAGPHMADCAWKLGELVWCTGGGLNDIFEAQKI  0.01  AWKLGELVW      14.29   \n",
       "4  KREAEAGPHMADCLYFLGELVWCTGGGLNDIFEAQKI  0.25  LYFLGELVW      14.29   \n",
       "5  KREAEAGPHMADCLLFLGELVWCTGGGLNDIFEAQKI  0.35  LLFLGELVW      14.29   \n",
       "\n",
       "   CDR1 Distance  Cluster  GRAVY_Index        pI  \n",
       "0              1        0     0.522222  5.241828  \n",
       "1              0        0     0.488889  4.050028  \n",
       "2              1        0     0.377778  6.047009  \n",
       "3              1        0     0.444444  6.046100  \n",
       "4              3        1     1.366667  4.050028  \n",
       "5              3        1     1.933333  4.050028  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdr_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "38e8b88f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "import modin.pandas as pd\n",
    "from IPython.display import display, HTML\n",
    "from ipywidgets import widgets\n",
    "import io\n",
    "import base64\n",
    "\n",
    "# Define the amino acids and their numerical values\n",
    "amino_acids = [(\"R\",0),(\"H\",1),(\"K\",2),(\"D\",3),(\"E\",4),(\"S\",5),(\"T\",6),(\"N\",7),(\"Q\",8),(\"C\",9),(\"G\",10),(\"P\",11),(\"A\",12),(\"V\",13),(\"I\",14),(\"L\",15),(\"M\",16),(\"F\",17),(\"Y\",18),(\"W\",19),(\"Del\",20)]\n",
    "aa_dict = {aa: num for aa, num in amino_acids}\n",
    "\n",
    "# Read in the data from the CSV file\n",
    "df = pd.read_csv(\"CDR_Parallel.csv\")\n",
    "\n",
    "# Replace the amino acid values with their numerical equivalents\n",
    "for col in df.columns:\n",
    "    if col.startswith(\"CDR\"):\n",
    "        df[col] = df[col].apply(lambda x: aa_dict[x])\n",
    "\n",
    "# Define the tick values and tick text for the plot\n",
    "tick_values = list(range(len(amino_acids)))\n",
    "tick_text = [aa[0] for aa in amino_acids]\n",
    "\n",
    "# Create a list of dictionaries to define the dimensions for the plot\n",
    "dimensions = []\n",
    "for col in df.columns:\n",
    "    if col.startswith(\"CDR\"):\n",
    "        dimensions.append(dict(range=[0,21], tickvals=tick_values, ticktext=tick_text, label=col, values=df[col]))\n",
    "\n",
    "# Create the parallel coordinates plot with custom height and width\n",
    "fig = go.FigureWidget(data=\n",
    "    go.Parcoords(\n",
    "        line=dict(colorscale='Electric', showscale=True, cmin=-4000, cmax=-100),\n",
    "        dimensions=dimensions\n",
    "    ),\n",
    "    layout=dict(height=1800, width=2500)\n",
    ")\n",
    "\n",
    "# Define the function to update the table based on the selected lines\n",
    "def update_table(trace, points, state):\n",
    "    selected_df = pd.DataFrame(points.point_data.data)\n",
    "    table = selected_df.drop(columns=[\"dimension\", \"trace\"])\n",
    "    table.columns = [c.replace(\"_\", \" \") for c in table.columns]\n",
    "    #table_widget.clear_output()\n",
    "    with table_widget:\n",
    "        display(table)\n",
    "\n",
    "# Add the brushing callback to the figure\n",
    "fig.data[0].on_selection(update_table)\n",
    "#Add brushing to table\n",
    "\n",
    "\n",
    "# Add a table of the data\n",
    "table = df.copy()\n",
    "table.columns = [c.replace(\"_\", \" \") for c in table.columns]\n",
    "table_widget = widgets.Output()\n",
    "with table_widget:\n",
    "    display(table)\n",
    "\n",
    "# Add the table and export button to the figure\n",
    "fig.add_trace(go.Table(\n",
    "    header=dict(values=list(table.columns), align='left'),\n",
    "    cells=dict(values=[table[c] for c in table.columns], align='left')\n",
    "))\n",
    "\n",
    "button = widgets.Button(description=\"Export to Excel\")\n",
    "output = widgets.Output()\n",
    "\n",
    "def on_button_click(b):\n",
    "    # Save the table to Excel and display a success message\n",
    "    with io.BytesIO() as buffer:\n",
    "        writer = pd.ExcelWriter(buffer, engine='xlsxwriter')\n",
    "        table.to_excel(writer)\n",
    "        writer.save()\n",
    "        buffer.seek(0)\n",
    "        data = buffer.read()\n",
    "        b64 = base64.b64encode(data).decode()\n",
    "        href = f'<a href=\"data:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;base64,{b64}\" download=\"CDR_Parallel_values.xlsx\">Download Excel file</a>'\n",
    "        with output:\n",
    "            print(\"Export successful!\")\n",
    "            display(HTML(href)) \n",
    "button.on_click(on_button_click)\n",
    "\n",
    "#Create a VBox container to hold the figure, table, and export button widgets\n",
    "#widgets.VBox([fig, table_widget, widgets.HBox([button, output])])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "10ef3a17",
   "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>0.50</td>\n",
       "      <td>AWHLGELVW</td>\n",
       "      <td>14.29</td>\n",
       "      <td>1</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>KREAEAGPHMADCAWDLGELVWCTGGGLNDIFEAQKI</td>\n",
       "      <td>1.00</td>\n",
       "      <td>AWDLGELVW</td>\n",
       "      <td>28.57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.488889</td>\n",
       "      <td>4.050028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KREAEAGPHMADCAWRLGELVWCTGGGLNDIFEAQKI</td>\n",
       "      <td>0.10</td>\n",
       "      <td>AWRLGELVW</td>\n",
       "      <td>14.29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.377778</td>\n",
       "      <td>6.047009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KREAEAGPHMADCAWKLGELVWCTGGGLNDIFEAQKI</td>\n",
       "      <td>0.01</td>\n",
       "      <td>AWKLGELVW</td>\n",
       "      <td>14.29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>6.046100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KREAEAGPHMADCLYFLGELVWCTGGGLNDIFEAQKI</td>\n",
       "      <td>0.25</td>\n",
       "      <td>LYFLGELVW</td>\n",
       "      <td>14.29</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.366667</td>\n",
       "      <td>4.050028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KREAEAGPHMADCLLFLGELVWCTGGGLNDIFEAQKI</td>\n",
       "      <td>0.35</td>\n",
       "      <td>LLFLGELVW</td>\n",
       "      <td>14.29</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.933333</td>\n",
       "      <td>4.050028</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Sequence    KD   CDR1 Seq  CDR1 Freq  \\\n",
       "0  KREAEAGPHMADCAWHLGELVWCTGGGLNDIFEAQKI  0.50  AWHLGELVW      14.29   \n",
       "1  KREAEAGPHMADCAWDLGELVWCTGGGLNDIFEAQKI  1.00  AWDLGELVW      28.57   \n",
       "2  KREAEAGPHMADCAWRLGELVWCTGGGLNDIFEAQKI  0.10  AWRLGELVW      14.29   \n",
       "3  KREAEAGPHMADCAWKLGELVWCTGGGLNDIFEAQKI  0.01  AWKLGELVW      14.29   \n",
       "4  KREAEAGPHMADCLYFLGELVWCTGGGLNDIFEAQKI  0.25  LYFLGELVW      14.29   \n",
       "5  KREAEAGPHMADCLLFLGELVWCTGGGLNDIFEAQKI  0.35  LLFLGELVW      14.29   \n",
       "\n",
       "   CDR1 Distance  Cluster  GRAVY_Index        pI  \n",
       "0              1        0     0.522222  5.241828  \n",
       "1              0        0     0.488889  4.050028  \n",
       "2              1        0     0.377778  6.047009  \n",
       "3              1        0     0.444444  6.046100  \n",
       "4              3        1     1.366667  4.050028  \n",
       "5              3        1     1.933333  4.050028  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdr_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ef56f462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[SeqRecord(seq=Seq('CDR1'), id='seq1', name='<unknown name>', description='<unknown description>', dbxrefs=[])]\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[1;32mIn [15]\u001b[0m, in \u001b[0;36m<cell line: 13>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28mprint\u001b[39m(seq_records)\n\u001b[0;32m     11\u001b[0m \u001b[38;5;66;03m# Check that all CDR1 sequences have the same length\u001b[39;00m\n\u001b[0;32m     12\u001b[0m \u001b[38;5;66;03m# Perform pairwise alignment using Needleman-Wunsch algorithm\u001b[39;00m\n\u001b[1;32m---> 13\u001b[0m alignments \u001b[38;5;241m=\u001b[39m pairwise2\u001b[38;5;241m.\u001b[39malign\u001b[38;5;241m.\u001b[39mglobalxx(seq_records[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mseq, \u001b[43mseq_records\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mseq)\n\u001b[0;32m     14\u001b[0m seq1_aligned, seq2_aligned, score, begin, end \u001b[38;5;241m=\u001b[39m alignments[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m     15\u001b[0m aligned_records \u001b[38;5;241m=\u001b[39m [seq_records[\u001b[38;5;241m0\u001b[39m], seq_records[\u001b[38;5;241m1\u001b[39m]]\n",
      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "from Bio import pairwise2\n",
    "from Bio.Align import MultipleSeqAlignment\n",
    "from Bio.Seq import Seq\n",
    "from Bio.SeqRecord import SeqRecord\n",
    "from Bio import AlignIO\n",
    "cdr_seqs = {'CDR1': ['AWHLGELVW','AWDLGELVW', 'AWHLGELVW']}\n",
    "seq_records = [SeqRecord(Seq(seq), id='seq{}'.format(i+1)) for i, seq in enumerate(cdr_seqs)]\n",
    "seq_records = [SeqRecord(Seq(seq.strip()), id='seq{}'.format(i+1)) for i, seq in enumerate(cdr_seqs)]\n",
    "\n",
    "print(seq_records)\n",
    "# Check that all CDR1 sequences have the same length\n",
    "# Perform pairwise alignment using Needleman-Wunsch algorithm\n",
    "alignments = pairwise2.align.globalxx(seq_records[0].seq, seq_records[1].seq)\n",
    "seq1_aligned, seq2_aligned, score, begin, end = alignments[0]\n",
    "aligned_records = [seq_records[0], seq_records[1]]\n",
    "aligned_records[0].seq = Seq(seq1_aligned)\n",
    "aligned_records[1].seq = Seq(seq2_aligned)\n",
    "\n",
    "for i in range(2, len(seq_records)):\n",
    "    alignments = pairwise2.align.globalxx(aligned_records[0].seq, seq_records[i].seq)\n",
    "    seq_aligned, aligned_seq, score, begin, end = alignments[0]\n",
    "    aligned_seq_record = SeqRecord(Seq(aligned_seq), id='seq{}'.format(i+1))\n",
    "    aligned_seq_record = aligned_seq.lstrip('-')\n",
    "    aligned_records.append(aligned_seq_record)\n",
    "\n",
    "alignment = MultipleSeqAlignment(aligned_records)\n",
    "\n",
    "# Write aligned sequences to a file in FASTA format\n",
    "with open('alignment.fa', 'w') as f:\n",
    "    AlignIO.write(alignment, f, 'fasta')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbee2fe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import logomaker\n",
    "from Bio import AlignIO\n",
    "\n",
    "# Read in the alignment file in FASTA format\n",
    "alignment = AlignIO.read(\"alignment.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('peptide_logo.png', dpi=300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fa5e73e8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[SeqRecord(seq=Seq('AWHLGELVW'), id='seq1', name='<unknown name>', description='<unknown description>', dbxrefs=[]), SeqRecord(seq=Seq('AWDLGELVW'), id='seq2', name='<unknown name>', description='<unknown description>', dbxrefs=[]), SeqRecord(seq=Seq('AWRLGELVW'), id='seq3', name='<unknown name>', description='<unknown description>', dbxrefs=[]), SeqRecord(seq=Seq('AWKLGELVW'), id='seq4', name='<unknown name>', description='<unknown description>', dbxrefs=[]), SeqRecord(seq=Seq('LYFLGELVW'), id='seq5', name='<unknown name>', description='<unknown description>', dbxrefs=[]), SeqRecord(seq=Seq('LLFLGELVW'), id='seq6', name='<unknown name>', description='<unknown description>', dbxrefs=[])]\n"
     ]
    }
   ],
   "source": [
    "print(seq_records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f29e29cd",
   "metadata": {},
   "outputs": [],
   "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('cdr1_global_alignment.fa', 'w') as f:\n",
    "    AlignIO.write(alignment, f, 'fasta')\n",
    "print(\"Global alignment completed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "14c9c569",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "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'cdr1_alignment_{cluster}.fa', 'w') as f:\n",
    "        SeqIO.write(alignment, f, 'fasta')\n",
    "print(\"Cluster alignments completed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ff43a01",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b060afe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a7d03ab",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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