library_design/generate_lib_mutations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bbfd72d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "import dms_tools2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3099440a",
   "metadata": {},
   "source": [
    "### Get list of targeted, viable mutations from primer pool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ac3f9432",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>primer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5005</th>\n",
       "      <td>hk19-rev-mut502S</td>\n",
       "      <td>gacttcagctcaacTGACTTGATCTGGAACC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5006</th>\n",
       "      <td>hk19-rev-mut502T</td>\n",
       "      <td>gacttcagctcaacTGTCTTGATCTGGAACC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5007</th>\n",
       "      <td>hk19-rev-mut502V</td>\n",
       "      <td>acttcagctcaacCACCTTGATCTGGAAC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5008</th>\n",
       "      <td>hk19-rev-mut502W</td>\n",
       "      <td>acttcagctcaacCCACTTGATCTGGAAC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5009</th>\n",
       "      <td>hk19-rev-mut502Y</td>\n",
       "      <td>tgacttcagctcaacATACTTGATCTGGAACC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  name                             primer\n",
       "5005  hk19-rev-mut502S    gacttcagctcaacTGACTTGATCTGGAACC\n",
       "5006  hk19-rev-mut502T    gacttcagctcaacTGTCTTGATCTGGAACC\n",
       "5007  hk19-rev-mut502V      acttcagctcaacCACCTTGATCTGGAAC\n",
       "5008  hk19-rev-mut502W      acttcagctcaacCCACTTGATCTGGAAC\n",
       "5009  hk19-rev-mut502Y   tgacttcagctcaacATACTTGATCTGGAACC"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load targeted primer dataset\n",
    "targeted_primers = pd.read_csv('hk19_primers.csv',\n",
    "                              header=None).rename(columns={0:'name', 1:'primer'})\n",
    "\n",
    "targeted_primers.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c88e17c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up function to pull targeted mutations from hk19_primers.csv\n",
    "def get_targeted_sites(primer_name, re_primer_prefix, targeted_muts_list):\n",
    "    primer_name = re_primer_prefix.sub('', primer_name) # gets rid of prefix\n",
    "    site_number = int((re.findall(r'\\d+', primer_name))[0]) # pull site number\n",
    "    chimeric_site = site_number + 21 # converts to chimeric sequential numbering\n",
    "    \n",
    "    aa_mut = primer_name[-1]\n",
    "    targeted_muts_list.append(str(chimeric_site)+aa_mut)\n",
    "    \n",
    "    return targeted_muts_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ce35fa45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['22A', '22D', '22E', '22F', '22G', '22H', '22K', '22L', '22M', '22N']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract a list of targeted sites + aa mutations based on primer names\n",
    "re_primer_prefix = re.compile(r'^(.*mut)')\n",
    "targeted_muts_list = []\n",
    "targeted_primers.apply(lambda x: get_targeted_sites(\n",
    "    x['name'], re_primer_prefix, targeted_muts_list), axis=1)\n",
    "\n",
    "# then, concat into df\n",
    "# targeted_muts = pd.DataFrame({'site':targeted_muts_sites, 'amino_acid':targeted_muts_aa})\n",
    "targeted_muts_list[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c375877b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>site</th>\n",
       "      <th>amino_acid</th>\n",
       "      <th>mutation_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>A</td>\n",
       "      <td>viable_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>D</td>\n",
       "      <td>viable_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>E</td>\n",
       "      <td>viable_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22</td>\n",
       "      <td>F</td>\n",
       "      <td>viable_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>G</td>\n",
       "      <td>viable_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>S</td>\n",
       "      <td>viable_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2501</th>\n",
       "      <td>523</td>\n",
       "      <td>T</td>\n",
       "      <td>viable_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2502</th>\n",
       "      <td>523</td>\n",
       "      <td>V</td>\n",
       "      <td>viable_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2503</th>\n",
       "      <td>523</td>\n",
       "      <td>W</td>\n",
       "      <td>viable_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2504</th>\n",
       "      <td>523</td>\n",
       "      <td>Y</td>\n",
       "      <td>viable_mutation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2505 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      site amino_acid    mutation_type\n",
       "0       22          A  viable_mutation\n",
       "1       22          D  viable_mutation\n",
       "2       22          E  viable_mutation\n",
       "3       22          F  viable_mutation\n",
       "4       22          G  viable_mutation\n",
       "...    ...        ...              ...\n",
       "2500   523          S  viable_mutation\n",
       "2501   523          T  viable_mutation\n",
       "2502   523          V  viable_mutation\n",
       "2503   523          W  viable_mutation\n",
       "2504   523          Y  viable_mutation\n",
       "\n",
       "[2505 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert to DF, with all labeled 'viable_mutation'\n",
    "targeted_muts = pd.DataFrame({'mutation':targeted_muts_list, 'mutation_type':'viable_mutation'})\n",
    "\n",
    "# split mutation into site and amino acid\n",
    "targeted_muts['site'] = targeted_muts['mutation'].str.replace('([A-Z]+)', '').astype(int)\n",
    "targeted_muts['amino_acid'] = targeted_muts['mutation'].str.extract('([A-Z]+)')\n",
    "targeted_muts = targeted_muts.drop(['mutation'], axis=1)\n",
    "\n",
    "# set column order\n",
    "cols = ['site', 'amino_acid', 'mutation_type']\n",
    "# targeted_muts = targeted_muts[cols]\n",
    "targeted_muts = targeted_muts[cols].drop_duplicates()\n",
    "targeted_muts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33c4d239",
   "metadata": {},
   "source": [
    "### Get list of single and paired epitope mutations\n",
    "Note that the final csv file should list each individual mutation. So we need to take the list of epitope sites, and then expand each site into 19 possible amino acid mutations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2008e985",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up function to pull epitope sites and convert to mutation list\n",
    "# handles both single and paired epitopes\n",
    "def get_epi_muts(epitope_df, aa_list, mutation_type):\n",
    "    stripped_name_numeric = epitope_df['name'].str.findall(r'\\d+').tolist()\n",
    "    \n",
    "    epitope_sites = []\n",
    "    for entry in stripped_name_numeric:\n",
    "        entry = entry[1:] # get rid of '19' from hk19\n",
    "        \n",
    "        # for handling single or paired epitope primers\n",
    "        for site in entry:\n",
    "            site=int(site)+3 # converts to chimeric sequential numbering\n",
    "            if site not in epitope_sites:\n",
    "                epitope_sites.append(site)\n",
    "    \n",
    "    \n",
    "    epitope_muts = []\n",
    "    for site in epitope_sites:\n",
    "        for aa in aa_list:\n",
    "            epitope_muts.append(str(site)+aa)\n",
    "            \n",
    "    # convert to DF, with all labeled 'viable_mutation'\n",
    "    muts_df = pd.DataFrame({'mutation':epitope_muts, 'mutation_type':mutation_type})\n",
    "\n",
    "    # split mutation into site and amino acid\n",
    "    muts_df['site'] = muts_df['mutation'].str.replace('(\\D+)', '').astype(int)\n",
    "    muts_df['amino_acid'] = muts_df['mutation'].str.extract('(\\D+)')\n",
    "    muts_df = muts_df.drop(['mutation'], axis=1)\n",
    "\n",
    "    # set column order\n",
    "    cols = ['site', 'amino_acid', 'mutation_type']\n",
    "    muts_df = muts_df[cols]\n",
    "    \n",
    "    return muts_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b2354e6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in df of single epitope primers\n",
    "single_epi_df = pd.read_csv('hk19_single_epitope_primers.csv', \n",
    "                              header=None).rename(columns={0:'name', 1:'primer'})\n",
    "\n",
    "paired_epi_df = pd.read_csv('hk19_paired_epitope_primers.csv', \n",
    "                              header=None).rename(columns={0:'name', 1:'primer'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "82b37194",
   "metadata": {},
   "outputs": [],
   "source": [
    "# there's probably a biopython tool for this??\n",
    "# would avoid having to incorporate dms_tools2 + updated pandas in same environment\n",
    "aa_list = dms_tools2.AAS_WITHSTOP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "81af4b1b",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>site</th>\n",
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       "      <td>111</td>\n",
       "      <td>A</td>\n",
       "      <td>epitope_mutation</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>111</td>\n",
       "      <td>C</td>\n",
       "      <td>epitope_mutation</td>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
       "      <td>111</td>\n",
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       "      <td>epitope_mutation</td>\n",
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       "      <th>4</th>\n",
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       "      <th>457</th>\n",
       "      <td>102</td>\n",
       "      <td>T</td>\n",
       "      <td>epitope_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>102</td>\n",
       "      <td>V</td>\n",
       "      <td>epitope_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>102</td>\n",
       "      <td>W</td>\n",
       "      <td>epitope_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>102</td>\n",
       "      <td>Y</td>\n",
       "      <td>epitope_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>102</td>\n",
       "      <td>*</td>\n",
       "      <td>epitope_mutation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>462 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     site amino_acid     mutation_type\n",
       "0     111          A  epitope_mutation\n",
       "1     111          C  epitope_mutation\n",
       "2     111          D  epitope_mutation\n",
       "3     111          E  epitope_mutation\n",
       "4     111          F  epitope_mutation\n",
       "..    ...        ...               ...\n",
       "457   102          T  epitope_mutation\n",
       "458   102          V  epitope_mutation\n",
       "459   102          W  epitope_mutation\n",
       "460   102          Y  epitope_mutation\n",
       "461   102          *  epitope_mutation\n",
       "\n",
       "[462 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_epi_muts = get_epi_muts(single_epi_df, aa_list, 'epitope_mutation').drop_duplicates()\n",
    "single_epi_muts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bbe76fce",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>site</th>\n",
       "      <th>amino_acid</th>\n",
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       "      <th>0</th>\n",
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       "      <td>A</td>\n",
       "      <td>paired_epitope_mutation</td>\n",
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       "      <th>1</th>\n",
       "      <td>150</td>\n",
       "      <td>C</td>\n",
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       "      <th>2</th>\n",
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       "      <td>D</td>\n",
       "      <td>paired_epitope_mutation</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>E</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>F</td>\n",
       "      <td>paired_epitope_mutation</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>217</td>\n",
       "      <td>T</td>\n",
       "      <td>paired_epitope_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>217</td>\n",
       "      <td>V</td>\n",
       "      <td>paired_epitope_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>217</td>\n",
       "      <td>W</td>\n",
       "      <td>paired_epitope_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>217</td>\n",
       "      <td>Y</td>\n",
       "      <td>paired_epitope_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>217</td>\n",
       "      <td>*</td>\n",
       "      <td>paired_epitope_mutation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>252 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     site amino_acid            mutation_type\n",
       "0     150          A  paired_epitope_mutation\n",
       "1     150          C  paired_epitope_mutation\n",
       "2     150          D  paired_epitope_mutation\n",
       "3     150          E  paired_epitope_mutation\n",
       "4     150          F  paired_epitope_mutation\n",
       "..    ...        ...                      ...\n",
       "247   217          T  paired_epitope_mutation\n",
       "248   217          V  paired_epitope_mutation\n",
       "249   217          W  paired_epitope_mutation\n",
       "250   217          Y  paired_epitope_mutation\n",
       "251   217          *  paired_epitope_mutation\n",
       "\n",
       "[252 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paired_epi_muts = get_epi_muts(paired_epi_df, aa_list, 'paired_epitope_mutation').drop_duplicates()\n",
    "paired_epi_muts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24519dc0",
   "metadata": {},
   "source": [
    "### Concat into final df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ec72cbd2",
   "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>site</th>\n",
       "      <th>amino_acid</th>\n",
       "      <th>mutation_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>A</td>\n",
       "      <td>viable_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>D</td>\n",
       "      <td>viable_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>E</td>\n",
       "      <td>viable_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22</td>\n",
       "      <td>F</td>\n",
       "      <td>viable_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>G</td>\n",
       "      <td>viable_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>217</td>\n",
       "      <td>T</td>\n",
       "      <td>paired_epitope_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>217</td>\n",
       "      <td>V</td>\n",
       "      <td>paired_epitope_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>217</td>\n",
       "      <td>W</td>\n",
       "      <td>paired_epitope_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>217</td>\n",
       "      <td>Y</td>\n",
       "      <td>paired_epitope_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>217</td>\n",
       "      <td>*</td>\n",
       "      <td>paired_epitope_mutation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3219 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     site amino_acid            mutation_type\n",
       "0      22          A          viable_mutation\n",
       "1      22          D          viable_mutation\n",
       "2      22          E          viable_mutation\n",
       "3      22          F          viable_mutation\n",
       "4      22          G          viable_mutation\n",
       "..    ...        ...                      ...\n",
       "247   217          T  paired_epitope_mutation\n",
       "248   217          V  paired_epitope_mutation\n",
       "249   217          W  paired_epitope_mutation\n",
       "250   217          Y  paired_epitope_mutation\n",
       "251   217          *  paired_epitope_mutation\n",
       "\n",
       "[3219 rows x 3 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aggregated_mutations = pd.concat([targeted_muts, single_epi_muts, paired_epi_muts])\n",
    "aggregated_mutations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "55b902e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "aggregated_mutations.to_csv('aggregated_mutations.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e5a1517",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be71b013",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab5ecab7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3748873d",
   "metadata": {},
   "source": [
    "scratch code -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aece58a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_targeted_muts_df(primer_df):\n",
    "    mut_sites=[]\n",
    "    mut_aa=[]\n",
    "    re_primer_prefix = re.compile(r'^(.*mut)')\n",
    "    \n",
    "    primer_name_list = primer_df['name'].str.tolist()\n",
    "    for primer in primer_name_list:\n",
    "        primer_name = re_primer_prefix.sub('', primer_name) # gets rid of prefix\n",
    "        site_number = int((re.findall(r'\\d+', primer_name))[0]) # pull site number\n",
    "        mut_sites.append(site_number+21) #converts to chimeric sequential numbering\n",
    "\n",
    "        aa_mut = primer_name[-1]\n",
    "        mut_aa.append(aa_mut)\n",
    "    \n",
    "    primer_\n",
    "\n",
    "# set up function to pull epitope sites and convert to mutation list\n",
    "# handles both single and paired epitopes\n",
    "def get_muts_df(primer_df, mut_type, aa_list):\n",
    "    \n",
    "    if mut_type = targeted:\n",
    "        mut_sites=[]\n",
    "        mut_aa=[]\n",
    "        primer_name_list = primer_df['name'].str.tolist()\n",
    "        for primer in primer_name_list:\n",
    "            re_primer_prefix = re.compile(r'^(.*mut)')\n",
    "            primer_name = re_primer_prefix.sub('', primer_name) # gets rid of prefix\n",
    "            site_number = int((re.findall(r'\\d+', primer_name))[0]) # pull site number\n",
    "            mut_sites.append(site_number+21) #converts to chimeric sequential numbering\n",
    "            \n",
    "            aa_mut = primer_name[-1]\n",
    "            mut_aa.append(aa_mut)\n",
    "        \n",
    "    else:\n",
    "        numeric_site_list = primer_df['name'].str.findall(r'\\d+').tolist()\n",
    "        \n",
    "        mut_sites=[]\n",
    "        for entry in stripped_name_numeric:\n",
    "            entry = entry[1:] # get rid of '19' from hk19\n",
    "            \n",
    "            for site in entry:\n",
    "                site=int(site)+3 # converts to chimeric sequential numbering\n",
    "                if site not in mut_sites:\n",
    "                    mut_sites.append(site)\n",
    "                    \n",
    "    \n",
    "    \n",
    "        primer_name = re_primer_prefix.sub('', primer_name) # gets rid of prefix\n",
    "    site_number = int((re.findall(r'\\d+', primer_name))[0]) # pull site number\n",
    "    chimeric_site = site_number + 21 # converts to chimeric sequential numbering\n",
    "    targeted_muts_sites.append(str(targeted_muts_sites))\n",
    "    \n",
    "    aa_mut = primer_name[-1]\n",
    "    targeted_muts_aa.append(aa_mut)\n",
    "    \n",
    "    \n",
    "    # note that the first number per primer is from hk19\n",
    "    stripped_name_numeric = epitope_df['name'].str.findall(r'\\d+').tolist()\n",
    "        \n",
    "        \n",
    "    mut_sites = []\n",
    "    for entry in stripped_name_numeric:\n",
    "        entry = entry[1:] # get rid of '19' from hk19\n",
    "        \n",
    "        # for handling single or paired epitope primers\n",
    "        for site in entry:\n",
    "            site=int(site)+3 # converts to chimeric sequential numbering\n",
    "            if site not in epitope_sites:\n",
    "                epitope_sites.append(site)\n",
    "    \n",
    "    \n",
    "    epitope_muts = []\n",
    "    for site in epitope_sites:\n",
    "        for aa in aa_list:\n",
    "            epitope_muts.append(str(site)+aa)\n",
    "    \n",
    "    return epitope_muts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f583c4a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert to df and remove duplicates from rev primers in original df\n",
    "targeted_muts = pd.DataFrame({})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9df8b70a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get list of paired epitopes from these primer names\n",
    "stripped_name = paired_epitope_df['name'].str.findall(r'\\d+').tolist()\n",
    "\n",
    "paired_epitopes = []\n",
    "for entry in stripped_name:\n",
    "    entry = entry[1:]\n",
    "    for site in entry:\n",
    "        site=int(site)+3 # to account for numbering shift in chimeras\n",
    "        if site not in paired_epitopes:\n",
    "            paired_epitopes.append(site)\n",
    "\n",
    "paired_epitopes"
   ]
  }
 ],
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