pub/most_studied_human_proteins.ipynb

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    "# Overview\n",
    "This notebook takes a list of top-studied human proteins, downloads PDB structures from AlphaFold to assess their quality, and representatively samples 25 proteins from that list. We used these 25 proteins to perform a meta-analysis of ProteinCartography results to identify the limits of the pipeline's current functionalities.\n",
    "\n",
    "## `conda` environment\n",
    "\n",
    "You can install the `conda` environment needed to run this notebook by running the following command from the within the `ProteinCartography` repo:\n",
    "\n",
    "```bash\n",
    "conda env create -f envs/cartography_pub.yml -n cartography_pub\n",
    "conda activate cartograph_pub\n",
    "```\n",
    "\n",
    "# 0. Setup\n",
    "\n",
    "The `most_studied_human_proteins.tsv` file was genereated by manually copying the proteins listed in Table 1 and Table 2 of this paper: [Li and Buck 2021, Protein Science](https://doi.org/10.1002/pro.4038) and aggregating their UniProt identifiers."
   ]
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   "source": [
    "#######################\n",
    "# Import dependencies #\n",
    "#######################\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from tqdm import tqdm\n",
    "\n",
    "##################################\n",
    "# Load Arcadia-specific packages #\n",
    "##################################\n",
    "import sys\n",
    "\n",
    "# set up specialized functions\n",
    "sys.path.append('../ProteinCartography/')\n",
    "from fetch_accession import fetch_pdb\n",
    "from assess_pdbs import assess_pdbs\n",
    "\n",
    "# set up colors and plot scheme\n",
    "import arcadia_pycolor as apc\n",
    "apc.mpl_setup()\n",
    "\n",
    "########################\n",
    "# Set global variables #\n",
    "########################\n",
    "output_dir = 'pub_proteins'\n",
    "\n",
    "# create output directory if it doesn't already exist\n",
    "if not os.path.exists(output_dir):\n",
    "    os.mkdir(output_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99cc255d-4b20-46ea-a5e0-5da6f22c1dc3",
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   "source": [
    "# 1. Downloading and assessing PDBs\n",
    "\n",
    "We include the `most_studied_human_proteins.tsv` file as part of this GitHub repository.  \n",
    "This block downloads a PDB structure for each protein in the dataset and assesses the proteins to determine the [mean pLDDT](https://alphafold.ebi.ac.uk/faq#faq-5) of their structures, which is a heuristic for structure quality."
   ]
  },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>uniprot id</th>\n",
       "      <th>protein</th>\n",
       "      <th>Length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P31947</td>\n",
       "      <td>14–3-3 protein sigma</td>\n",
       "      <td>248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>O15530</td>\n",
       "      <td>3-phosphoinositide-dependent protein kinase 1</td>\n",
       "      <td>556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P08708</td>\n",
       "      <td>40S ribosomal protein S17</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P62266</td>\n",
       "      <td>40S ribosomal protein S23</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P62753</td>\n",
       "      <td>40S ribosomal protein S6</td>\n",
       "      <td>249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>P00749</td>\n",
       "      <td>Urokinase-type plasminogen activator</td>\n",
       "      <td>431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>P35968</td>\n",
       "      <td>Vascular endothelial growth factor receptor 2</td>\n",
       "      <td>1356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>P11473</td>\n",
       "      <td>Vitamin D3 receptor</td>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>P40337</td>\n",
       "      <td>von Hippel–Lindau disease tumor suppressor</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>P61964</td>\n",
       "      <td>WD repeat-containing protein 5</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>193 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    uniprot id                                        protein  Length\n",
       "0       P31947                           14–3-3 protein sigma     248\n",
       "1       O15530  3-phosphoinositide-dependent protein kinase 1     556\n",
       "2       P08708                      40S ribosomal protein S17     135\n",
       "3       P62266                      40S ribosomal protein S23     143\n",
       "4       P62753                       40S ribosomal protein S6     249\n",
       "..         ...                                            ...     ...\n",
       "188     P00749           Urokinase-type plasminogen activator     431\n",
       "189     P35968  Vascular endothelial growth factor receptor 2    1356\n",
       "190     P11473                            Vitamin D3 receptor     427\n",
       "191     P40337     von Hippel–Lindau disease tumor suppressor     213\n",
       "192     P61964                 WD repeat-containing protein 5     334\n",
       "\n",
       "[193 rows x 3 columns]"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 193/193 [00:00<00:00, 1829.52it/s]\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>protid</th>\n",
       "      <th>pdb_origin</th>\n",
       "      <th>pdb_confidence</th>\n",
       "      <th>pdb_chains</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P08603</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>79.112146</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P40337</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>85.590887</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P28074</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>83.014641</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P14174</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>98.594309</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P62753</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>94.005390</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>P18031</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>82.030609</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>P29373</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>96.710100</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>P0DP23</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>85.087947</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>P25788</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>94.693955</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>P04746</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>96.663322</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>187 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     protid pdb_origin  pdb_confidence pdb_chains\n",
       "0    P08603  AlphaFold       79.112146        [A]\n",
       "1    P40337  AlphaFold       85.590887        [A]\n",
       "2    P28074  AlphaFold       83.014641        [A]\n",
       "3    P14174  AlphaFold       98.594309        [A]\n",
       "4    P62753  AlphaFold       94.005390        [A]\n",
       "..      ...        ...             ...        ...\n",
       "182  P18031  AlphaFold       82.030609        [A]\n",
       "183  P29373  AlphaFold       96.710100        [A]\n",
       "184  P0DP23  AlphaFold       85.087947        [A]\n",
       "185  P25788  AlphaFold       94.693955        [A]\n",
       "186  P04746  AlphaFold       96.663322        [A]\n",
       "\n",
       "[187 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# read input TSV and display it\n",
    "proteins_df = pd.read_csv('most_studied_human_proteins.tsv', sep = '\\t')\n",
    "display(proteins_df)\n",
    "\n",
    "# extract list of uniprot IDs\n",
    "proteins_list = proteins_df['uniprot id'].values\n",
    "\n",
    "# download PDB files for all of the proteins from AlphaFold    \n",
    "for protid in tqdm(proteins_list):\n",
    "    if not os.path.exists(os.path.join(output_dir, f'{protid}.pdb')):\n",
    "        fetch_pdb(protid, output_dir)\n",
    "\n",
    "# get list of file paths to those PDBs\n",
    "protein_file_list = [os.path.join(output_dir, i) for i in os.listdir(output_dir) if '.pdb' in i]\n",
    "\n",
    "# calculate mean pLDDT for each\n",
    "assessment_df = assess_pdbs(protein_file_list, os.path.join(output_dir, 'pub_proteins_analysis.tsv'))\n",
    "display(assessment_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcf03f13-a7b5-41ab-910a-f06cbd64e6e5",
   "metadata": {},
   "source": [
    "# 3. Combine data\n",
    "\n",
    "This cell merges the two DataFrames into one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "32cef74d-1cf2-42bd-9751-950fafab3431",
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    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>protid</th>\n",
       "      <th>protein</th>\n",
       "      <th>Length</th>\n",
       "      <th>pdb_origin</th>\n",
       "      <th>pdb_confidence</th>\n",
       "      <th>pdb_chains</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P08603</td>\n",
       "      <td>Complement factor H</td>\n",
       "      <td>1231</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>79.112146</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P40337</td>\n",
       "      <td>von Hippel–Lindau disease tumor suppressor</td>\n",
       "      <td>213</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>85.590887</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P28074</td>\n",
       "      <td>Proteasome subunit beta type-5</td>\n",
       "      <td>263</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>83.014641</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P14174</td>\n",
       "      <td>Macrophage migration inhibitory factor</td>\n",
       "      <td>115</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>98.594309</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P62753</td>\n",
       "      <td>40S ribosomal protein S6</td>\n",
       "      <td>249</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>94.005390</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>P18031</td>\n",
       "      <td>Tyrosine-protein phosphatase non-receptor type 1</td>\n",
       "      <td>435</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>82.030609</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>P29373</td>\n",
       "      <td>Cellular retinoic acid-binding protein 2</td>\n",
       "      <td>138</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>96.710100</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>P0DP23</td>\n",
       "      <td>Calmodulin-1</td>\n",
       "      <td>149</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>85.087947</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>P25788</td>\n",
       "      <td>Proteasome subunit alpha type-3</td>\n",
       "      <td>255</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>94.693955</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>P04746</td>\n",
       "      <td>Pancreatic alpha-amylase</td>\n",
       "      <td>511</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>96.663322</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>187 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     protid                                           protein  Length   \n",
       "0    P08603                               Complement factor H    1231  \\\n",
       "1    P40337        von Hippel–Lindau disease tumor suppressor     213   \n",
       "2    P28074                    Proteasome subunit beta type-5     263   \n",
       "3    P14174            Macrophage migration inhibitory factor     115   \n",
       "4    P62753                          40S ribosomal protein S6     249   \n",
       "..      ...                                               ...     ...   \n",
       "182  P18031  Tyrosine-protein phosphatase non-receptor type 1     435   \n",
       "183  P29373          Cellular retinoic acid-binding protein 2     138   \n",
       "184  P0DP23                                      Calmodulin-1     149   \n",
       "185  P25788                   Proteasome subunit alpha type-3     255   \n",
       "186  P04746                          Pancreatic alpha-amylase     511   \n",
       "\n",
       "    pdb_origin  pdb_confidence pdb_chains  \n",
       "0    AlphaFold       79.112146        [A]  \n",
       "1    AlphaFold       85.590887        [A]  \n",
       "2    AlphaFold       83.014641        [A]  \n",
       "3    AlphaFold       98.594309        [A]  \n",
       "4    AlphaFold       94.005390        [A]  \n",
       "..         ...             ...        ...  \n",
       "182  AlphaFold       82.030609        [A]  \n",
       "183  AlphaFold       96.710100        [A]  \n",
       "184  AlphaFold       85.087947        [A]  \n",
       "185  AlphaFold       94.693955        [A]  \n",
       "186  AlphaFold       96.663322        [A]  \n",
       "\n",
       "[187 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "proteins_df.rename(columns = {'uniprot id': 'protid'}, inplace = True)\n",
    "\n",
    "merged_df = proteins_df.merge(assessment_df, on = 'protid', how = 'right')\n",
    "display(merged_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5664ddd-aa65-40b6-b58b-e7e9b162b8a9",
   "metadata": {},
   "source": [
    "# 4. Inspect data\n",
    "\n",
    "This cell generates a plot that contains distributions of pLDDT and protein length, as well as a scatter plot comparing pLDDT and protein length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4ba503ea-715b-4a8d-941a-c06a4783670b",
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    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x450 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make figure\n",
    "plt.figure(figsize = (8, 4.5))\n",
    "\n",
    "# create subplot grid\n",
    "plt.subplot(2, 2, 1)\n",
    "\n",
    "# plot histogram of protein lengths\n",
    "plt.hist(merged_df['Length'], bins = 20, color = 'arcadia:oat')\n",
    "plt.xlabel('Protein length (aa)')\n",
    "plt.ylabel('# of proteins')\n",
    "\n",
    "# plot histogram of pLDDT scores\n",
    "plt.subplot(2, 2, 3)\n",
    "n, bins, patches = plt.hist(merged_df['pdb_confidence'], bins = np.arange(40, 101, 2), color = 'arcadia:amber')\n",
    "\n",
    "for bn, p in zip(bins, patches):\n",
    "    if bn >= 90:\n",
    "        plt.setp(p, 'facecolor', '#4A72B0')\n",
    "    elif bn >= 70:\n",
    "        plt.setp(p, 'facecolor', apc.All['arcadia:vitalblue'])\n",
    "    elif bn >= 50:\n",
    "        plt.setp(p, 'facecolor', apc.All['arcadia:canary'])\n",
    "    else:\n",
    "        plt.setp(p, 'facecolor', apc.All['arcadia:amber'])\n",
    "plt.xlabel('AlphaFold average pLDDT')\n",
    "plt.ylabel('# of proteins')\n",
    "\n",
    "# plot scatterplot of protein length vs. pLDDT\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.scatter(x = merged_df['Length'], y = merged_df['pdb_confidence'], s = 20, alpha = 0.8, color = 'arcadia:denim')\n",
    "\n",
    "plt.xlabel('Protein length')\n",
    "plt.ylabel('AlphaFold average pLDDT')\n",
    "\n",
    "# set global parameters\n",
    "plt.suptitle('Protein length vs. average pLDDT for top-studied human proteins')\n",
    "plt.tight_layout()\n",
    "\n",
    "# save figure and show\n",
    "plt.savefig('top_studied_proteins_distribution.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aca31be7-3be0-4113-9861-e3db6bac24c6",
   "metadata": {},
   "source": [
    "## Look at top 10 longest and highest quality proteins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c3d3c2f4-74d6-42c7-9eb0-5f12c0e94f48",
   "metadata": {
    "tags": []
   },
   "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>protid</th>\n",
       "      <th>protein</th>\n",
       "      <th>Length</th>\n",
       "      <th>pdb_origin</th>\n",
       "      <th>pdb_confidence</th>\n",
       "      <th>pdb_chains</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>P02751</td>\n",
       "      <td>Fibronectin</td>\n",
       "      <td>2477</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>70.332757</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Q92793</td>\n",
       "      <td>CREB-binding protein</td>\n",
       "      <td>2442</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>54.295475</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Q9UIF8</td>\n",
       "      <td>Bromodomain adjacent to zinc finger domain pro...</td>\n",
       "      <td>2168</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>55.871608</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>P29375</td>\n",
       "      <td>Lysine-specific histone demethylase 1A</td>\n",
       "      <td>1690</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>70.565702</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>P01024</td>\n",
       "      <td>Complement C3</td>\n",
       "      <td>1663</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>79.812489</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Q9UM73</td>\n",
       "      <td>ALK tyrosine kinase receptor</td>\n",
       "      <td>1620</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>67.276466</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Q15648</td>\n",
       "      <td>Mediator of RNA polymerase II transcription su...</td>\n",
       "      <td>1581</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>51.295623</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Q15596</td>\n",
       "      <td>Nuclear receptor coactivator 2</td>\n",
       "      <td>1464</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>48.459262</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Q15788</td>\n",
       "      <td>Nuclear receptor coactivator 1</td>\n",
       "      <td>1441</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>47.389764</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>P29475</td>\n",
       "      <td>Nitric oxide synthase, brain</td>\n",
       "      <td>1434</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>80.829277</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     protid                                            protein  Length   \n",
       "126  P02751                                        Fibronectin    2477  \\\n",
       "118  Q92793                               CREB-binding protein    2442   \n",
       "28   Q9UIF8  Bromodomain adjacent to zinc finger domain pro...    2168   \n",
       "157  P29375             Lysine-specific histone demethylase 1A    1690   \n",
       "29   P01024                                      Complement C3    1663   \n",
       "94   Q9UM73                       ALK tyrosine kinase receptor    1620   \n",
       "170  Q15648  Mediator of RNA polymerase II transcription su...    1581   \n",
       "65   Q15596                     Nuclear receptor coactivator 2    1464   \n",
       "111  Q15788                     Nuclear receptor coactivator 1    1441   \n",
       "116  P29475                       Nitric oxide synthase, brain    1434   \n",
       "\n",
       "    pdb_origin  pdb_confidence pdb_chains  \n",
       "126  AlphaFold       70.332757        [A]  \n",
       "118  AlphaFold       54.295475        [A]  \n",
       "28   AlphaFold       55.871608        [A]  \n",
       "157  AlphaFold       70.565702        [A]  \n",
       "29   AlphaFold       79.812489        [A]  \n",
       "94   AlphaFold       67.276466        [A]  \n",
       "170  AlphaFold       51.295623        [A]  \n",
       "65   AlphaFold       48.459262        [A]  \n",
       "111  AlphaFold       47.389764        [A]  \n",
       "116  AlphaFold       80.829277        [A]  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.sort_values('Length', ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e94b0f93-ea5c-4772-81d8-cbc10013a170",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>protid</th>\n",
       "      <th>protein</th>\n",
       "      <th>Length</th>\n",
       "      <th>pdb_origin</th>\n",
       "      <th>pdb_confidence</th>\n",
       "      <th>pdb_chains</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P14174</td>\n",
       "      <td>Macrophage migration inhibitory factor</td>\n",
       "      <td>115</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>98.594309</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Q99497</td>\n",
       "      <td>Parkinson disease protein 7</td>\n",
       "      <td>189</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>98.419174</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>P15121</td>\n",
       "      <td>Aldo-keto reductase family 1 member B1</td>\n",
       "      <td>316</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>98.309818</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>P16083</td>\n",
       "      <td>Ribosyldihydronicotinamide dehydrogenase [quin...</td>\n",
       "      <td>231</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>98.174401</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>P09211</td>\n",
       "      <td>Glutathione S-transferase P</td>\n",
       "      <td>210</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>98.070115</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>P69905</td>\n",
       "      <td>Hemoglobin subunit alpha</td>\n",
       "      <td>142</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>98.029907</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>P62937</td>\n",
       "      <td>Peptidyl-prolyl cis-trans isomerase A</td>\n",
       "      <td>165</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>98.024976</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>P00441</td>\n",
       "      <td>Superoxide dismutase [cu-Zn]</td>\n",
       "      <td>154</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>97.821216</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>P62873</td>\n",
       "      <td>Guanine nucleotide-binding protein G(I)/G(S)/G...</td>\n",
       "      <td>340</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>97.213644</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>P68871</td>\n",
       "      <td>Hemoglobin subunit beta</td>\n",
       "      <td>147</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>96.969726</td>\n",
       "      <td>[A]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     protid                                            protein  Length   \n",
       "3    P14174             Macrophage migration inhibitory factor     115  \\\n",
       "18   Q99497                        Parkinson disease protein 7     189   \n",
       "12   P15121             Aldo-keto reductase family 1 member B1     316   \n",
       "113  P16083  Ribosyldihydronicotinamide dehydrogenase [quin...     231   \n",
       "83   P09211                        Glutathione S-transferase P     210   \n",
       "106  P69905                           Hemoglobin subunit alpha     142   \n",
       "88   P62937              Peptidyl-prolyl cis-trans isomerase A     165   \n",
       "178  P00441                       Superoxide dismutase [cu-Zn]     154   \n",
       "81   P62873  Guanine nucleotide-binding protein G(I)/G(S)/G...     340   \n",
       "38   P68871                            Hemoglobin subunit beta     147   \n",
       "\n",
       "    pdb_origin  pdb_confidence pdb_chains  \n",
       "3    AlphaFold       98.594309        [A]  \n",
       "18   AlphaFold       98.419174        [A]  \n",
       "12   AlphaFold       98.309818        [A]  \n",
       "113  AlphaFold       98.174401        [A]  \n",
       "83   AlphaFold       98.070115        [A]  \n",
       "106  AlphaFold       98.029907        [A]  \n",
       "88   AlphaFold       98.024976        [A]  \n",
       "178  AlphaFold       97.821216        [A]  \n",
       "81   AlphaFold       97.213644        [A]  \n",
       "38   AlphaFold       96.969726        [A]  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.sort_values('pdb_confidence', ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47ad4000-ecb3-4f13-8afb-2abe0dc9a95c",
   "metadata": {},
   "source": [
    "# 5. Sample proteins representatively based on bins of length and pdb confidence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ba7a48c8-7230-48e5-8c6f-0dd9cfac1b54",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>protid</th>\n",
       "      <th>protein</th>\n",
       "      <th>Length</th>\n",
       "      <th>pdb_origin</th>\n",
       "      <th>pdb_confidence</th>\n",
       "      <th>pdb_chains</th>\n",
       "      <th>Length_bin</th>\n",
       "      <th>pdb_confidence_bin</th>\n",
       "      <th>combined</th>\n",
       "      <th>combined_weight</th>\n",
       "      <th>sampled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P08603</td>\n",
       "      <td>Complement factor H</td>\n",
       "      <td>1231</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>79.112146</td>\n",
       "      <td>[A]</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>(10, 3)</td>\n",
       "      <td>0.032086</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P40337</td>\n",
       "      <td>von Hippel–Lindau disease tumor suppressor</td>\n",
       "      <td>213</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>85.590887</td>\n",
       "      <td>[A]</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>(3, 5)</td>\n",
       "      <td>0.005348</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P28074</td>\n",
       "      <td>Proteasome subunit beta type-5</td>\n",
       "      <td>263</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>83.014641</td>\n",
       "      <td>[A]</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>(4, 4)</td>\n",
       "      <td>0.010695</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P14174</td>\n",
       "      <td>Macrophage migration inhibitory factor</td>\n",
       "      <td>115</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>98.594309</td>\n",
       "      <td>[A]</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>(1, 10)</td>\n",
       "      <td>0.026738</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P62753</td>\n",
       "      <td>40S ribosomal protein S6</td>\n",
       "      <td>249</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>94.005390</td>\n",
       "      <td>[A]</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>(3, 9)</td>\n",
       "      <td>0.021390</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>P18031</td>\n",
       "      <td>Tyrosine-protein phosphatase non-receptor type 1</td>\n",
       "      <td>435</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>82.030609</td>\n",
       "      <td>[A]</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>(6, 3)</td>\n",
       "      <td>0.026738</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>P29373</td>\n",
       "      <td>Cellular retinoic acid-binding protein 2</td>\n",
       "      <td>138</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>96.710100</td>\n",
       "      <td>[A]</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>(1, 10)</td>\n",
       "      <td>0.026738</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>P0DP23</td>\n",
       "      <td>Calmodulin-1</td>\n",
       "      <td>149</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>85.087947</td>\n",
       "      <td>[A]</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>(2, 5)</td>\n",
       "      <td>0.005348</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>P25788</td>\n",
       "      <td>Proteasome subunit alpha type-3</td>\n",
       "      <td>255</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>94.693955</td>\n",
       "      <td>[A]</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>(3, 9)</td>\n",
       "      <td>0.021390</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>P04746</td>\n",
       "      <td>Pancreatic alpha-amylase</td>\n",
       "      <td>511</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>96.663322</td>\n",
       "      <td>[A]</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>(7, 10)</td>\n",
       "      <td>0.005348</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>187 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     protid                                           protein  Length   \n",
       "0    P08603                               Complement factor H    1231  \\\n",
       "1    P40337        von Hippel–Lindau disease tumor suppressor     213   \n",
       "2    P28074                    Proteasome subunit beta type-5     263   \n",
       "3    P14174            Macrophage migration inhibitory factor     115   \n",
       "4    P62753                          40S ribosomal protein S6     249   \n",
       "..      ...                                               ...     ...   \n",
       "182  P18031  Tyrosine-protein phosphatase non-receptor type 1     435   \n",
       "183  P29373          Cellular retinoic acid-binding protein 2     138   \n",
       "184  P0DP23                                      Calmodulin-1     149   \n",
       "185  P25788                   Proteasome subunit alpha type-3     255   \n",
       "186  P04746                          Pancreatic alpha-amylase     511   \n",
       "\n",
       "    pdb_origin  pdb_confidence pdb_chains Length_bin pdb_confidence_bin   \n",
       "0    AlphaFold       79.112146        [A]         10                  3  \\\n",
       "1    AlphaFold       85.590887        [A]          3                  5   \n",
       "2    AlphaFold       83.014641        [A]          4                  4   \n",
       "3    AlphaFold       98.594309        [A]          1                 10   \n",
       "4    AlphaFold       94.005390        [A]          3                  9   \n",
       "..         ...             ...        ...        ...                ...   \n",
       "182  AlphaFold       82.030609        [A]          6                  3   \n",
       "183  AlphaFold       96.710100        [A]          1                 10   \n",
       "184  AlphaFold       85.087947        [A]          2                  5   \n",
       "185  AlphaFold       94.693955        [A]          3                  9   \n",
       "186  AlphaFold       96.663322        [A]          7                 10   \n",
       "\n",
       "    combined  combined_weight sampled  \n",
       "0    (10, 3)         0.032086     yes  \n",
       "1     (3, 5)         0.005348     yes  \n",
       "2     (4, 4)         0.010695     yes  \n",
       "3    (1, 10)         0.026738     yes  \n",
       "4     (3, 9)         0.021390      no  \n",
       "..       ...              ...     ...  \n",
       "182   (6, 3)         0.026738      no  \n",
       "183  (1, 10)         0.026738      no  \n",
       "184   (2, 5)         0.005348      no  \n",
       "185   (3, 9)         0.021390      no  \n",
       "186  (7, 10)         0.005348      no  \n",
       "\n",
       "[187 rows x 11 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>protid</th>\n",
       "      <th>protein</th>\n",
       "      <th>Length</th>\n",
       "      <th>pdb_origin</th>\n",
       "      <th>pdb_confidence</th>\n",
       "      <th>pdb_chains</th>\n",
       "      <th>Length_bin</th>\n",
       "      <th>pdb_confidence_bin</th>\n",
       "      <th>combined</th>\n",
       "      <th>combined_weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Q9UM73</td>\n",
       "      <td>ALK tyrosine kinase receptor</td>\n",
       "      <td>1620</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>67.276466</td>\n",
       "      <td>[A]</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>(10, 1)</td>\n",
       "      <td>0.037433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>P98170</td>\n",
       "      <td>E3 ubiquitin-protein ligase XIAP</td>\n",
       "      <td>497</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>74.874344</td>\n",
       "      <td>[A]</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>(7, 2)</td>\n",
       "      <td>0.010695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>P49841</td>\n",
       "      <td>Glycogen synthase kinase-3 beta</td>\n",
       "      <td>420</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>89.045659</td>\n",
       "      <td>[A]</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>(5, 6)</td>\n",
       "      <td>0.021390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Q93009</td>\n",
       "      <td>Ubiquitin carboxyl-terminal hydrolase 7</td>\n",
       "      <td>1102</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>86.436554</td>\n",
       "      <td>[A]</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>(9, 5)</td>\n",
       "      <td>0.010695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>O75530</td>\n",
       "      <td>Polycomb protein EED</td>\n",
       "      <td>441</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>87.337825</td>\n",
       "      <td>[A]</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>(6, 5)</td>\n",
       "      <td>0.016043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>P53779</td>\n",
       "      <td>Mitogen-activated protein kinase 10</td>\n",
       "      <td>464</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>80.358883</td>\n",
       "      <td>[A]</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>(6, 3)</td>\n",
       "      <td>0.026738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Q13451</td>\n",
       "      <td>Peptidyl-prolyl cis-trans isomerase FKBP5</td>\n",
       "      <td>457</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>92.878441</td>\n",
       "      <td>[A]</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>(6, 8)</td>\n",
       "      <td>0.010695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>P00441</td>\n",
       "      <td>Superoxide dismutase [cu-Zn]</td>\n",
       "      <td>154</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>97.821216</td>\n",
       "      <td>[A]</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>(2, 10)</td>\n",
       "      <td>0.037433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Q15596</td>\n",
       "      <td>Nuclear receptor coactivator 2</td>\n",
       "      <td>1464</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>48.459262</td>\n",
       "      <td>[A]</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>(10, 1)</td>\n",
       "      <td>0.037433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>P19793</td>\n",
       "      <td>Retinoic acid receptor RXR-alpha</td>\n",
       "      <td>462</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>76.512264</td>\n",
       "      <td>[A]</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>(6, 3)</td>\n",
       "      <td>0.026738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P14174</td>\n",
       "      <td>Macrophage migration inhibitory factor</td>\n",
       "      <td>115</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>98.594309</td>\n",
       "      <td>[A]</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>(1, 10)</td>\n",
       "      <td>0.026738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>P68871</td>\n",
       "      <td>Hemoglobin subunit beta</td>\n",
       "      <td>147</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>96.969726</td>\n",
       "      <td>[A]</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>(2, 10)</td>\n",
       "      <td>0.037433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P40337</td>\n",
       "      <td>von Hippel–Lindau disease tumor suppressor</td>\n",
       "      <td>213</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>85.590887</td>\n",
       "      <td>[A]</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>(3, 5)</td>\n",
       "      <td>0.005348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P08603</td>\n",
       "      <td>Complement factor H</td>\n",
       "      <td>1231</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>79.112146</td>\n",
       "      <td>[A]</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>(10, 3)</td>\n",
       "      <td>0.032086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>P00374</td>\n",
       "      <td>Dihydrofolate reductase</td>\n",
       "      <td>187</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>96.339755</td>\n",
       "      <td>[A]</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>(2, 10)</td>\n",
       "      <td>0.037433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>P01112</td>\n",
       "      <td>GTPase HRas</td>\n",
       "      <td>189</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>92.561087</td>\n",
       "      <td>[A]</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>(2, 8)</td>\n",
       "      <td>0.021390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Q99497</td>\n",
       "      <td>Parkinson disease protein 7</td>\n",
       "      <td>189</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>98.419174</td>\n",
       "      <td>[A]</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>(2, 10)</td>\n",
       "      <td>0.037433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>P62937</td>\n",
       "      <td>Peptidyl-prolyl cis-trans isomerase A</td>\n",
       "      <td>165</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>98.024976</td>\n",
       "      <td>[A]</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>(2, 10)</td>\n",
       "      <td>0.037433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>P50120</td>\n",
       "      <td>Retinol-binding protein 2</td>\n",
       "      <td>134</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>96.454094</td>\n",
       "      <td>[A]</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>(1, 10)</td>\n",
       "      <td>0.026738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>P04439</td>\n",
       "      <td>HLA class I histocompatibility antigen, A alph...</td>\n",
       "      <td>365</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>88.274294</td>\n",
       "      <td>[A]</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>(5, 6)</td>\n",
       "      <td>0.021390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>P01834</td>\n",
       "      <td>Immunoglobulin kappa constant</td>\n",
       "      <td>107</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>96.852177</td>\n",
       "      <td>[A]</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>(1, 10)</td>\n",
       "      <td>0.026738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>P43235</td>\n",
       "      <td>Cathepsin K</td>\n",
       "      <td>329</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>95.094008</td>\n",
       "      <td>[A]</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>(4, 9)</td>\n",
       "      <td>0.026738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>P27986</td>\n",
       "      <td>Phosphatidylinositol 3-kinase regulatory subun...</td>\n",
       "      <td>724</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>83.787419</td>\n",
       "      <td>[A]</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>(8, 4)</td>\n",
       "      <td>0.021390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P28074</td>\n",
       "      <td>Proteasome subunit beta type-5</td>\n",
       "      <td>263</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>83.014641</td>\n",
       "      <td>[A]</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>(4, 4)</td>\n",
       "      <td>0.010695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Q96RI1</td>\n",
       "      <td>Bile acid receptor</td>\n",
       "      <td>486</td>\n",
       "      <td>AlphaFold</td>\n",
       "      <td>69.765294</td>\n",
       "      <td>[A]</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>(6, 2)</td>\n",
       "      <td>0.010695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     protid                                            protein  Length   \n",
       "94   Q9UM73                       ALK tyrosine kinase receptor    1620  \\\n",
       "17   P98170                   E3 ubiquitin-protein ligase XIAP     497   \n",
       "26   P49841                    Glycogen synthase kinase-3 beta     420   \n",
       "19   Q93009            Ubiquitin carboxyl-terminal hydrolase 7    1102   \n",
       "96   O75530                               Polycomb protein EED     441   \n",
       "152  P53779                Mitogen-activated protein kinase 10     464   \n",
       "56   Q13451          Peptidyl-prolyl cis-trans isomerase FKBP5     457   \n",
       "178  P00441                       Superoxide dismutase [cu-Zn]     154   \n",
       "65   Q15596                     Nuclear receptor coactivator 2    1464   \n",
       "50   P19793                   Retinoic acid receptor RXR-alpha     462   \n",
       "3    P14174             Macrophage migration inhibitory factor     115   \n",
       "38   P68871                            Hemoglobin subunit beta     147   \n",
       "1    P40337         von Hippel–Lindau disease tumor suppressor     213   \n",
       "0    P08603                                Complement factor H    1231   \n",
       "44   P00374                            Dihydrofolate reductase     187   \n",
       "120  P01112                                        GTPase HRas     189   \n",
       "18   Q99497                        Parkinson disease protein 7     189   \n",
       "88   P62937              Peptidyl-prolyl cis-trans isomerase A     165   \n",
       "36   P50120                          Retinol-binding protein 2     134   \n",
       "35   P04439  HLA class I histocompatibility antigen, A alph...     365   \n",
       "143  P01834                      Immunoglobulin kappa constant     107   \n",
       "165  P43235                                        Cathepsin K     329   \n",
       "138  P27986  Phosphatidylinositol 3-kinase regulatory subun...     724   \n",
       "2    P28074                     Proteasome subunit beta type-5     263   \n",
       "173  Q96RI1                                 Bile acid receptor     486   \n",
       "\n",
       "    pdb_origin  pdb_confidence pdb_chains Length_bin pdb_confidence_bin   \n",
       "94   AlphaFold       67.276466        [A]         10                  1  \\\n",
       "17   AlphaFold       74.874344        [A]          7                  2   \n",
       "26   AlphaFold       89.045659        [A]          5                  6   \n",
       "19   AlphaFold       86.436554        [A]          9                  5   \n",
       "96   AlphaFold       87.337825        [A]          6                  5   \n",
       "152  AlphaFold       80.358883        [A]          6                  3   \n",
       "56   AlphaFold       92.878441        [A]          6                  8   \n",
       "178  AlphaFold       97.821216        [A]          2                 10   \n",
       "65   AlphaFold       48.459262        [A]         10                  1   \n",
       "50   AlphaFold       76.512264        [A]          6                  3   \n",
       "3    AlphaFold       98.594309        [A]          1                 10   \n",
       "38   AlphaFold       96.969726        [A]          2                 10   \n",
       "1    AlphaFold       85.590887        [A]          3                  5   \n",
       "0    AlphaFold       79.112146        [A]         10                  3   \n",
       "44   AlphaFold       96.339755        [A]          2                 10   \n",
       "120  AlphaFold       92.561087        [A]          2                  8   \n",
       "18   AlphaFold       98.419174        [A]          2                 10   \n",
       "88   AlphaFold       98.024976        [A]          2                 10   \n",
       "36   AlphaFold       96.454094        [A]          1                 10   \n",
       "35   AlphaFold       88.274294        [A]          5                  6   \n",
       "143  AlphaFold       96.852177        [A]          1                 10   \n",
       "165  AlphaFold       95.094008        [A]          4                  9   \n",
       "138  AlphaFold       83.787419        [A]          8                  4   \n",
       "2    AlphaFold       83.014641        [A]          4                  4   \n",
       "173  AlphaFold       69.765294        [A]          6                  2   \n",
       "\n",
       "    combined  combined_weight  \n",
       "94   (10, 1)         0.037433  \n",
       "17    (7, 2)         0.010695  \n",
       "26    (5, 6)         0.021390  \n",
       "19    (9, 5)         0.010695  \n",
       "96    (6, 5)         0.016043  \n",
       "152   (6, 3)         0.026738  \n",
       "56    (6, 8)         0.010695  \n",
       "178  (2, 10)         0.037433  \n",
       "65   (10, 1)         0.037433  \n",
       "50    (6, 3)         0.026738  \n",
       "3    (1, 10)         0.026738  \n",
       "38   (2, 10)         0.037433  \n",
       "1     (3, 5)         0.005348  \n",
       "0    (10, 3)         0.032086  \n",
       "44   (2, 10)         0.037433  \n",
       "120   (2, 8)         0.021390  \n",
       "18   (2, 10)         0.037433  \n",
       "88   (2, 10)         0.037433  \n",
       "36   (1, 10)         0.026738  \n",
       "35    (5, 6)         0.021390  \n",
       "143  (1, 10)         0.026738  \n",
       "165   (4, 9)         0.026738  \n",
       "138   (8, 4)         0.021390  \n",
       "2     (4, 4)         0.010695  \n",
       "173   (6, 2)         0.010695  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# assign each protein to an equally-sized bin for Length or pdb_confidence\n",
    "merged_df['Length_bin'] = pd.qcut(merged_df['Length'], 10, [i for i in range(1, 11)])\n",
    "merged_df['pdb_confidence_bin'] = pd.qcut(merged_df['pdb_confidence'], 10, [i for i in range(1, 11)])\n",
    "\n",
    "# generate combined binning for value counting\n",
    "# see: https://stackoverflow.com/questions/64967847/pandas-representative-sampling-across-multiple-columns\n",
    "merged_df['combined'] = list(zip(merged_df['Length_bin'], merged_df['pdb_confidence_bin']))\n",
    "\n",
    "# generate value counts for zipped weights, normalizing to get weight\n",
    "combined_weight = merged_df['combined'].value_counts(normalize = True)\n",
    "# map weights back to combined value\n",
    "merged_df['combined_weight'] = merged_df['combined'].apply(lambda x: combined_weight[x])\n",
    "\n",
    "# sample using weights\n",
    "merged_df_sample = merged_df.sample(25, weights=merged_df['combined_weight'], random_state = 20230821)\n",
    "\n",
    "# indicate in original data whether or not a protein was sampled\n",
    "merged_df['sampled'] = merged_df['protid'].apply(lambda x: 'yes' if x in merged_df_sample['protid'].values else 'no')\n",
    "\n",
    "display(merged_df)\n",
    "display(merged_df_sample)\n",
    "\n",
    "# output sampled proteins to a TSV\n",
    "merged_df.to_csv('most_studied_human_proteins_analysis.tsv', sep = '\\t')\n",
    "merged_df_sample.to_csv('sampled_proteins.tsv', sep = '\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37064dce-7547-4970-b047-a08d6a0ca0ab",
   "metadata": {},
   "source": [
    "# 6. Plot distribution of proteins that were sampled as interactive HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a024741f-6c64-4ec4-a7bf-c9014cb5259e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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        "width": 600,
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        },
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         "anchor": "y2",
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   ],
   "source": [
    "fig = px.scatter(\n",
    "    merged_df, \n",
    "    x = 'Length', \n",
    "    y = 'pdb_confidence', \n",
    "    marginal_x = 'histogram',\n",
    "    marginal_y = 'histogram',\n",
    "    color = 'sampled', \n",
    "    hover_name = 'protid', \n",
    "    hover_data = ['protein', 'Length', 'pdb_confidence'], \n",
    "    opacity = 0.8, \n",
    "    category_orders = {'sampled': ['no', 'yes']},\n",
    "    color_discrete_map = {'yes': apc.All['arcadia:dragon'], 'no': apc.All['arcadia:brightgrey']}\n",
    ")\n",
    "\n",
    "#fig.update_traces(marker_color = apc.All['arcadia:aegean'])\n",
    "fig.update_layout(\n",
    "    plot_bgcolor = apc.All['arcadia:paper'],\n",
    "    width = 600,\n",
    "    height = 570,\n",
    "    font_family = 'Arial'\n",
    ")\n",
    "fig.show()\n",
    "\n",
    "fig.write_html('sampled_proteins.html')"
   ]
  }
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