gget_elm_IUPred3_tutorial.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyN+cdMqO1RID6FRoZTY09Rg",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pachterlab/gget_examples/blob/main/gget_elm_IUPred3_tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Filter results returned by `gget.elm` using the [IUPred3](https://iupred.elte.hu/) API\n",
        "This tutorial shows how results returned by `gget.elm` can be filtered based on IUPred scores to identify disordered protein regions."
      ],
      "metadata": {
        "id": "3YzF_EuZFP-7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Install and import software:"
      ],
      "metadata": {
        "id": "gz3JlZ08DFp1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -qU gget\n",
        "\n",
        "import gget\n",
        "import requests\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%config InlineBackend.figure_format='retina'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zgQZ-lfQAqFc",
        "outputId": "6e1698d9-a7be-4226-ed51-bb87e6b9b53b"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m25.2/25.2 MB\u001b[0m \u001b[31m14.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for gget (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Download the ELM database for use with `gget.elm`:"
      ],
      "metadata": {
        "id": "dT9qUfbwDKEF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gget.setup(\"elm\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zv00bWIMBJW0",
        "outputId": "36178444-7dd7-4f83-9ac4-3492628ee559"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r  0 2123k    0 14073    0     0  36970      0  0:00:58 --:--:--  0:00:58 36937\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r 38 36919   38 14085    0     0  35831      0  0:00:01 --:--:--  0:00:01 35839\r100 36919  100 36919    0     0  66898      0 --:--:-- --:--:-- --:--:-- 66882\n",
            "\r100 87336  100 87336    0     0   102k      0 --:--:-- --:--:-- --:--:--  102k\n",
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r  7  914k    7 73461    0     0   132k      0  0:00:06 --:--:--  0:00:06  132k\r 18 2123k   18  388k    0     0   266k      0  0:00:07  0:00:01  0:00:06  266k\r 77  914k   77  710k    0     0   463k      0  0:00:01  0:00:01 --:--:--  463k\r 70 2123k   70 1499k    0     0   612k      0  0:00:03  0:00:02  0:00:01  612k\r100  914k  100  914k    0     0   533k      0  0:00:01  0:00:01 --:--:--  533k\n",
            "\r100 2123k  100 2123k    0     0   778k      0  0:00:02  0:00:02 --:--:--  778k\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Get IUPred scores for a UniProt Accession:"
      ],
      "metadata": {
        "id": "NT7zRQ-vDL_L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the UniProt Accession\n",
        "uniprot_acc = \"P04637\"\n",
        "\n",
        "# Define IUPred type (\"long\" or \"short\")\n",
        "disorder = \"long\""
      ],
      "metadata": {
        "id": "iQTSVzowA7Qr"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zz6QxXp3AkCe",
        "outputId": "1fe904f3-0481-4231-bcb3-af20e00dd6fa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "200"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# Get IUPred2 scores using the IUPred3 API\n",
        "url = f\"http://iupred3.elte.hu/iupred3/{disorder}/{uniprot_acc}.json\"\n",
        "\n",
        "r = requests.get(url)\n",
        "r.status_code"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plot the IUPred2 scores (residues with a predicted score above 0.5 are considered disordered, while residues with lower scores considered to be ordered):"
      ],
      "metadata": {
        "id": "FKzVJuK4FAY1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(17,5))\n",
        "fontsize = 14\n",
        "\n",
        "# Plot IUPred2 scores returned by API\n",
        "scores = r.json()[\"iupred2\"]\n",
        "ax.plot(np.arange(len(scores)+1)[1:], scores, lw=2)\n",
        "\n",
        "# Set x and y axis labels\n",
        "ax.set_xlabel(\"Residue\", fontsize=fontsize)\n",
        "ax.set_ylabel(\"IUPred2 score\", fontsize=fontsize)\n",
        "\n",
        "ax.axhline(0.5, color=\"black\", lw=1, ls=\"--\", zorder=-1)\n",
        "ax.text(0, 0.52, \" ↑ Disordered\", fontsize=fontsize-2)\n",
        "ax.text(0, 0.45, \" ↓ Ordered\", fontsize=fontsize-2)\n",
        "\n",
        "ax.set_ylim(0,1)\n",
        "ax.margins(x=0)\n",
        "ax.tick_params(axis=\"both\", labelsize=fontsize)\n",
        "ax.grid(True, which=\"both\", color=\"lightgray\", ls=\"--\", lw=1)\n",
        "ax.set_axisbelow(True)\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "C7hS8h9MA2gd",
        "outputId": "7b362e16-4e84-4885-8956-12f56e5504fd"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1700x500 with 1 Axes>"
            ],
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jhnoBAAAAAAAAAAAAIMcM9QIAAAAAAAAAAABAjhnqBQAAAAAAAAAAAIAcM9QLAAAAAAAAAAAAADlmqBcAAAAAAAAAAAAAcsxQLwAAAAAAAAAAAADkmKFeAAAAAAAAAAAAAMgxQ70AAAAAAAAAAAAAkGOGegEAAAAAAAAAAAAgxwz1AgAAAAAAAAAAAECOGeoFAAAAAAAAAAAAgBwz1AsAAAAAAAAAAAAAOWaoFwAAAAAAAAAAAAByzFAvAAAAAAAAAAAAAOSYoV4AAAAAAAAAAAAAyDFDvQAAAAAAAAAAAACQY4Z6AQAAAAAAAAAAACDHDPUCAAAAAAAAAAAAQI4Z6gUAAAAAAAAAAACAHDPUCwAAAAAAAAAAAAA59v8By36qIeSwXDwAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "image/png": {
              "width": 1402,
              "height": 465
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Find putative ELMs for the UniProt Accession using `gget elm`:"
      ],
      "metadata": {
        "id": "8KTLJOoEDVlS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ortho, regex = gget.elm(uniprot_acc, uniprot=True)"
      ],
      "metadata": {
        "id": "nD7qCDv1BXS5"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Filter the ELMs returned by `gget elm` based on the IUPred3 scores. In this example, we will filter the regex data frame for motifs in ordered protein regions by only keeping motifs for which each residue has a IUPred scored > 0.5."
      ],
      "metadata": {
        "id": "9MUR6swcDXdh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get amino acid positions of all disordered residues (thresholded as IUPred score > 0.5)\n",
        "disordered = np.arange(len(scores)+1)[1:][np.array(scores) > 0.5]\n",
        "\n",
        "# Get amino acid positions of all ordered residues (thresholded as IUPred score < 0.5)\n",
        "ordered = np.arange(len(scores)+1)[1:][np.array(scores) < 0.5]"
      ],
      "metadata": {
        "id": "0N3IipALN20A"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def is_subset(arr1, arr2):\n",
        "  \"\"\"\n",
        "  Function to check if an array (arr2) is a subset of anoter array (arr1).\n",
        "\n",
        "  Rerturns True is arr2 is a subset of arr1.\n",
        "  \"\"\"\n",
        "  m = len(arr1)\n",
        "  n = len(arr2)\n",
        "  s = set()\n",
        "  for i in range(m):\n",
        "      s.add(arr1[i])\n",
        "\n",
        "  p = len(s)\n",
        "  for i in range(n):\n",
        "      s.add(arr2[i])\n",
        "\n",
        "  if (len(s) == p):\n",
        "    return True\n",
        "  else:\n",
        "    return False"
      ],
      "metadata": {
        "id": "LSwvdRAjV1kN"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Categorize each motif based on the IUPred scores of its residues\n",
        "\n",
        "iupred = []\n",
        "for index, row in regex.iterrows():\n",
        "  # Get the start and end amino acid positions of the motif\n",
        "  start = row['motif_start_in_query']\n",
        "  end = row['motif_end_in_query']\n",
        "\n",
        "  # Check if all positions covered by the motif are within ordered or disordered IUPred scores thresholds\n",
        "  if is_subset(ordered, list(range(start, end+1))):\n",
        "    iupred.append(\"ordered\")\n",
        "\n",
        "  elif is_subset(disordered, list(range(start, end+1))):\n",
        "    iupred.append(\"disordered\")\n",
        "\n",
        "  else:\n",
        "    iupred.append(\"inbetween\")\n",
        "\n",
        "# Add IUPred categorization to the regex dataframe\n",
        "regex[\"IUPred\"] = iupred"
      ],
      "metadata": {
        "id": "jEXi-utBDXsV"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Only keey motifs that fall into ordered protein regions\n",
        "regex[regex[\"IUPred\"] == \"ordered\"]"
      ],
      "metadata": {
        "id": "pHmWif2iU926",
        "outputId": "f7ef69f3-0aab-4d92-d3c7-0f61ae8fbc9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 930
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Instance_accession       ELMIdentifier  \\\n",
              "0           ELME000321  CLV_C14_Caspase3-7   \n",
              "3           ELME000102       CLV_NRD_NRD_1   \n",
              "4           ELME000102       CLV_NRD_NRD_1   \n",
              "8           ELME000108     CLV_PCSK_KEX2_1   \n",
              "9           ELME000108     CLV_PCSK_KEX2_1   \n",
              "..                 ...                 ...   \n",
              "184         ELME000012      TRG_ER_diArg_1   \n",
              "185         ELME000012      TRG_ER_diArg_1   \n",
              "186         ELME000012      TRG_ER_diArg_1   \n",
              "187         ELME000193      TRG_NES_CRM1_1   \n",
              "193         ELME000462  TRG_Pf-PMV_PEXEL_1   \n",
              "\n",
              "                          FunctionalSiteName ELMType  \\\n",
              "0                     Caspase cleavage motif     CLV   \n",
              "3                          NRD cleavage site     CLV   \n",
              "4                          NRD cleavage site     CLV   \n",
              "8                         PCSK cleavage site     CLV   \n",
              "9                         PCSK cleavage site     CLV   \n",
              "..                                       ...     ...   \n",
              "184  di Arginine retention/retrieving signal     TRG   \n",
              "185  di Arginine retention/retrieving signal     TRG   \n",
              "186  di Arginine retention/retrieving signal     TRG   \n",
              "187               NES Nuclear Export Signals     TRG   \n",
              "193                Apicomplexan export motif     TRG   \n",
              "\n",
              "                                           Description InteractionDomainId  \\\n",
              "0               Caspase-3 and Caspase-7 cleavage site.             PF00656   \n",
              "3    N-Arg dibasic convertase (NRD/Nardilysin) clea...             PF00675   \n",
              "4    N-Arg dibasic convertase (NRD/Nardilysin) clea...             PF00675   \n",
              "8    Yeast kexin 2 cleavage site (K-R-|-X or R-R-|-X).             PF00082   \n",
              "9    Yeast kexin 2 cleavage site (K-R-|-X or R-R-|-X).             PF00082   \n",
              "..                                                 ...                 ...   \n",
              "184  The di-Arg ER retention motif is defined by tw...             PF00400   \n",
              "185  The di-Arg ER retention motif is defined by tw...             PF00400   \n",
              "186  The di-Arg ER retention motif is defined by tw...             PF00400   \n",
              "187  Many proteins re-exported from the nucleus con...             PF08389   \n",
              "193  Plasmodium Export Element, PEXEL, is a traffic...             PF00026   \n",
              "\n",
              "    InteractionDomainDescription              InteractionDomainName  \\\n",
              "0                  Peptidase_C14                     Caspase domain   \n",
              "3                  Peptidase_M16  Insulinase (Peptidase family M16)   \n",
              "4                  Peptidase_M16  Insulinase (Peptidase family M16)   \n",
              "8                   Peptidase_S8                   Subtilase family   \n",
              "9                   Peptidase_S8                   Subtilase family   \n",
              "..                           ...                                ...   \n",
              "184                         WD40           WD domain, G-beta repeat   \n",
              "185                         WD40           WD domain, G-beta repeat   \n",
              "186                         WD40           WD domain, G-beta repeat   \n",
              "187                         Xpo1            Exportin 1-like protein   \n",
              "193                          Asp       Eukaryotic aspartyl protease   \n",
              "\n",
              "                                                 Regex  \\\n",
              "0                          [DSTE][^P][^DEWHFYC]D[GSAN]   \n",
              "3                                      (.RK)|(RR[^KR])   \n",
              "4                                      (.RK)|(RR[^KR])   \n",
              "8                                               [KR]R.   \n",
              "9                                               [KR]R.   \n",
              "..                                                 ...   \n",
              "184  ([LIVMFYWPR]R[^YFWDE]{0,1}R)|(R[^YFWDE]{0,1}R[...   \n",
              "185  ([LIVMFYWPR]R[^YFWDE]{0,1}R)|(R[^YFWDE]{0,1}R[...   \n",
              "186  ([LIVMFYWPR]R[^YFWDE]{0,1}R)|(R[^YFWDE]{0,1}R[...   \n",
              "187  ([DEQ].{0,1}[LIM].{2,3}[LIVMF][^P]{2,3}[LMVF]....   \n",
              "193                (R.[LI].[EDQ])|(R.L..[EDQ])|(K.L.E)   \n",
              "\n",
              "    Instances (Matched Sequence)  motif_start_in_query  motif_end_in_query  \\\n",
              "0                          SDSDG                   183                 187   \n",
              "3                            RRC                   174                 176   \n",
              "4                            RRP                   248                 250   \n",
              "8                            RRC                   174                 176   \n",
              "9                            RRP                   248                 250   \n",
              "..                           ...                   ...                 ...   \n",
              "184                          VRR                   173                 175   \n",
              "185                          RRP                   248                 250   \n",
              "186                         IRGR                   332                 335   \n",
              "187             QHLIRVEGNLRVEYLD                   192                 207   \n",
              "193                        KLLPE                    24                  28   \n",
              "\n",
              "     InstanceLogic  #Instances  #Instances_in_PDB   IUPred  \n",
              "0    true positive          41                  0  ordered  \n",
              "3    true positive           2                  0  ordered  \n",
              "4    true positive           2                  0  ordered  \n",
              "8    true positive           1                  0  ordered  \n",
              "9    true positive           1                  0  ordered  \n",
              "..             ...         ...                ...      ...  \n",
              "184  true positive          27                  0  ordered  \n",
              "185  true positive          27                  0  ordered  \n",
              "186  true positive          27                  0  ordered  \n",
              "187  true positive          18                  3  ordered  \n",
              "193  true positive          24                  0  ordered  \n",
              "\n",
              "[76 rows x 16 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-775dc735-81aa-4254-ab8f-5f4cf2878f88\" class=\"colab-df-container\">\n",
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              "        vertical-align: middle;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Instance_accession</th>\n",
              "      <th>ELMIdentifier</th>\n",
              "      <th>FunctionalSiteName</th>\n",
              "      <th>ELMType</th>\n",
              "      <th>Description</th>\n",
              "      <th>InteractionDomainId</th>\n",
              "      <th>InteractionDomainDescription</th>\n",
              "      <th>InteractionDomainName</th>\n",
              "      <th>Regex</th>\n",
              "      <th>Instances (Matched Sequence)</th>\n",
              "      <th>motif_start_in_query</th>\n",
              "      <th>motif_end_in_query</th>\n",
              "      <th>InstanceLogic</th>\n",
              "      <th>#Instances</th>\n",
              "      <th>#Instances_in_PDB</th>\n",
              "      <th>IUPred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ELME000321</td>\n",
              "      <td>CLV_C14_Caspase3-7</td>\n",
              "      <td>Caspase cleavage motif</td>\n",
              "      <td>CLV</td>\n",
              "      <td>Caspase-3 and Caspase-7 cleavage site.</td>\n",
              "      <td>PF00656</td>\n",
              "      <td>Peptidase_C14</td>\n",
              "      <td>Caspase domain</td>\n",
              "      <td>[DSTE][^P][^DEWHFYC]D[GSAN]</td>\n",
              "      <td>SDSDG</td>\n",
              "      <td>183</td>\n",
              "      <td>187</td>\n",
              "      <td>true positive</td>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>ordered</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ELME000102</td>\n",
              "      <td>CLV_NRD_NRD_1</td>\n",
              "      <td>NRD cleavage site</td>\n",
              "      <td>CLV</td>\n",
              "      <td>N-Arg dibasic convertase (NRD/Nardilysin) clea...</td>\n",
              "      <td>PF00675</td>\n",
              "      <td>Peptidase_M16</td>\n",
              "      <td>Insulinase (Peptidase family M16)</td>\n",
              "      <td>(.RK)|(RR[^KR])</td>\n",
              "      <td>RRC</td>\n",
              "      <td>174</td>\n",
              "      <td>176</td>\n",
              "      <td>true positive</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>ordered</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ELME000102</td>\n",
              "      <td>CLV_NRD_NRD_1</td>\n",
              "      <td>NRD cleavage site</td>\n",
              "      <td>CLV</td>\n",
              "      <td>N-Arg dibasic convertase (NRD/Nardilysin) clea...</td>\n",
              "      <td>PF00675</td>\n",
              "      <td>Peptidase_M16</td>\n",
              "      <td>Insulinase (Peptidase family M16)</td>\n",
              "      <td>(.RK)|(RR[^KR])</td>\n",
              "      <td>RRP</td>\n",
              "      <td>248</td>\n",
              "      <td>250</td>\n",
              "      <td>true positive</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>ordered</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>ELME000108</td>\n",
              "      <td>CLV_PCSK_KEX2_1</td>\n",
              "      <td>PCSK cleavage site</td>\n",
              "      <td>CLV</td>\n",
              "      <td>Yeast kexin 2 cleavage site (K-R-|-X or R-R-|-X).</td>\n",
              "      <td>PF00082</td>\n",
              "      <td>Peptidase_S8</td>\n",
              "      <td>Subtilase family</td>\n",
              "      <td>[KR]R.</td>\n",
              "      <td>RRC</td>\n",
              "      <td>174</td>\n",
              "      <td>176</td>\n",
              "      <td>true positive</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>ordered</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>ELME000108</td>\n",
              "      <td>CLV_PCSK_KEX2_1</td>\n",
              "      <td>PCSK cleavage site</td>\n",
              "      <td>CLV</td>\n",
              "      <td>Yeast kexin 2 cleavage site (K-R-|-X or R-R-|-X).</td>\n",
              "      <td>PF00082</td>\n",
              "      <td>Peptidase_S8</td>\n",
              "      <td>Subtilase family</td>\n",
              "      <td>[KR]R.</td>\n",
              "      <td>RRP</td>\n",
              "      <td>248</td>\n",
              "      <td>250</td>\n",
              "      <td>true positive</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>ordered</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>184</th>\n",
              "      <td>ELME000012</td>\n",
              "      <td>TRG_ER_diArg_1</td>\n",
              "      <td>di Arginine retention/retrieving signal</td>\n",
              "      <td>TRG</td>\n",
              "      <td>The di-Arg ER retention motif is defined by tw...</td>\n",
              "      <td>PF00400</td>\n",
              "      <td>WD40</td>\n",
              "      <td>WD domain, G-beta repeat</td>\n",
              "      <td>([LIVMFYWPR]R[^YFWDE]{0,1}R)|(R[^YFWDE]{0,1}R[...</td>\n",
              "      <td>VRR</td>\n",
              "      <td>173</td>\n",
              "      <td>175</td>\n",
              "      <td>true positive</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>ordered</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>185</th>\n",
              "      <td>ELME000012</td>\n",
              "      <td>TRG_ER_diArg_1</td>\n",
              "      <td>di Arginine retention/retrieving signal</td>\n",
              "      <td>TRG</td>\n",
              "      <td>The di-Arg ER retention motif is defined by tw...</td>\n",
              "      <td>PF00400</td>\n",
              "      <td>WD40</td>\n",
              "      <td>WD domain, G-beta repeat</td>\n",
              "      <td>([LIVMFYWPR]R[^YFWDE]{0,1}R)|(R[^YFWDE]{0,1}R[...</td>\n",
              "      <td>RRP</td>\n",
              "      <td>248</td>\n",
              "      <td>250</td>\n",
              "      <td>true positive</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>ordered</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>186</th>\n",
              "      <td>ELME000012</td>\n",
              "      <td>TRG_ER_diArg_1</td>\n",
              "      <td>di Arginine retention/retrieving signal</td>\n",
              "      <td>TRG</td>\n",
              "      <td>The di-Arg ER retention motif is defined by tw...</td>\n",
              "      <td>PF00400</td>\n",
              "      <td>WD40</td>\n",
              "      <td>WD domain, G-beta repeat</td>\n",
              "      <td>([LIVMFYWPR]R[^YFWDE]{0,1}R)|(R[^YFWDE]{0,1}R[...</td>\n",
              "      <td>IRGR</td>\n",
              "      <td>332</td>\n",
              "      <td>335</td>\n",
              "      <td>true positive</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>ordered</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>187</th>\n",
              "      <td>ELME000193</td>\n",
              "      <td>TRG_NES_CRM1_1</td>\n",
              "      <td>NES Nuclear Export Signals</td>\n",
              "      <td>TRG</td>\n",
              "      <td>Many proteins re-exported from the nucleus con...</td>\n",
              "      <td>PF08389</td>\n",
              "      <td>Xpo1</td>\n",
              "      <td>Exportin 1-like protein</td>\n",
              "      <td>([DEQ].{0,1}[LIM].{2,3}[LIVMF][^P]{2,3}[LMVF]....</td>\n",
              "      <td>QHLIRVEGNLRVEYLD</td>\n",
              "      <td>192</td>\n",
              "      <td>207</td>\n",
              "      <td>true positive</td>\n",
              "      <td>18</td>\n",
              "      <td>3</td>\n",
              "      <td>ordered</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>ELME000462</td>\n",
              "      <td>TRG_Pf-PMV_PEXEL_1</td>\n",
              "      <td>Apicomplexan export motif</td>\n",
              "      <td>TRG</td>\n",
              "      <td>Plasmodium Export Element, PEXEL, is a traffic...</td>\n",
              "      <td>PF00026</td>\n",
              "      <td>Asp</td>\n",
              "      <td>Eukaryotic aspartyl protease</td>\n",
              "      <td>(R.[LI].[EDQ])|(R.L..[EDQ])|(K.L.E)</td>\n",
              "      <td>KLLPE</td>\n",
              "      <td>24</td>\n",
              "      <td>28</td>\n",
              "      <td>true positive</td>\n",
              "      <td>24</td>\n",
              "      <td>0</td>\n",
              "      <td>ordered</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>76 rows × 16 columns</p>\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8b108389-2425-4ddd-a900-af0e325f4084 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**IUPred3 references:**\n",
        "\n",
        "*Primary citations*\n",
        "\n",
        "Gábor Erdős, Mátyás Pajkos, Zsuzsanna Dosztányi\n",
        "IUPred3: prediction of protein disorder enhanced with unambiguous experimental annotation and visualization of evolutionary conservation\n",
        "Nucleic Acids Research 2021;49(W1):W297-W303.\n",
        "\n",
        "Bálint Mészáros, Gábor Erdős, Zsuzsanna Dosztányi\n",
        "IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding\n",
        "Nucleic Acids Research 2018;46(W1):W329-W337.\n",
        "\n",
        "Gábor Erdős, Zsuzsanna Dosztányi\n",
        "Analyzing Protein Disorder with IUPred2A\n",
        "Current Protocols in Bioinformatics 2020;70(1):e99\n",
        "\n",
        "*Additional citations*\n",
        "\n",
        "Zsuzsanna Dosztányi\n",
        "Prediction of protein disorder based on IUPred\n",
        "Protein Science 2017;27:331-340.\n",
        "\n",
        "Dosztányi Z, Csizmók V, Tompa P, Simon I.\n",
        "The pairwise energy content estimated from amino acid composition discriminates between folded and intrinsically unstructured proteins\n",
        "J Mol Biol. 2005;347:827-39.\n",
        "\n",
        "Mészáros B, Simon I, Dosztányi Z.\n",
        "Prediction of protein binding regions in disordered proteins\n",
        "PLoS Comput Biol. 2009;5:e1000376."
      ],
      "metadata": {
        "id": "y1iALubZIN-R"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "esUCY9ykIP_Y"
      },
      "execution_count": 10,
      "outputs": []
    }
  ]
}