Predict_SS3.ipynb

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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "qDaEDFNd9t6P"
      },
      "source": [
        "## Armin Behjati\n",
        "\n",
        "### Predict secondary structure 3 states using transformers (Q3)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P7tTzo_VjNWX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a568d97-f12f-416d-b8b6-109335ef176a"
      },
      "source": [
        "!pip install -q transformers"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 1.3MB 6.9MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.1MB 17.3MB/s \n",
            "\u001b[K     |████████████████████████████████| 890kB 27.7MB/s \n",
            "\u001b[K     |████████████████████████████████| 2.9MB 34.1MB/s \n",
            "\u001b[?25h  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DzGbyjvbjdIs"
      },
      "source": [
        "from transformers import AutoTokenizer, AutoModelForTokenClassification, TokenClassificationPipeline\n",
        "import re"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z42szJu0-HAl"
      },
      "source": [
        "### Loading the model"
      ]
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            "d68765ced59c4323aa84e96c4ef44bcc",
            "fa2c0a3acf9144fbb21c0afe64b82848",
            "8e8ed99388c1433a89b214b47cb934bf",
            "f9f4e91fd57344ef855587bd19ed8fbe",
            "d30e93b0214749028d4cc835d1ec4fe5"
          ]
        },
        "outputId": "be936468-d546-471d-b218-b512fff12d5f"
      },
      "source": [
        "pipeline = TokenClassificationPipeline(\n",
        "    model=AutoModelForTokenClassification.from_pretrained(\"Rostlab/prot_bert_bfd_ss3\"),\n",
        "    tokenizer=AutoTokenizer.from_pretrained(\"Rostlab/prot_bert_bfd_ss3\", skip_special_tokens=True),\n",
        "    device=0\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=632.0, style=ProgressStyle(description_…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "87b4d585c053417ebedea66ab38c5696",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1676061559.0, style=ProgressStyle(descr…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=81.0, style=ProgressStyle(description_w…"
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=112.0, style=ProgressStyle(description_…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "54e5c533aabb41a488915ca096822eac",
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r812U7GwnSXF"
      },
      "source": [
        "**3. Create or load sequences and map rarely occured amino acids (U,Z,O,B) to (X)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uOaEESGWkXJu"
      },
      "source": [
        "sequences_Example = [\"M G A E E E D T A I L Y P F T I S G N D R N G N F T I N F K G T P N S T N N G C I G Y S Y N G D W E K I E W E G S C D G N G N L V V E V P M S K I P A G V T S G E I Q I W W H S G D L K M T D Y K A L E H H H H H H\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OkZLVmYRkZ6k"
      },
      "source": [
        "sequences_Example = [re.sub(r\"[UZOB]\", \"X\", sequence) for sequence in sequences_Example]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z-aGAcDhnZKF"
      },
      "source": [
        "**4. Make Prediction**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "noEGrN0hkbJ6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0d45db7-c4d5-45ac-879c-4ee06e336a90"
      },
      "source": [
        "pipeline(sequences_Example)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hl6vNHbH9rh3"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}