BringYourOwnSequences-CysPressoESM.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#Run all cells and go to the end to try out your own sequences"
      ],
      "metadata": {
        "id": "H_aGyKe31r6e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Install ESM and import dependencies"
      ],
      "metadata": {
        "id": "FWGosJzFlK6C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install fair-esm"
      ],
      "metadata": {
        "id": "5qphEK-tlTvn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import esm"
      ],
      "metadata": {
        "id": "8dWrUBsLlZb1"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import esm\n",
        "\n",
        "# Load ESM-2 model\n",
        "model, alphabet = esm.pretrained.esm2_t33_650M_UR50D()\n",
        "batch_converter = alphabet.get_batch_converter()\n",
        "model.eval().cuda()\n",
        "\n",
        "def return_sequence_representation(sequences):\n",
        "  data = [(f'peptide{i}', sequence) for i,sequence in enumerate(sequences)]\n",
        "  batch_labels, batch_strs, batch_tokens = batch_converter(data)\n",
        "  batch_lens = (batch_tokens != alphabet.padding_idx).sum(1)\n",
        "\n",
        "  with torch.no_grad():\n",
        "      results = model(batch_tokens.cuda(), repr_layers=[33], return_contacts=True)\n",
        "  token_representations = results[\"representations\"][33]\n",
        "\n",
        "  # Removing start and end tokens\n",
        "  sequence_representations = []\n",
        "  for i, tokens_len in enumerate(batch_lens):\n",
        "    sequence_representations.append(token_representations[i, 1 : tokens_len - 1].cpu().numpy())\n",
        "\n",
        "  return sequence_representations"
      ],
      "metadata": {
        "id": "Epbiaw9-lR_Y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6aa6ed6e-8f62-48c4-fece-07ffdbda9067"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://dl.fbaipublicfiles.com/fair-esm/models/esm2_t33_650M_UR50D.pt\" to /root/.cache/torch/hub/checkpoints/esm2_t33_650M_UR50D.pt\n",
            "Downloading: \"https://dl.fbaipublicfiles.com/fair-esm/regression/esm2_t33_650M_UR50D-contact-regression.pt\" to /root/.cache/torch/hub/checkpoints/esm2_t33_650M_UR50D-contact-regression.pt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import glob\n",
        "from sklearn import preprocessing\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LogisticRegression, RidgeClassifier, LogisticRegressionCV, RidgeClassifierCV\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import auc, roc_auc_score\n",
        "from sklearn import model_selection\n",
        "from sklearn import metrics"
      ],
      "metadata": {
        "id": "5ROh6dV9lJl4"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip install sktime"
      ],
      "metadata": {
        "id": "Smp4V609syBS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sktime.transformations.panel.rocket import Rocket"
      ],
      "metadata": {
        "id": "31MniDnRspl-"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dimensions = return_sequence_representation(['GGGGGG'])[0].shape[1]"
      ],
      "metadata": {
        "id": "im7H_WPrmzp9"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dimensions"
      ],
      "metadata": {
        "id": "IqX49B2MnrL9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Load the CDP dataset and embeddings"
      ],
      "metadata": {
        "id": "ZnTV_ToUwSXu"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "AuK5HfESI6z2"
      },
      "outputs": [],
      "source": [
        "# Load the cysteine-dense peptides dataset (CDPs.csv) from GitHub\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/Zebreu/cyspresso/main/CDPs.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "14C0ue1NtGdv"
      },
      "outputs": [],
      "source": [
        "# Process the dataset (omit extraneous columns and label \"Expressibility\" as True or False)\n",
        "express = {'+': True, '+-PR': True, '-': False}\n",
        "df['Expressibility'] = df['Expressibility'].replace(express)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "hBD3OJT_3Jrb",
        "outputId": "32d3c9d2-379a-47f7-de24-8c9157c03ee1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "     Uniprot                                           Sequence  \\\n",
              "0     P01030          AKRCCQDGLTRLPMARTCEQRAARVQQPACREPFLSCCQFA   \n",
              "1     P46162               PQSCRWNMGVCIPFLCRVGMRQIGTCFGPRVPCCRR   \n",
              "2     P46163              PQSCRWNMGVCIPISCPGNMRQIGTCFGPRVPCCRRW   \n",
              "3     P46167                FVTCRINRGFCVPIRCPGHRRQIGTCLAPQIKCCR   \n",
              "4     P01223        GLACGQAMSFCIPTEYMMHVERKECAYCLTINTTVCAGYCMTR   \n",
              "...      ...                                                ...   \n",
              "1244  B6UHE2  ADLCVTRSRTFKGWCHQSENCITVCKSEGNTGGFCKLGACMCTKECVRS   \n",
              "1245  P0C1Y5  GGGCGYKDVNKAPFNSMGACGNVPIFKDGLGCGSCFEIKCDKPAECSGK   \n",
              "1246  B6SJ49  ARTCQSQSHRFRGPCLRRSNCANVCRTEGFPGGRCRGFRRRCFCTTHCH   \n",
              "1247  B6SQK6  AQICYSRSKTFKGWCYHSTNCISVCITEGEISGFCQHGICMCTYECLTG   \n",
              "1248  A1DMY0                    RDHCGQVCLNKTGCGGKCPKCDMRSLTCKKA   \n",
              "\n",
              "      Expressibility Is Knottin? Uniprot  \n",
              "0              False                   N  \n",
              "1              False                   N  \n",
              "2              False                   N  \n",
              "3              False                   N  \n",
              "4              False                   N  \n",
              "...              ...                 ...  \n",
              "1244            True                   N  \n",
              "1245           False                   N  \n",
              "1246           False                   N  \n",
              "1247           False                   N  \n",
              "1248            True                   Y  \n",
              "\n",
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              "      <th>Uniprot</th>\n",
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              "      <td>ARTCQSQSHRFRGPCLRRSNCANVCRTEGFPGGRCRGFRRRCFCTTHCH</td>\n",
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              "      <th>1247</th>\n",
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              "      <td>AQICYSRSKTFKGWCYHSTNCISVCITEGEISGFCQHGICMCTYECLTG</td>\n",
              "      <td>False</td>\n",
              "      <td>N</td>\n",
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              "    <tr>\n",
              "      <th>1248</th>\n",
              "      <td>A1DMY0</td>\n",
              "      <td>RDHCGQVCLNKTGCGGKCPKCDMRSLTCKKA</td>\n",
              "      <td>True</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1249,\n  \"fields\": [\n    {\n      \"column\": \"Uniprot\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1227,\n        \"samples\": [\n          \"F2CTD8\",\n          \"B0W2M6\",\n          \"P25404\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sequence\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1227,\n        \"samples\": [\n          \"ARTCESQSHKFKGACFSDTNCASVCRTEDFPRGHCSTHYAARKCYCERDC\",\n          \"RATCDLLSGLGVNDSACAAHCIARGNRGGYCNSKKVCVCRN\",\n          \"IEACIGNGGRCNENVGPPYCCSGFCLRQPNQGYGVCRNR\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Expressibility\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Is Knottin? Uniprot\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Y\",\n          \"N\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "df = df[['Uniprot','Sequence','Expressibility','Is Knottin? Uniprot']]\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Avoids running out of memory on consumer GPUs\n",
        "embeddings = []\n",
        "for i in range(13):\n",
        "  subset = df['Sequence'].values[i*100:(i+1)*100]\n",
        "  embeddings.extend(return_sequence_representation(subset))"
      ],
      "metadata": {
        "id": "2vz5D9Hqn2JM"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AWuGUIfLPM1N"
      },
      "source": [
        "### Embedding preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Rocket works best when zero-padded\n",
        "\n",
        "features = [[] for i in range(dimensions)]\n",
        "new_names = []\n",
        "for name, embedding in zip(df['Uniprot'], embeddings):\n",
        "  for i in range(dimensions):\n",
        "    f = embedding[:,i]\n",
        "    d = 50 - len(f) #Pad values with 0s to ensure equal length (50, in this case)\"\n",
        "    f = np.pad(f, (0,d))\n",
        "    features[i].append(pd.Series(f))\n",
        "  new_names.append(name)"
      ],
      "metadata": {
        "id": "F21zsuSwnfUu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YmVv8girslWz",
        "tags": []
      },
      "outputs": [],
      "source": [
        "cc = pd.DataFrame(new_names, columns=['Uniprot'])\n",
        "for i,f in enumerate(features):\n",
        "  cc[i] = pd.Series(f)\n",
        "  combined = pd.merge(cc, df, left_on='Uniprot', right_on='Uniprot')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined"
      ],
      "metadata": {
        "id": "0bN3ntWrsaU2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Llmoxv8sKfBb"
      },
      "outputs": [],
      "source": [
        "# Omit duplicates from dataset\n",
        "combined = combined.drop_duplicates('Uniprot').sort_values('Uniprot')\n",
        "combined['Expressibility'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TuTnCTMWPM1O"
      },
      "source": [
        "### Split the dataset between knottins and non-knottins if applicable"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nmfHQGy3PM1O"
      },
      "outputs": [],
      "source": [
        "# Select all rows that correspond to knottin proteins based on Uniprot identification\n",
        "if False: # Skipped for generalization given this notebook is designed to allow users to try out easily, but if you care about knottins then you train a more specific model using this subset\n",
        "  knottin = combined[combined['Is Knottin? Uniprot'] == 'Y']\n",
        "  knottin = knottin.sort_values('Uniprot')\n",
        "  combined = knottin"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###ROCKET transformation and model training"
      ],
      "metadata": {
        "id": "fEutIrvYoeuN"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZRxT9OGz5NIa"
      },
      "outputs": [],
      "source": [
        "# ROCKET transformation on the knottin dataset\n",
        "\n",
        "feat_cols = list(range(dimensions))\n",
        "\n",
        "rocket = Rocket(num_kernels=10000, random_state=42)\n",
        "rocket.fit(combined[feat_cols])\n",
        "transformed = rocket.transform(combined[feat_cols])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "m59huiPSPM1O"
      },
      "outputs": [],
      "source": [
        "# Train a logistic regression model\n",
        "X_train = transformed.values\n",
        "y_train = combined['Expressibility'].values\n",
        "\n",
        "scaler = preprocessing.StandardScaler()\n",
        "scaler.fit(X_train)\n",
        "tr = scaler.transform(X_train)\n",
        "regression_model = LogisticRegression(C=0.0001)\n",
        "regression_model.fit(tr, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def predict(sequences):\n",
        "  embeddings = return_sequence_representation(sequences)\n",
        "\n",
        "  features = [[] for i in range(dimensions)]\n",
        "  new_names = []\n",
        "  for name, embedding in zip(sequences, embeddings):\n",
        "    for i in range(dimensions):\n",
        "      f = embedding[:,i]\n",
        "      d = 50 - len(f) #Pad values with 0s to ensure equal length (50 being the maximum length in this case)\"\n",
        "      f = np.pad(f, (0,d))\n",
        "      features[i].append(pd.Series(f))\n",
        "    new_names.append(name)\n",
        "\n",
        "  cc = pd.DataFrame(new_names, columns=['Sequences'])\n",
        "  for i,f in enumerate(features):\n",
        "    cc[i] = pd.Series(f)\n",
        "\n",
        "  transformed = rocket.transform(cc[feat_cols])\n",
        "  return regression_model.predict_proba(transformed)"
      ],
      "metadata": {
        "id": "GwDghJkOvEmN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Bring your own sequences and get expression predictions"
      ],
      "metadata": {
        "id": "Nvj5Eu6EtSon"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sequences = [\n",
        "  'PAPCVATRDSCKPPAPACCDPCASCQCRFFRSACSCRVLTRTC',\n",
        "  'ERECLGFGKGCNPSNDQCCKSSNLVCSRKHRWCKYE'\n",
        "]\n",
        "\n",
        "predictions = predict(sequences)"
      ],
      "metadata": {
        "id": "sFBpsZYmtdCR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions # the second column is the probability of expression"
      ],
      "metadata": {
        "id": "7ZkR08Asx-do"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
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