esm2_masked_lm.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Masked Protein Modeling and Visualization"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Top $n$ Predictions for a Masked Amino Acid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E\n",
      "K\n",
      "S\n",
      "D\n",
      "A\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoTokenizer, EsmForMaskedLM\n",
    "import torch\n",
    "\n",
    "# Load pre-trained model and tokenizer\n",
    "model_name = \"facebook/esm2_t6_8M_UR50D\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "model = EsmForMaskedLM.from_pretrained(model_name)\n",
    "\n",
    "# Prepare the masked input (masked a V)\n",
    "masked_input = \"MA<mask>ESRVTQEEIKKEPEK\"\n",
    "inputs = tokenizer(masked_input, return_tensors=\"pt\")\n",
    "\n",
    "# Get the predictions from the model\n",
    "with torch.no_grad():\n",
    "    outputs = model(**inputs)\n",
    "    predictions = outputs.logits\n",
    "\n",
    "# Retrieve the index of the mask token\n",
    "mask_token_index = torch.where(inputs[\"input_ids\"][0] == tokenizer.mask_token_id)\n",
    "\n",
    "# Get the top 5 predicted token ids for the mask token\n",
    "n = 5\n",
    "top_5_predicted_token_ids = torch.topk(predictions[0, mask_token_index], n).indices[0]\n",
    "\n",
    "# Convert the token ids to tokens and print them\n",
    "for token_id in top_5_predicted_token_ids:\n",
    "    print(tokenizer.decode([token_id]))\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Top $n$ Predictions for a Subset of Masked Amino Acids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer, EsmForMaskedLM\n",
    "import torch\n",
    "\n",
    "def predict_masked_positions(sequence, positions, n):\n",
    "    # Load pre-trained model and tokenizer\n",
    "    model_name = \"facebook/esm2_t6_8M_UR50D\"\n",
    "    tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "    model = EsmForMaskedLM.from_pretrained(model_name)\n",
    "\n",
    "    # Store the top n predictions for each position\n",
    "    predictions = []\n",
    "\n",
    "    # For each position to be masked\n",
    "    for pos in positions:\n",
    "        # Create a new sequence with the position masked\n",
    "        masked_sequence = sequence[:pos] + \"<mask>\" + sequence[pos+1:]\n",
    "        inputs = tokenizer(masked_sequence, return_tensors=\"pt\")\n",
    "\n",
    "        # Get the predictions from the model\n",
    "        with torch.no_grad():\n",
    "            outputs = model(**inputs)\n",
    "            prediction = outputs.logits\n",
    "\n",
    "        # Retrieve the index of the mask token\n",
    "        mask_token_index = torch.where(inputs[\"input_ids\"][0] == tokenizer.mask_token_id)\n",
    "\n",
    "        # Get the top n predicted token ids for the mask token\n",
    "        top_n_predicted_token_ids = torch.topk(prediction[0, mask_token_index], n).indices[0]\n",
    "\n",
    "        # Convert the token ids to tokens\n",
    "        predicted_tokens = [tokenizer.decode([token_id]) for token_id in top_n_predicted_token_ids]\n",
    "\n",
    "        # Store the predictions\n",
    "        predictions.append(predicted_tokens)\n",
    "\n",
    "    return predictions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['L', 'S', 'V', 'K', 'A'],\n",
       " ['K', 'E', 'V', 'L', 'G'],\n",
       " ['K', 'E', 'L', 'V', 'S']]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence = \"MAVESRVTQEEIKKEPEK\"\n",
    "positions = [3, 4, 7]  # mask the 2nd, 6th, and 10th positions\n",
    "n = 5  # top 5 predictions\n",
    "\n",
    "predict_masked_positions(sequence, positions, n)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get all Sequences Predicted by Previous Cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "\n",
    "def get_predicted_sequences(sequence, positions, predictions):\n",
    "    # Create a list to store the possible sequences\n",
    "    sequences = []\n",
    "\n",
    "    # Get the Cartesian product of the predictions\n",
    "    for predicted_tokens in itertools.product(*predictions):\n",
    "        # Copy the original sequence\n",
    "        new_sequence = list(sequence)\n",
    "\n",
    "        # Replace the masked positions with the predicted tokens\n",
    "        for pos, token in zip(positions, predicted_tokens):\n",
    "            new_sequence[pos] = token\n",
    "\n",
    "        # Add the new sequence to the list\n",
    "        sequences.append(''.join(new_sequence))\n",
    "\n",
    "    return sequences\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAVLKRVKQEEIKKEPEK\n",
      "MAVLKRVEQEEIKKEPEK\n",
      "MAVLERVKQEEIKKEPEK\n",
      "MAVLERVEQEEIKKEPEK\n",
      "MAVSKRVKQEEIKKEPEK\n",
      "MAVSKRVEQEEIKKEPEK\n",
      "MAVSERVKQEEIKKEPEK\n",
      "MAVSERVEQEEIKKEPEK\n"
     ]
    }
   ],
   "source": [
    "sequence = \"MAVESRVTQEEIKKEPEK\" # original sequence\n",
    "positions = [3, 4, 7]  # mask these positions\n",
    "n = 2  # top n predictions\n",
    "\n",
    "# Get the predictions for the masked positions\n",
    "predictions = predict_masked_positions(sequence, positions, n)\n",
    "\n",
    "# Generate all possible sequences\n",
    "sequences = get_predicted_sequences(sequence, positions, predictions)\n",
    "\n",
    "# Print the sequences (of size n^m, where m = len(positions))\n",
    "for seq in sequences:\n",
    "    print(seq)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict Folded Atomic Coordinates\n",
    "\n",
    "Now, we can predict the $3D$ atomic coordinates of all of the atoms in one of the protein sequences predicted above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of EsmForProteinFolding were not initialized from the model checkpoint at facebook/esmfold_v1 and are newly initialized: ['esm.contact_head.regression.weight', 'esm.contact_head.regression.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoTokenizer, EsmForProteinFolding\n",
    "\n",
    "model = EsmForProteinFolding.from_pretrained(\"facebook/esmfold_v1\")\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"facebook/esmfold_v1\")\n",
    "inputs = tokenizer([\"MAVESRVTQEEIKKEPEK\"], return_tensors=\"pt\", add_special_tokens=False)  #A tiny random peptide\n",
    "outputs = model(**inputs)\n",
    "folded_positions = outputs.positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "\n",
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       "           ...,\n",
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       "\n",
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       "           ...,\n",
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       "           [ 0.0000e+00,  0.0000e+00,  0.0000e+00]],\n",
       "\n",
       "          [[ 1.7976e+00,  7.6888e+00,  1.4486e+01],\n",
       "           [ 1.9015e+00,  8.8333e+00,  1.5387e+01],\n",
       "           [ 3.2006e+00,  8.7877e+00,  1.6186e+01],\n",
       "           ...,\n",
       "           [ 0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
       "           [ 0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
       "           [ 0.0000e+00,  0.0000e+00,  0.0000e+00]]]],\n",
       "\n",
       "\n",
       "\n",
       "        [[[[-7.6381e+00, -2.3369e+00, -2.7188e+01],\n",
       "           [-7.1675e+00, -3.1392e+00, -2.6062e+01],\n",
       "           [-7.1305e+00, -2.3116e+00, -2.4782e+01],\n",
       "           ...,\n",
       "           [-0.0000e+00, -0.0000e+00, -0.0000e+00],\n",
       "           [-0.0000e+00, -0.0000e+00, -0.0000e+00],\n",
       "           [-0.0000e+00, -0.0000e+00, -0.0000e+00]],\n",
       "\n",
       "          [[-8.0822e+00, -2.4465e+00, -2.4213e+01],\n",
       "           [-8.1645e+00, -1.5831e+00, -2.3038e+01],\n",
       "           [-7.4075e+00, -2.1879e+00, -2.1859e+01],\n",
       "           ...,\n",
       "           [-0.0000e+00, -0.0000e+00, -0.0000e+00],\n",
       "           [-0.0000e+00, -0.0000e+00, -0.0000e+00],\n",
       "           [-0.0000e+00, -0.0000e+00, -0.0000e+00]],\n",
       "\n",
       "          [[-6.5337e+00, -1.6095e+00, -2.1439e+01],\n",
       "           [-5.6417e+00, -2.1140e+00, -2.0400e+01],\n",
       "           [-6.2788e+00, -1.9098e+00, -1.9027e+01],\n",
       "           ...,\n",
       "           [-0.0000e+00, -0.0000e+00, -0.0000e+00],\n",
       "           [-0.0000e+00, -0.0000e+00, -0.0000e+00],\n",
       "           [-0.0000e+00, -0.0000e+00, -0.0000e+00]],\n",
       "\n",
       "          ...,\n",
       "\n",
       "          [[-2.4816e-01,  8.0500e+00,  9.3643e+00],\n",
       "           [ 1.0327e+00,  8.3849e+00,  9.9909e+00],\n",
       "           [ 1.0870e+00,  7.9964e+00,  1.1467e+01],\n",
       "           ...,\n",
       "           [ 0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
       "           [ 0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
       "           [ 0.0000e+00,  0.0000e+00,  0.0000e+00]],\n",
       "\n",
       "          [[ 2.3595e+00,  7.2858e+00,  1.1563e+01],\n",
       "           [ 2.6552e+00,  7.0779e+00,  1.2978e+01],\n",
       "           [ 2.6124e+00,  8.3935e+00,  1.3750e+01],\n",
       "           ...,\n",
       "           [ 0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
       "           [ 0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
       "           [ 0.0000e+00,  0.0000e+00,  0.0000e+00]],\n",
       "\n",
       "          [[ 1.3621e+00,  8.4536e+00,  1.4745e+01],\n",
       "           [ 1.4545e+00,  9.5826e+00,  1.5666e+01],\n",
       "           [ 2.7727e+00,  9.5589e+00,  1.6434e+01],\n",
       "           ...,\n",
       "           [ 0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
       "           [ 0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
       "           [ 0.0000e+00,  0.0000e+00,  0.0000e+00]]]],\n",
       "\n",
       "\n",
       "\n",
       "        [[[[-7.5219e+00, -2.6207e+00, -2.7341e+01],\n",
       "           [-7.0540e+00, -3.3666e+00, -2.6176e+01],\n",
       "           [-7.0469e+00, -2.4845e+00, -2.4932e+01],\n",
       "           ...,\n",
       "           [-0.0000e+00, -0.0000e+00, -0.0000e+00],\n",
       "           [-0.0000e+00, -0.0000e+00, -0.0000e+00],\n",
       "           [-0.0000e+00, -0.0000e+00, -0.0000e+00]],\n",
       "\n",
       "          [[-8.1294e+00, -2.3045e+00, -2.4369e+01],\n",
       "           [-8.2219e+00, -1.5016e+00, -2.3153e+01],\n",
       "           [-7.5064e+00, -2.1833e+00, -2.1990e+01],\n",
       "           ...,\n",
       "           [-0.0000e+00, -0.0000e+00, -0.0000e+00],\n",
       "           [-0.0000e+00, -0.0000e+00, -0.0000e+00],\n",
       "           [-0.0000e+00, -0.0000e+00, -0.0000e+00]],\n",
       "\n",
       "          [[-6.2750e+00, -1.7300e+00, -2.1582e+01],\n",
       "           [-5.4169e+00, -2.2029e+00, -2.0500e+01],\n",
       "           [-6.0819e+00, -1.9258e+00, -1.9154e+01],\n",
       "           ...,\n",
       "           [-0.0000e+00, -0.0000e+00, -0.0000e+00],\n",
       "           [-0.0000e+00, -0.0000e+00, -0.0000e+00],\n",
       "           [-0.0000e+00, -0.0000e+00, -0.0000e+00]],\n",
       "\n",
       "          ...,\n",
       "\n",
       "          [[ 2.7116e-02,  8.2006e+00,  9.3191e+00],\n",
       "           [ 1.2849e+00,  8.5908e+00,  9.9604e+00],\n",
       "           [ 1.3288e+00,  8.2277e+00,  1.1443e+01],\n",
       "           ...,\n",
       "           [ 0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
       "           [ 0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
       "           [ 0.0000e+00,  0.0000e+00,  0.0000e+00]],\n",
       "\n",
       "          [[ 2.3006e+00,  7.2797e+00,  1.1860e+01],\n",
       "           [ 2.6410e+00,  7.1167e+00,  1.3270e+01],\n",
       "           [ 2.5463e+00,  8.4437e+00,  1.4017e+01],\n",
       "           ...,\n",
       "           [ 0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
       "           [ 0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
       "           [ 0.0000e+00,  0.0000e+00,  0.0000e+00]],\n",
       "\n",
       "          [[ 1.2808e+00,  8.7240e+00,  1.4796e+01],\n",
       "           [ 1.3794e+00,  9.8470e+00,  1.5724e+01],\n",
       "           [ 2.7314e+00,  9.8585e+00,  1.6431e+01],\n",
       "           ...,\n",
       "           [ 0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
       "           [ 0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
       "           [ 0.0000e+00,  0.0000e+00,  0.0000e+00]]]]],\n",
       "       grad_fn=<StackBackward0>)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "folded_positions"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the Protein Sequence and Highlight the Masked Amino Acids\n",
    "\n",
    "Now, we can plot one of the predicted sequences from above. This can be used to compare the topologies of each predicted sequence in the above list as the masked amino acids are changed. The atoms corresponding to the positions `[3, 4, 7]` are highlighted in red, and each atom is labeled by the amino acid it lies in. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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   "source": [
    "import torch\n",
    "import plotly.graph_objects as go\n",
    "import numpy as np\n",
    "\n",
    "# flatten the tensor to 2D shape\n",
    "folded_positions_flat = folded_positions.view(-1, 3)\n",
    "\n",
    "# remove points with all zeros\n",
    "folded_positions_filtered = folded_positions_flat[~torch.all(folded_positions_flat == 0, dim=1)]\n",
    "\n",
    "# detach the tensor, we don't need gradients here\n",
    "folded_positions_filtered = folded_positions_filtered.detach()\n",
    "\n",
    "# extract individual coordinates\n",
    "x = folded_positions_filtered[:, 0].numpy()\n",
    "y = folded_positions_filtered[:, 1].numpy()\n",
    "z = folded_positions_filtered[:, 2].numpy()\n",
    "\n",
    "# define which residues are to be colored red\n",
    "protein_sequence = \"MAVESRVTQEEIKKEPEK\"\n",
    "residue_indices = [3, 4, 7]\n",
    "residue_atom_counts = {\n",
    "    'A': 13,\n",
    "    'R': 24,\n",
    "    'N': 14,\n",
    "    'D': 12,\n",
    "    'C': 11,\n",
    "    'E': 15,\n",
    "    'Q': 17,\n",
    "    'G': 10,\n",
    "    'H': 20,\n",
    "    'I': 22,\n",
    "    'L': 22,\n",
    "    'K': 22,\n",
    "    'M': 17,\n",
    "    'F': 20,\n",
    "    'P': 14,\n",
    "    'S': 11,\n",
    "    'T': 14,\n",
    "    'W': 27,\n",
    "    'Y': 21,\n",
    "    'V': 16\n",
    "}  # atom counts for each residue type\n",
    "\n",
    "color_indices = []\n",
    "labels = []\n",
    "index = 0\n",
    "for i, residue in enumerate(protein_sequence):\n",
    "    for _ in range(residue_atom_counts[residue]):\n",
    "        labels.append(residue)\n",
    "        if i in residue_indices:\n",
    "            color_indices.append(index)\n",
    "        index += 1\n",
    "\n",
    "colors = ['red' if i in color_indices else 'blue' for i in range(len(x))]\n",
    "\n",
    "# distance threshold\n",
    "epsilon = 0.0  # change to desired value\n",
    "\n",
    "# prepare data for lines\n",
    "Xe, Ye, Ze = [], [], []\n",
    "for i in range(len(x)):\n",
    "    for j in range(i + 1, len(x)):\n",
    "        if np.sqrt((x[i] - x[j])**2 + (y[i] - y[j])**2 + (z[i] - z[j])**2) < epsilon:\n",
    "            Xe += [x[i], x[j], None]\n",
    "            Ye += [y[i], y[j], None]\n",
    "            Ze += [z[i], z[j], None]\n",
    "\n",
    "trace_edges = go.Scatter3d(x=Xe, y=Ye, z=Ze, mode='lines', line=dict(color='black', width=2))\n",
    "\n",
    "# create a 3D scatter plot\n",
    "trace_nodes = go.Scatter3d(\n",
    "    x=x,\n",
    "    y=y,\n",
    "    z=z,\n",
    "    mode='markers+text',\n",
    "    marker=dict(\n",
    "        size=5,\n",
    "        color=colors,                # set color to an array/list of desired values\n",
    "        opacity=0.8\n",
    "    ),\n",
    "    text=labels,  # Add labels\n",
    "    textposition=\"top center\"\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=[trace_edges, trace_nodes])\n",
    "\n",
    "# set labels for axes\n",
    "fig.update_layout(scene = dict(\n",
    "                    xaxis_title='X',\n",
    "                    yaxis_title='Y',\n",
    "                    zaxis_title='Z'))\n",
    "\n",
    "fig.show()\n"
   ]
  }
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