{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PeptideSmilesEncoder: Encoding Peptides to SMILES\n",
    "\n",
    "SMILES representations of peptides with PTMs enable advanced ML applications in proteomics:\n",
    "\n",
    "* Graph Neural Networks (GNNs): SMILES convert to molecular graphs, allowing GNNs to capture complex structural relationships, including PTM arrangements.\n",
    "* Transformer Models: Treating SMILES as sequences enables use of state-of-the-art transformer architectures, adept at learning long-range dependencies in peptide structures.\n",
    "* Transfer Learning: Pre-trained models on large chemical datasets can be fine-tuned for specific proteomic tasks, leveraging broader chemical knowledge.\n",
    "* Interpretability: Graph-based models provide insights into which parts of the peptide, including PTMs, contribute most to predicted properties.\n",
    "\n",
    "This notebook demonstrates the functionality of the `PeptideSmilesEncoder` class, which is used to encode peptide sequences into SMILES (Simplified Molecular Input Line Entry System) strings. We'll go through various examples, including peptides with and without modifications, and visualize the resulting molecules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import Descriptors, Draw\n",
    "from rdkit.Chem import rdCoordGen\n",
    "from rdkit.Chem import rdFingerprintGenerator\n",
    "from rdkit.Chem import MACCSkeys\n",
    "from rdkit.Chem.AtomPairs import Pairs\n",
    "\n",
    "from alphabase.peptide.precursor import get_mod_seq_formula\n",
    "from alphabase.constants.atom import CHEM_MONO_MASS, MASS_H2O\n",
    "from alphabase.smiles.peptide import PeptideSmilesEncoder\n",
    "\n",
    "# Initialize the encoder\n",
    "encoder = PeptideSmilesEncoder()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Encoding a Peptide without Modifications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Peptide sequence: QMNPHIR\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=300x300>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SMILES: [H]N([H])[C@@H](CCC(N)=O)C(=O)N([H])[C@@H](CCSC)C(=O)N([H])[C@@H](CC(N)=O)C(=O)N1CCC[C@H]1C(=O)N([H])[C@@H](Cc1cnc[nH]1)C(=O)N([H])[C@H](C(=O)N([H])[C@@H](CCCNC(=N)N)C(=O)O)[C@@H](C)CC\n",
      "Exact Mass: 876.4388\n",
      "Expected mass: 876.4388\n",
      "Mass difference: 0.0000\n"
     ]
    }
   ],
   "source": [
    "def visualize_peptide(smiles, title):\n",
    "    mol = Chem.MolFromSmiles(smiles)\n",
    "    rdCoordGen.AddCoords(mol)\n",
    "    img = Draw.MolToImage(mol)\n",
    "    display(img)\n",
    "    print(f\"SMILES: {smiles}\")\n",
    "    print(f\"Exact Mass: {Descriptors.ExactMolWt(mol) - MASS_H2O:.4f}\")\n",
    "\n",
    "sequence = \"QMNPHIR\"\n",
    "smiles = encoder.peptide_to_smiles(sequence)\n",
    "\n",
    "print(f\"Peptide sequence: {sequence}\")\n",
    "visualize_peptide(smiles, \"Peptide without modifications\")\n",
    "\n",
    "# Verify mass\n",
    "expected_mass = np.sum([CHEM_MONO_MASS[elem] * n for elem, n in get_mod_seq_formula(sequence, \"\")])\n",
    "actual_mass = Descriptors.ExactMolWt(Chem.MolFromSmiles(smiles)) - MASS_H2O\n",
    "print(f\"Expected mass: {expected_mass:.4f}\")\n",
    "print(f\"Mass difference: {abs(actual_mass - expected_mass):.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Encoding a Peptide with Modifications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Peptide sequence: QMNPHIR\n",
      "Modifications: Gln->pyro-Glu@Q^Any_N-term;Oxidation@M\n",
      "Modification sites: 1;2\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=300x300>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SMILES: [H]N(C(=O)[C@@H]1CCC(=O)N1[H])C(CCS(C)=O)C(=O)N([H])[C@@H](CC(N)=O)C(=O)N1CCC[C@H]1C(=O)N([H])[C@@H](Cc1cnc[nH]1)C(=O)N([H])[C@H](C(=O)N([H])[C@@H](CCCNC(=N)N)C(=O)O)[C@@H](C)CC\n",
      "Exact Mass: 875.4072\n",
      "Expected mass: 875.4072\n",
      "Mass difference: 0.0000\n"
     ]
    }
   ],
   "source": [
    "sequence = \"QMNPHIR\"\n",
    "mods = \"Gln->pyro-Glu@Q^Any_N-term;Oxidation@M\"\n",
    "mod_sites = \"1;2\"\n",
    "\n",
    "smiles = encoder.peptide_to_smiles(sequence, mods, mod_sites)\n",
    "\n",
    "print(f\"Peptide sequence: {sequence}\")\n",
    "print(f\"Modifications: {mods}\")\n",
    "print(f\"Modification sites: {mod_sites}\")\n",
    "visualize_peptide(smiles, \"Peptide with modifications\")\n",
    "\n",
    "# Verify mass\n",
    "expected_mass = np.sum([CHEM_MONO_MASS[elem] * n for elem, n in get_mod_seq_formula(sequence, mods)])\n",
    "actual_mass = Descriptors.ExactMolWt(Chem.MolFromSmiles(smiles)) - MASS_H2O\n",
    "print(f\"Expected mass: {expected_mass:.4f}\")\n",
    "print(f\"Mass difference: {abs(actual_mass - expected_mass):.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. N-terminal Modification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Peptide sequence: QMNPHIR\n",
      "Modifications: Acetyl@Any_N-term\n",
      "Modification sites: 0\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=300x300>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SMILES: [H]N(C(=O)[C@H](Cc1cnc[nH]1)N([H])C(=O)[C@@H]1CCCN1C(=O)[C@H](CC(N)=O)N([H])C(=O)[C@H](CCSC)N([H])C(=O)[C@H](CCC(N)=O)N([H])C(C)=O)[C@H](C(=O)N([H])[C@@H](CCCNC(=N)N)C(=O)O)[C@@H](C)CC\n",
      "Exact Mass: 918.4494\n",
      "Expected mass: 918.4494\n",
      "Mass difference: 0.0000\n"
     ]
    }
   ],
   "source": [
    "sequence = \"QMNPHIR\"\n",
    "mods = \"Acetyl@Any_N-term\"\n",
    "mod_sites = \"0\"\n",
    "\n",
    "smiles = encoder.peptide_to_smiles(sequence, mods, mod_sites)\n",
    "\n",
    "print(f\"Peptide sequence: {sequence}\")\n",
    "print(f\"Modifications: {mods}\")\n",
    "print(f\"Modification sites: {mod_sites}\")\n",
    "visualize_peptide(smiles, \"Peptide with N-terminal modification\")\n",
    "\n",
    "# Verify mass\n",
    "expected_mass = np.sum([CHEM_MONO_MASS[elem] * n for elem, n in get_mod_seq_formula(sequence, mods)])\n",
    "actual_mass = Descriptors.ExactMolWt(Chem.MolFromSmiles(smiles)) - MASS_H2O\n",
    "print(f\"Expected mass: {expected_mass:.4f}\")\n",
    "print(f\"Mass difference: {abs(actual_mass - expected_mass):.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. C-terminal Modification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Peptide sequence: QMNPHIR\n",
      "Modifications: Amidated@Any_C-term\n",
      "Modification sites: -1\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=300x300>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SMILES: [H]N([H])[C@@H](CCC(N)=O)C(=O)N([H])[C@@H](CCSC)C(=O)N([H])[C@@H](CC(N)=O)C(=O)N1CCC[C@H]1C(=O)N([H])[C@@H](Cc1cnc[nH]1)C(=O)N([H])[C@H](C(=O)N([H])[C@@H](CCCNC(=N)N)C(N)=O)[C@@H](C)CC\n",
      "Exact Mass: 875.4548\n",
      "Expected mass: 875.4548\n",
      "Mass difference: 0.0000\n"
     ]
    }
   ],
   "source": [
    "sequence = \"QMNPHIR\"\n",
    "mods = \"Amidated@Any_C-term\"\n",
    "mod_sites = \"-1\"\n",
    "\n",
    "smiles = encoder.peptide_to_smiles(sequence, mods, mod_sites)\n",
    "\n",
    "print(f\"Peptide sequence: {sequence}\")\n",
    "print(f\"Modifications: {mods}\")\n",
    "print(f\"Modification sites: {mod_sites}\")\n",
    "visualize_peptide(smiles, \"Peptide with C-terminal modification\")\n",
    "\n",
    "# Verify mass\n",
    "expected_mass = np.sum([CHEM_MONO_MASS[elem] * n for elem, n in get_mod_seq_formula(sequence, mods)])\n",
    "actual_mass = Descriptors.ExactMolWt(Chem.MolFromSmiles(smiles)) - MASS_H2O\n",
    "print(f\"Expected mass: {expected_mass:.4f}\")\n",
    "print(f\"Mass difference: {abs(actual_mass - expected_mass):.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Multiple Modifications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Peptide sequence: QMNPSIR\n",
      "Modifications: Acetyl@Any_N-term;Oxidation@M;Phospho@S\n",
      "Modification sites: 0;2;5\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=300x300>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SMILES: [H]N(C(=O)[C@H](CCC(N)=O)N([H])C(C)=O)C(CCS(C)=O)C(=O)N([H])[C@@H](CC(N)=O)C(=O)N1CCC[C@H]1C(=O)N([H])[C@@H](COP(=O)(O)O)C(=O)N([H])[C@H](C(=O)N([H])[C@@H](CCCNC(=N)N)C(=O)O)[C@@H](C)CC\n",
      "Exact Mass: 964.3838\n",
      "Expected mass: 964.3838\n",
      "Mass difference: 0.0000\n"
     ]
    }
   ],
   "source": [
    "sequence = \"QMNPSIR\"\n",
    "mods = \"Acetyl@Any_N-term;Oxidation@M;Phospho@S\"\n",
    "mod_sites = \"0;2;5\"\n",
    "\n",
    "smiles = encoder.peptide_to_smiles(sequence, mods, mod_sites)\n",
    "\n",
    "print(f\"Peptide sequence: {sequence}\")\n",
    "print(f\"Modifications: {mods}\")\n",
    "print(f\"Modification sites: {mod_sites}\")\n",
    "visualize_peptide(smiles, \"Peptide with multiple modifications\")\n",
    "\n",
    "# Verify mass\n",
    "expected_mass = np.sum([CHEM_MONO_MASS[elem] * n for elem, n in get_mod_seq_formula(sequence, mods)])\n",
    "actual_mass = Descriptors.ExactMolWt(Chem.MolFromSmiles(smiles)) - MASS_H2O\n",
    "print(f\"Expected mass: {expected_mass:.4f}\")\n",
    "print(f\"Mass difference: {abs(actual_mass - expected_mass):.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further usage:\n",
    "\n",
    "### Molecular fingerprints\n",
    "\n",
    "As we stated before, being able to represent a peptide as a SMILES opens some doors for us. Let's take a look at feature extraction using RDKit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2048,)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_fingerprint(mol, fp_type='morgan', **kwargs):\n",
    "    \"\"\"\n",
    "    Generate molecular fingerprints for an RDKit mol object.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    mol : rdkit.Chem.Mol\n",
    "        RDKit mol object.\n",
    "    fp_type : str, optional\n",
    "        Type of fingerprint. Options are 'morgan', 'topological', 'maccs', 'atompair'.\n",
    "        Default is 'morgan'.\n",
    "    **kwargs : dict\n",
    "        Additional keyword arguments for specific fingerprint parameters.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    list\n",
    "        Fingerprint bit vector.\n",
    "    \"\"\"\n",
    "    if fp_type == 'morgan':\n",
    "        radius = kwargs.get('radius', 2)\n",
    "        nBits = kwargs.get('nBits', 2048)\n",
    "        mfpgen = rdFingerprintGenerator.GetMorganGenerator(radius=radius,fpSize=nBits)\n",
    "        fp = mfpgen.GetFingerprint(mol)\n",
    "    \n",
    "    elif fp_type == 'topological':\n",
    "        minPath = kwargs.get('minPath', 1)\n",
    "        maxPath = kwargs.get('maxPath', 7)\n",
    "        fpSize = kwargs.get('fpSize', 2048)\n",
    "        fp = Chem.RDKFingerprint(mol, minPath=minPath, maxPath=maxPath, fpSize=fpSize)\n",
    "    \n",
    "    elif fp_type == 'maccs':\n",
    "        fp = MACCSkeys.GenMACCSKeys(mol)\n",
    "    \n",
    "    elif fp_type == 'atompair':\n",
    "        nBits = kwargs.get('nBits', 2048)\n",
    "        fp = Pairs.GetAtomPairFingerprintAsBitVect(mol, nBits=nBits)\n",
    "    \n",
    "    else:\n",
    "        raise ValueError(f\"Unknown fingerprint type: {fp_type}\")\n",
    "    \n",
    "    return fp.ToList()\n",
    "\n",
    "\n",
    "sequence = \"QMNPSIR\"\n",
    "mods = \"Acetyl@Any_N-term;Oxidation@M;Phospho@S\"\n",
    "mod_sites = \"0;2;5\"\n",
    "\n",
    "smiles = encoder.peptide_to_smiles(sequence, mods, mod_sites)\n",
    "np.array(get_fingerprint(Chem.MolFromSmiles(smiles), fp_type='topological')).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adjacency matrix\n",
    "\n",
    "Now we can easily get an adjacency matrix to work with graph algorithms, numbers specify the bond type between the atoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(66, 66)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 0., ..., 0., 0., 0.],\n",
       "       [1., 0., 2., ..., 0., 0., 0.],\n",
       "       [0., 2., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 1.],\n",
       "       [0., 0., 0., ..., 0., 1., 0.]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mol = Chem.MolFromSmiles(smiles)\n",
    "adj_matrix = Chem.GetAdjacencyMatrix(mol, useBO=True)\n",
    "print(adj_matrix.shape)\n",
    "adj_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "here we can see the types of corresponding atoms themselves:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N 0 2 1 0 False\n",
      "C 1 3 0 0 False\n",
      "O 2 1 0 0 False\n",
      "C 3 3 1 0 False\n",
      "C 4 2 2 0 False\n",
      "C 5 2 2 0 False\n",
      "C 6 3 0 0 False\n",
      "N 7 1 2 0 False\n",
      "O 8 1 0 0 False\n",
      "N 9 2 1 0 False\n",
      "C 10 3 0 0 False\n",
      "C 11 1 3 0 False\n",
      "O 12 1 0 0 False\n",
      "C 13 3 1 0 False\n",
      "C 14 2 2 0 False\n",
      "C 15 2 2 0 False\n",
      "S 16 3 0 0 False\n",
      "C 17 1 3 0 False\n",
      "O 18 1 0 0 False\n",
      "C 19 3 0 0 False\n",
      "O 20 1 0 0 False\n",
      "N 21 2 1 0 False\n",
      "C 22 3 1 0 False\n",
      "C 23 2 2 0 False\n",
      "C 24 3 0 0 False\n",
      "N 25 1 2 0 False\n",
      "O 26 1 0 0 False\n",
      "C 27 3 0 0 False\n",
      "O 28 1 0 0 False\n",
      "N 29 3 0 0 False\n",
      "C 30 2 2 0 False\n",
      "C 31 2 2 0 False\n",
      "C 32 2 2 0 False\n",
      "C 33 3 1 0 False\n",
      "C 34 3 0 0 False\n",
      "O 35 1 0 0 False\n",
      "N 36 2 1 0 False\n",
      "C 37 3 1 0 False\n",
      "C 38 2 2 0 False\n",
      "O 39 2 0 0 False\n",
      "P 40 4 0 0 False\n",
      "O 41 1 0 0 False\n",
      "O 42 1 1 0 False\n",
      "O 43 1 1 0 False\n",
      "C 44 3 0 0 False\n",
      "O 45 1 0 0 False\n",
      "N 46 2 1 0 False\n",
      "C 47 3 1 0 False\n",
      "C 48 3 0 0 False\n",
      "O 49 1 0 0 False\n",
      "N 50 2 1 0 False\n",
      "C 51 3 1 0 False\n",
      "C 52 2 2 0 False\n",
      "C 53 2 2 0 False\n",
      "C 54 2 2 0 False\n",
      "N 55 2 1 0 False\n",
      "C 56 3 0 0 False\n",
      "N 57 1 1 0 False\n",
      "N 58 1 2 0 False\n",
      "C 59 3 0 0 False\n",
      "O 60 1 0 0 False\n",
      "O 61 1 1 0 False\n",
      "C 62 3 1 0 False\n",
      "C 63 1 3 0 False\n",
      "C 64 2 2 0 False\n",
      "C 65 1 3 0 False\n"
     ]
    }
   ],
   "source": [
    "for atom in mol.GetAtoms():\n",
    "    print(atom.GetSymbol(), atom.GetIdx(), atom.GetDegree(), atom.GetTotalNumHs(), atom.GetFormalCharge(), atom.GetIsAromatic())"
   ]
  }
 ],
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