{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with Amino Acids and Post-Translational Modifications using SMILES Notation\n",
    "\n",
    "This notebook introduces working with amino acids and post-translational modifications on the molecular level, using the SMILES (Simplified Molecular Input Line Entry System) notation. We'll explore how to represent amino acids, add modifications, and visualize the results using RDKit.\n",
    "\n",
    "## Introduction to SMILES and RDKit\n",
    "\n",
    "SMILES is a line notation for describing the structure of chemical species using short ASCII strings. For example, the SMILES for ethanol is `CCO`.\n",
    "\n",
    "RDKit is an open-source cheminformatics software that we'll use to work with and visualize molecular structures.\n",
    "\n",
    "Let's start by importing the necessary functions and data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import Draw\n",
    "\n",
    "from alphabase.smiles.smiles import AminoAcidModifier\n",
    "\n",
    "\n",
    "aa_modifier = AminoAcidModifier()\n",
    "modify_amino_acid = aa_modifier.modify_amino_acid\n",
    "aa_smiles = aa_modifier.aa_smiles\n",
    "n_term_modifications = aa_modifier.n_term_modifications\n",
    "c_term_modifications = aa_modifier.c_term_modifications\n",
    "ptm_dict = aa_modifier.ptm_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understanding the Data Structure\n",
    "\n",
    "Our data is organized into several dictionaries:\n",
    "\n",
    "1. `aa_smiles`: Contains SMILES representations of amino acids\n",
    "2. `n_term_modifications`: Contains N-terminal modifications\n",
    "3. `c_term_modifications`: Contains C-terminal modifications\n",
    "4. `ptm_dict`: Contains post-translational modifications\n",
    "\n",
    "Let's examine the SMILES representation of an amino acid, such as Lysine (K):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lysine SMILES with dummy atoms: N([Fl])([Fl])[C@@]([H])(CCCCN)C(=O)[Ts]\n"
     ]
    },
    {
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      "image/png": "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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=300x300>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Lysine SMILES with dummy atoms:\", aa_smiles[\"K\"])\n",
    "mol = Chem.MolFromSmiles(aa_smiles[\"K\"])\n",
    "Draw.MolToImage(mol)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this SMILES representation:\n",
    "- `[Fl]` represents placeholder atoms for the N-terminus\n",
    "- `[Ts]` represents placeholder atoms for the C-terminus\n",
    "\n",
    "These placeholders allow for easy addition of N- and C-terminal modifications."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## N-terminal Modifications\n",
    "\n",
    "Let's look at the available N-terminal modifications:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available N-terminal modifications:\n",
      "- Acetyl@Protein_N-term\n",
      "- Acetyl@Any_N-term\n",
      "- Biotin@Any_N-term\n",
      "- Carbamidomethyl@Any_N-term\n",
      "- Carbamyl@Any_N-term\n",
      "- Carbamyl@Protein_N-term\n",
      "- Propionamide@Any_N-term\n",
      "- Pyridylacetyl@Any_N-term\n",
      "- Methyl@Protein_N-term\n",
      "- Methyl@Any_N-term\n",
      "- Dimethyl@Protein_N-term\n",
      "- Dimethyl@Any_N-term\n",
      "- Propionyl@Protein_N-term\n",
      "- Propionyl@Any_N-term\n",
      "- Dimethyl:2H(6)13C(2)@Protein_N-term\n",
      "- Dimethyl:2H(6)13C(2)@Any_N-term\n",
      "- Dimethyl:2H(4)@Protein_N-term\n",
      "- Dimethyl:2H(4)@Any_N-term\n",
      "- Dimethyl:2H(4)13C(2)@Protein_N-term\n",
      "- Dimethyl:2H(4)13C(2)@Any_N-term\n",
      "- mTRAQ@Any_N-term\n",
      "- mTRAQ:13C(3)15N(1)@Any_N-term\n",
      "- mTRAQ:13C(6)15N(2)@Any_N-term\n",
      "- mTRAQ@Protein_N-term\n",
      "- mTRAQ:13C(3)15N(1)@Protein_N-term\n",
      "- mTRAQ:13C(6)15N(2)@Protein_N-term\n",
      "- Biotin@Protein_N-term\n",
      "- Carbamidomethyl@Protein_N-term\n",
      "- Propionamide@Protein_N-term\n",
      "- Pyridylacetyl@Protein_N-term\n",
      "\n",
      "Biotin SMILES: C(=O)CCCCC1SCC2NC(=O)NC21\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=300x300>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Available N-terminal modifications:\")\n",
    "for mod in n_term_modifications.keys():\n",
    "    print(f\"- {mod}\")\n",
    "\n",
    "# Let's visualize one of these modifications, e.g., Biotin\n",
    "print(\"\\nBiotin SMILES:\", n_term_modifications[\"Biotin@Any_N-term\"])\n",
    "biotin_mol = Chem.MolFromSmiles(n_term_modifications[\"Biotin@Any_N-term\"])\n",
    "Draw.MolToImage(biotin_mol)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The `modify_amino_acid` Function\n",
    "\n",
    "Now, let's explore the `modify_amino_acid` function, which allows us to add modifications to amino acids. This function takes three arguments:\n",
    "\n",
    "1. `aa_smiles`: SMILES string of an amino acid\n",
    "2. `n_term_mod`: N-terminal modification (optional)\n",
    "3. `c_term_mod`: C-terminal modification (optional)\n",
    "\n",
    "Let's see it in action:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modified Lysine SMILES: [H]N(C(=O)CCCCC1SCC2NC(=O)NC21)[C@@H](CCCCN)C(=O)O\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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18PBQqswBA+jgQXr4Ybp27UxAwNPZ2dn1Kyc/P/+777575ZVX2rRpM27cuOTk5D179vj7+ycmJlLjtXnzZiKqZfe1BQb9a6B4rK3c1KlTRRNC8ZJTU1n//oMq+2+6XqrL8iaXL19evnz5yJEjjR2DKpXKy8tr+vTpXbp0EVPYG+v9MC8vz87OzsbGxvTxPaUG/e+FECpM/FmfPHnSHIVnZGT06dNHrIh15syZ+784NTU1PDy8+vqRGo1GrB955fclMktLSydMmFC5saTz7t27WaPz+eefE9EzzzzTQAb9a4QQKikvL0+lUjk6OtZ1tbjay8/PHzx48B/dvvR6fVxcnFar7datm7Gy4+joKNaPFA9D3qWiokKMfdnZ2W0UK9o2IqMq11h44GLTtfTss88S0f+UWurwdwihknbu3ElEgwcPNutZSktL//KXv1S/fel0utjY2JCQEDc3N2P2XF1dg4KCNm/e/MClAAwGg1arFdXUTz75hDW6umiWQveuNWvWENHzSj9wjRAq6d///relVoXSifWsbGxs3N3dH3roIWP2PDw8xPMiFXXcki88PFwsyhwaGsoahbVr1xLR6NGjlSowIyNDrVY7Ojoqu8QJQqgksUfNtm3bLHAu0ZYzzq5ydXU1fUzyyy+/FJ03M2fObAR7ezz99NNEVNe59fc3aBDvG3vgwkV1ghAqRq/Xi1mI6ffuHm4G7pUbgi1cuFDMpfr0008VKTYqKkpsgPXXv/5Vkb09ioqKvvnmm86dO48fP/63335jlpKdnW1jY2Nra5ubm6tgsQsXLiSioKAgBctECBXza+UeqB4eHhY416XKHTFdXFz0er1YL0fB5YkPHjzYrFkzMcBd71U8cnNzIyIiAgMDjeusE1H79u0vm7JzfF2sXr2aiPz8/JiiUlJSxGqaCu4+hBAqZtWqVUT08ssvW+BcYszKz88vJydHpVI5OTnpFF1o+uzZs6KPp3///tnZ2bV/Y1pa2pIlS4YOHar5fY6sWq0eOHDgpEmTxK3bzc3tgYMrCjYN1q9fz5TWo0cPIoqJiVGqQIRQMa9UPhGoVLXw/l6vfLrpgw8+EM/ImWME+cKFC4899hgRdevW7YG3r3v3tDOOSRr3BTEOrrRo0eLo0aPMnLKyssxRFxXeeeedyun1s5QqECFUjOLVwvvw8vISH8bz5s0jorffftscZ0lPT3/88cfFUyB3rYNcfUxSzE+ozZ52paWl48ePF4Mre/bsYWazciXfFtrf399804MffvhhU/rAqkMIlWGmamGNSkpK7Ozs1Gp1QUHB8OHDxYRGM50rPz/fx8dH3L6OVa7HWFJSsnfv3pCQkOrrHbVs2TIoKCgqKqq0tPT+BVZUVEybNk3MDRDLZJnDsGHDiCgiIsIchRsMhvbt2ys4LwohVIb5qoX3Onz4MBH17t27oqJCdHuY8pDOA926dcvPz0/ExtPT09nZ2Zi9Tp06vfXWW0ePHq3TeIbBYPjnP/8pqqyrzLBuUnp6ukajsbe3N99OlbNm8TWF33nnHUVKQwiVYdZq4V0qliy52bPn6XffvXLmTN+2bT09Pc19Rp1ON2LECGP2unfvrtVq4+LiTCkzLCxMNCBDlZ4bsHz5cnPMa6kuJiaGiHr06KFIaQihMsxdLbzD+PH84cIvvmArVzKisunTLXBOvV6/atWqKVOmJCcnK1XmF198IeYG/OMf/1BkbsDNmze//fZbsVesOe6xRjqdTowJK7K7JkKoAMtUC29zc+MhTE5mkybxg5Ur2Z9WZGSkg4MDEU2cOLHeI2/Z2dkRERH+/v7G+UO2trZubm6HDh1i5hEdHS2a5Yo0OxFCBfzyyy9iQEzZGVI1S0ur2oHWYGCenvy4vnuhNhD79+8XdxVfX9/CwsLavzElJWXhwoXe3t5iyqtoZA4ZMuS9994Tvcc2NjZarVbZ+XfFxcWzZs0SFel+/fqVKLHiCEJoqvj4eOMyhyqVasmSJeY9n9gx3d+fZWbyA2dn5feDtri4uDixnqeXl1dOTs79X1ybPe10Op1WqxXhHDFihFK7R8THx4sxG1tbWwXjjRDWx72P7dnZ2QUEBBifQlBqBKkGs2dXrde9fXvVsjONQmpqqqenp+j1MT5zbFS/Pe1iYmLatm1LRK1btzZxWNJgMKxZs8bJyUmsbKDsaDBCWAdlpaW7du167bXXxP9aY/ODiDp06JCenr5hwwbx7eTJk801YNivH8/evn3s7bf5gUK95A3B9evXRZ2iY8eOYqp3cXFxVFTUa6+91sa4QHLl+p9iTLI2T05fu3ZNjBmq1epFi943GOr2eJeQmZkpNqIQU7cV36oZIayFW7dYVBQLCqrw9LT5vfnxyCOPhISE7N2799KlSz179iSiRx99NDk5ee/evaKTJiAgQJEGwx2Ki/nmFRoNKyzkuxkS8RURG5GcnJwBAwaIGmbXrl3F8xzGcZF58+b99NNPda1l6PX6sLAwjUazb9+gpKQh5eVX6/T2goI90dH9mjRxbtmyZWRkJDMD6wvh6dO3Nzs+doyJ8dziYh6zDRtY9QWU0tPZ6tVs9Ojbm7YQzQ8IWLBgwenTp6sXmZubKx6xbdu27alTp44cOeLi4kJErwYEMGX3Ej14kF/GE0/wdqCzM1OpWF1mV/8pFBUViRnS1cckExISTCw2KenQ6dPt4uLo119b3bixqzZv0etvXboUHBdHcXG0YUOw4tsSWnEI336b7dxZdTxpEs/krVts8GC2fDn7/nvexNq/n23bxgYOZGp1VfY0GjZsGAsP5z2Tf6CoqEgsZ9K8efNDhw4lJCQ83qnTzZ49+QZLCu7jYzCwxER25Ai7fJn16MG6dGGNUWFh4SeffBIUFKTsc086XVZKyrOVoVJdvhxiMNyvNltcfDYhoVdcHP38s316ehhjZnzEGSE8zW93H39c9ZO0NJ639et59hwcmK8vz17tHtItKyt74YUXxNPuW7ZsqbhwgT32GC+nSxd28aIyF3/+PJs+nQ/Wv/cev3ubbTmpxsuQmRn+88+2cXGUmNivtDT1j19jFxdH8fHdb90y+wiQVYZw0CA2bhz/at+eh3Du3DtaVl26sLw8tmULv0PWUUVFRXBwsI1afcnHh61bxzIyeNWRiLVrx09kosJCXpp4miEigk2caGqB1qqo6KezZx+Ni6NTp5rm5fFJ5AZDeXl5BmP68vL05OTR4m558eJren2d/wbqwSpDeNed8KOP+K1PKCrif+im+W3xYh48lYotWcKT4+vLv3Vx4dVIU0RF8c8Lox49GsEIoSw6Xe7588/HxdG5cwMKCqKTknzS0qZcufLm5cuz4+Lo9Om2BQWWW4UV2/wQvfACvfwy+flRixb0/vv82DSd584lBwcKCaE336SMDPrhBwoKou+/5/uhffcdVa5dWR95eeTqevvbZs2ooOCOn0Ct2di08PSMzMr6tFkz//T0BQ8//J8mTfgDxwZDEWMVbm7zbWyU3wz0DzFrs2ED++WXquPly6u6Q48d43fFwEA+D1OpcfYNG5itLb8HTpnCt1OaOpUf29mxlJR6Fnj8OHvpparjkhLWvbsy12n18vO3xsd3z8xcVlGhxG47dYedes0pJobGjaOiIr4f78aNpNWSkxPNn1//Al98ke8k+sQTfB/t557jexeCEioqsnNzv7xxI7JLl1iyOITQzI4c4Zti5+fz3bGjongIi4po/36+EfbQodSqVd1KY4y/98oV8vKiagvdg2kYkYqIJSR06949QaWq9TauCkGb0My8venwYRo1ipo2JXt7ysnhmZwyhZo04bfH1auppj3u/5BKRdUergVFXL8+v6TkrF5/s0WLly2fQNwJLeXqVWrdmuzseMdP+/ZV1cjjx2npUl5NBflECqqWirMwbBJqEe7uPIFElJxMvXpV/bB3b/4tNAgqWQlECC3O1ZXXSIXsbGrZUvL1QAOAEFrWxIn03/9SYSHpdPTBBzRpkuwLAvnQJrS43bvpyy95CP39eQ8NWD2EEEAyVEcBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBJEMIASRDCAEkQwgBSK7/B0v95tG/CNuOAAAAAElFTkSuQmCC",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=300x300>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Modify Lysine with Biotin N-terminal modification\n",
    "modified_lys = modify_amino_acid(aa_smiles[\"K\"], n_term_mod=\"Biotin@Any_N-term\")\n",
    "print(\"Modified Lysine SMILES:\", modified_lys)\n",
    "mod_lys_mol = Chem.MolFromSmiles(modified_lys)\n",
    "Draw.MolToImage(mod_lys_mol)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To obtained the unmodified aminoacid, just pass the corresponding SMILES to the function with no additional arguments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lysine SMILES: [H]N([H])[C@@H](CCCCN)C(=O)O\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=300x300>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "non_modified_lys = modify_amino_acid(aa_smiles[\"K\"])\n",
    "print(\"Lysine SMILES:\", non_modified_lys)\n",
    "Draw.MolToImage(Chem.MolFromSmiles(non_modified_lys))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the Biotin modification has been added to the N-terminus of Lysine.\n",
    "\n",
    "## Working with Post-Translational Modifications (PTMs)\n",
    "\n",
    "The `ptm_dict` contains various post-translational modifications. Let's examine one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phosphorylated Serine SMILES: O=P(O)(O)OC[C@@H](C(=O)[Ts])N([Fl])([Fl])\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=300x300>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Phosphorylated Serine SMILES:\", ptm_dict[\"Phospho@S\"])\n",
    "phos_ser_mol = Chem.MolFromSmiles(ptm_dict[\"Phospho@S\"])\n",
    "Draw.MolToImage(phos_ser_mol)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the `modify_amino_acid` function with these PTMs as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modified Phosphorylated Serine SMILES: [H]N(C(C)=O)[C@@H](COP(=O)(O)O)C(=O)O\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=300x300>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Modify phosphorylated Serine with Acetyl N-terminal modification\n",
    "mod_phos_ser = modify_amino_acid(ptm_dict[\"Phospho@S\"], n_term_mod=\"Acetyl@Any_N-term\")\n",
    "print(\"Modified Phosphorylated Serine SMILES:\", mod_phos_ser)\n",
    "mod_phos_ser_mol = Chem.MolFromSmiles(mod_phos_ser)\n",
    "Draw.MolToImage(mod_phos_ser_mol)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "In this tutorial, we've explored how to work with amino acids and post-translational modifications using SMILES notation and the `modify_amino_acid` function. We've seen how to:\n",
    "\n",
    "1. Represent amino acids using SMILES\n",
    "2. Add N-terminal modifications\n",
    "3. Work with post-translational modifications\n",
    "4. Visualize molecular structures using RDKit\n",
    "\n"
   ]
  }
 ],
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    "name": "ipython",
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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