{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ramachandran plot (gerdos/PyRAMA engine)\n",
    "\n",
    "In this minitool I want to share with you the implemetation of Gerdos pyRAMA tool for ploting Ramachandran plots of proteins inside of jupyter notebook. The full code of pyRAMA can be found on gerdos Github page (https://github.com/gerdos/PyRAMA).\n",
    "\n",
    "The aim of this minitool is not just to visualize the Ramachandran plots but also perform some structural visualization using nglview. Let's put the hands on it.\n",
    "\n",
    "For Ramachandran plots we need the pyRAMA code. Next cell contains such code ready to work directly on jupyter as funtion, but before to run the funtion we need 2 things and they are mandatory.\n",
    "\n",
    "1- The pdb file we want to analyze (from the PDB database or a model that we made).\n",
    "2- The rama500- .data files which store the density profiles or occupancies as a grid (download from my github at: https://github.com/AngelRuizMoreno/Cheminformatics_workflows_source/blob/master/Others/rama-500.zip )\n",
    "\n",
    "Now let's run the script"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdb_file='1eve.pdb' #This is the pdb we will analize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import sys\n",
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from Bio import PDB\n",
    "from matplotlib import colors\n",
    "\n",
    "def plot_ramachandran(file):\n",
    "    __file__=file\n",
    "\n",
    "    \"\"\"\n",
    "    The preferences were calculated from the following artice:\n",
    "    Lovell et al. Structure validation by Calpha geometry: phi,psi and Cbeta deviation. 2003\n",
    "    DOI: 10.1002/prot.10286\n",
    "    \"\"\"\n",
    "\n",
    "    # General variable for the background preferences\n",
    "    rama_preferences = {\n",
    "        \"General\": {\n",
    "            \"file\": \"rama500-general.data\",\n",
    "            \"cmap\": colors.ListedColormap(['#FFFFFF', '#B3E8FF', '#7FD9FF']),\n",
    "            \"bounds\": [0, 0.0005, 0.02, 1],\n",
    "        },\n",
    "        \"GLY\": {\n",
    "            \"file\": \"rama500-gly-sym.data\",\n",
    "            \"cmap\": colors.ListedColormap(['#FFFFFF', '#FFE8C5', '#FFCC7F']),\n",
    "            \"bounds\": [0, 0.002, 0.02, 1],\n",
    "        },\n",
    "        \"PRO\": {\n",
    "            \"file\": \"rama500-pro.data\",\n",
    "            \"cmap\": colors.ListedColormap(['#FFFFFF', '#D0FFC5', '#7FFF8C']),\n",
    "            \"bounds\": [0, 0.002, 0.02, 1],\n",
    "        },\n",
    "        \"PRE-PRO\": {\n",
    "            \"file\": \"rama500-prepro.data\",\n",
    "            \"cmap\": colors.ListedColormap(['#FFFFFF', '#B3E8FF', '#7FD9FF']),\n",
    "            \"bounds\": [0, 0.002, 0.02, 1],\n",
    "        }\n",
    "    }\n",
    "\n",
    "    # Read in the expected torsion angles\n",
    "    __location__ = './' #You must set the ptah of the .data files here\n",
    "    rama_pref_values = {}\n",
    "    for key, val in rama_preferences.items():\n",
    "        rama_pref_values[key] = np.full((360, 360), 0, dtype=np.float64)\n",
    "        with open(os.path.join(__location__, val[\"file\"])) as fn:\n",
    "            for line in fn:\n",
    "                if not line.startswith(\"#\"):\n",
    "                    # Preference file has values for every second position only\n",
    "                    rama_pref_values[key][int(float(line.split()[1])) + 180][int(float(line.split()[0])) + 180] = float(\n",
    "                        line.split()[2])\n",
    "                    rama_pref_values[key][int(float(line.split()[1])) + 179][int(float(line.split()[0])) + 179] = float(\n",
    "                        line.split()[2])\n",
    "                    rama_pref_values[key][int(float(line.split()[1])) + 179][int(float(line.split()[0])) + 180] = float(\n",
    "                        line.split()[2])\n",
    "                    rama_pref_values[key][int(float(line.split()[1])) + 180][int(float(line.split()[0])) + 179] = float(\n",
    "                        line.split()[2])\n",
    "\n",
    "    normals = {}\n",
    "    outliers = {}\n",
    "    for key, val in rama_preferences.items():\n",
    "        normals[key] = {\"x\": [], \"y\": [],'Res':[]}\n",
    "        outliers[key] = {\"x\": [], \"y\": []}\n",
    "\n",
    "    # Calculate the torsion angle of the inputs\n",
    "    #for inp in sys.argv[1:]:\n",
    "        #if not os.path.isfile(inp):\n",
    "            #print(\"{} not found!\".format(inp))\n",
    "            #continue\n",
    "    structure = PDB.PDBParser().get_structure('input_structure', __file__)\n",
    "    for model in structure:\n",
    "        for chain in model:\n",
    "            polypeptides = PDB.PPBuilder().build_peptides(chain)\n",
    "            for poly_index, poly in enumerate(polypeptides):\n",
    "                phi_psi = poly.get_phi_psi_list()\n",
    "                for res_index, residue in enumerate(poly):\n",
    "                    res_name = \"{}\".format(residue.resname)\n",
    "                    res_num = residue.id[1]\n",
    "                    phi, psi = phi_psi[res_index]\n",
    "                    if phi and psi:\n",
    "                        aa_type = \"\"\n",
    "                        if str(poly[res_index + 1].resname) == \"PRO\":\n",
    "                            aa_type = \"PRE-PRO\"\n",
    "                        elif res_name == \"PRO\":\n",
    "                            aa_type = \"PRO\"\n",
    "                        elif res_name == \"GLY\":\n",
    "                            aa_type = \"GLY\"\n",
    "                        else:\n",
    "                            aa_type = \"General\"\n",
    "                        if rama_pref_values[aa_type][int(math.degrees(psi)) + 180][int(math.degrees(phi)) + 180] < \\\n",
    "                                rama_preferences[aa_type][\"bounds\"][1]:\n",
    "                            print(\"{} {} {} {}{} is an outlier\".format(inp, model, chain, res_name, res_num))\n",
    "                            outliers[aa_type][\"x\"].append(math.degrees(phi))\n",
    "                            outliers[aa_type][\"y\"].append(math.degrees(psi))\n",
    "                        else:\n",
    "                            normals[aa_type][\"x\"].append(math.degrees(phi))\n",
    "                            normals[aa_type][\"y\"].append(math.degrees(psi))\n",
    "                            normals[aa_type]['Res'].append(res_name+'_'+str(res_num))\n",
    "\n",
    "    # Generate the plots\n",
    "    plt.figure(figsize=(10,10))\n",
    "    for idx, (key, val) in enumerate(sorted(rama_preferences.items(), key=lambda x: x[0].lower())):\n",
    "        plt.subplot(2, 2, idx + 1)\n",
    "        plt.title(key,fontsize=20)\n",
    "        plt.imshow(rama_pref_values[key], cmap=rama_preferences[key][\"cmap\"],\n",
    "                   norm=colors.BoundaryNorm(rama_preferences[key][\"bounds\"], rama_preferences[key][\"cmap\"].N),\n",
    "                   extent=(-180, 180, 180, -180),alpha=0.7)\n",
    "\n",
    "        plt.scatter(normals[key][\"x\"], normals[key][\"y\"],s=[15],marker='.')\n",
    "\n",
    "        #for key in normals:\n",
    "            #for i, name in enumerate (normals[key]['Res']):\n",
    "                #plt.annotate(name, (normals[key][\"x\"][i], normals[key][\"y\"][i]))\n",
    "\n",
    "        plt.scatter(outliers[key][\"x\"], outliers[key][\"y\"],color=\"red\",s=[15],marker='.')\n",
    "        plt.xlim([-180, 180])\n",
    "        plt.ylim([-180, 180])\n",
    "        plt.plot([-180, 180], [0, 0],color=\"k\",alpha=0.7)\n",
    "        plt.plot([0, 0], [-180, 180],color=\"k\",alpha=0.7)\n",
    "        plt.xlabel(r'$\\phi$',fontsize=12)\n",
    "        plt.ylabel(r'$\\psi$',fontsize=12)\n",
    "        plt.grid(linestyle='dotted')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    # plt.savefig(\"asd.png\", dpi=300) #Uncommet this line of you want so save the plot in a specific location\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time to visualize the Ramachandran plot of the 1eve.pdb file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ramachandran(pdb_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d8b98eabab624076bdfa0261cd951728",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "_ColormakerRegistry()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import nglview as nv #Let's see the 3D structure of the protein"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "67c99d23abaf4aa286eac99c1bd69aee",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nv.show_structure_file(pdb_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I know that this is something simple and not very useful. But it could be the first idea to perform more deep analysis and figures. The pyRAMA code can be easily modified to convert the simple plot into an interactive one. Such modification can be useful to identify outlayer residues and then visualize them using nglview. \n",
    "\n",
    "Independently what you whant to do, I hope you find useful this minitool. At least to make cool Ramachandran plots of your protein structures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ".. disqus::"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}