{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from pymolint import base, struct_int\n",
    "import numpy as np\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "sys.path.insert(1, '../seqplot/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "from seqplot.p9tools import "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 4, 0.8660254037844386),\n",
       " (2, 4, 0.8660254037844386),\n",
       " (3, 4, 2.598076211353316),\n",
       " (1, 5, 2.598076211353316),\n",
       " (2, 5, 0.8660254037844386),\n",
       " (3, 5, 0.8660254037844386),\n",
       " (2, 6, 2.598076211353316),\n",
       " (3, 6, 0.8660254037844386)]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base.find_contacts([(1,1,1),(2,2,2),(3,3,3)],[1,2,3],[(1.5,1.5,1.5),(2.5,2.5,2.5),(3.5,3.5,3.5)],[4,5,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import MDAnalysis as mda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import io\n",
    "h=io.StringIO(requests.get('https://files.rcsb.org/download/1KX5.pdb').content.decode(\"utf-8\") )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "struct=mda.Universe(h,format='PDB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nglview as nv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0015bb03b55d46fca6fc8352dfef54a1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nv.show_mdanalysis(struct.select_atoms('all'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "d=struct_int.struct2cont(struct,'segid B and protein')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=d.get_df()\n",
    "dp=d.get_num_int_profile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "from seqplot.pdb_plot import plot_prof4pdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3840x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8757847463688)>"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_prof4pdb(pdbchid='1KX5_B',column='num_int',data=dp,ymin=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A_atom_segid</th>\n",
       "      <th>A_atom_resid</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>A</td>\n",
       "      <td>131</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>A</td>\n",
       "      <td>132</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>A</td>\n",
       "      <td>133</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>A</td>\n",
       "      <td>134</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>A</td>\n",
       "      <td>135</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    A_atom_segid  A_atom_resid   0\n",
       "0              A             1  10\n",
       "1              A             2   6\n",
       "2              A             9   2\n",
       "3              A            14   2\n",
       "4              A            17   4\n",
       "..           ...           ...  ..\n",
       "100            A           131  20\n",
       "101            A           132   3\n",
       "102            A           133   9\n",
       "103            A           134  14\n",
       "104            A           135  16\n",
       "\n",
       "[105 rows x 3 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['A_atom_segid','A_atom_resid']).size().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cont_dict={}\n",
    "for k in ['1AOI','1KX5','3LZ0']:\n",
    "    struct_u=mda.Universe('structs/%s_nrf.pdb'%k)\n",
    "    seln=struct_u.select_atoms('nucleic and segid I')\n",
    "    selp=struct_u.select_atoms('protein and %s'%ngd[k]['hist_core'])\n",
    "    pid2resid={a.id:a.resid for a in selp.atoms}\n",
    "    pid2name={a.id:a.name for a in selp.atoms}\n",
    "    pid2segid={a.id:a.segid for a in selp.atoms}\n",
    "    nid2resid={a.id:a.resid for a in seln.atoms}\n",
    "    nid2name={a.id:a.name for a in seln.atoms}\n",
    "    nid2segid={a.id:a.segid for a in seln.atoms}\n",
    "    cont=find_contacts(seln.positions,seln.ids,selp.positions,selp.ids)\n",
    "    contdf=pd.DataFrame({'natom':[i[0] for i in cont],'patom':[i[1] for i in cont],'patom_name':[pid2name[i[1]] for i in cont],'patom_resid':[pid2resid[i[1]] for i in cont],'patom_segid':[pid2segid[i[1]] for i in cont],'natom_name':[nid2name[i[0]] for i in cont],'natom_resid':[nid2resid[i[0]] for i in cont],'natom_segid':[nid2segid[i[0]] for i in cont]})\n",
    "    #Let's construct dataframe that has info about DNA atoms and their classification\n",
    "    dna_at=pd.DataFrame()\n",
    "    for i in range(-73,74):\n",
    "        sel_bases=struct_u.select_atoms(ngd[k]['BP%d_DNA'%i]+' and (name N1 N2 N3 N4 N6 N7 N9 C2 C4 C5 C6 C7 C8 C4 O2 O6)')\n",
    "        sel_sugar=struct_u.select_atoms(ngd[k]['BP%d_DNA'%i]+' and (name O4\\' C1\\' C2\\' C3\\' C4\\' C5\\')')\n",
    "        sel_phosph=struct_u.select_atoms(ngd[k]['BP%d_DNA'%i]+' and (name O5\\' O3\\' P OP1 OP2 O1P O2P)')\n",
    "        bids=list(sel_bases.ids)\n",
    "        sids=list(sel_sugar.ids)\n",
    "        pids=list(sel_phosph.ids)\n",
    "        l=len(bids)+len(sids)+len(pids)\n",
    "        df=pd.DataFrame({'id':(bids+sids+pids),'bp_num':[i]*l,'type':(['1base']*len(bids)+['2sugar']*len(sids)+['3phosph']*len(pids))})\n",
    "        dna_at=dna_at.append(df,ignore_index = True)\n",
    "#     print(dna_at)\n",
    "    #Let's classify contacts according to dna_at\n",
    "        cont_dict[k]=contdf.merge(dna_at,how='left',left_on='natom',right_on='id')\n",
    "\n",
    "\n"
   ]
  }
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