{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 結晶構造との比較"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## モデルのばらつきによるモデルの分解能の評価\n",
    "\n",
    "結晶構造の比較する前に、モデルの分解能について調べてみる。\n",
    "\n",
    "モデルの分解能はFourier Shell Correlation (FSC)でビーズモデルのばらつきから、モデルの分解能がわかるという報告がある。[７]\n",
    "\n",
    "これは、`damaver` を実行した際に生成されるログファイル`damsel.log`に記述されている。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-20T00:53:06.153800Z",
     "start_time": "2019-12-20T00:53:06.132716Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Ensemble Resolution =    32 +-  3 Angstrom\n",
      "\n"
     ]
    }
   ],
   "source": [
    "flog=open('damsel.log','r')\n",
    "lines=flog.readlines()\n",
    "res = [i for i in lines if 'Ensemble Resolution' in i]\n",
    "print(res[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 元のpdbファイルとの重ね合わせ\n",
    "\n",
    "結晶構造と比較するには代表的ビーズモデルの向きをあわせてやる（アラインメント）してあげる必要がある。\n",
    "\n",
    "`6lyz.pdb`に`damfilt.pdb`をアラインメントしてみよう。\n",
    "\n",
    "ここでは`supalm` [８]という`ATSAS`のプログラムを使用する。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T05:45:15.597192Z",
     "start_time": "2020-04-17T05:45:14.711703Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' Read file .............................................. : 6lyz.pdb',\n",
       " ' Number of atoms read ................................... : 1102',\n",
       " ' Fineness of the template ............................... : 1.514',\n",
       " ' Read file .............................................. : damfilt.pdb',\n",
       " ' Number of atoms read ................................... : 853',\n",
       " ' Fineness of the superimposed structure ................. : 3.500',\n",
       " '',\n",
       " '  Principal axes orientation table',\n",
       " '',\n",
       " '    Distance                       Orientation',\n",
       " '',\n",
       " '   1.1360454524805610                1          -1          -1',\n",
       " '   1.1419180336932468                1           1          -1',\n",
       " '   1.1706840733884827               -1           1           1',\n",
       " '   1.1720072826064973               -1          -1           1',\n",
       " '   1.1741133266525163                1           1           1',\n",
       " '   1.1992066476404875               -1          -1          -1',\n",
       " ' ---------------------------------------------',\n",
       " '   1.1996296950877527                1          -1           1',\n",
       " '   1.2065759032678545               -1           1          -1',\n",
       " ' Calculating amplitudes from 6lyz.pdb',\n",
       " ' --WARNING: Recompute the intensity with LM= 13',\n",
       " ' Calculating amplitudes from damfilt.pdb',\n",
       " '                                Initial       Final',\n",
       " '              Orientation   Correlation Correlation',\n",
       " '              1    -1    -1     0.87072     0.87756',\n",
       " '              1     1    -1     0.87217     0.87629',\n",
       " '             -1     1     1     0.86609     0.87156',\n",
       " '             -1    -1     1     0.86081     0.87057',\n",
       " '              1     1     1     0.86799     0.88473',\n",
       " '             -1    -1    -1     0.85997     0.86985',\n",
       " ' Final distance (NSD) =    1.1673967678946129     ',\n",
       " ' Wrote file ......... :    damfiltr.pdb',\n",
       " '']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!!supalm  6lyz.pdb damfilt.pdb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`6lyz.pdb`にアラインメントされた`damfilt.pdb`は`damfiltr.pdb`として保存されているのでそれを読み込む。\n",
    "\n",
    "通常なら別のプログラムを立ち上げて確認することになるのだが、jupyter-notebook上で３D表示し、視点を変えることもできる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T05:45:26.516659Z",
     "start_time": "2020-04-17T05:45:26.472898Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8e99c9f5e4014bb1862873acc593639f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import nglview\n",
    "view = nglview.show_file(\"damfiltr.pdb\")  # ビーズファイルを指定する\n",
    "view.add_surface('.CA', opacity=0.1)  # ビーズファイルの表面だけ表示する\n",
    "#以下の部分はグラフィックボードとの相性で同時に表示されないこともあるのでコメントアウトしている。\n",
    "mol2=nglview.FileStructure(\"6lyz.pdb\") #pdbファイルを指定する\n",
    "view.add_structure(mol2)\n",
    "view.camera = 'orthographic'\n",
    "view.center()\n",
    "view\n"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![beads-fit2-1.jpg](attachment:beads-fit2-1.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "気に入った向きで画像を動かして、次のコマンドで保存する。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T05:45:51.655379Z",
     "start_time": "2020-04-17T05:45:51.648471Z"
    }
   },
   "outputs": [],
   "source": [
    "view.download_image('beads.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalized Spatial Discrepancy (NSD)値\n",
    "構造の差異を表す指標としてRMSD（Root Mean Square Deviaiton)平均自乗偏差が良く使われるが、結晶構造とビーズモデルでは分解能が違うのでRMSDは使えない。\n",
    "\n",
    "そこで、タンパク質溶液散乱ではNSD(Normalized Spatial Discrepancy)という指標を使用する。[６]\n",
    "\n",
    "NSDは、元のPDBファイルとの重ね合わせでもNSDが最小になるようPDBの方向を変えているので、`damfiltr.pdb`ファイルにも記載されている。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T05:52:03.851670Z",
     "start_time": "2020-04-17T05:52:03.843494Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REMARK 265  Final distance (NSD)     :   1.1674\n",
      "\n"
     ]
    }
   ],
   "source": [
    "flog=open('damfiltr.pdb','r')\n",
    "lines=flog.readlines()\n",
    "res = [i for i in lines if 'Final distance' in i]\n",
    "print(res[0])"
   ]
  }
 ],
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