{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NOTEBOOK_HEADER-->\n",
    "*This notebook contains material from [PyRosetta](https://RosettaCommons.github.io/PyRosetta.notebooks);\n",
    "content is available [on Github](https://github.com/RosettaCommons/PyRosetta.notebooks.git).*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Distributed analysis example: exhaustive ddG PSSM](http://nbviewer.jupyter.org/github/RosettaCommons/PyRosetta.notebooks/blob/master/notebooks/16.01-PyData-ddG-pssm.ipynb) | [Contents](toc.ipynb) | [Index](index.ipynb) | [Example of Using PyRosetta with GNU Parallel](http://nbviewer.jupyter.org/github/RosettaCommons/PyRosetta.notebooks/blob/master/notebooks/16.03-GNU-Parallel-Via-Slurm.ipynb) ><p><a href=\"https://colab.research.google.com/github/RosettaCommons/PyRosetta.notebooks/blob/master/notebooks/16.02-PyData-miniprotein-design.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open in Google Colaboratory\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distributed computation example: miniprotein design\n",
    "\n",
    "## Notes\n",
    "This tutorial will walk you through how to design miniproteins in PyRosetta using the PyData stack for analysis and distributed computing.\n",
    "\n",
    "This Jupyter notebook uses parallelization and is not meant to be executed within a Google Colab environment.\n",
    "\n",
    "## Setup\n",
    "Please see setup instructions in Chapter 16.00\n",
    "\n",
    "## Citation\n",
    "[Integration of the Rosetta Suite with the Python Software Stack via reproducible packaging and core programming interfaces for distributed simulation](https://doi.org/10.1002/pro.3721)\n",
    "\n",
    "Alexander S. Ford, Brian D. Weitzner, Christopher D. Bahl\n",
    "\n",
    "## Manual\n",
    "Documentation for the `pyrosetta.distributed` namespace can be found here: https://nbviewer.jupyter.org/github/proteininnovation/Rosetta-PyData_Integration/blob/master/distributed_overview.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install pyrosettacolabsetup\n",
    "import pyrosettacolabsetup; pyrosettacolabsetup.install_pyrosetta()\n",
    "import pyrosetta; pyrosetta.init()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyrosetta\n",
    "import dask.bag\n",
    "import dask.distributed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas\n",
    "import seaborn\n",
    "from matplotlib import pylab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyrosetta.distributed.tasks.score import ScorePoseTask\n",
    "from pyrosetta.distributed.io import pose_from_pdbstring\n",
    "from pyrosetta.distributed.packed_pose import to_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import zipfile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Init\n",
    "\n",
    "Setup LocalCluster to full utilize the current machine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3>Client</h3>\n",
       "<ul>\n",
       "  <li><b>Scheduler: </b>tcp://127.0.0.1:60557\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3>Cluster</h3>\n",
       "<ul>\n",
       "  <li><b>Workers: </b>8</li>\n",
       "  <li><b>Cores: </b>8</li>\n",
       "  <li><b>Memory: </b>17.18 GB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: scheduler='tcp://127.0.0.1:60557' processes=8 cores=8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if not os.getenv(\"DEBUG\"):\n",
    "    cluster = dask.distributed.LocalCluster(n_workers=1, threads_per_worker=1)\n",
    "    client = dask.distributed.Client(cluster)\n",
    "else:\n",
    "    client = None\n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load \n",
    "\n",
    "Load decoys from batch compute run, adding annotations for designed sequence, cystine count and cystine location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_source(decoy):\n",
    "    src_pdb = zipfile.ZipFile(library).open(decoy).read()\n",
    "    p = pose_from_pdbstring(src_pdb)\n",
    "    \n",
    "    cys_locations=[i for i, c in enumerate(p.pose.sequence()) if c == \"C\"]\n",
    "    p = p.update_scores(\n",
    "        library=library,\n",
    "        decoy=decoy,\n",
    "        sequence=p.pose.sequence(),\n",
    "        num_res = len(p.pose.sequence()),\n",
    "        num_cys=len(cys_locations),\n",
    "        cys_locations=\",\".join(map(str, cys_locations))\n",
    "    )\n",
    "\n",
    "    return p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.getenv(\"DEBUG\"):\n",
    "    library = \"inputs/EHEE_library.zip\"\n",
    "    decoy_names = [f.filename for f in zipfile.ZipFile(library).filelist if f.filename.endswith(\".pdb\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.getenv(\"DEBUG\"):\n",
    "    decoys = dask.bag.from_sequence(decoy_names).map(load_source).map(ScorePoseTask()).persist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.getenv(\"DEBUG\"):\n",
    "    result_frame = pandas.DataFrame.from_records(decoys.map(to_dict).compute()).sort_values(\"total_score\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis\n",
    "\n",
    "We anticipate a distribution of score results, with higher scores with more disulfide insertions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc3404f9860>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if not os.getenv(\"DEBUG\"):\n",
    "    seaborn.boxplot(x=\"num_cys\", y=\"total_score\", data=result_frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc3404a5240>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if not os.getenv(\"DEBUG\"):\n",
    "    seaborn.boxplot(x=\"cys_locations\", y=\"total_score\", data=result_frame)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select \n",
    "\n",
    "Select the best model by total_Score for each inserted disulfide location, allowing us to test a variety of disulfide architectures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.getenv(\"DEBUG\"):\n",
    "    best_by_location = to_dict(result_frame.groupby(\"cys_locations\").head(1).reset_index(drop=True))\n",
    "    print(len(best_by_location))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.getenv(\"DEBUG\"):\n",
    "    with open(\"EHEE.best_by_location.fasta\", \"w\") as out:\n",
    "        for entry in best_by_location:\n",
    "            print(f\">{entry['decoy']}\", file=out)\n",
    "            print(entry['sequence'], file=out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">30652025_EHEE_254_0001.pdb\r\n",
      "GDYQLHTCNTSEEELKKLTETLRRRLQTECKLERHGDCYTITCHV\r\n",
      ">30887334_EHEE_738_0001.pdb\r\n",
      "GCKTLTFCGYDDEQAKKIQKDISKTVQRPVEVHKHGSCWEFHVCV\r\n",
      ">30856927_EHEE_395_0001.pdb\r\n",
      "GCTTWEFHNVDPNEVKKALRELSEKTGAECHLEQHGNTFHITCCV\r\n",
      ">30884953_EHEE_269_0001_0004.pdb\r\n",
      "GQKCVTFCGQDPREVKKIAEEIARRLQVPYEIRRHGSCITVCFKV\r\n",
      ">30696637_EHEE_467_0001_0002.pdb\r\n",
      "GTKTFTYDGVDPTEVDKSRERLEKELKTRCELECRGNQCHIHCHV\r\n"
     ]
    }
   ],
   "source": [
    "if not os.getenv(\"DEBUG\"):\n",
    "    !head expected_outputs/EHEE.best_by_location.fasta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize \n",
    "\n",
    "Visualize using Py3Dmol or PyMolMover as you have learned before. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nimport py3Dmol\\nview = py3Dmol.view(viewergrid=(3, 3), linked=False, width=900, height=900)\\nfor i in range(9):\\n    view.addModel( pyrosetta.distributed.io.to_pdbstring(best_by_location[i]), \"pdb\", viewer=(i/3, i%3),)\\n    \\nview.setStyle({\\'cartoon\\':{\\'color\\':\\'spectrum\\'}})\\nview.setStyle({\"resn\": \"CYS\"}, {\\'stick\\': {}, \\'cartoon\\':{\\'color\\':\\'spectrum\\'}} )\\nview.zoomTo()\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "import py3Dmol\n",
    "view = py3Dmol.view(viewergrid=(3, 3), linked=False, width=900, height=900)\n",
    "for i in range(9):\n",
    "    view.addModel( pyrosetta.distributed.io.to_pdbstring(best_by_location[i]), \"pdb\", viewer=(i/3, i%3),)\n",
    "    \n",
    "view.setStyle({'cartoon':{'color':'spectrum'}})\n",
    "view.setStyle({\"resn\": \"CYS\"}, {'stick': {}, 'cartoon':{'color':'spectrum'}} )\n",
    "view.zoomTo()\n",
    "'''"
   ]
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    "<!--NAVIGATION-->\n",
    "< [Distributed analysis example: exhaustive ddG PSSM](http://nbviewer.jupyter.org/github/RosettaCommons/PyRosetta.notebooks/blob/master/notebooks/16.01-PyData-ddG-pssm.ipynb) | [Contents](toc.ipynb) | [Index](index.ipynb) | [Example of Using PyRosetta with GNU Parallel](http://nbviewer.jupyter.org/github/RosettaCommons/PyRosetta.notebooks/blob/master/notebooks/16.03-GNU-Parallel-Via-Slurm.ipynb) ><p><a href=\"https://colab.research.google.com/github/RosettaCommons/PyRosetta.notebooks/blob/master/notebooks/16.02-PyData-miniprotein-design.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open in Google Colaboratory\"></a>"
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