{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 05_plot_the_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Determination of memory status is not supported on this \n",
      " platform, measuring for memoryleaks will never fail\n"
     ]
    }
   ],
   "source": [
    "from pigeon_feather.data import *\n",
    "from pigeon_feather.plot import *\n",
    "from pigeon_feather.hxio import *\n",
    "from pigeon_feather.spectra import *\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import datetime\n",
    "import os\n",
    "import pickle\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peptides with experimental max_d: 358\n",
      "Number of peptides with no experimental max_d: 12\n"
     ]
    }
   ],
   "source": [
    "# load the pickle file we saved in the previous notebook\n",
    "\n",
    "\n",
    "today = datetime.date.today().strftime(\"%Y%m%d\")\n",
    "today = \"20240722\"\n",
    "\n",
    "with open(f\"./data/hdxms_data_raw_{today}.pkl\", \"rb\") as f:\n",
    "    hdxms_data_list = pickle.load(f)\n",
    "\n",
    "\n",
    "# back exchange correction for peptides with experimental full deuteration data based its closest match in the database\n",
    "tools.backexchange_correction(hdxms_data_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make folders for results\n",
    "\n",
    "today_date = datetime.date.today().strftime(\"%Y%m%d\")\n",
    "# today_date = '20240722'\n",
    "results_path = f\"ecDHFR_results_{today_date}\"\n",
    "\n",
    "if not os.path.exists(results_path):\n",
    "    os.makedirs(results_path)\n",
    "\n",
    "\n",
    "out_path = \"./data/PF_input_20240722\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create an `Analysis` object and load the results. **Note:** The temperature and pH are crucial for calculating the intrinsic exchange rates, so make sure to input the correct values. The chunk size and chunk number can be altered by the user. In this tutorial, they were automatically determined by a helper function.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pigeon_feather.analysis import Analysis, get_index_offset\n",
    "from pigeon_feather.tools import optimal_chunks\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "RUN_NUM = 3\n",
    "\n",
    "apo_states = [\n",
    "    state\n",
    "    for data in hdxms_data_list\n",
    "    for state in data.states\n",
    "    if state.state_name == \"APO\"\n",
    "]\n",
    "\n",
    "tri_states = [\n",
    "    state\n",
    "    for data in hdxms_data_list\n",
    "    for state in data.states\n",
    "    if state.state_name == \"TRI\"\n",
    "]\n",
    "\n",
    "ana_apo_1 = Analysis(apo_states, temperature=293.0, pH=7.0)\n",
    "\n",
    "chunk_num, chunk_size = optimal_chunks(len(ana_apo_1.protein_sequence))\n",
    "ana_apo_1.load_bayesian_hdx_oupt_chunks(\n",
    "    chunk_size=chunk_size,\n",
    "    chunk_num=chunk_num,\n",
    "    state_name=\"APO\",\n",
    "    run_num=RUN_NUM,\n",
    "    N=200,\n",
    "    bayesian_hdx_data_folder=f\"{out_path}/bayesian_hdx_output_chunks\",\n",
    ")\n",
    "\n",
    "\n",
    "ana_tri_1 = Analysis(tri_states, temperature=293.0, pH=7.0)\n",
    "\n",
    "ana_tri_1.load_bayesian_hdx_oupt_chunks(\n",
    "    chunk_size=chunk_size,\n",
    "    chunk_num=chunk_num,\n",
    "    state_name=\"TRI\",\n",
    "    run_num=RUN_NUM,\n",
    "    N=200,\n",
    "    bayesian_hdx_data_folder=f\"{out_path}/bayesian_hdx_output_chunks\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## barplot of the kex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "barplot showing the FEATHER derived exchange rates. index_offset is determined by comparison to the sequence in HX/MS and the pdb file provided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 4000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# APO state only\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "fig, axes = plt.subplots(2, 1, figsize=(40, 10), sharey=True, sharex=True)\n",
    "\n",
    "ana_apo_1.plot_kex_bar(\n",
    "    ax=axes[0], resolution_indicator_pos=15, label=\"APO\", show_seq=False, color=\"k\"\n",
    ")\n",
    "\n",
    "ana_tri_1.plot_kex_bar(\n",
    "    ax=axes[1], resolution_indicator_pos=15, label=\"TRI\", show_seq=False, color=\"red\"\n",
    ")\n",
    "\n",
    "from matplotlib.pyplot import step\n",
    "from matplotlib.ticker import FuncFormatter, MultipleLocator\n",
    "\n",
    "from pigeon_feather.analysis import get_index_offset\n",
    "\n",
    "pdb_file = \"./data/6XG5_TRI.pdb\"\n",
    "index_offset = get_index_offset(ana_apo_1, pdb_file)\n",
    "\n",
    "# # ax.xaxis.set_major_locator(plt.MultipleLocator(2))\n",
    "axes[0].set_xticks(axes[0].get_xticks()[::2])\n",
    "axes[0].set_xticklabels(axes[0].get_xticklabels(), fontdict={\"fontsize\": 24})\n",
    "\n",
    "\n",
    "def format_func(value, tick_number):\n",
    "    return f\"{int(value - index_offset +1)}\"\n",
    "\n",
    "\n",
    "axes[0].xaxis.set_major_formatter(FuncFormatter(format_func))\n",
    "\n",
    "seq_pos = 17\n",
    "for ii in range(0, len(ana_apo_1.protein_sequence[:]), 2):\n",
    "    axes[0].text(\n",
    "        ii,\n",
    "        seq_pos,\n",
    "        ana_apo_1.protein_sequence[ii],\n",
    "        ha=\"center\",\n",
    "        va=\"center\",\n",
    "        fontsize=22,\n",
    "    )\n",
    "\n",
    "\n",
    "for ax in axes:\n",
    "    ax.set_ylabel(\"-log(k$_{ex}$)\")\n",
    "    #ax.legend(loc=\"lower left\")\n",
    "    ax.yaxis.set_major_locator(plt.MultipleLocator(5))\n",
    "\n",
    "    ax.plot(\n",
    "        [-3, len(ana_apo_1.protein_sequence) + 3],\n",
    "        [5, 5],\n",
    "        color=\"gray\",\n",
    "        lw=1,\n",
    "        linestyle=\"--\",\n",
    "        alpha=0.5,\n",
    "    )\n",
    "    ax.plot(\n",
    "        [-3, len(ana_apo_1.protein_sequence) + 3],\n",
    "        [10, 10],\n",
    "        color=\"gray\",\n",
    "        lw=1,\n",
    "        linestyle=\"--\",\n",
    "        alpha=0.5,\n",
    "    )\n",
    "\n",
    "\n",
    "plt.subplots_adjust(hspace=0.08)\n",
    "fig.savefig(f\"{results_path}/ecDHFR_kex_bar_APO_TRI_{today_date}.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "logP projection to a pdb structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pigeon_feather.analysis import BFactorPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfactor_plot = BFactorPlot(\n",
    "    ana_apo_1,\n",
    "    pdb_file=\"./data/6XG5_TRI.pdb\",\n",
    "    plot_deltaG=True,\n",
    "    temperature=293.15,\n",
    "    logP_threshold=10,\n",
    ")\n",
    "bfactor_plot.plot(f\"{results_path}/6XG5_APO_deltaG.pdb\")\n",
    "\n",
    "# bfactor_plot = BFactorPlot(\n",
    "#     ana_apo_1,\n",
    "#     pdb_file=\"./data/6XG5_APO_relaxed_best_solvated.pdb\",\n",
    "#     plot_deltaG=True,\n",
    "#     temperature=293.15,\n",
    "# )\n",
    "# bfactor_plot.plot(f\"{out_path}/6XG5_APO_deltaG.pdb\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## delta log(PF)s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "if there are mutiple states availble, you can map the difference to the pdb strutcure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfactor_plot = BFactorPlot(\n",
    "    ana_apo_1,\n",
    "    ana_tri_1,  \n",
    "    pdb_file=\"./data/6XG5_TRI.pdb\",\n",
    "    plot_deltaG=True,\n",
    "    temperature=293.15,\n",
    "    logP_threshold=10,\n",
    ")\n",
    "bfactor_plot.plot(f\"{results_path}/6XG5_deltaG_APO-TRI.pdb\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can visualize the results in PyMOL by running the following commands:\n",
    "\n",
    "```bash\n",
    "spectrum b, green_white_magenta, minimum=-20, maximum=20; select nans, not (b=0 or b>0 or b<0); color grey20, nans;\n",
    "```\n",
    "\n",
    "`nans` are Prolines and residues with no coverage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "export logPFs to a csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import MDAnalysis\n",
    "from pigeon_feather.analysis import get_res_avg_logP, get_res_avg_logP_std\n",
    "\n",
    "pdb_file = \"./data/6XG5_TRI.pdb\"\n",
    "index_offset = get_index_offset(ana_apo_1, pdb_file)\n",
    "\n",
    "\n",
    "def logPF_to_deltaG(ana_obj, logPF):\n",
    "    \"\"\"\n",
    "    :param logPF: logP value\n",
    "    :return: deltaG in kJ/mol, local unfolding energy\n",
    "    \"\"\"\n",
    "\n",
    "    return 8.3145 * ana_obj.temperature * np.log(10) * logPF / 1000\n",
    "\n",
    "\n",
    "def create_logP_df(ana_obj, index_offset):\n",
    "    df_logPF = pd.DataFrame()\n",
    "\n",
    "    for res_i, _ in enumerate(ana_obj.results_obj.protein_sequence):\n",
    "        res_obj_i = ana_obj.results_obj.get_residue_by_resindex(res_i)\n",
    "\n",
    "        avg_logP, std_logP = get_res_avg_logP(res_obj_i)\n",
    "        #std_logP = get_res_avg_logP_std(res_obj_i)\n",
    "\n",
    "        df_i = pd.DataFrame(\n",
    "            {\n",
    "                \"resid\": [res_obj_i.resid - index_offset],\n",
    "                #\"resname\": [res_obj_i.resname],\n",
    "                \"resname\": [MDAnalysis.lib.util.convert_aa_code(res_obj_i.resname)],\n",
    "                'avg_dG': [logPF_to_deltaG(ana_obj, avg_logP)],\n",
    "                'std_dG': [logPF_to_deltaG(ana_obj, std_logP)],\n",
    "                \"avg_logP\": [avg_logP],\n",
    "                \"std_logP\": [max(std_logP, 0.35)],\n",
    "                \"is_nan\": [res_obj_i.is_nan()],\n",
    "            }\n",
    "        )\n",
    "\n",
    "        if res_obj_i.is_nan():\n",
    "            df_i[\"single resloved\"] = [np.nan]\n",
    "            df_i[\"min_pep logPs\"] = [np.nan]\n",
    "\n",
    "        else:\n",
    "            df_i[\"single resloved\"] = [res_obj_i.mini_pep.if_single_residue()]\n",
    "            df_i[\"min_pep logPs\"] = [res_obj_i.clustering_results_logP]\n",
    "\n",
    "        df_logPF = pd.concat([df_logPF, df_i])\n",
    "\n",
    "    df_logPF = df_logPF.reset_index(drop=True)\n",
    "\n",
    "    return df_logPF\n",
    "\n",
    "\n",
    "df = create_logP_df(ana_apo_1, index_offset)\n",
    "df.to_csv(f\"{results_path}/logP_APO_{today_date}.csv\", index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fecth two df and merge\n",
    "df_apo = create_logP_df(ana_apo_1, index_offset)\n",
    "df_tri = create_logP_df(ana_tri_1, index_offset)\n",
    "\n",
    "df = pd.merge(df_apo, df_tri, on=\"resid\", suffixes=(\"_APO\", \"_TRI\"))\n",
    "\n",
    "df.to_csv(f\"{results_path}/logP_APO_TRI_{today_date}.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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