PMV_mutation_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:numexpr.utils:Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
      "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plot\n",
    "import matplotlib.pylab as plt\n",
    "from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)\n",
    "import mdtraj as md\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import os\n",
    "import pickle\n",
    "from enspara import ra\n",
    "from enspara.msm import msm, builders, transition_matrices\n",
    "from functools import partial\n",
    "import scipy.stats\n",
    "\n",
    "import pandas as pd\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style(\"white\")\n",
    "plt.rcParams['font.family'] = 'serif'\n",
    "plt.rcParams['font.serif'] = 'Ubuntu'\n",
    "plt.rcParams['font.monospace'] = 'Ubuntu Mono'\n",
    "plt.rcParams['font.size'] = 20\n",
    "plt.rcParams['axes.labelsize'] = 22\n",
    "plt.rcParams['axes.labelweight'] = 'bold'\n",
    "plt.rcParams['axes.linewidth'] = 3\n",
    "plt.rcParams['axes.titlesize'] = 19\n",
    "plt.rcParams['axes.labelpad'] = 15\n",
    "plt.rcParams['xtick.labelsize'] = 19\n",
    "plt.rcParams['ytick.labelsize'] = 19\n",
    "plt.rcParams['legend.fontsize'] = 25\n",
    "plt.rcParams['axes.titlesize'] = 25\n",
    "plt.rcParams['axes.titleweight'] = 'bold'\n",
    "plt.rcParams['figure.figsize'] = (8,8)\n",
    "plt.rcParams['lines.linewidth'] = 3\n",
    "plt.rcParams['errorbar.capsize'] = 3.0\n",
    "\n",
    "wtc='#5050fb'\n",
    "nepc='#52007a'\n",
    "C13Sc='#9696ff'\n",
    "C24Sc='#9676af'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_histogram(data, pop_vals, n_bins, min_val, max_val):\n",
    "    freq, edges =np.histogram(data, bins=n_bins, weights = pop_vals,\n",
    "                             range = (min_val, max_val))\n",
    "    xvals = (edges[1:]   edges[:-1]) / 2 \n",
    "    return freq,xvals\n",
    "\n",
    "def plot_1dhistogram(xvals, freq, sigma_val, color_string):\n",
    "    F_vals = gaussian_filter1d(freq, sigma=sigma_val)\n",
    "    plt.plot(xvals, F_vals, lw=1.75, color=xkcd_rgb[color_string])\n",
    "    plt.tight_layout()\n",
    "    return F_vals\n",
    "\n",
    "def plot_wErrorbars(xvals, yvals, yerr_vals, color_string, label_name):\n",
    "    plt.errorbar(xvals, yvals, yerr=yerr_vals, color=xkcd_rgb[color_string], label=label_name)\n",
    "    plt.tight_layout()\n",
    "\n",
    "\n",
    "def plot_wError_Range(xvals, yvals, yerr_vals, color_string, label_name):\n",
    "    plt.fill_between(xvals, y1 = yvals - yerr_vals, y2 = yvals yerr_vals,\n",
    "                     color=xkcd_rgb[color_string], alpha=0.4, zorder=1)\n",
    "    plt.plot(xvals, yvals, color=xkcd_rgb[color_string], label=label_name, zorder=2)\n",
    "    plt.tight_layout()\n",
    "    \n",
    "def calc_distance(trajectory, residues, atom_names,min_avg):\n",
    "    apairs=[np.concatenate([trajectory.top.select(\"resSeq %d and (%s)\" % (i, atom_names))\n",
    "                           for i in r]) for r in residues]\n",
    "    apairs=list(itertools.product(apairs[0], apairs[1]))\n",
    "    adists=md.compute_distances(trajectory, atom_pairs=apairs)\n",
    "    if min_avg==\"average\":\n",
    "        dists=adists.mean(axis=1)\n",
    "    elif min_avg==\"minn\":\n",
    "        dists=np.amin(adists, axis=1)\n",
    "    return dists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_Hbond_to_residue_bond(Topology, Hbonds):\n",
    "    #Converts output from md.wernet_nilsson into a ragged list (frames) of lists (bonds in frame)\n",
    "    #of lists (residues involved in bond) and returns the residue number list. Residue corresponds to the\n",
    "    #numbering in the topology file.\n",
    "    residue_bonds=[]\n",
    "    for frame in range(len(Hbonds)):\n",
    "        tmpframelist=[]\n",
    "        for bond in range(len(Hbonds[frame])):\n",
    "            templist=[0,0]\n",
    "            templist[0]=Topology.atom(Hbonds[frame][bond][0]).residue.resSeq\n",
    "            templist[1]=Topology.atom(Hbonds[frame][bond][2]).residue.resSeq\n",
    "            tmpframelist.append(templist)\n",
    "        residue_bonds.append(tmpframelist)\n",
    "        \n",
    "    return residue_bonds\n",
    "\n",
    "def check_bond_status(residue_bonds, bond_pairs_list, strict_search):\n",
    "#Iterates through the frames of a Hbond residue list and checks if a bond exists in each frame.\n",
    "#Returns a mask array of length frames.\n",
    "\n",
    "#If strict_search=True, residues provided must be in order of H-bond donor, H-bond acceptor,\n",
    "#If strict_search=False, residue order is irrelevant\n",
    "\n",
    "#Initialize a storage array that is of length bonds to check by frames.\n",
    "\n",
    "    masked_bonds=np.zeros((len(bond_pairs_list),len(residue_bonds)))\n",
    "    bond_pair_index=0\n",
    "\n",
    "    #For each frame, check whether the bond-pair exists\n",
    "    if strict_search==True:\n",
    "        for potential_bond in bond_pairs_list:\n",
    "            for frame in range(len(residue_bonds)):\n",
    "                if potential_bond in residue_bonds[frame]:\n",
    "                    masked_bonds[bond_pair_index, frame]=1\n",
    "            bond_pair_index =1\n",
    "\n",
    "    else:  \n",
    "        for potential_bond in bond_pairs_list:\n",
    "            for frame in range(len(residue_bonds)):\n",
    "                if potential_bond in residue_bonds[frame] or potential_bond[::-1] in residue_bonds[frame]:\n",
    "                    masked_bonds[bond_pair_index, frame]=1\n",
    "            bond_pair_index =1\n",
    "    return masked_bonds\n",
    "\n",
    "def is_resi_bonded(residue_bonds, residue):\n",
    "#Iterates through the frames of a Hbond residue list and checks if a bond exists in each frame.\n",
    "#Returns a mask array of length frames.\n",
    "\n",
    "#Initialize a storage array that is of length bonds to check by frames.\n",
    "\n",
    "    masked_bonds=np.zeros((len(residue_bonds)))\n",
    "    \n",
    "    #For each frame, check whether the bond-pair exists\n",
    "    for frame in range(len(residue_bonds)):\n",
    "        if any(residue in sublist for sublist in residue_bonds[frame]):\n",
    "            masked_bonds[frame]=1\n",
    "\n",
    "    return masked_bonds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rmsf_calc(centers, ref_frame=0):\n",
    "    \"\"\"Calculated the RMSF from a frame in a MSM\n",
    "\n",
    "    Attributes\n",
    "    ----------\n",
    "    centers : md.trajectory, shape=(n_states,),\n",
    "        The cluster centers to use for RMSF calculations.\n",
    "    ref_frame : int, default=0,\n",
    "        The reference state in the MSM to use for calculation\n",
    "        deviations from. If not supplied, first frame is used.\n",
    "\n",
    "    Returns\n",
    "    ----------\n",
    "    rmsfs : nd.array, shape=(n_frames,n_residues),\n",
    "        Returns the RMSF of each residue for each frame.\n",
    "    \"\"\"\n",
    "    # align all states to reference frame\n",
    "    centers = centers.superpose(centers[ref_frame])\n",
    "\n",
    "    # get differences between coordinates\n",
    "    diffs = centers.xyz - centers.xyz[ref_frame]\n",
    "\n",
    "    # dot product differences\n",
    "    dists_per_atom_sq = np.einsum('ijk,ijk->ij', diffs, diffs)\n",
    "\n",
    "    # obtain indices of all atoms partitioned by residues\n",
    "    atom_iis_per_resi = np.array(\n",
    "        [[a.index for a in r.atoms] for r in centers.top.residues])\n",
    "\n",
    "    # average the dot products within each residue\n",
    "    avg_resi_dists = np.array(\n",
    "        [\n",
    "            np.mean(dists_per_atom_sq[:, iis], axis=1)\n",
    "            for iis in atom_iis_per_resi])\n",
    "    rmsfs=np.transpose(avg_resi_dists)\n",
    "\n",
    "    return rmsfs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_chi_angles(chi1_array, chi2_array, topfile, residue_of_interest):\n",
    "    #First find the atom index of the CA (CAs are involved in chi1/chi2 bond definitions!)\n",
    "    CA_index=topfile.select(f\"resSeq {residue_of_interest} and name CA\")\n",
    "    #Chi1/2 arrays are saved as following: [(chi angles (variable based on residue IDs, 4 atom indices))/\n",
    "    #,(nframes, n chi_angles)]\n",
    "    chi1s=(chi1_array[1][:,np.where(chi1_array[0]==CA_index)[0]].flatten())\n",
    "    chi2s=(chi2_array[1][:,np.where(chi2_array[0]==CA_index)[0]].flatten())\n",
    "    chis=np.array([chi1s, chi2s])\n",
    "    return chis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "d='/home/jjmiller/bowmore/jjmiller/Simulations/PlasmepsinV/MSMs'\n",
    "Top_WT=d '/WT-prot-masses.pdb'\n",
    "Top_Ser1=d '/Ser1-prot-masses.pdb'\n",
    "Top_Ser2=d '/Ser2-prot-masses.pdb'\n",
    "Top_Nep_mut=d '/NepMut-prot-masses.pdb'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "centersWT=md.load('./WT_center_structures.xtc',top=Top_WT)\n",
    "centersNep=md.load('./Nep_center_structures.xtc',top=Top_Nep_mut)\n",
    "centersSer1=md.load('./Ser1_center_structures.xtc',top=Top_Ser1)\n",
    "centersSer2=md.load('./Ser2_center_structures.xtc',top=Top_Ser2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Bootstrapped the eq_probs by resampling the assignments file iteratively excluding a trajectory\n",
    "bs_wt_pops=np.load('./bootstrapping/strap_output_WT_lagtime250_eqpops.npy')\n",
    "bs_nep_pops=np.load('./bootstrapping//strap_output_Nep_lagtime250_eqpops.npy')\n",
    "bs_ser1_pops=np.load('./bootstrapping//strap_output_Ser1_lagtime250_eqpops.npy')\n",
    "bs_ser2_pops=np.load('./bootstrapping//strap_output_Ser2_lagtime250_eqpops.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "popsWT=np.mean(bs_wt_pops,axis=0)\n",
    "popsNep=np.mean(bs_nep_pops,axis=0)\n",
    "popsSer1=np.mean(bs_ser1_pops,axis=0)\n",
    "popsSer2=np.mean(bs_ser2_pops,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def histonorm(values, nbins, weights, histrange):\n",
    "    counts_vals, bins_vals=np.histogram(values, bins=nbins, weights=weights, range=histrange)\n",
    "    normcounts=counts_vals/sum(counts_vals)\n",
    "    return normcounts, bins_vals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How does the RMSF of the loop change?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "WTtop=md.load(Top_WT).topology\n",
    "Ser1top=md.load(Top_Ser1).topology\n",
    "Ser2top=md.load(Top_Ser2).topology\n",
    "Neptop=md.load(Top_Nep_mut).topology\n",
    "WT_aligned=md.load('.//wt_aligned.xtc', top=WTtop)\n",
    "Ser1_aligned=md.load('.//ser1_aligned.xtc', top=Ser1top)\n",
    "Ser2_aligned=md.load('.//ser2_aligned.xtc', top=Ser2top)\n",
    "Nep_aligned=md.load('.//nep_aligned.xtc', top=Neptop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jjmiller/modules/yes/envs/condaenvir/lib/python3.6/site-packages/ipykernel_launcher.py:28: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n"
     ]
    }
   ],
   "source": [
    "#Calculate the residue RMSF by frame\n",
    "rmsfs_wt=rmsf_calc(WT_aligned)\n",
    "rmsfs_ser1=rmsf_calc(Ser1_aligned)\n",
    "rmsfs_ser2=rmsf_calc(Ser2_aligned)\n",
    "rmsfs_nep=rmsf_calc(Nep_aligned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Select the loop residues\n",
    "WT_loop_indcs=WTtop.select('(resSeq 153 to 173) and backbone')\n",
    "Ser1_loop_indcs=Ser1top.select('(resSeq 153 to 173) and backbone')\n",
    "Ser2_loop_indcs=Ser2top.select('(resSeq 153 to 173) and backbone')\n",
    "Nep_loop_indcs=Neptop.select('(resSeq 153 to 175) and backbone')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "wt_loop_rmsf=(np.mean(rmsfs_wt[:,70:90],axis=1))\n",
    "ser1_loop_rmsf=(np.mean(rmsfs_ser1[:,70:90],axis=1))\n",
    "ser2_loop_rmsf=(np.mean(rmsfs_ser2[:,70:90],axis=1))\n",
    "nep_loop_rmsf=(np.mean(rmsfs_nep[:,71:93],axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "numbins=50\n",
    "historange=(0,1.0)\n",
    "\n",
    "normCountsWT,binsWT= histonorm(wt_loop_rmsf, numbins, popsWT, historange)\n",
    "normCountsNep, binsNep= histonorm(nep_loop_rmsf, numbins, popsNep, historange)\n",
    "normCountsSer1, binsSer1= histonorm(ser1_loop_rmsf, numbins, popsSer1, historange)\n",
    "normCountsSer2, binsSer2= histonorm(ser2_loop_rmsf, numbins, popsSer2, historange)\n",
    "\n",
    "binscenters_WT=(binsWT[:-1]   binsWT[1:]) / 2\n",
    "width=binsWT[1]-binsWT[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 540x900 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs=plt.subplots(4,1,sharey=True, sharex=True, figsize=(1.5,2.5), dpi=360)\n",
    "fig.text(-0.1, 0.5, 'Probability', va='center',rotation='vertical', fontsize=8)\n",
    "fig.text(0.5, -0.05, 'Nep Insert RMSD/residue (nm)', ha='center',fontsize=8)\n",
    "plt.xlim(0, 1.0)\n",
    "axs[0].bar(binscenters_WT,normCountsWT, align='center', width=width, label=\"WT\", color=wtc,linewidth=0.25)\n",
    "axs[1].bar(binscenters_WT,normCountsNep, align='center', width=width, label=\"Nep$^{Nep1A}$\", color=nepc,linewidth=0.25)\n",
    "axs[2].bar(binscenters_WT,normCountsSer1, align='center', width=width, label=\"Nep$^{C1,3S}$\", color=C13Sc,linewidth=0.25)\n",
    "axs[3].bar(binscenters_WT,normCountsSer2, align='center', width=width, label=\"Nep$^{C2,4S}$\", color=C24Sc,linewidth=0.25)\n",
    "\n",
    "for ax in axs:\n",
    "    ax.legend(fontsize=8,frameon=False, loc='upper right', handlelength=0, handletextpad=0, fancybox=True)\n",
    "    ax.get_xticklabels()    \n",
    "    ax.tick_params(which='major', direction='out', length=3, width=1.5, colors='black')\n",
    "    ax.tick_params(which='minor', direction='out', length=2, width=1, colors='black')\n",
    "\n",
    "    ax.xaxis.set_major_locator(MultipleLocator(.5))\n",
    "    ax.xaxis.set_minor_locator(MultipleLocator(.1))\n",
    "    ax.yaxis.set_major_locator(MultipleLocator(0.05))\n",
    "    ax.yaxis.set_minor_locator(MultipleLocator(0.025))\n",
    "    \n",
    "    for axis in ['top','bottom','left','right']:\n",
    "        ax.spines[axis].set_linewidth(1)\n",
    "    \n",
    "    ax.tick_params(axis = 'x', which = 'both', bottom = True, top=False, labelsize=8)\n",
    "    ax.tick_params(axis= 'y', which = 'both', left = True, right=False, labelsize=8)\n",
    "fig.tight_layout(pad=0.05)\n",
    "plt.savefig('./Figures/Nep_insert_RMSD_smaller.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#RMSF of the FLAP?\n",
    "wt_flap_rmsf=(np.mean(rmsfs_wt[:,91:101],axis=1))\n",
    "ser1_flap_rmsf=(np.mean(rmsfs_ser1[:,91:101],axis=1))\n",
    "ser2_flap_rmsf=(np.mean(rmsfs_ser2[:,91:101],axis=1))\n",
    "nep_flap_rmsf=(np.mean(rmsfs_nep[:,94:104],axis=1))\n",
    "\n",
    "numbins=75\n",
    "historange=(0,3.5)\n",
    "\n",
    "normCountsWT,binsWT= histonorm(wt_flap_rmsf, numbins, popsWT, historange)\n",
    "normCountsNep, binsNep= histonorm(nep_flap_rmsf, numbins, popsNep, historange)\n",
    "normCountsSer1, binsSer1= histonorm(ser1_flap_rmsf, numbins, popsSer1, historange)\n",
    "normCountsSer2, binsSer2= histonorm(ser2_flap_rmsf, numbins, popsSer2, historange)\n",
    "\n",
    "binscenters_WT=(binsWT[:-1]   binsWT[1:]) / 2\n",
    "width=binsWT[1]-binsWT[0]\n",
    "\n",
    "fig, axs=plt.subplots(4,1,sharey=True, sharex=True, figsize=(6,8))\n",
    "fig.text(-0.1, 0.5, 'Probability', va='center',rotation='vertical')\n",
    "fig.text(0.5, -0.05, 'Flap (174-184) RMSD/residue (nm)', ha='center',)\n",
    "plt.xlim(0, 3.5)\n",
    "axs[0].bar(binscenters_WT,normCountsWT, align='center', width=width, label=\"WT\", color=wtc)\n",
    "axs[1].bar(binscenters_WT,normCountsNep, align='center', width=width, label=\"Nep$^{Nep1A}$\", color=nepc)\n",
    "axs[2].bar(binscenters_WT,normCountsSer1, align='center', width=width, label=\"Nep$^{C1,3S}$\", color=C13Sc)\n",
    "axs[3].bar(binscenters_WT,normCountsSer2, align='center', width=width, label=\"Nep$^{C2,4S}$\", color=C24Sc)\n",
    "\n",
    "for ax in axs:\n",
    "    ax.legend(fontsize='small',frameon=False, loc='upper right', handlelength=0, handletextpad=0, fancybox=True)\n",
    "    ax.get_xticklabels()    \n",
    "    ax.tick_params(which='major', direction='out', length=10, width=3, colors='black')\n",
    "    ax.tick_params(which='minor', direction='out', length=5, width=1.5, colors='black')\n",
    "\n",
    "    ax.xaxis.set_major_locator(MultipleLocator(0.5))\n",
    "    ax.xaxis.set_minor_locator(MultipleLocator(0.1))\n",
    "    ax.yaxis.set_major_locator(MultipleLocator(0.05))\n",
    "    ax.yaxis.set_minor_locator(MultipleLocator(0.01))\n",
    "    \n",
    "    ax.tick_params(axis = 'x', which = 'both', bottom = True, top=False)\n",
    "    ax.tick_params(axis= 'y', which = 'both', left = True, right=False)\n",
    "fig.tight_layout(pad=-0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How is flap opening impacted?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f02585fee48>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 540x900 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Use the distance between D118 and Cys178 (WT) as a metric of flap opening\n",
    "WT_FLAP=calc_distance(centersWT,[[118],[178]],\"sidechain\",\"minn\")\n",
    "NepMutFLAP=calc_distance(centersNep,[[118],[180]],\"sidechain\",\"minn\")\n",
    "Ser1FLAP=calc_distance(centersSer1,[[118],[178]],\"sidechain\",\"minn\")\n",
    "Ser2FLAP=calc_distance(centersSer2,[[118],[178]],\"sidechain\",\"minn\")\n",
    "# NepTruncFLAP=calc_distance(NepTruncTrjs,[[37],[97]],\"protein\")\n",
    "\n",
    "\n",
    "numbins=50\n",
    "historange=(0,3.5)\n",
    "\n",
    "normCountsWT,binsWT= histonorm(WT_FLAP, numbins, popsWT, historange)\n",
    "normCountsNep, binsNep= histonorm(NepMutFLAP, numbins, popsNep, historange)\n",
    "normCountsSer1, binsSer1= histonorm(Ser1FLAP, numbins, popsSer1, historange)\n",
    "normCountsSer2, binsSer2= histonorm(Ser2FLAP, numbins, popsSer2, historange)\n",
    "\n",
    "binscenters_WT=(binsWT[:-1]   binsWT[1:]) / 2\n",
    "width=binsWT[1]-binsWT[0]\n",
    "\n",
    "fig, axs=plt.subplots(4,1,sharey=True, sharex=True, figsize=(1.5,2.5), dpi=360)\n",
    "fig.text(-0.1, 0.5, 'Probability', va='center',rotation='vertical',fontsize=8)\n",
    "fig.text(0.5, -0.05, 'Asp118-Cys178 distance (nm)', ha='center',fontsize=8)\n",
    "plt.xlim(0,3.5)\n",
    "axs[0].bar(binscenters_WT,normCountsWT, align='center', width=width, label=\"WT\", color=wtc, linewidth=0.25)\n",
    "axs[1].bar(binscenters_WT,normCountsNep, align='center', width=width, label=\"Nep$^{Nep1A}$\", color=nepc, linewidth=0.25)\n",
    "axs[2].bar(binscenters_WT,normCountsSer1, align='center', width=width, label=\"Nep$^{C1,3S}$\", color=C13Sc, linewidth=0.25)\n",
    "axs[3].bar(binscenters_WT,normCountsSer2, align='center', width=width, label=\"Nep$^{C2,4S}$\", color=C24Sc, linewidth=0.25)\n",
    "\n",
    "for ax in axs:\n",
    "    ax.legend(fontsize=8,frameon=False, loc='upper right', handlelength=0, handletextpad=0, fancybox=True)\n",
    "    ax.get_xticklabels()    \n",
    "    ax.tick_params(which='major', direction='out', length=3, width=1.5, colors='black')\n",
    "    ax.tick_params(which='minor', direction='out', length=2, width=1, colors='black')\n",
    "\n",
    "    ax.xaxis.set_major_locator(MultipleLocator(1))\n",
    "    ax.xaxis.set_minor_locator(MultipleLocator(0.25))\n",
    "    ax.yaxis.set_major_locator(MultipleLocator(0.05))\n",
    "    ax.yaxis.set_minor_locator(MultipleLocator(0.01))\n",
    "    for axis in ['top','bottom','left','right']:\n",
    "        ax.spines[axis].set_linewidth(1)    \n",
    "    \n",
    "    ax.tick_params(axis = 'x', which = 'both', bottom = True, top=False,labelsize=8)\n",
    "    ax.tick_params(axis= 'y', which = 'both', left = True, right=False,labelsize=8)\n",
    "fig.tight_layout(pad=0.05)\n",
    "plt.axvline(x=1.5,lw=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WT Extra Open Active Site % is: 0.6872264650250091  /- 0.14710123335130865 %\n",
      "Nep Mut Extra Open Active Site % is: 20.96680928977383  /- 5.744351028251319 %\n",
      "Ser 1 Extra Open Active Site % is: 8.938057158839786  /- 2.024481444477831 %\n",
      "Ser 2 Extra Open Active Site % is: 14.246168936621023  /- 4.473128481283297 %\n"
     ]
    }
   ],
   "source": [
    "cutoff=1.5\n",
    "wt_indcs=np.where(WT_FLAP>cutoff)\n",
    "nep_indcs=np.where(NepMutFLAP>cutoff)\n",
    "s1_indcs=np.where(Ser1FLAP>cutoff)\n",
    "s2_indcs=np.where(Ser2FLAP>cutoff)\n",
    "\n",
    "wt_vals=[np.sum(bs_wt_pops[i][wt_indcs]) for i in range(len(bs_wt_pops))]\n",
    "nep_vals=[np.sum(bs_nep_pops[i][nep_indcs]) for i in range(len(bs_nep_pops))]\n",
    "s1_vals=[np.sum(bs_ser1_pops[i][s1_indcs]) for i in range(len(bs_ser1_pops))]\n",
    "s2_vals=[np.sum(bs_ser2_pops[i][s2_indcs]) for i in range(len(bs_ser2_pops))]\n",
    "\n",
    "print(\"WT Extra Open Active Site % is:\", (100*np.mean(wt_vals)), \" /-\", 100*np.std(wt_vals), \"%\")\n",
    "print(\"Nep Mut Extra Open Active Site % is:\", (100*np.mean(nep_vals)), \" /-\", 100*np.std(nep_vals), \"%\")\n",
    "print(\"Ser 1 Extra Open Active Site % is:\", (100*np.mean(s1_vals)), \" /-\", 100*np.std(s1_vals), \"%\")\n",
    "print(\"Ser 2 Extra Open Active Site % is:\", (100*np.mean(s2_vals)), \" /-\", 100*np.std(s2_vals), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.25 - 0.375\n",
      "center: 1018\n",
      "0.375 - 0.5\n",
      "center: 1904\n",
      "0.5 - 0.625\n",
      "center: 372\n",
      "0.625 - 0.75\n",
      "center: 987\n",
      "0.75 - 0.875\n",
      "center: 738\n",
      "0.875 - 1.0\n",
      "center: 433\n",
      "1.0 - 1.125\n",
      "center: 781\n",
      "1.125 - 1.25\n",
      "center: 932\n",
      "1.25 - 1.375\n",
      "center: 2475\n",
      "1.375 - 1.5\n",
      "center: 2728\n",
      "1.5 - 1.625\n",
      "center: 2279\n",
      "1.625 - 1.75\n",
      "center: 712\n",
      "1.75 - 1.875\n",
      "center: 829\n"
     ]
    }
   ],
   "source": [
    "#Iterate through a series of distances to pull out the maximum likely structure for each range.\n",
    "\n",
    "opening_distances=(np.arange(0.25,2,.125))\n",
    "save_structures=False\n",
    "\n",
    "\n",
    "for i in range(len(opening_distances)-1):\n",
    "    print(opening_distances[i],\"-\",opening_distances[i 1])\n",
    "    print(\"center:\", np.where(popsWT==np.amax(popsWT[np.where((WT_FLAP>opening_distances[i]) & (WT_FLAP<opening_distances[i 1]))]))[0][0])\n",
    "    centerindex=np.where(popsWT==np.amax(popsWT[np.where((WT_FLAP>opening_distances[i]) & (WT_FLAP<opening_distances[i 1]))]))[0][0]\n",
    "    distance=WT_FLAP[centerindex]\n",
    "    if save_structures==True:\n",
    "        cmd = f'echo \"System\" | gmx trjconv -f .//WT_center_structures.xtc -s ../ProcessedRuns/WT-prot-masses.pdb \\\n",
    "        -o Structures_for_Figures/WT_FLAP_Opening/WT_center{i}_open{distance}.pdb -b {centerindex} -e {centerindex}'\n",
    "        os.system(cmd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  37  251  268  453 1220 1597 1887 2372 2440 2606 2721]\n"
     ]
    }
   ],
   "source": [
    "collapsed_flaps=np.where((Ser2FLAP<0.4))[0]\n",
    "print(collapsed_flaps)\n",
    "\n",
    "save_structures=False\n",
    "#Optionally save out structures\n",
    "if save_structures==True:\n",
    "    for i in collapsed_flaps:\n",
    "#         cmd = f'echo \"System\" | gmx trjconv -f WT_center_structures.xtc -s ../ProcessedRuns/WT-prot-masses.pdb \\\n",
    "#         -o Structures_for_Figures/Large_Flap_Opening/WT_center{i}.pdb -b {i} -e {i}'\n",
    "        \n",
    "        cmd = f'echo \"System\" | gmx trjconv -f Ser2_center_structures.xtc -s ../ProcessedRuns/Ser2-prot-masses.pdb \\\n",
    "        -o Structures_for_Figures/C24S_collapsed_flap/C24S_center{i}.pdb -b {i} -e {i}'\n",
    "        os.system(cmd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How closed is the flap?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define one set of residues as ResSeq176-181\n",
    "#Define the other set as ResSeq 485-493\n",
    "Chain1ids=np.arange(93,99)\n",
    "Chain2ids=np.arange(402,411)\n",
    "pairwise_flap_residues=[]\n",
    "nep_pairwise_flap_residues=[]\n",
    "for i in Chain1ids:\n",
    "    for j in Chain2ids:\n",
    "        pairwise_flap_residues.append([i,j])\n",
    "        nep_pairwise_flap_residues.append([i 3,j 3])\n",
    "pairwise_flap_residues=np.array(pairwise_flap_residues)\n",
    "nep_pairwise_flap_residues=np.array(nep_pairwise_flap_residues)\n",
    "\n",
    "#Calculate the closest contacts\n",
    "wt_flap_contacts=md.compute_contacts(centersWT,contacts=pairwise_flap_residues, scheme='closest')\n",
    "nep_flap_contacts=md.compute_contacts(centersNep, contacts=nep_pairwise_flap_residues, scheme='closest')\n",
    "ser1_flap_contacts=md.compute_contacts(centersSer1, contacts=pairwise_flap_residues, scheme='closest')\n",
    "ser2_flap_contacts=md.compute_contacts(centersSer2, contacts=pairwise_flap_residues, scheme='closest')\n",
    "\n",
    "#Pull out the min flap distance\n",
    "wtminflap_dist=np.amin(wt_flap_contacts[0], axis=1)\n",
    "nepminflap_dist=np.amin(nep_flap_contacts[0], axis=1)\n",
    "ser1minflap_dist=np.amin(ser1_flap_contacts[0], axis=1)\n",
    "ser2minflap_dist=np.amin(ser2_flap_contacts[0], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 540x900 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numbins=50\n",
    "historange=(0,2.5)\n",
    "\n",
    "normCountsWT,binsWT= histonorm(wtminflap_dist, numbins, popsWT, historange)\n",
    "normCountsNep, binsNep= histonorm(nepminflap_dist, numbins, popsNep, historange)\n",
    "normCountsSer1, binsSer1= histonorm(ser1minflap_dist, numbins, popsSer1, historange)\n",
    "normCountsSer2, binsSer2= histonorm(ser2minflap_dist, numbins, popsSer2, historange)\n",
    "\n",
    "binscenters_WT=(binsWT[:-1]   binsWT[1:]) / 2\n",
    "width=binsWT[1]-binsWT[0]\n",
    "\n",
    "\n",
    "fig, axs=plt.subplots(4,1,sharey=True, sharex=True, figsize=(1.5, 2.5), dpi=360)\n",
    "fig.text(-0.1, 0.5, 'Probability', va='center',rotation='vertical', fontsize=8)\n",
    "fig.text(0.5, -0.05, 'Channel Opening Distance (nm)', ha='center', fontsize=8)\n",
    "plt.xlim(0,2.5)\n",
    "axs[0].bar(binscenters_WT,normCountsWT, align='center', width=width, label=\"WT\", color=wtc, linewidth=0.25)\n",
    "axs[1].bar(binscenters_WT,normCountsNep, align='center', width=width, label=\"Nep$^{Nep1A}$\", color=nepc, linewidth=0.25)\n",
    "axs[2].bar(binscenters_WT,normCountsSer1, align='center', width=width, label=\"Nep$^{C1,3S}$\", color=C13Sc, linewidth=0.25)\n",
    "axs[3].bar(binscenters_WT,normCountsSer2, align='center', width=width, label=\"Nep$^{C2,4S}$\", color=C24Sc, linewidth=0.25)\n",
    "\n",
    "for ax in axs:\n",
    "    ax.legend(fontsize=8,frameon=False, loc='upper right', handlelength=0, handletextpad=0, fancybox=True)\n",
    "    ax.get_xticklabels()    \n",
    "    ax.tick_params(which='major', direction='out', length=3, width=1.5, colors='black')\n",
    "    ax.tick_params(which='minor', direction='out', length=2, width=1, colors='black')\n",
    "\n",
    "    ax.xaxis.set_major_locator(MultipleLocator(0.5))\n",
    "    ax.xaxis.set_minor_locator(MultipleLocator(0.25))\n",
    "    ax.yaxis.set_major_locator(MultipleLocator(0.05))\n",
    "    ax.yaxis.set_minor_locator(MultipleLocator(0.01))\n",
    "    for axis in ['top','bottom','left','right']:\n",
    "        ax.spines[axis].set_linewidth(1)        \n",
    "    \n",
    "    ax.tick_params(axis = 'x', which = 'both', bottom = True, top=False, labelsize=8)\n",
    "    ax.tick_params(axis= 'y', which = 'both', left = True, right=False, labelsize = 8)\n",
    "fig.tight_layout(pad=0.05)\n",
    "plt.axvline(x=0.35,lw=0.2)\n",
    "plt.savefig('./Figures/Channel_Opening_smaller.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WT Closed Active Site % is: 64.30847791578528  /- 7.333870652206922 %\n",
      "Nep Mut Closed Active Site % is: 82.55699224008805  /- 4.308769487296378 %\n",
      "Ser 1 Closed Active Site % is: 71.34258096393201  /- 4.3125208040784 %\n",
      "Ser 2 Closed Active Site % is: 44.91990669141524  /- 10.055061427604201 %\n"
     ]
    }
   ],
   "source": [
    "# what is the probability of being more closed than nm?\n",
    "cutoff=0.4\n",
    "wt_indcs=np.where(wtminflap_dist>cutoff)\n",
    "nep_indcs=np.where(nepminflap_dist>cutoff)\n",
    "s1_indcs=np.where(ser1minflap_dist>cutoff)\n",
    "s2_indcs=np.where(ser2minflap_dist>cutoff)\n",
    "\n",
    "wt_vals=[np.sum(bs_wt_pops[i][wt_indcs]) for i in range(len(bs_wt_pops))]\n",
    "nep_vals=[np.sum(bs_nep_pops[i][nep_indcs]) for i in range(len(bs_nep_pops))]\n",
    "s1_vals=[np.sum(bs_ser1_pops[i][s1_indcs]) for i in range(len(bs_ser1_pops))]\n",
    "s2_vals=[np.sum(bs_ser2_pops[i][s2_indcs]) for i in range(len(bs_ser2_pops))]\n",
    "\n",
    "print(\"WT Closed Active Site % is:\", (100*np.mean(wt_vals)), \" /-\", 100*np.std(wt_vals), \"%\")\n",
    "print(\"Nep Mut Closed Active Site % is:\", (100*np.mean(nep_vals)), \" /-\", 100*np.std(nep_vals), \"%\")\n",
    "print(\"Ser 1 Closed Active Site % is:\", (100*np.mean(s1_vals)), \" /-\", 100*np.std(s1_vals), \"%\")\n",
    "print(\"Ser 2 Closed Active Site % is:\", (100*np.mean(s2_vals)), \" /-\", 100*np.std(s2_vals), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 - 0.25\n",
      "center: 1904    distance: 0.179\n",
      "0.25 - 0.5\n",
      "center: 2432    distance: 0.478\n",
      "0.5 - 0.75\n",
      "center: 433    distance: 0.553\n",
      "0.75 - 1.0\n",
      "center: 781    distance: 0.825\n",
      "1.0 - 1.25\n",
      "center: 1029    distance: 1.16\n",
      "1.25 - 1.5\n",
      "center: 2475    distance: 1.322\n",
      "1.5 - 1.75\n",
      "center: 478    distance: 1.548\n"
     ]
    }
   ],
   "source": [
    "#Iterate through a series of distances to pull out the maximum likely structure for each range.\n",
    "\n",
    "opening_distances=(np.arange(0,2,.25))\n",
    "#opening_distances=np.array([0.33,0.37])\n",
    "save_structures=False\n",
    "\n",
    "\n",
    "for i in range(len(opening_distances)-1):\n",
    "    centerindex=np.where(popsWT==np.amax(popsWT[np.where((wtminflap_dist>opening_distances[i]) & (wtminflap_dist<opening_distances[i 1]))]))[0][0]\n",
    "    distance=np.round(wtminflap_dist[centerindex],3)\n",
    "    print(opening_distances[i],\"-\",opening_distances[i 1])\n",
    "    print(\"center:\", centerindex, \"   distance:\", distance)\n",
    "\n",
    "    if save_structures==True:\n",
    "        cmd = f'echo \"System\" | gmx trjconv -f WT_center_structures.xtc -s ../ProcessedRuns/WT-prot-masses.pdb \\\n",
    "        -o Structures_for_Figures/channel_opening/WT_center{i}_open{distance}.pdb -b {centerindex} -e {centerindex}'\n",
    "        os.system(cmd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How is Hbonding impacted?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## There are two potential H-donors for the active site serine (125). His (212- NE2) and Gln 177-NE2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "WTtop=md.load(Top_WT).topology\n",
    "Neptop=md.load(Top_Nep_mut).topology\n",
    "Ser1top=md.load(Top_Ser1).topology\n",
    "Ser2top=md.load(Top_Ser2).topology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "WT\n",
      "SER125-OG\n",
      "GLN175-OE1 GLN175\n",
      "HIS212-NE2\n",
      "\n",
      "Nep\n",
      "SER125-OG\n",
      "GLN177-OE1\n",
      "HIS214-NE2\n",
      "\n",
      "Ser1\n",
      "SER125-OG\n",
      "GLN175-OE1\n",
      "HIS212-NE2\n",
      "\n",
      "Ser2\n",
      "SER125-OG\n",
      "GLN175-OE1\n",
      "HIS212-NE2\n"
     ]
    }
   ],
   "source": [
    "print()\n",
    "print('WT')\n",
    "print(WTtop.atom(683))\n",
    "print(WTtop.atom(1443), WTtop.atom(1443).residue)\n",
    "print(WTtop.atom(2024))\n",
    "print()\n",
    "print('Nep')\n",
    "print(Neptop.atom(694))\n",
    "print(Neptop.atom(1464))\n",
    "print(Neptop.atom(2045))\n",
    "print()\n",
    "print('Ser1')\n",
    "print(Ser1top.atom(683))\n",
    "print(Ser1top.atom(1445))\n",
    "print(Ser1top.atom(2026))\n",
    "print()\n",
    "print('Ser2')\n",
    "print(Ser2top.atom(683))\n",
    "print(Ser2top.atom(1445))\n",
    "print(Ser2top.atom(2026))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate the Wernet_Nilsson H-bond pairs for each frame in the trajectory\n",
    "#Returns a jagged array of length of centers, with each center having variable number of bonds.\n",
    "#For each bond, returns 3 atom indicies of the bond- donor atom, hydrogen atom, acceptor atom\n",
    "ser1Hbonds=md.wernet_nilsson(centersSer1, sidechain_only=True)\n",
    "ser2Hbonds=md.wernet_nilsson(centersSer2, sidechain_only=True)\n",
    "NepHbonds=md.wernet_nilsson(centersNep, sidechain_only=True)\n",
    "WTHbonds=md.wernet_nilsson(centersWT, sidechain_only=True)\n",
    "\n",
    "#Convert the atom numbers into residue numbers, drop the H-atom\n",
    "residue_numbers_ser1=convert_Hbond_to_residue_bond(Ser1top, ser1Hbonds)\n",
    "residue_numbers_ser2=convert_Hbond_to_residue_bond(Ser2top, ser2Hbonds)\n",
    "residue_numbers_WT=convert_Hbond_to_residue_bond(WTtop, WTHbonds)\n",
    "residue_numbers_Nep=convert_Hbond_to_residue_bond(Neptop, NepHbonds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">WT</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Nep$^{Nep1A}$</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Nep$^{C1,3S}$</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Nep$^{C2,4S}$</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Percent Occupancy</th>\n",
       "      <th>SD</th>\n",
       "      <th>Percent Occupancy</th>\n",
       "      <th>SD</th>\n",
       "      <th>Percent Occupancy</th>\n",
       "      <th>SD</th>\n",
       "      <th>Percent Occupancy</th>\n",
       "      <th>SD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>86.640231</td>\n",
       "      <td>1.734540</td>\n",
       "      <td>71.823700</td>\n",
       "      <td>3.143307</td>\n",
       "      <td>68.491344</td>\n",
       "      <td>2.389580</td>\n",
       "      <td>89.256208</td>\n",
       "      <td>2.302295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>83.707758</td>\n",
       "      <td>2.053436</td>\n",
       "      <td>71.170737</td>\n",
       "      <td>3.229668</td>\n",
       "      <td>68.209380</td>\n",
       "      <td>2.353873</td>\n",
       "      <td>88.697842</td>\n",
       "      <td>2.421009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>8.156820</td>\n",
       "      <td>0.812845</td>\n",
       "      <td>5.081515</td>\n",
       "      <td>0.811768</td>\n",
       "      <td>4.586554</td>\n",
       "      <td>0.637796</td>\n",
       "      <td>9.508361</td>\n",
       "      <td>2.240725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>69.629847</td>\n",
       "      <td>4.199151</td>\n",
       "      <td>12.648329</td>\n",
       "      <td>1.974453</td>\n",
       "      <td>40.008204</td>\n",
       "      <td>3.816232</td>\n",
       "      <td>59.792313</td>\n",
       "      <td>8.796965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>68.828321</td>\n",
       "      <td>4.875690</td>\n",
       "      <td>38.771332</td>\n",
       "      <td>5.166595</td>\n",
       "      <td>36.689764</td>\n",
       "      <td>5.202845</td>\n",
       "      <td>60.709542</td>\n",
       "      <td>8.471745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>18.663133</td>\n",
       "      <td>3.048990</td>\n",
       "      <td>19.561004</td>\n",
       "      <td>2.032546</td>\n",
       "      <td>29.336451</td>\n",
       "      <td>2.384413</td>\n",
       "      <td>46.596700</td>\n",
       "      <td>8.479640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>60.507879</td>\n",
       "      <td>6.350046</td>\n",
       "      <td>54.708123</td>\n",
       "      <td>5.036478</td>\n",
       "      <td>69.764449</td>\n",
       "      <td>3.101749</td>\n",
       "      <td>54.543276</td>\n",
       "      <td>1.783407</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   WT               Nep$^{Nep1A}$               Nep$^{C1,3S}$  \\\n",
       "    Percent Occupancy        SD Percent Occupancy        SD Percent Occupancy   \n",
       "118         86.640231  1.734540         71.823700  3.143307         68.491344   \n",
       "121         83.707758  2.053436         71.170737  3.229668         68.209380   \n",
       "123          8.156820  0.812845          5.081515  0.811768          4.586554   \n",
       "125         69.629847  4.199151         12.648329  1.974453         40.008204   \n",
       "175         68.828321  4.875690         38.771332  5.166595         36.689764   \n",
       "177         18.663133  3.048990         19.561004  2.032546         29.336451   \n",
       "212         60.507879  6.350046         54.708123  5.036478         69.764449   \n",
       "\n",
       "                  Nep$^{C2,4S}$            \n",
       "           SD Percent Occupancy        SD  \n",
       "118  2.389580         89.256208  2.302295  \n",
       "121  2.353873         88.697842  2.421009  \n",
       "123  0.637796          9.508361  2.240725  \n",
       "125  3.816232         59.792313  8.796965  \n",
       "175  5.202845         60.709542  8.471745  \n",
       "177  2.384413         46.596700  8.479640  \n",
       "212  3.101749         54.543276  1.783407  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Catalytic Aspartate D118 needs a H-donor.\n",
    "residues=[118,121,123,125,175,177,212]\n",
    "Nep_residues=[118,121,123,125,177,179,214]\n",
    "Resi_vals=[]\n",
    "Resi_errs=[]\n",
    "errors=[]\n",
    "for residue in range(len(residues)):\n",
    "    ser1_resi=is_resi_bonded(residue_numbers_ser1, residues[residue])\n",
    "    ser2_resi=is_resi_bonded(residue_numbers_ser2, residues[residue])\n",
    "    WT_resi=is_resi_bonded(residue_numbers_WT, residues[residue])\n",
    "    Nep_resi=is_resi_bonded(residue_numbers_Nep, Nep_residues[residue])\n",
    "    \n",
    "    wt_vals=[np.sum(bs_wt_pops[i]*WT_resi) for i in range(len(bs_wt_pops))]\n",
    "    nep_vals=[np.sum(bs_nep_pops[i]*Nep_resi) for i in range(len(bs_nep_pops))]\n",
    "    s1_vals=[np.sum(bs_ser1_pops[i]*ser1_resi) for i in range(len(bs_ser1_pops))]\n",
    "    s2_vals=[np.sum(bs_ser2_pops[i]*ser2_resi) for i in range(len(bs_ser2_pops))]\n",
    "    \n",
    "    Resi_vals.append([np.mean(wt_vals),np.mean(nep_vals), np.mean(s1_vals),np.mean(s2_vals)])\n",
    "    Resi_errs.append([np.std(wt_vals),np.std(nep_vals), np.std(s1_vals),np.std(s2_vals)])\n",
    "    \n",
    "    \n",
    "#Combine the values and errors into the shape of (bond_pair, mutant, 2) \n",
    "#where 2 is value, error\n",
    "comb_val_err_int=np.dstack((Resi_vals,Resi_errs))\n",
    "#reshape so you can feed into a pd dataframe...\n",
    "comb_val_err=comb_val_err_int.reshape(len(residues),8)\n",
    "#Convert to %\n",
    "resi_val_err=np.multiply(comb_val_err,100)\n",
    "\n",
    "cols = pd.MultiIndex.from_product([[\"WT\",r\"Nep$^{Nep1A}$\",r\"Nep$^{C1,3S}$\",r\"Nep$^{C2,4S}$\"], ['Percent Occupancy', 'SD']])\n",
    "combined_val_err_df_ones=pd.DataFrame(resi_val_err, index=residues,columns=cols)\n",
    "combined_val_err_df_ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "bond_pairs_list=[[125, 212],[125, 175],[118,125],[118,227],[118,177],[118,121],[121,175],[123,175], [121, 177], [123,177],[125,177],[173,212],[173,125],[177,123],[177,125],[177,487],[177,222],[179,488],[177,216],[123,125],[177,219]]\n",
    "ser1_bonds=check_bond_status(residue_numbers_ser1, bond_pairs_list, strict_search=False)\n",
    "ser2_bonds=check_bond_status(residue_numbers_ser2, bond_pairs_list, strict_search=False)\n",
    "WT_bonds=check_bond_status(residue_numbers_WT, bond_pairs_list, strict_search=False)\n",
    "\n",
    "Nep_bond_pairs_list=[[125,214],[125,177],[125,118],[118,229],[118,179],[118,121],[121,177],[123,177], [179,121],[179,123],[179,125],[175,214],[175,125],[179,123],[179,125],[179,489],[179,224],[179,490],[179,218],[123,125],[179,221]]\n",
    "Nep_bonds=check_bond_status(residue_numbers_Nep, Nep_bond_pairs_list, strict_search=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# wt_vals=[np.sum(bs_wt_pops[i]*WT_resi) for i in range(len(bs_wt_pops))]\n",
    "eq_wtd_Ser1_Hbonds=[np.sum(ser1_bonds*bs_ser1_pops[i],axis=1) for i in range(len(bs_ser1_pops))]\n",
    "eq_wtd_Ser2_Hbonds=[np.sum(ser2_bonds*bs_ser2_pops[i],axis=1) for i in range(len(bs_ser2_pops))]\n",
    "eq_wtd_wt_Hbonds=[np.sum(WT_bonds*bs_wt_pops[i],axis=1) for i in range(len(bs_wt_pops))]\n",
    "eq_wtd_nep_Hbonds=[np.sum(Nep_bonds*bs_nep_pops[i],axis=1) for i in range(len(bs_nep_pops))]\n",
    "\n",
    "#Set the eq_wtd_dataset up to be a nice dataframe\n",
    "combined_eq_Hbonds=np.stack((eq_wtd_wt_Hbonds,eq_wtd_nep_Hbonds,eq_wtd_Ser1_Hbonds,eq_wtd_Ser2_Hbonds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">WT</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Nep$^{Nep1A}$</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Nep$^{C1,3S}$</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Nep$^{C2,4S}$</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Percent Occupancy</th>\n",
       "      <th>SD</th>\n",
       "      <th>Percent Occupancy</th>\n",
       "      <th>SD</th>\n",
       "      <th>Percent Occupancy</th>\n",
       "      <th>SD</th>\n",
       "      <th>Percent Occupancy</th>\n",
       "      <th>SD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>125-212</th>\n",
       "      <td>20.024892</td>\n",
       "      <td>4.817551</td>\n",
       "      <td>3.736533</td>\n",
       "      <td>0.603231</td>\n",
       "      <td>30.538516</td>\n",
       "      <td>2.714401</td>\n",
       "      <td>30.313642</td>\n",
       "      <td>4.055703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125-175</th>\n",
       "      <td>53.461502</td>\n",
       "      <td>6.543816</td>\n",
       "      <td>7.584546</td>\n",
       "      <td>1.298059</td>\n",
       "      <td>12.866755</td>\n",
       "      <td>2.837826</td>\n",
       "      <td>38.921828</td>\n",
       "      <td>12.622595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118-125</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118-227</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118-177</th>\n",
       "      <td>0.256949</td>\n",
       "      <td>0.069723</td>\n",
       "      <td>0.488574</td>\n",
       "      <td>0.091424</td>\n",
       "      <td>0.930057</td>\n",
       "      <td>0.301674</td>\n",
       "      <td>0.761313</td>\n",
       "      <td>0.261127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118-121</th>\n",
       "      <td>83.523918</td>\n",
       "      <td>2.044623</td>\n",
       "      <td>71.151775</td>\n",
       "      <td>3.230214</td>\n",
       "      <td>68.209380</td>\n",
       "      <td>2.353873</td>\n",
       "      <td>88.680941</td>\n",
       "      <td>2.423962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121-175</th>\n",
       "      <td>1.093341</td>\n",
       "      <td>0.245857</td>\n",
       "      <td>0.002061</td>\n",
       "      <td>0.000714</td>\n",
       "      <td>0.170493</td>\n",
       "      <td>0.040061</td>\n",
       "      <td>0.749908</td>\n",
       "      <td>0.189918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123-175</th>\n",
       "      <td>3.704249</td>\n",
       "      <td>0.311140</td>\n",
       "      <td>4.766937</td>\n",
       "      <td>0.794753</td>\n",
       "      <td>4.047563</td>\n",
       "      <td>0.537218</td>\n",
       "      <td>8.786222</td>\n",
       "      <td>2.210968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121-177</th>\n",
       "      <td>0.164847</td>\n",
       "      <td>0.051031</td>\n",
       "      <td>0.260983</td>\n",
       "      <td>0.085538</td>\n",
       "      <td>0.053659</td>\n",
       "      <td>0.013959</td>\n",
       "      <td>0.023960</td>\n",
       "      <td>0.010323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123-177</th>\n",
       "      <td>0.037496</td>\n",
       "      <td>0.013265</td>\n",
       "      <td>0.080369</td>\n",
       "      <td>0.019886</td>\n",
       "      <td>0.012283</td>\n",
       "      <td>0.004969</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125-177</th>\n",
       "      <td>0.145482</td>\n",
       "      <td>0.039891</td>\n",
       "      <td>0.760173</td>\n",
       "      <td>0.170420</td>\n",
       "      <td>0.710671</td>\n",
       "      <td>0.188117</td>\n",
       "      <td>7.268266</td>\n",
       "      <td>3.127451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173-212</th>\n",
       "      <td>27.337380</td>\n",
       "      <td>6.472839</td>\n",
       "      <td>33.824513</td>\n",
       "      <td>5.363259</td>\n",
       "      <td>35.766714</td>\n",
       "      <td>5.389343</td>\n",
       "      <td>15.782428</td>\n",
       "      <td>4.089485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173-125</th>\n",
       "      <td>11.048669</td>\n",
       "      <td>3.958168</td>\n",
       "      <td>0.622849</td>\n",
       "      <td>0.181871</td>\n",
       "      <td>0.372800</td>\n",
       "      <td>0.043726</td>\n",
       "      <td>0.735724</td>\n",
       "      <td>0.142676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177-123</th>\n",
       "      <td>0.037496</td>\n",
       "      <td>0.013265</td>\n",
       "      <td>0.080369</td>\n",
       "      <td>0.019886</td>\n",
       "      <td>0.012283</td>\n",
       "      <td>0.004969</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177-125</th>\n",
       "      <td>0.145482</td>\n",
       "      <td>0.039891</td>\n",
       "      <td>0.760173</td>\n",
       "      <td>0.170420</td>\n",
       "      <td>0.710671</td>\n",
       "      <td>0.188117</td>\n",
       "      <td>7.268266</td>\n",
       "      <td>3.127451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177-487</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008595</td>\n",
       "      <td>0.003404</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177-222</th>\n",
       "      <td>8.942152</td>\n",
       "      <td>2.648422</td>\n",
       "      <td>5.047431</td>\n",
       "      <td>1.191366</td>\n",
       "      <td>5.520056</td>\n",
       "      <td>1.465022</td>\n",
       "      <td>30.407985</td>\n",
       "      <td>11.761720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179-488</th>\n",
       "      <td>5.438315</td>\n",
       "      <td>2.152143</td>\n",
       "      <td>0.216902</td>\n",
       "      <td>0.074680</td>\n",
       "      <td>1.169841</td>\n",
       "      <td>0.364000</td>\n",
       "      <td>3.041128</td>\n",
       "      <td>1.244398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177-216</th>\n",
       "      <td>7.980356</td>\n",
       "      <td>2.296248</td>\n",
       "      <td>8.552969</td>\n",
       "      <td>2.331437</td>\n",
       "      <td>17.493823</td>\n",
       "      <td>2.811571</td>\n",
       "      <td>3.301799</td>\n",
       "      <td>1.255471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123-125</th>\n",
       "      <td>0.233779</td>\n",
       "      <td>0.065276</td>\n",
       "      <td>0.030089</td>\n",
       "      <td>0.008068</td>\n",
       "      <td>0.181435</td>\n",
       "      <td>0.060117</td>\n",
       "      <td>0.546970</td>\n",
       "      <td>0.173163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177-219</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       WT               Nep$^{Nep1A}$            \\\n",
       "        Percent Occupancy        SD Percent Occupancy        SD   \n",
       "125-212         20.024892  4.817551          3.736533  0.603231   \n",
       "125-175         53.461502  6.543816          7.584546  1.298059   \n",
       "118-125          0.000000  0.000000          0.000000  0.000000   \n",
       "118-227          0.000000  0.000000          0.000000  0.000000   \n",
       "118-177          0.256949  0.069723          0.488574  0.091424   \n",
       "118-121         83.523918  2.044623         71.151775  3.230214   \n",
       "121-175          1.093341  0.245857          0.002061  0.000714   \n",
       "123-175          3.704249  0.311140          4.766937  0.794753   \n",
       "121-177          0.164847  0.051031          0.260983  0.085538   \n",
       "123-177          0.037496  0.013265          0.080369  0.019886   \n",
       "125-177          0.145482  0.039891          0.760173  0.170420   \n",
       "173-212         27.337380  6.472839         33.824513  5.363259   \n",
       "173-125         11.048669  3.958168          0.622849  0.181871   \n",
       "177-123          0.037496  0.013265          0.080369  0.019886   \n",
       "177-125          0.145482  0.039891          0.760173  0.170420   \n",
       "177-487          0.000000  0.000000          0.008595  0.003404   \n",
       "177-222          8.942152  2.648422          5.047431  1.191366   \n",
       "179-488          5.438315  2.152143          0.216902  0.074680   \n",
       "177-216          7.980356  2.296248          8.552969  2.331437   \n",
       "123-125          0.233779  0.065276          0.030089  0.008068   \n",
       "177-219          0.000000  0.000000          0.000000  0.000000   \n",
       "\n",
       "            Nep$^{C1,3S}$               Nep$^{C2,4S}$             \n",
       "        Percent Occupancy        SD Percent Occupancy         SD  \n",
       "125-212         30.538516  2.714401         30.313642   4.055703  \n",
       "125-175         12.866755  2.837826         38.921828  12.622595  \n",
       "118-125          0.000000  0.000000          0.000000   0.000000  \n",
       "118-227          0.000000  0.000000          0.000000   0.000000  \n",
       "118-177          0.930057  0.301674          0.761313   0.261127  \n",
       "118-121         68.209380  2.353873         88.680941   2.423962  \n",
       "121-175          0.170493  0.040061          0.749908   0.189918  \n",
       "123-175          4.047563  0.537218          8.786222   2.210968  \n",
       "121-177          0.053659  0.013959          0.023960   0.010323  \n",
       "123-177          0.012283  0.004969          0.000000   0.000000  \n",
       "125-177          0.710671  0.188117          7.268266   3.127451  \n",
       "173-212         35.766714  5.389343         15.782428   4.089485  \n",
       "173-125          0.372800  0.043726          0.735724   0.142676  \n",
       "177-123          0.012283  0.004969          0.000000   0.000000  \n",
       "177-125          0.710671  0.188117          7.268266   3.127451  \n",
       "177-487          0.000000  0.000000          0.000000   0.000000  \n",
       "177-222          5.520056  1.465022         30.407985  11.761720  \n",
       "179-488          1.169841  0.364000          3.041128   1.244398  \n",
       "177-216         17.493823  2.811571          3.301799   1.255471  \n",
       "123-125          0.181435  0.060117          0.546970   0.173163  \n",
       "177-219          0.000000  0.000000          0.000000   0.000000  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find the mean of the replicates\n",
    "mean_Ser1_Hbond=np.mean(eq_wtd_Ser1_Hbonds, axis=0)\n",
    "mean_Ser2_Hbond=np.mean(eq_wtd_Ser2_Hbonds, axis=0)\n",
    "mean_WT_Hbond=np.mean(eq_wtd_wt_Hbonds, axis=0)\n",
    "mean_nep_Hbond=np.mean(eq_wtd_nep_Hbonds, axis=0)\n",
    "\n",
    "\n",
    "#Find the Standard Deviation\n",
    "std_Ser1_Hbond=np.std(eq_wtd_Ser1_Hbonds, axis=0)\n",
    "std_Ser2_Hbond=np.std(eq_wtd_Ser2_Hbonds, axis=0)\n",
    "std_WT_Hbond=np.std(eq_wtd_wt_Hbonds, axis=0)\n",
    "std_nep_Hbond=np.std(eq_wtd_nep_Hbonds, axis=0)\n",
    "\n",
    "#Make it into a nice dataframe\n",
    "per_bond_vals=np.column_stack([mean_WT_Hbond,mean_nep_Hbond,mean_Ser1_Hbond, mean_Ser2_Hbond])\n",
    "per_bond_errs=np.column_stack([std_WT_Hbond,std_nep_Hbond,std_Ser1_Hbond, std_Ser2_Hbond])\n",
    "\n",
    "bond_list_cond=[]\n",
    "for i in range(len(bond_pairs_list)):\n",
    "    bond_list_cond.append(str(bond_pairs_list[i][0]) \"-\" str(bond_pairs_list[i][1]))\n",
    "\n",
    "#Combine the values and errors into the shape of (bond_pair, mutant, 2) \n",
    "#where 2 is value, error\n",
    "comb_val_err_int=np.dstack((per_bond_vals,per_bond_errs))\n",
    "#reshape so you can feed into a pd dataframe...\n",
    "comb_val_err=comb_val_err_int.reshape(len(bond_list_cond),8)\n",
    "#Convert to %\n",
    "comb_val_err=np.multiply(comb_val_err,100)\n",
    "\n",
    "\n",
    "per_bond_df=pd.DataFrame(np.multiply(per_bond_vals,100), index=bond_list_cond, columns=[\"WT\",r\"Nep$^{Nep1A}$\",r\"Nep$^{C1,3S}$\",r\"Nep$^{C2,4S}$\"])\n",
    "cols = pd.MultiIndex.from_product([[\"WT\",r\"Nep$^{Nep1A}$\",r\"Nep$^{C1,3S}$\",r\"Nep$^{C2,4S}$\"], ['Percent Occupancy', 'SD']])\n",
    "combined_val_err_df=pd.DataFrame(comb_val_err, index=bond_list_cond,columns=cols)\n",
    "\n",
    "combined_val_err_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 633  679 2284  896  599 2448 1433 2453 2698 2983]\n"
     ]
    }
   ],
   "source": [
    "#What do WT and Nep look like when 125 is not H-bonded?\n",
    "\n",
    "#First find all the structures that don't have a 125 H-bond.\n",
    "\n",
    "#Above we defined an array of size: \n",
    "#(3000-centers, variable-bond pairs, variable by center, 2- residue numbers of residues involved in bond pair)\n",
    "#These are #residue_numbers_WT and residue_numbers_Nep\n",
    "\n",
    "#We also made a function that checks if a residue is in a pair (is_resi_bonded). This returns a mask array of \n",
    "#size (centers), with a 1 (residue bonded) or 0 (unbonded)\n",
    "WT_resi_125=is_resi_bonded(residue_numbers_WT, 125)\n",
    "Nep_resi_125=is_resi_bonded(residue_numbers_Nep, 125)\n",
    "\n",
    "#Find the mean eq prob for each center (doing all now as we'll use the others later..)\n",
    "mean_wt_eq=np.mean(bs_wt_pops,axis=0)\n",
    "mean_nep_eq=np.mean(bs_nep_pops,axis=0)\n",
    "mean_s1_eq=np.mean(bs_ser1_pops,axis=0)\n",
    "mean_s2_eq=np.mean(bs_ser2_pops,axis=0)\n",
    "\n",
    "subsectioned_wt=np.where(WT_resi_125==0)[0]\n",
    "subsectioned_nep=np.where(Nep_resi_125==0)[0]\n",
    "\n",
    "#Since we are going to argsort and argsort returns the indicies of the provided array, make a\n",
    "#Temp center indicies array to re-recover the correct indicies.\n",
    "temp_indcs_wt=np.arange(0,3000)[subsectioned_wt]\n",
    "temp_indcs_nep=np.arange(0,3000)[subsectioned_nep]\n",
    "#Find the indicies where there is no bond\n",
    "wt_top_no_125=temp_indcs_wt[np.argpartition(mean_wt_eq[subsectioned_wt],-10)[-10:]]\n",
    "nep_top_no_125=temp_indcs_nep[np.argpartition(mean_nep_eq[subsectioned_nep],-10)[-10:]]\n",
    "\n",
    "#Print out the structures!\n",
    "save_structures=False\n",
    "\n",
    "center_indcs=nep_top_no_125\n",
    "print(center_indcs)\n",
    "if save_structures==True:\n",
    "    for i in center_indcs:\n",
    "        cmd = f'echo \"System\" | gmx trjconv -f Nep_center_structures.xtc -s ../ProcessedRuns/NepMut-prot-masses.pdb \\\n",
    "        -o Structures_for_Figures/Nep_no_125/Nep_center{i}.pdb -b {i} -e {i}'\n",
    "        os.system(cmd)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1241 2935 2964 1205 1860 1372 1973 2169  903 2997  679 1056  633 2698\n",
      "    6 1568 1792 1819 1857 1868 2212  366  279 2284  133  130 2358 2406\n",
      " 2448 1433]\n"
     ]
    }
   ],
   "source": [
    "##We have many structures that fit into an \"active\" conformation, but do not see H-bonding with S125.\n",
    "##What do these specifically look like?\n",
    "structures=30\n",
    "flapcutoff=0.6\n",
    "flap_upper=1.25\n",
    "channelcutoff=0.5\n",
    "\n",
    "active_open_no_125=np.where((NepMutFLAP>flapcutoff)&(Nep_resi_125==0)&(NepMutFLAP<flap_upper)&(nepminflap_dist>channelcutoff))[0]\n",
    "#Since we are going to argsort and argsort returns the indicies of the provided array, make a\n",
    "#Temp center indicies array to re-recover the correct indicies.\n",
    "temp_eq_array=mean_nep_eq[active_open_no_125]\n",
    "temp_indcs_array=np.arange(0,3000)[active_open_no_125]\n",
    "\n",
    "nep_top_no_125=temp_indcs_array[np.argpartition(temp_eq_array, -structures)[-structures:]]\n",
    "\n",
    "#Print out the structures!\n",
    "save_structures=False\n",
    "center_indcs=nep_top_no_125\n",
    "\n",
    "print(center_indcs)\n",
    "if save_structures==True:\n",
    "    for i in center_indcs:\n",
    "        cmd = f'echo \"System\" | gmx trjconv -f Nep_center_structures.xtc -s ../ProcessedRuns/NepMut-prot-masses.pdb \\\n",
    "        -o Structures_for_Figures/Nep_no_125/center{i}_nep_active.pdb -b {i} -e {i}'\n",
    "        os.system(cmd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#What is the highest probability structure with 125-175 or 125-212 but not both?\n",
    "#125-212 is saved in: mean_eq_wtd_hbonds_wt[0]\n",
    "#125-175 is saved in mean_eq_wtd_hbonds_wt[1]\n",
    "\n",
    "mean_eq_wtd_hbonds_wt=np.multiply(WT_bonds,mean_wt_eq)\n",
    "mean_eq_wtd_hbonds_nep=np.multiply(Nep_bonds,mean_nep_eq)\n",
    "mean_eq_wtd_hbonds_s1=np.multiply(ser1_bonds,mean_s1_eq)\n",
    "mean_eq_wtd_hbonds_s2=np.multiply(ser2_bonds,mean_s2_eq)\n",
    "\n",
    "\n",
    "#Find where there are no 125-212 bonds, return the highest prob 125-175 bond\n",
    "no212_w175=np.argmax(mean_eq_wtd_hbonds_wt[1][np.where(mean_eq_wtd_hbonds_wt[0]==0)[0]])\n",
    "no175_w212=np.argmax(mean_eq_wtd_hbonds_wt[0][np.where(mean_eq_wtd_hbonds_wt[1]==0)[0]])\n",
    "\n",
    "save_structures=False\n",
    "if save_structures==True:\n",
    "        cmd = f'echo \"System\" | gmx trjconv -f WT_center_structures.xtc -s ../ProcessedRuns/WT-prot-masses.pdb \\\n",
    "        -o Structures_for_Figures//WT_hbonds/125_212_no175.pdb -b {no175_w212} -e {no175_w212}'\n",
    "        os.system(cmd)\n",
    "\n",
    "        cmd = f'echo \"System\" | gmx trjconv -f WT_center_structures.xtc -s ../ProcessedRuns/WT-prot-masses.pdb \\\n",
    "        -o Structures_for_Figures//WT_hbonds/125_175_no212.pdb -b {no212_w175} -e {no212_w175}'\n",
    "        os.system(cmd)        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What contacts change when 125 is unbound?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairwise_interactions_with_flap=[]\n",
    "#Flap starts at residue 89 (0 indexed) and ends at 102 for WT/all others. Only need to incldue these in the computation.\n",
    "for i in range(89,103):\n",
    "    for j in range(439):\n",
    "        pairwise_interactions_with_flap.append([i,j])\n",
    "\n",
    "pairwise_interactions_with_flap_nep=[]\n",
    "#Flap starts at residue 92 (0 indexed) and ends at 105 for Nep. Only need to incldue these in the computation.\n",
    "for i in range(92,106):\n",
    "    for j in range(442):\n",
    "        pairwise_interactions_with_flap_nep.append([i,j])\n",
    "        \n",
    "#Compute contacts\n",
    "allcontactsnep,resipairs=md.compute_contacts(centersNep, contacts=pairwise_interactions_with_flap_nep,scheme='closest')\n",
    "allcontactswt,resipairs=md.compute_contacts(centersWT, contacts=pairwise_interactions_with_flap,scheme='closest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def SectionAndWtContacts(contacts, subset_indices, contact_cutoff, eq_probs, contact_dims):\n",
    "    #Contacts should be an array of all frames with all contacts\n",
    "    #Subset_indices is a np array (e.g from np.where) with the indices you want to subsection\\\n",
    "    #Contact cutoff is in nm, what you consider a contact\n",
    "    #contact dims is the size of the list of residues you input to the compute_contacts_calculation\n",
    "    \n",
    "    #Select contacts that are closer than the subset.\n",
    "    #This is a Bool array\n",
    "    contact_subset=contacts[subset_indices]<contact_cutoff\n",
    "\n",
    "    #reweight the eq_probs\n",
    "    rwt_pops=eq_probs[subset_indices]/np.sum(eq_probs[subset_indices])\n",
    "\n",
    "    #eq_weight the bonded pairs\n",
    "    eq_wtd_bonds=[d*p for d,p in zip(contact_subset,rwt_pops)]\n",
    "\n",
    "    #Return the eq_probability of being in a bond\n",
    "    eq_contact=np.sum(eq_wtd_bonds, axis=0)\n",
    "\n",
    "    #Make the array 2D\n",
    "    n=0\n",
    "    eq_contacts_2d=np.zeros((contact_dims[0],contact_dims[1]))\n",
    "    for i in range(contact_dims[0]):\n",
    "        for j in range(contact_dims[1]):\n",
    "            eq_contacts_2d[i,j]=eq_contact[n]\n",
    "            n =1\n",
    "    return eq_contacts_2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Bin the data\n",
    "def bin_data(twoD_eq_contact):\n",
    "    binned_contacts=np.zeros(np.shape(twoD_eq_contact))\n",
    "    for i in range(np.shape(twoD_eq_contact)[0]):\n",
    "        for j in range(np.shape(twoD_eq_contact)[1]):\n",
    "            if twoD_eq_contact[i,j]<0.01:\n",
    "                binned_contacts[i,j]=0\n",
    "            elif twoD_eq_contact[i,j]<0.1:\n",
    "                binned_contacts[i,j]=0.0\n",
    "            elif twoD_eq_contact[i,j]<0.25:\n",
    "                binned_contacts[i,j]=0.25\n",
    "            elif twoD_eq_contact[i,j]<0.5:\n",
    "                binned_contacts[i,j]=0.5\n",
    "            elif twoD_eq_contact[i,j]<0.75:\n",
    "                binned_contacts[i,j]=0.75\n",
    "            elif twoD_eq_contact[i,j]<0.9:\n",
    "                binned_contacts[i,j]=0.9\n",
    "            else:\n",
    "                binned_contacts[i,j]=1\n",
    "    return binned_contacts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "flapcutoff=0.6\n",
    "flap_upper=1.25\n",
    "channelcutoff=0.5\n",
    "nep_no_125_indcs=np.where((NepMutFLAP>flapcutoff)&(Nep_resi_125==0)&(NepMutFLAP<flap_upper)&(nepminflap_dist>channelcutoff))[0]\n",
    "nep_active_with_125_indcs=np.where((NepMutFLAP>flapcutoff)&(Nep_resi_125==1)&(NepMutFLAP<flap_upper)&(nepminflap_dist>channelcutoff))[0]\n",
    "\n",
    "# nep_no_125_indcs=np.where((Nep_resi_125==0))[0]\n",
    "# nep_active_with_125_indcs=np.where((Nep_resi_125==1))[0]\n",
    "\n",
    "\n",
    "\n",
    "contact_distance=0.25\n",
    "nep_no_125_contacts=SectionAndWtContacts(allcontactsnep, nep_no_125_indcs, contact_distance, popsNep, [13,442])\n",
    "nep_with_125_contacts=SectionAndWtContacts(allcontactsnep, nep_active_with_125_indcs, contact_distance, popsNep, [13,442])\n",
    "\n",
    "nep_no_125_binned=bin_data(nep_no_125_contacts)\n",
    "nep_with_125_binned=bin_data(nep_with_125_contacts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 4320x5400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What does the contact map look like?\n",
    "fig,ax=plt.subplots(figsize=(12,15), dpi=360)\n",
    "\n",
    "plt.imshow(nep_no_125_binned-nep_with_125_binned, origin='lower', cmap='bwr', vmin=-1, vmax=1)\n",
    "\n",
    "plt.colorbar(fraction=0.03, label='Probability')\n",
    "\n",
    "ax.xaxis.set_major_locator(MultipleLocator(25))\n",
    "# ax.xaxis.set_major_formatter('{x:.0f}')\n",
    "ax.xaxis.set_minor_locator(MultipleLocator(5))\n",
    "\n",
    "ax.yaxis.set_major_locator(MultipleLocator(25))\n",
    "# ax.yaxis.set_major_formatter('{x:.0f}')\n",
    "ax.yaxis.set_minor_locator(MultipleLocator(5))\n",
    "\n",
    "plt.xticks(ticks=range(26,170,25), labels=range(27 82,171 82,25));\n",
    "plt.yticks(ticks=range(0,13,5), labels=range(174,187,5));\n",
    "plt.xlim(26,180)\n",
    "\n",
    "plt.title(\"Nep No 125 - 125 bound\")\n",
    "\n",
    "plt.ylabel('residue');\n",
    "plt.xlabel('residue');\n",
    "\n",
    "np.savetxt('./Figures/Lost_contacts.csv', nep_no_125_binned-nep_with_125_binned)\n",
    "#Positive values reflect increased contacts when 125 bond is absent,\n",
    "#Negative values reflect contacts that are decreased when 125 is gone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 540x900 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Use the distance between E175 and N236 (WT) as loop flipping\n",
    "wt_flip=calc_distance(centersWT,[[236],[175]],\"sidechain\",\"minn\")\n",
    "nep_flip=calc_distance(centersNep,[[177],[238]],\"sidechain\",\"minn\")\n",
    "ser1_flip=calc_distance(centersSer1,[[236],[175]],\"sidechain\",\"minn\")\n",
    "ser2_flip=calc_distance(centersSer2,[[236],[175]],\"sidechain\",\"minn\")\n",
    "# NepTruncFLAP=calc_distance(NepTruncTrjs,[[37],[97]],\"protein\")\n",
    "\n",
    "numbins=50\n",
    "min_val=0\n",
    "max_val=3\n",
    "countsWT, binsWT=np.histogram(wt_flip, bins=numbins,weights=popsWT, range=(min_val,max_val))\n",
    "countsNep, bins=np.histogram(nep_flip, bins=numbins,weights=popsNep,range=(min_val,max_val))\n",
    "countsSer1, bins=np.histogram(ser1_flip, bins=numbins,weights=popsSer1, range=(min_val,max_val))\n",
    "countsSer2, bins=np.histogram(ser2_flip, bins=numbins,weights=popsSer2, range=(min_val,max_val))\n",
    "# countsNepTrunc, bins=np.histogram(NepTruncFLAP, bins=50,range=(0.5,3.5))\n",
    "\n",
    "normCountsWT= countsWT/sum(countsWT)\n",
    "normCountsNep= countsNep/sum(countsNep)\n",
    "normCountsSer1= countsSer1/sum(countsSer1)\n",
    "normCountsSer2= countsSer2/sum(countsSer2)\n",
    "# normCountsNepTrunc= countsNepTrunc/sum(countsNepTrunc)\n",
    "\n",
    "binscenters_WT=(binsWT[:-1]   binsWT[1:]) / 2\n",
    "width=binsWT[1]-binsWT[0]\n",
    "\n",
    "fig, axs=plt.subplots(4,1,sharey=True, sharex=True,  figsize=(1.5, 2.5), dpi=360)\n",
    "fig.text(-0.1, 0.5, 'Probability', va='center',rotation='vertical',fontsize=8)\n",
    "fig.text(0.5, -0.05, 'Gln175-Asn238 distance (nm)', ha='center',fontsize=8)\n",
    "plt.xlim(0,3)\n",
    "axs[0].bar(binscenters_WT,normCountsWT, align='center', width=width, label=\"WT\", color=wtc, linewidth=0.25)\n",
    "axs[1].bar(binscenters_WT,normCountsNep, align='center', width=width, label=\"Nep$^{Nep1A}$\", color=nepc, linewidth=0.25)\n",
    "axs[2].bar(binscenters_WT,normCountsSer1, align='center', width=width, label=\"Nep$^{C1,3S}$\", color=C13Sc, linewidth=0.25)\n",
    "axs[3].bar(binscenters_WT,normCountsSer2, align='center', width=width, label=\"Nep$^{C2,4S}$\", color=C24Sc, linewidth=0.25)\n",
    "\n",
    "for ax in axs:\n",
    "    ax.legend(fontsize=8,frameon=False, loc='upper right', handlelength=0, handletextpad=0, fancybox=True)\n",
    "    ax.get_xticklabels()    \n",
    "    ax.tick_params(which='major', direction='out', length=3, width=1.5, colors='black')\n",
    "    ax.tick_params(which='minor', direction='out', length=2, width=1, colors='black')\n",
    "\n",
    "    ax.xaxis.set_major_locator(MultipleLocator(1))\n",
    "    ax.xaxis.set_minor_locator(MultipleLocator(0.25))\n",
    "    ax.yaxis.set_major_locator(MultipleLocator(0.05))\n",
    "    ax.yaxis.set_minor_locator(MultipleLocator(0.01))\n",
    "    for axis in ['top','bottom','left','right']:\n",
    "        ax.spines[axis].set_linewidth(1)    \n",
    "    \n",
    "    ax.tick_params(axis = 'x', which = 'both', bottom = True, top=False,labelsize=8)\n",
    "    ax.tick_params(axis= 'y', which = 'both', left = True, right=False,labelsize=8)\n",
    "fig.tight_layout(pad=0.05)\n",
    "plt.axvline(x=0.5,lw=0.2)\n",
    "\n",
    "\n",
    "# plt.savefig('./Figures/Loop_flipping_smaller.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WT Flipped Distal loop % is: 100.0  /- 4.196248604251576e-15 %\n",
      "Nep Mut Flipped Distal loop % is: 83.66266359771302  /- 6.6249168867839785 %\n",
      "Ser 1 Flipped Distal loop % is: 99.90669586362975  /- 0.02917259186412124 %\n",
      "Ser 2 Flipped Distal loop % is: 100.0  /- 9.383097129595137e-15 %\n"
     ]
    }
   ],
   "source": [
    "cutoff=0.5\n",
    "wt_indcs=np.where(wt_flip>cutoff)\n",
    "nep_indcs=np.where(nep_flip>cutoff)\n",
    "s1_indcs=np.where(ser1_flip>cutoff)\n",
    "s2_indcs=np.where(ser2_flip>cutoff)\n",
    "\n",
    "wt_vals=[np.sum(bs_wt_pops[i][wt_indcs]) for i in range(len(bs_wt_pops))]\n",
    "nep_vals=[np.sum(bs_nep_pops[i][nep_indcs]) for i in range(len(bs_nep_pops))]\n",
    "s1_vals=[np.sum(bs_ser1_pops[i][s1_indcs]) for i in range(len(bs_ser1_pops))]\n",
    "s2_vals=[np.sum(bs_ser2_pops[i][s2_indcs]) for i in range(len(bs_ser2_pops))]\n",
    "\n",
    "print(\"WT Flipped Distal loop % is:\", (100*np.mean(wt_vals)), \" /-\", 100*np.std(wt_vals), \"%\")\n",
    "print(\"Nep Mut Flipped Distal loop % is:\", (100*np.mean(nep_vals)), \" /-\", 100*np.std(nep_vals), \"%\")\n",
    "print(\"Ser 1 Flipped Distal loop % is:\", (100*np.mean(s1_vals)), \" /-\", 100*np.std(s1_vals), \"%\")\n",
    "print(\"Ser 2 Flipped Distal loop % is:\", (100*np.mean(s2_vals)), \" /-\", 100*np.std(s2_vals), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}