analyses.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cf918093",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import mdtraj as md\n",
    "from scipy.optimize import curve_fit, least_squares\n",
    "from scipy.ndimage import gaussian_filter1d\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "from matplotlib.colors import LogNorm\n",
    "import os, sys\n",
    "from scipy.stats import binned_statistic\n",
    "from scipy.stats import pearsonr, spearmanr\n",
    "import seaborn as sns\n",
    "\n",
    "def calcProfiles(name,protein,replica=0,L=25,nskip=500):\n",
    "    \"\"\"Function to calculate csat and ccon from concentration profiles\"\"\"\n",
    "    df_proteins = pd.read_csv('direct_coexistence/proteins.csv',index_col=0)\n",
    "    filename = f'{name:s}_{replica:d}.npy'\n",
    "    if os.path.isfile(filename):\n",
    "        h = np.load(filename)[nskip:]\n",
    "        fasta = df_proteins.loc[protein].fasta\n",
    "        N = len(fasta)\n",
    "        conv = 100/6.022/N/L/L*1e3\n",
    "        h = h*conv \n",
    "        lz = h.shape[1]+1\n",
    "        edges = np.arange(-lz/2.,lz/2.,1)/10\n",
    "        dz = (edges[1]-edges[0])/2.\n",
    "        z = edges[:-1]+dz\n",
    "        hm = np.mean(h,axis=0)\n",
    "        \n",
    "        cutoffs1 = [-10,10]\n",
    "        cutoffs2 = [-60,60]\n",
    "\n",
    "        bool1 = np.logical_and(z>cutoffs1[0],z<cutoffs1[1])\n",
    "        bool2 = np.logical_or(z<cutoffs2[0],z>cutoffs2[1])\n",
    "\n",
    "        dilarray = np.apply_along_axis(lambda a: a[bool2].mean(), 1, h)\n",
    "        denarray = np.apply_along_axis(lambda a: a[bool1].mean(), 1, h)\n",
    "        \n",
    "        dil = hm[bool2].mean()\n",
    "        den = hm[bool1].mean()\n",
    "\n",
    "        return dil, den, dilarray, denarray\n",
    "    else:\n",
    "        print('DATA NOT FOUND FOR',name)\n",
    "        \n",
    "def plotMap(ax,mat,vmin,vmax,xlabel,ylabel,cmap=plt.cm.bwr,\n",
    "            ori='vertical',\n",
    "            cbar=True,cbarlabel='$U_E$ (pH 6) - $U_E$ (pH 8)  /  J mol$^{-1}$'):\n",
    "    im = ax.imshow(mat.T,extent=[0, mat.shape[1],0, mat.shape[0]], \n",
    "                 cmap=cmap,\n",
    "                 origin='lower',alpha=1,\n",
    "                 vmin=vmin,vmax=vmax)\n",
    "    \"\"\"Function to plot contact maps\"\"\"\n",
    "    if cbar:\n",
    "        divider = make_axes_locatable(ax)\n",
    "        if ori == 'vertical':\n",
    "            cax = divider.new_horizontal(size=\"{:.1f}%\".format(5), pad=.1)\n",
    "        else:\n",
    "            cax = divider.new_vertical(size=\"{:.1f}%\".format(5), pad=.1)\n",
    "        f.add_axes(cax)\n",
    "        cb = f.colorbar(im, cax=cax, orientation=ori,\n",
    "                    label=cbarlabel)\n",
    "        cax.xaxis.set_label_position('top'); cax.xaxis.set_ticks_position('top')\n",
    "    ax.set_xlabel(xlabel)\n",
    "    ax.set_ylabel(ylabel)\n",
    "    \n",
    "def calc_X_Y_mat(X_pos,Y_pos,mat):\n",
    "    \"\"\"Function to extract contacts between residue types from contact maps\"\"\"\n",
    "    df = pd.DataFrame(0,index=X_pos,columns=Y_pos)\n",
    "    for res1 in X_pos:\n",
    "        for res2 in Y_pos:\n",
    "            df.loc[res1,res2] = mat[res1,res2]\n",
    "    return df\n",
    "\n",
    "def calc_X_Y_mat_replica(var,X_pos,Y_pos,replica):\n",
    "    \"\"\"Function to extract contacts between residue types from contact maps\"\"\"\n",
    "    df = pd.DataFrame(0,index=X_pos,columns=Y_pos)\n",
    "    shape = np.load(f'direct_coexistence/contact_maps/replica_{replica:d}/{var:s}_{replica:d}_0_contact_map.npy').shape\n",
    "    mat = np.zeros(shape)\n",
    "    for chunk in range(20):\n",
    "        mat += np.load(f'direct_coexistence/contact_maps/replica_{replica:d}/{var:s}_{replica:d}_{chunk:d}_contact_map.npy')\n",
    "    mat = mat/20\n",
    "    for res1 in X_pos:\n",
    "        for res2 in Y_pos:\n",
    "            df.loc[res1,res2] = mat[res1,res2]\n",
    "    return df\n",
    "\n",
    "def calc_ave_mat(var,N,replicas=[0,2,3]):\n",
    "    \"\"\"average over contact matrices calculated for segments of a simulation trajectory\"\"\"\n",
    "    mat = np.zeros((N,N))\n",
    "    for replica in replicas:\n",
    "        mat_replica = np.zeros((N,N))\n",
    "        for chunk in range(20):\n",
    "            mat_replica += np.load(\n",
    "                f'direct_coexistence/contact_maps/replica_{replica:d}/{var:s}_{replica:d}_{chunk:d}_contact_map.npy')\n",
    "        np.save(f'direct_coexistence/contact_maps/{var:s}_{replica:d}_contact_map.npy',mat_replica/20)\n",
    "        mat += mat_replica/20\n",
    "    np.save(f'direct_coexistence/contact_maps/{var:s}_contact_map.npy',mat/len(replicas))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bd41c9f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isfile(f'direct_coexistence/contact_maps/CPEB4_contact_map.npy'): \n",
    "    calc_ave_mat('CPEB4',448,replicas=[0,2,3])\n",
    "if not os.path.isfile(f'direct_coexistence/contact_maps/CPEB4pH6_contact_map.npy'): \n",
    "    calc_ave_mat('CPEB4pH6',448,replicas=[0,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "024ff79c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_proteins = pd.read_csv('direct_coexistence/proteins.csv',index_col=0)\n",
    "df_proteins.fasta = df_proteins.fasta.apply(list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bf49b28b",
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir figures"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d32aa78d",
   "metadata": {},
   "source": [
    "### Methods\n",
    "\n",
    "Molecular dynamics simulations were performed using the single-bead-per-residue model\n",
    "CALVADOS 2 implemented in OpenMM v.7.5. All simulations were conducted in the NVT ensemble at 20 ºC using a Langevin integrator with a time step of 10 fs and friction coefficient of 0.01 ps$^{-1}$. Configurations were saved every 2 ns for direct-coexistence simulations and every 5 ns for multimer simulations. In our simulations, pH is modeled through its effect on the charge on the histidine residues ($q_{His}$).\n",
    "\n",
    "Direct-coexistence simulations were performed with 100 chains in a cuboidal box of side lengths [Lx,Ly,Lz] = [25, 25, 300] nm. Simulations were run in $n=3$ independent replicas for at least 55 µs each. The systems readily formed a protein-rich slab spanning the periodic boundaries along the x- and y-coordinates. The initial 2 μs of the simulation trajectories were discarded and time-averaged concentration profiles along the z-axis were calculated after centering the condensates in the box as previously described. \n",
    "\n",
    "To model protein multimers, 400 chains were initially placed at random positions in a cubic box of side length 188 nm and simulations were run in $n=2$ replicas for 16 μs each. The formation and dissolution of protein multimers was monitored via a cluster analysis implemented in OVITO. Proteins were clustered based on the distances between their centers of mass, using a cutoff of 1.5 times the average radius of gyration of the protein. Contact maps were calculated between a chain in the middle of the condensate or multimer and the surrounding chains. Contacts were quantified via the switching function $c(r)= 0.5 − 0.5 × \\tanh{[(r-\\sigma_0)/r_0]}$, where $r$ is the intermolecular distance between two residues, $\\sigma_0=1$ nm, and $r_0=0.3$ nm. \n",
    "\n",
    "To match the conditions of the experiments with which the simulation results are compared, direct-coexistence and multimer simulations were performed at ionic strengths of 150 mM and 60 mM, respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50ed7659",
   "metadata": {},
   "source": [
    "### Time evolution of multimer sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a213dcb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.666015625 16.375\n",
      "8.63671875 16.58\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x250 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(1, 2, sharex=False, sharey=True, figsize=(5,2.5))\n",
    "plt.rc('font', **{'family':'sans-serif','sans-serif':['Arial'], 'size': 8})\n",
    "\n",
    "edges = np.arange(1,401,1)\n",
    "x = edges[:-1]+(edges[1]-edges[0])/2.\n",
    "\n",
    "for i in range(2):\n",
    "    clust = pd.read_pickle(f'multimers/replica_{i:d}/cmtraj/clusters_CPEB4_60.pkl')\n",
    "    rg = np.load(f'multimers/replica_{i:d}/cmtraj/CPEB4_400_293_60_rg.npy')\n",
    "    print(rg.mean()*1.5,rg.shape[1]*5/1000)\n",
    "    h = np.apply_along_axis(\n",
    "        lambda a: np.histogram(a,bins=edges)[0]/x, 1, clust.size_chain_B)\n",
    "    h = np.concatenate(\n",
    "        [np.mean(h[i:i+10],axis=0,keepdims=True) for i in range(0,h.shape[0]//10*10,10)])\n",
    "    im = axes[i].pcolor(h.T,norm=LogNorm(vmin=1,vmax=100))\n",
    "    axes[i].set_ylim(1,250)\n",
    "    divider = make_axes_locatable(axes[i])\n",
    "    cax = divider.new_vertical(size=\"5%\", pad=.1)\n",
    "    f.add_axes(cax)\n",
    "    cb = f.colorbar(im, cax=cax, orientation=\"horizontal\",label='Number of multimers')\n",
    "    cax.xaxis.set_label_position('top'); cax.xaxis.set_ticks_position('top')\n",
    "    axes[i].set_xticks(range(0,h.shape[0]//10*10,100),\n",
    "                         labels=[i/20 for i in range(0,h.shape[0]//10*10,100)])\n",
    "\n",
    "    axes[i].set_yscale('log')\n",
    "    axes[i].axhspan(130, 170, alpha=0.2, color='k', lw=0)\n",
    " \n",
    "axes[0].set_ylabel('Chains per multimer')\n",
    "axes[0].set_xlabel('Time  /  µs')\n",
    "axes[1].set_xlabel('Time  /  µs')\n",
    "    \n",
    "plt.tight_layout()\n",
    "plt.savefig('figures/multimers.png',dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0279371",
   "metadata": {},
   "source": [
    "### Fig 2\n",
    "\n",
    "_Left:_ Ratio of intermolecular contact maps at pH 6 and 8 calculated between a chain in the middle of the condensate and the surrounding chains. _Right:_ Ratio of contacts at pH 7 and 8 between His, Arg, Lys, Phe, Tyr, Trp, Asp, and Glu residues on a chain in the middle of the condensate and the histidine residues on the surrounding chains."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "368e6774",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/y_/2x90pfvj0138gctc5qx9196m0000gn/T/ipykernel_84830/1253001535.py:76: FutureWarning: save is not part of the public API, usage can give unexpected results and will be removed in a future version\n",
      "  writer.save()\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'CPEB4'\n",
    "H_pos = np.where(np.asarray(df_proteins.loc[var].fasta)=='H')[0]\n",
    "R_pos = np.where(np.asarray(df_proteins.loc[var].fasta)=='R')[0]\n",
    "K_pos = np.where(np.asarray(df_proteins.loc[var].fasta)=='K')[0]\n",
    "E_pos = np.where(np.asarray(df_proteins.loc[var].fasta)=='E')[0]\n",
    "D_pos = np.where(np.asarray(df_proteins.loc[var].fasta)=='D')[0]\n",
    "F_pos = np.where(np.asarray(df_proteins.loc[var].fasta)=='F')[0]\n",
    "Y_pos = np.where(np.asarray(df_proteins.loc[var].fasta)=='Y')[0]\n",
    "W_pos = np.where(np.asarray(df_proteins.loc[var].fasta)=='W')[0]\n",
    "N_pos = np.sort(np.concatenate([E_pos,D_pos]))\n",
    "Aro_pos = np.sort(np.concatenate([F_pos,Y_pos,W_pos]))\n",
    "\n",
    "f, (ax1,ax2) = plt.subplots(1, 2, sharex=False, sharey=False, figsize=(5,2))\n",
    "plt.rc('font', **{'family':'sans-serif','sans-serif':['Arial'], 'size': 8})\n",
    "\n",
    "writer = pd.ExcelWriter('Fig2hk.xlsx', engine='xlsxwriter')\n",
    "\n",
    "colors = [(1, 1, 1), sns.color_palette('viridis_r', 2)[0], (0, 0, 0)] # Experiment with this\n",
    "cm = mpl.colors.LinearSegmentedColormap.from_list('viridis_green', colors, N=100)\n",
    "\n",
    "mat_pH8 = np.load('direct_coexistence/contact_maps/CPEB4_contact_map.npy')\n",
    "\n",
    "mat_pH6 = np.load('direct_coexistence/contact_maps/CPEB4pH6_contact_map.npy')\n",
    "\n",
    "ratio = mat_pH6 / mat_pH8\n",
    "\n",
    "pd.DataFrame(ratio,index=np.arange(1,ratio.shape[0]+1),\n",
    "            columns=np.arange(1,ratio.shape[0]+1)).to_excel(writer, sheet_name='contact map ratio')\n",
    "\n",
    "plotMap(ax1,ratio,.5,2.2,xlabel='Central chain',\n",
    "    ylabel='Chains in condensate',cmap=cm,\n",
    "        ori='vertical',cbar=True,cbarlabel='Contact ratio')\n",
    "\n",
    "ax1.set_yticks([0.5]+list(np.arange(49.5,409.5,100))+[447.5],\n",
    "                   ['{:d}'.format(int(i)) for i in [1]+list(range(100,410,100))+[448]])\n",
    "ax1.set_xticks([0.5]+list(np.arange(49.5,409.5,100))+[447.5],\n",
    "                   ['{:d}'.format(int(i)) for i in [1]+list(range(100,410,100))+[448]])\n",
    "\n",
    "####################################\n",
    "\n",
    "ylorbr = mpl.colormaps['viridis']\n",
    "colors = ['#33B779','#3954A4','#3954A4','#19B0A9','#19B0A9','#19B0A9','#EC2427','#EC2427']\n",
    "\n",
    "residues = np.empty(0)\n",
    "contacts = np.empty(0)\n",
    "for res_pos,res in zip([H_pos,R_pos,K_pos,F_pos,Y_pos,W_pos,D_pos,E_pos],\n",
    "                       ['H', 'R', 'K', 'F', 'Y', 'W', 'D', 'E']):\n",
    "    contacts_8 = calc_X_Y_mat(res_pos,H_pos,mat_pH8) \n",
    "    contacts_6 = calc_X_Y_mat(res_pos,H_pos,mat_pH6) \n",
    "    du = (contacts_6/contacts_8).mean(axis=1).values.flatten()\n",
    "    contacts = np.append(contacts,du.tolist())\n",
    "    residues = np.append(residues,[res]*du.size)\n",
    "H_contacts = pd.DataFrame({'residues':residues,'contacts':contacts})\n",
    "\n",
    "H_contacts.set_index('residues').to_excel(writer, sheet_name='aa–H contact ratio')\n",
    "\n",
    "sns.swarmplot(ax=ax2,data=H_contacts,x='residues',y='contacts',hue='residues',\n",
    "              size=2,legend=False,s=1.9,palette=colors)\n",
    "PROPS = {\n",
    "    'boxprops':{'facecolor':'none', 'edgecolor':'k'},\n",
    "    'medianprops':{'color':'k'},\n",
    "    'whiskerprops':{'color':'k'},\n",
    "    'capprops':{'color':'k'}\n",
    "}\n",
    "sns.boxplot(ax=ax2,data=H_contacts,x='residues',y='contacts',color='w', \n",
    "            whis=(0, 100),linewidth=.5,**PROPS)\n",
    "\n",
    "####################################\n",
    "\n",
    "ax2.set_ylabel('aa–H contact ratio')\n",
    "ax2.set_xlabel('')\n",
    "ax2.hlines(1,-.8,7.8,ls=':',color='k',lw=1)\n",
    "ax2.set_xlim(-.8,7.8)\n",
    "ax2.set_ylim(.5,2)\n",
    "ax2.set_yticks([.5,1,1.5,2])\n",
    "writer.save()\n",
    "    \n",
    "plt.tight_layout(w_pad=0)\n",
    "plt.savefig('figures/fig2hi.svg')    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5aa1e17",
   "metadata": {},
   "source": [
    "### Extended Data Fig 3\n",
    "\n",
    "Top panel: ratio of contacts at $q_{His}=0.50$ vs $q_{His}=0.01$ between His (green circles) or Arg residues (blue squares) and charged residues (closed symbols), and vice versa (open circles). Bottom panel: ratio of contacts at $q_{His}=0.50$ vs $q_{His}=0.01$ between aromatic (cyan circles) or Arg residues (blue squares) and His residues (closed symbols), and vice versa (open symbols). Both open and closed symbols show contacts between residues on a chain in the middle of the condensate and the surrounding chains. Data represents mean ± SD over $n=3$ indipendent simulation replicas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4270b098",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27 36 36 36\n",
      "18 36 36 36\n",
      "27 36 27 27\n",
      "18 36 18 18\n",
      "27 36 (27, 2)\n",
      "27 18 (27, 2)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/y_/2x90pfvj0138gctc5qx9196m0000gn/T/ipykernel_84830/2785912629.py:69: FutureWarning: save is not part of the public API, usage can give unexpected results and will be removed in a future version\n",
      "  writer.save()\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1,ax2) = plt.subplots(2, 1, sharex=True, sharey=False, figsize=(5,3))\n",
    "plt.rc('font', **{'family':'sans-serif','sans-serif':['Arial'], 'size': 8})\n",
    "\n",
    "colors = ['#33B779','#3954A4','#3954A4','#19B0A9','#19B0A9','#19B0A9','#EC2427','#EC2427']\n",
    "\n",
    "replicas = [0,2,3]\n",
    "\n",
    "writer = pd.ExcelWriter('EDfig3c.xlsx', engine='xlsxwriter')\n",
    "\n",
    "for res_pos,label,c,m in zip([H_pos,R_pos],['D/E – H','D/E – R'],\n",
    "                           ['#33B779','#3954A4'],['o','s']):\n",
    "\n",
    "    mean_std = pd.DataFrame()\n",
    "    for i in replicas:\n",
    "        contact_8 = calc_X_Y_mat_replica('CPEB4',N_pos,res_pos,i) \n",
    "        contact_6 = calc_X_Y_mat_replica('CPEB4pH6',N_pos,res_pos,i)\n",
    "        mean_std[i] = (contact_6/contact_8).agg('mean',axis=1)\n",
    "    mean_std = mean_std.agg(['mean','std'],axis=1)\n",
    "    mean_std.to_excel(writer, sheet_name=label.replace('/','_')+' open symbols')\n",
    "    print(res_pos.size,N_pos.size,mean_std['mean'].size,mean_std['std'].size)\n",
    "    ax1.errorbar(x=N_pos,y=mean_std['mean'],color=c,lw=0,marker=m,mfc='w',\n",
    "        yerr=mean_std['std'],elinewidth=0.5,capsize=2,ms=4,label=label)\n",
    "\n",
    "for res_pos,label,c,m in zip([H_pos,R_pos],['D/E – H','D/E – R'],\n",
    "                           ['#33B779','#3954A4'],['o','s']):\n",
    "    \n",
    "    mean_std = pd.DataFrame()\n",
    "    for i in replicas:\n",
    "        contact_8 = calc_X_Y_mat_replica('CPEB4',res_pos,N_pos,i) \n",
    "        contact_6 = calc_X_Y_mat_replica('CPEB4pH6',res_pos,N_pos,i)\n",
    "        mean_std[i] = (contact_6/contact_8).agg('mean',axis=1)\n",
    "    mean_std = mean_std.agg(['mean','std'],axis=1)\n",
    "    mean_std.to_excel(writer, sheet_name=label.replace('/','_')+' closed symbols')\n",
    "    print(res_pos.size,N_pos.size,mean_std['mean'].size,mean_std['std'].size)\n",
    "    ax1.errorbar(x=res_pos,y=mean_std['mean'],color=c,lw=0,marker=m,\n",
    "        yerr=mean_std['std'],elinewidth=0.5,capsize=2,ms=4,label=' ')\n",
    "    \n",
    "for res_pos,label,c,m in zip([Aro_pos,R_pos],['H – W/Y/F','H – R'],\n",
    "                           ['#19B0A9','#3954A4'],['o','s']):\n",
    "    \n",
    "    mean_std = pd.DataFrame()\n",
    "    for i in replicas:\n",
    "        contact_8 = calc_X_Y_mat_replica('CPEB4',H_pos,res_pos,i) \n",
    "        contact_6 = calc_X_Y_mat_replica('CPEB4pH6',H_pos,res_pos,i)\n",
    "        mean_std[i] = (contact_6/contact_8).agg('mean',axis=1)\n",
    "    mean_std = mean_std.agg(['mean','std'],axis=1)\n",
    "    mean_std.to_excel(writer, sheet_name=label.replace('/','_')+' open symbols')\n",
    "    print(H_pos.size,res_pos.size,mean_std.shape)\n",
    "    ax2.errorbar(x=H_pos,y=mean_std['mean'],color=c,lw=0,marker=m,mfc='w',\n",
    "        yerr=mean_std['std'],elinewidth=0.5,capsize=2,label=label,ms=4)\n",
    "    \n",
    "for res_pos,label,c,m in zip([Aro_pos,R_pos],['H – W/Y/F','H – R'],\n",
    "                           ['#19B0A9','#3954A4'],['o','s']):\n",
    "    \n",
    "    mean_std = pd.DataFrame()\n",
    "    for i in replicas:\n",
    "        contact_8 = calc_X_Y_mat_replica('CPEB4',res_pos,H_pos,i) \n",
    "        contact_6 = calc_X_Y_mat_replica('CPEB4pH6',res_pos,H_pos,i)\n",
    "        mean_std[i] = (contact_6/contact_8).agg('mean',axis=1)\n",
    "    mean_std = mean_std.agg(['mean','std'],axis=1)\n",
    "    mean_std.to_excel(writer, sheet_name=label.replace('/','_')+' closed symbols')\n",
    "    ax2.errorbar(x=res_pos,y=mean_std['mean'],color=c,lw=0,marker=m,\n",
    "        yerr=mean_std['std'],elinewidth=0.5,capsize=2,ms=4,label=' ')\n",
    "\n",
    "ax1.hlines(1,1,448,ls=':',color='k')\n",
    "\n",
    "ax2.hlines(1,1,448,ls=':',color='k')\n",
    "\n",
    "writer.save()\n",
    "\n",
    "for ax in [ax1,ax2]:\n",
    "    ax.axvspan(229, 252, alpha=0.2, color='tab:cyan', lw=0)\n",
    "    ax.axvspan(403, 410, alpha=0.2, color='tab:blue', lw=0)\n",
    "\n",
    "ax2.set_xlim(1-5,448+5)\n",
    "ax2.set_xticks([1]+list(range(50,410,50))+[448])\n",
    "\n",
    "ax1.set_ylabel(r'Contact ratio')\n",
    "ax2.set_ylabel(r'Contact ratio')\n",
    "\n",
    "ax1.legend(loc=(1,.25),handletextpad=.2,frameon=False,ncol=2,columnspacing=0)\n",
    "ax2.legend(loc=(1,.25),handletextpad=.2,frameon=False,ncol=2,columnspacing=0)\n",
    "\n",
    "ax2.set_xlabel('Residue number')\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures/EDfig3c.svg')    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0ca1bac",
   "metadata": {},
   "source": [
    "### Extended Data Fig 3\n",
    "\n",
    "Total contacts formed between residues on a chain in the middle of a multimeric particle and all the residues on the surrounding chains in the CALVADOS simulations. Contacts are obtained from molecular simulations averaging over multimers of 130-170 chains with an average radius of gyration of 17.5±0.8 nm. Data are displayed as average and difference (shaded area) over $n=2$ independent replicas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "72e42a56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "152.6531903190319 17.515087408339213 0.7600868379032106 0.04968462089630787\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 320x180 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(1,1,figsize=(3.2,1.8))\n",
    "plt.rc('font', **{'family':'sans-serif','sans-serif':['Arial'], 'size': 8})\n",
    "\n",
    "\n",
    "rgs = np.empty(0)\n",
    "kappas = np.empty(0)\n",
    "sizes = np.empty(0)\n",
    "\n",
    "n_frames_tot = 0\n",
    "profile_0 = np.zeros(448)\n",
    "replica = 0\n",
    "for i,s in enumerate(range(130,171)):\n",
    "    filename = f'multimers/replica_{replica:d}/contact_maps/CPEB4_contacts_293_60_{s:d}.npy'\n",
    "    if os.path.isfile(filename):\n",
    "        size,rg,kappa = np.loadtxt(\n",
    "            f'multimers/replica_{replica:d}/contact_maps/CPEB4_size_rg_kappa_293_60_{s:d}.dat',unpack=True)\n",
    "        sizes = np.append(sizes,size)\n",
    "        rgs = np.append(rgs,rg)\n",
    "        kappas = np.append(kappas,kappa)\n",
    "        n_frames = size.size\n",
    "        profile_0 += np.load(filename).sum(axis=1)*n_frames\n",
    "        n_frames_tot += n_frames \n",
    "\n",
    "profile_0 /= n_frames_tot\n",
    "\n",
    "n_frames_tot = 0\n",
    "profile_1 = np.zeros(448)\n",
    "replica = 1\n",
    "for i,s in enumerate(range(130,171)):\n",
    "    filename = f'multimers/replica_{replica:d}/contact_maps/CPEB4_contacts_293_60_{s:d}.npy'\n",
    "    if os.path.isfile(filename):\n",
    "        size,rg,kappa = np.loadtxt(\n",
    "            f'multimers/replica_{replica:d}/contact_maps/CPEB4_size_rg_kappa_293_60_{s:d}.dat',unpack=True)\n",
    "        sizes = np.append(sizes,size)\n",
    "        rgs = np.append(rgs,rg)\n",
    "        kappas = np.append(kappas,kappa)\n",
    "        n_frames = size.size\n",
    "        profile_1 += np.load(filename).sum(axis=1)*n_frames\n",
    "        n_frames_tot += n_frames \n",
    "\n",
    "profile_1 /= n_frames_tot\n",
    "\n",
    "profile = (profile_0 + profile_1) / 2\n",
    "\n",
    "profile_err = np.abs(profile_0 - profile_1)\n",
    "\n",
    "res_num = np.arange(1,profile.size+1,1)\n",
    "    \n",
    "ax1.plot(res_num,profile,color='#beddc0',label='Droplet',lw=2)\n",
    "\n",
    "ax1.fill_between(res_num,y1=profile-profile_err,\n",
    "                 y2=profile+profile_err,lw=0,color='#beddc0',alpha=.5)\n",
    "\n",
    "pd.DataFrame(data={'mean':profile,'diff':profile_err},index=res_num).to_excel('EDfig3l.xlsx')\n",
    "\n",
    "print(np.mean(sizes),np.mean(rgs),np.std(rgs),np.mean(kappas))\n",
    "\n",
    "plt.xlim(1-5,448+5)\n",
    "plt.xticks([1]+list(range(50,410,100))+[448])\n",
    "\n",
    "plt.axvspan(229, 252, alpha=0.2, color='tab:cyan', lw=0)\n",
    "plt.axvspan(403, 410, alpha=0.2, color='tab:blue', lw=0)\n",
    "\n",
    "plt.xlim(1,448)\n",
    "plt.ylim(3,6.8)\n",
    "\n",
    "plt.xlabel('Residue number')\n",
    "plt.ylabel('Contacts')\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures/EDfig3l.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15f98ca4",
   "metadata": {},
   "source": [
    "### Extended Data Fig 3\n",
    "\n",
    "Comparison between the saturation concentrations from experiments (30 μM nCPEB4-NTD with 100 mM NaCl at 40 ºC and pH 8) and molecular simulations (at 20 ºC with $q_{His}$ 0.01) of nCPEB4-NTD and the His to Ser variants. The experimental saturation concentrations are represented as mean ± SD of $n=3$ measurements of the same sample. $q_{His}$ indicates the charge of the His residues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ccb55c9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "variants = ['WT_pH6','WT_pH7','WT','H25S_Clust','H25S','H50S_Clust','H50S']\n",
    "\n",
    "df_conc = pd.DataFrame(index=variants)\n",
    "\n",
    "names = ['CPEB4pH6','CPEB4pH7','CPEB4','CPEB4H25S_Clust','CPEB4H25S','CPEB4H50S_Clust','CPEB4H50S']\n",
    "\n",
    "if not os.path.isfile('EDfig2ab.csv'):\n",
    "    for i,(name,variant) in enumerate(zip(names,variants)):\n",
    "        csat_replicas = []\n",
    "        ccon_replicas = []\n",
    "        for replica in range(4):\n",
    "            filename = f'direct_coexistence/{name:s}_{replica:d}.npy'\n",
    "            if os.path.exists(filename):\n",
    "                csat, ccon, csatarray, _ = calcProfiles('direct_coexistence/'+name,name,\n",
    "                                                        replica=replica,L=25,nskip=1000)\n",
    "                print(name,replica,csatarray.size*.002,csatarray.size,csat*1e3)\n",
    "                csat_replicas.append(csat*1e3)\n",
    "                ccon_replicas.append(ccon)\n",
    "                df_conc.loc[variant,f'csat_{replica:d}/µM'] = csat*1e3\n",
    "                df_conc.loc[variant,f'ccon_{replica:d}/mM'] = ccon\n",
    "        df_conc.loc[variant,'csat_mean/µM'] = np.mean(csat_replicas)\n",
    "        df_conc.loc[variant,'csat_sd/µM'] = np.std(csat_replicas)\n",
    "        df_conc.loc[variant,'ccon_mean/mM'] = np.mean(ccon_replicas)\n",
    "        df_conc.loc[variant,'ccon_sd/mM'] = np.std(ccon_replicas)\n",
    "    df_conc.to_csv('EDfig2ab.csv')\n",
    "    df_conc.to_excel('EDfig2ab.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "758aed95",
   "metadata": {},
   "outputs": [],
   "source": [
    "variants = ['WT','H25S','H50S','H25S_Clust','H50S_Clust']\n",
    "\n",
    "cs_100 = pd.read_csv('direct_coexistence/csat_exp_30uMprotein_100mMNaCl_40C.csv',\n",
    "                   index_col=0).loc[variants]\n",
    "\n",
    "df_c2 = pd.read_csv('EDfig2ab.csv',index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c89face0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9217252792841676\n",
      "-0.971409900325881\n",
      "-0.917858223582317\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 650x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6.5,4))\n",
    "ax1 = plt.subplot2grid( (2,6), (0,0), rowspan=1, colspan=3 )\n",
    "ax2 = plt.subplot2grid( (2,6), (0,3), rowspan=1, colspan=3 )\n",
    "ax3 = plt.subplot2grid( (2,6), (1,0), rowspan=1, colspan=2 )\n",
    "ax4 = plt.subplot2grid( (2,6), (1,2), rowspan=1, colspan=2 )\n",
    "ax5 = plt.subplot2grid( (2,6), (1,4), rowspan=1, colspan=2 )\n",
    "plt.rc('font', **{'family':'sans-serif','sans-serif':['Arial'], 'size': 8})\n",
    "                  \n",
    "variants = ['WT_pH6','WT_pH7','WT','H25S_Clust','H25S','H50S_Clust','H50S']\n",
    "\n",
    "replicas = [0,2,3]\n",
    "\n",
    "df_csat_box_60_293 = pd.DataFrame(dict(zip(variants,[[df_c2.loc[variant,f'csat_{replica:d}/µM'] \n",
    "     for replica in replicas] \n",
    "     for variant in variants])))\n",
    "df_csat_box_60_293 = df_csat_box_60_293.unstack().reset_index().drop('level_1',axis=1).rename(\n",
    "    {'level_0':'variants',0:'csat/µM'},axis=1)\n",
    "\n",
    "df_ccon_box_60_293 = pd.DataFrame(dict(zip(variants,[[df_c2.loc[variant,f'ccon_{replica:d}/mM'] \n",
    "     for replica in replicas] \n",
    "     for variant in variants])))\n",
    "df_ccon_box_60_293 = df_ccon_box_60_293.unstack().reset_index().drop('level_1',axis=1).rename(\n",
    "    {'level_0':'variants',0:'ccon/mM'},axis=1)\n",
    "\n",
    "PROPS = {\n",
    "    'boxprops':{'facecolor':'none', 'edgecolor':'k'},\n",
    "    'medianprops':{'color':'k'},\n",
    "    'whiskerprops':{'color':'k'},\n",
    "    'capprops':{'color':'k'}\n",
    "}\n",
    "\n",
    "sns.swarmplot(ax=ax1,data=df_csat_box_60_293,x='variants',y='csat/µM',\n",
    "              size=2,legend=False,s=4,color='w',edgecolor='k',linewidth=.5)\n",
    "\n",
    "sns.swarmplot(ax=ax2,data=df_ccon_box_60_293,x='variants',y='ccon/mM',\n",
    "              size=2,legend=False,s=4,color='w',edgecolor='k',linewidth=.5)\n",
    "\n",
    "ax1.errorbar(variants, df_c2.loc[variants,'csat_mean/µM'],\n",
    "         marker='o',yerr=df_c2.loc[variants,'csat_sd/µM'], \n",
    "         lw=0, ms=5, elinewidth=.5, capsize=2, capthick=.5, \n",
    "         color='k',zorder=0)\n",
    "ax2.errorbar(variants, df_c2.loc[variants,'ccon_mean/mM'],\n",
    "         marker='o',yerr=df_c2.loc[variants,'ccon_sd/mM'],\n",
    "         lw=0, ms=5, elinewidth=.5, capsize=2, capthick=.5, \n",
    "         color='k',zorder=0)\n",
    "\n",
    "ax1.set_ylabel('$c_{sat}$  /  µM')\n",
    "ax2.set_ylabel('$c_{con}$  /  mM')\n",
    "ax1.set_xlabel('')\n",
    "ax2.set_xlabel('')\n",
    "\n",
    "labels = [name.replace('_',' ') for name in variants]\n",
    "for ax in (ax1,ax2):\n",
    "    for tick in ax.get_xticklabels():\n",
    "        tick.set_rotation(60)\n",
    "    ax.set_xticks(variants)\n",
    "    ax.set_xticklabels(labels)\n",
    "    \n",
    "variants = ['WT','H25S_Clust','H25S','H50S_Clust','H50S']\n",
    "\n",
    "csat_exp = pd.read_csv('direct_coexistence/csat_exp_30uMprotein_100mMNaCl_40C.csv',\n",
    "                   index_col=0)\n",
    "\n",
    "x = np.linspace(0, 30,1000)\n",
    "\n",
    "\n",
    "ax3.errorbar(csat_exp.loc[variants].csat_mean, df_c2.loc[variants,'csat_mean/µM'],\n",
    "         xerr=csat_exp.loc[variants].csat_std,yerr=df_c2.loc[variants,'csat_sd/µM'], \n",
    "         marker='o', lw=0, ms=5, elinewidth=.5, capsize=2, capthick=.5,\n",
    "         color='k')\n",
    "popt,_ = curve_fit(lambda x,a,b : a*x+b,csat_exp.loc[variants].csat_mean, df_c2.loc[variants,'csat_mean/µM'])\n",
    "\n",
    "pr = pearsonr(csat_exp.loc[variants].csat_mean, df_c2.loc[variants,'csat_mean/µM'])[0]\n",
    "ax3.plot(x,popt[0]*x+popt[1],ls=':',lw=2,zorder=4,color='k',label=f'$R^2={pr**2:.2f}$')\n",
    "print(pr)\n",
    "\n",
    "ax3.set_ylabel('Simulated $c_{sat}$  /  µM')\n",
    "ax3.set_xlabel('Experimental $c_{sat}$  /  µM')\n",
    "\n",
    "ax3.legend()\n",
    "\n",
    "#####\n",
    "\n",
    "x = np.linspace(60, 120,1000)\n",
    "\n",
    "ax4.errorbar(df_c2.loc[variants,'csat_mean/µM'], df_c2.loc[variants,'ccon_mean/mM'],\n",
    "         xerr=df_c2.loc[variants,'csat_sd/µM'],yerr=df_c2.loc[variants,'ccon_sd/mM'], \n",
    "         marker='o', lw=0, ms=5, elinewidth=.5, capsize=2, capthick=.5,\n",
    "         color='k')\n",
    "popt,_ = curve_fit(lambda x,a,b : a*x+b,df_c2.loc[variants,'csat_mean/µM'], \n",
    "                   df_c2.loc[variants,'ccon_mean/mM'])\n",
    "\n",
    "pr = pearsonr(df_c2.loc[variants,'csat_mean/µM'], df_c2.loc[variants,'ccon_mean/mM'])[0]\n",
    "ax4.plot(x,popt[0]*x+popt[1],ls=':',lw=2,zorder=4,color='k',label=f'$R^2={pr**2:.2f}$')\n",
    "print(pr)\n",
    "\n",
    "ax4.set_ylabel('Simulated $c_{con}$  /  mM')\n",
    "ax4.set_xlabel('Simulated $c_{sat}$  /  µM')\n",
    "\n",
    "ax4.legend()\n",
    "\n",
    "#####\n",
    "\n",
    "x = np.linspace(0, 30,1000)\n",
    "\n",
    "ax5.errorbar(csat_exp.loc[variants].csat_mean, df_c2.loc[variants,'ccon_mean/mM'],\n",
    "         xerr=csat_exp.loc[variants].csat_std,yerr=df_c2.loc[variants,'ccon_sd/mM'], \n",
    "         marker='o', lw=0, ms=5, elinewidth=.5, capsize=2, capthick=.5,\n",
    "         color='k')\n",
    "popt,_ = curve_fit(lambda x,a,b : a*x+b,csat_exp.loc[variants].csat_mean, df_c2.loc[variants,'ccon_mean/mM'])\n",
    "\n",
    "pr = pearsonr(csat_exp.loc[variants].csat_mean, df_c2.loc[variants,'ccon_mean/mM'])[0]\n",
    "ax5.plot(x,popt[0]*x+popt[1],ls=':',lw=2,zorder=4,color='k',label=f'$R^2={pr**2:.2f}$')\n",
    "print(pr)\n",
    "\n",
    "ax5.set_ylabel('Simulated $c_{con}$  /  mM')\n",
    "ax5.set_xlabel('Experimental $c_{sat}$  /  µM')\n",
    "\n",
    "ax5.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures/EDfig2ab.svg')    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9ecfa2b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}