Fig4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "893aaf3d-a6b9-4c55-ab06-430f0a5e3f1e",
   "metadata": {},
   "source": [
    "# Path to Zenodo files folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b925c0f4-1990-4ab5-b255-4a2b377026f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = 'PATH/TO/ZENODO/FILES/FOLDER'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abb1dd01-540e-4746-b167-955eba78b714",
   "metadata": {},
   "source": [
    "# Figure 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "34dc95ee-f98c-438b-8824-33f648d3bb55",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definitions\n",
    "\n",
    "P06241 = 'ACGCTTTTCGTTGCATTGTATGATTATGAAGCAAGAACTGAAGATGATTTGTCTTTCCATAAAGGTGAAAAATTCCAAATCTTGAATTCTTCTGAAGGTGATTGGTGGGAAGCAAGATCTTTGACTACTGGTGAAACTGGTTATATCCCATCTAACTATGTCGCGCCTGTG'\n",
    "P01053 = 'AAAACTGAATGGCCAGAATTGGTTGGTAAATCTGTCGAAGAAGCAAAAAAAGTTATCTTGCAAGATAAACCAGAAGCACAAATCATTGTTTTGCCAGTTGGTACTATTGTTACTATGGAATATAGAATTGATAGAGTCAGATTGTTTGTCGATAAATTGGATAATATCGCACAAGTTCCAAGAGTTGGT'\n",
    "\n",
    "cores = {'P01053' : [11,18,27,45,47,49,55],\n",
    "        'P06241' : [2,16,18,24,26,48,53]}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49b257c2-dae0-4d33-81e7-72a0134f5aad",
   "metadata": {},
   "source": [
    "## Panel a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "61c2bc94-024e-4f22-a574-42ba3cb7856f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 140x160 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib.patches import Rectangle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "coefs = pd.read_csv(path+'Fig3/FYN-SH3/FYN_mochi_order2/task_1/weights/weights_Folding.txt',\n",
    "                       sep='\\t')\n",
    "coefs.rename(columns={'mean_kcal/mol':'mean_kcal_mol', 'std_kcal/mol':'std_kcal_mol', 'ci95_kcal/mol':'ci95_kcal_mol'}, inplace=True)\n",
    "for index, row in coefs.iterrows():\n",
    "    if row.id_ref =='WT':\n",
    "        coefs.at[index,'order']=0\n",
    "    else:\n",
    "        coefs.at[index,'order']=len(row.id.split('_'))\n",
    "#coefs=coefs.set_index('id')\n",
    "\n",
    "\n",
    "firstorderterms=coefs.loc[coefs['order']==1].copy()\n",
    "\n",
    "for index, row in firstorderterms.iterrows():\n",
    "    firstorderterms.at[index,'mut_from']=row.id[0:-1]\n",
    "    firstorderterms.at[index,'mut_to']=row.id[-1]\n",
    "\n",
    "real_pos = [2,16,18,24,26,48,53]\n",
    "mutated_positions = []\n",
    "\n",
    "for index, row in firstorderterms.iterrows():\n",
    "    if row.id !='WT' and row.mut_from not in mutated_positions:\n",
    "        mutated_positions.append(row.mut_from)\n",
    "\n",
    "coefs_hm=pd.DataFrame(columns=mutated_positions)\n",
    "\n",
    "for index, row in firstorderterms.iterrows():\n",
    "    if row.id !='WT':\n",
    "        coefs_hm.at[row.mut_to,row.mut_from]=row.mean_kcal_mol\n",
    "\n",
    "coefs_sparse_model = pd.read_csv(path+'Fig3/FYN-SH3/FYN_mochi_order2_sparse_regularized/task_4/weights/weights_Folding.txt',\n",
    "                       sep='\\t')\n",
    "coefs_sparse_model.rename(columns={'mean_kcal/mol':'mean_kcal_mol', 'std_kcal/mol':'std_kcal_mol', 'ci95_kcal/mol':'ci95_kcal_mol'}, inplace=True)\n",
    "for index, row in coefs_sparse_model.iterrows():\n",
    "    if row.id_ref == 'WT':\n",
    "        coefs_sparse_model.at[index,'order']=0\n",
    "    else:\n",
    "        coefs_sparse_model.at[index,'order']=len(row.id.split('_'))\n",
    "coefs_sparse_model=coefs_sparse_model.set_index('id')\n",
    "#coefs_sparse_model=coefs_sparse_model.loc[coefs_sparse_model['order']==1].copy()\n",
    "\n",
    "for index, row in coefs_sparse_model.iterrows():\n",
    "    coefs_sparse_model.at[index,'mut_from']=row.id_ref[0:-1]\n",
    "    coefs_sparse_model.at[index,'mut_to']=row.id_ref[-1]\n",
    "    \n",
    "coefs_hm_sparse_model = pd.DataFrame(columns=mutated_positions)\n",
    "\n",
    "for index, row in coefs_sparse_model.iterrows():\n",
    "    if row.id_ref !='WT' and row.order==1:\n",
    "        coefs_hm_sparse_model.at[row.mut_to,row.mut_from]='*'\n",
    "coefs_hm_sparse_model=coefs_hm_sparse_model.fillna('')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(1.4,1.6))\n",
    "\n",
    "#ax=sns.heatmap(coefs_hm.fillna(0), cmap='coolwarm', vmin=-1*max(coefs_hm.fillna(0).max()), vmax=max(coefs_hm.fillna(0).max()),cbar_kws={\"orientation\": \"horizontal\", \"pad\": 0.02, 'location':'bottom'}, xticklabels=True, yticklabels=True)\n",
    "singles_boundary=2.1\n",
    "ax=sns.heatmap(coefs_hm.fillna(0), cmap='coolwarm', vmin=-1*singles_boundary, vmax=singles_boundary,cbar_kws={\"orientation\": \"horizontal\", \"pad\": 0.03, 'location':'bottom'}, xticklabels=True, yticklabels=True,\n",
    "               annot=coefs_hm_sparse_model.to_numpy().tolist(), fmt= '', annot_kws={'size':4})\n",
    "ax.figure.axes[-1].set_xticks(ticks=np.arange(-2,3,1),labels=np.arange(-2,3,1), fontsize=7)\n",
    "ax.figure.axes[-1].set_xlabel(r'Inferred ${ΔΔG_{f}}$ \\ kcal mol$^-$$^1$', rotation=0, labelpad=5, fontsize=7)\n",
    "plt.tick_params(axis='both', which='both', labelsize=7, labelbottom = False, bottom=False, top = True, labeltop=True)\n",
    "plt.yticks(fontsize=6)\n",
    "plt.xticks(fontsize=6, rotation=45)\n",
    "plt.xlabel('WT residue',  fontsize=7)\n",
    "ax.xaxis.set_label_position('top') \n",
    "plt.ylabel('Substitution AA', fontsize=7)\n",
    "ax.spines[['right','top','left','bottom']].set_visible(True)\n",
    "#plt.title('Background-averaged\\nmutational effects',fontsize=18)\n",
    "for spine in ax.figure.axes[-1].spines.values():\n",
    "    spine.set(visible=True,lw=1,edgecolor='black')\n",
    "\n",
    "for i in mutated_positions:\n",
    "    for j in mutated_positions:\n",
    "        if i[0]==j[0]:\n",
    "            col=coefs_hm.columns.get_loc(i)\n",
    "            row=coefs_hm.index.get_loc(j[0])\n",
    "            ax.add_patch(Rectangle((col, row), 1, 1, fill=True,edgecolor='black', lw=0.0001, facecolor='black'))\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "#plt.savefig('FYN_Mochi_1storderterms_2ndordermodel_ddGs.svg', dpi=300, transparent=True, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a0af996e-3371-4f2b-8263-57f7819d021d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "rocklin = pd.read_csv(path+\"Fig4/Tsuboyama2023_Dataset1_20230416.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c4531c35-80d2-4314-9c9a-0085d8608d58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_kcal_mol</th>\n",
       "      <th>std_kcal_mol</th>\n",
       "      <th>deltaG</th>\n",
       "      <th>deltaG_95CI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F3I</th>\n",
       "      <td>0.560962</td>\n",
       "      <td>0.102125</td>\n",
       "      <td>2.907970</td>\n",
       "      <td>0.142996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F3L</th>\n",
       "      <td>0.268793</td>\n",
       "      <td>0.054489</td>\n",
       "      <td>3.429269</td>\n",
       "      <td>0.208434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F3M</th>\n",
       "      <td>0.049056</td>\n",
       "      <td>0.097655</td>\n",
       "      <td>4.509852</td>\n",
       "      <td>0.159364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F3V</th>\n",
       "      <td>-0.127578</td>\n",
       "      <td>0.086498</td>\n",
       "      <td>4.803552</td>\n",
       "      <td>0.085831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L17F</th>\n",
       "      <td>-0.046252</td>\n",
       "      <td>0.101517</td>\n",
       "      <td>4.494621</td>\n",
       "      <td>0.104096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L17I</th>\n",
       "      <td>-0.178472</td>\n",
       "      <td>0.123540</td>\n",
       "      <td>4.907191</td>\n",
       "      <td>0.196571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L17M</th>\n",
       "      <td>-0.098930</td>\n",
       "      <td>0.098577</td>\n",
       "      <td>5.205858</td>\n",
       "      <td>0.144808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L17V</th>\n",
       "      <td>0.817100</td>\n",
       "      <td>0.119803</td>\n",
       "      <td>3.766750</td>\n",
       "      <td>0.096021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F19I</th>\n",
       "      <td>0.070597</td>\n",
       "      <td>0.166062</td>\n",
       "      <td>3.777897</td>\n",
       "      <td>0.105124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F19L</th>\n",
       "      <td>0.263111</td>\n",
       "      <td>0.111710</td>\n",
       "      <td>3.692061</td>\n",
       "      <td>0.217474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F19M</th>\n",
       "      <td>0.170728</td>\n",
       "      <td>0.140218</td>\n",
       "      <td>4.584430</td>\n",
       "      <td>0.115614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F19V</th>\n",
       "      <td>0.817373</td>\n",
       "      <td>0.127921</td>\n",
       "      <td>3.008392</td>\n",
       "      <td>0.166778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F25I</th>\n",
       "      <td>0.152441</td>\n",
       "      <td>0.097748</td>\n",
       "      <td>3.558718</td>\n",
       "      <td>0.186997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F25L</th>\n",
       "      <td>0.039251</td>\n",
       "      <td>0.088284</td>\n",
       "      <td>4.466229</td>\n",
       "      <td>0.196913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F25M</th>\n",
       "      <td>0.447850</td>\n",
       "      <td>0.102017</td>\n",
       "      <td>4.058212</td>\n",
       "      <td>0.161085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F25V</th>\n",
       "      <td>0.763668</td>\n",
       "      <td>0.087280</td>\n",
       "      <td>2.904034</td>\n",
       "      <td>0.088955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I27F</th>\n",
       "      <td>1.048974</td>\n",
       "      <td>0.154208</td>\n",
       "      <td>2.990466</td>\n",
       "      <td>0.227945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I27L</th>\n",
       "      <td>-0.005840</td>\n",
       "      <td>0.117941</td>\n",
       "      <td>4.188002</td>\n",
       "      <td>0.176829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I27M</th>\n",
       "      <td>0.486032</td>\n",
       "      <td>0.104133</td>\n",
       "      <td>3.365049</td>\n",
       "      <td>0.085706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I27V</th>\n",
       "      <td>-0.348961</td>\n",
       "      <td>0.139382</td>\n",
       "      <td>4.815995</td>\n",
       "      <td>0.102864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I49F</th>\n",
       "      <td>1.638282</td>\n",
       "      <td>0.353252</td>\n",
       "      <td>1.656378</td>\n",
       "      <td>0.083294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I49L</th>\n",
       "      <td>0.321149</td>\n",
       "      <td>0.126758</td>\n",
       "      <td>4.228166</td>\n",
       "      <td>0.183150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I49M</th>\n",
       "      <td>1.643793</td>\n",
       "      <td>0.159612</td>\n",
       "      <td>2.328278</td>\n",
       "      <td>0.137080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I49V</th>\n",
       "      <td>-0.296858</td>\n",
       "      <td>0.097721</td>\n",
       "      <td>4.655269</td>\n",
       "      <td>0.123218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V54F</th>\n",
       "      <td>-0.301742</td>\n",
       "      <td>0.129874</td>\n",
       "      <td>4.651693</td>\n",
       "      <td>0.083245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V54I</th>\n",
       "      <td>-0.042016</td>\n",
       "      <td>0.171811</td>\n",
       "      <td>4.895318</td>\n",
       "      <td>0.125708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V54L</th>\n",
       "      <td>-0.022368</td>\n",
       "      <td>0.136617</td>\n",
       "      <td>4.961295</td>\n",
       "      <td>0.235019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V54M</th>\n",
       "      <td>0.100514</td>\n",
       "      <td>0.132332</td>\n",
       "      <td>4.647918</td>\n",
       "      <td>0.274096</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mean_kcal_mol  std_kcal_mol    deltaG  deltaG_95CI\n",
       "id                                                      \n",
       "F3I        0.560962      0.102125  2.907970     0.142996\n",
       "F3L        0.268793      0.054489  3.429269     0.208434\n",
       "F3M        0.049056      0.097655  4.509852     0.159364\n",
       "F3V       -0.127578      0.086498  4.803552     0.085831\n",
       "L17F      -0.046252      0.101517  4.494621     0.104096\n",
       "L17I      -0.178472      0.123540  4.907191     0.196571\n",
       "L17M      -0.098930      0.098577  5.205858     0.144808\n",
       "L17V       0.817100      0.119803  3.766750     0.096021\n",
       "F19I       0.070597      0.166062  3.777897     0.105124\n",
       "F19L       0.263111      0.111710  3.692061     0.217474\n",
       "F19M       0.170728      0.140218  4.584430     0.115614\n",
       "F19V       0.817373      0.127921  3.008392     0.166778\n",
       "F25I       0.152441      0.097748  3.558718     0.186997\n",
       "F25L       0.039251      0.088284  4.466229     0.196913\n",
       "F25M       0.447850      0.102017  4.058212     0.161085\n",
       "F25V       0.763668      0.087280  2.904034     0.088955\n",
       "I27F       1.048974      0.154208  2.990466     0.227945\n",
       "I27L      -0.005840      0.117941  4.188002     0.176829\n",
       "I27M       0.486032      0.104133  3.365049     0.085706\n",
       "I27V      -0.348961      0.139382  4.815995     0.102864\n",
       "I49F       1.638282      0.353252  1.656378     0.083294\n",
       "I49L       0.321149      0.126758  4.228166     0.183150\n",
       "I49M       1.643793      0.159612  2.328278     0.137080\n",
       "I49V      -0.296858      0.097721  4.655269     0.123218\n",
       "V54F      -0.301742      0.129874  4.651693     0.083245\n",
       "V54I      -0.042016      0.171811  4.895318     0.125708\n",
       "V54L      -0.022368      0.136617  4.961295     0.235019\n",
       "V54M       0.100514      0.132332  4.647918     0.274096"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from Bio.Seq import Seq\n",
    "\n",
    "query_protein='P06241'\n",
    "pdb='1A0N'\n",
    "DTS = ['F', 'L', 'I', 'V', 'M']\n",
    "\n",
    "for index, row in rocklin[rocklin['name'].str.startswith(pdb)].iterrows():\n",
    "    if len(row['name'].split('_'))>1:\n",
    "        if row['name'].split('_')[1]=='scramble':\n",
    "            rocklin.at[index,'scramble']=True\n",
    "\n",
    "query_protein_wt = str(Seq(P06241).translate())\n",
    "rocklin_query_protein = rocklin[rocklin['name'].str.startswith(pdb)].loc[rocklin['scramble']!=True].copy()\n",
    "\n",
    "for index, row in rocklin_query_protein.iterrows():\n",
    "    mutations=[]\n",
    "    core_mutant=[]\n",
    "    iterator=0\n",
    "    for wt_res, var_res in zip(query_protein_wt, str(Seq(row.dna_seq).translate())[8:-7]):\n",
    "        if wt_res != var_res:\n",
    "            mutations.append(wt_res+str(iterator)+var_res)\n",
    "            if iterator in cores[query_protein]:\n",
    "                core_mutant.append(True)\n",
    "            else:\n",
    "                core_mutant.append(False)\n",
    "        iterator+=1\n",
    "    if all(i==True for i in core_mutant)==True:\n",
    "        rocklin_query_protein.at[index,'core_mutant']=True\n",
    "    else:\n",
    "        rocklin_query_protein.at[index,'core_mutant']=False\n",
    "    var_core=[]\n",
    "    for core_pos in cores[query_protein]:\n",
    "        var_core.append(str(Seq(row.dna_seq).translate())[8:-7][core_pos])\n",
    "    rocklin_query_protein.at[index,'core']=''.join(var_core)\n",
    "    if set(var_core) <= set(DTS):\n",
    "        rocklin_query_protein.at[index,'DTS_core']=True\n",
    "    else:\n",
    "        rocklin_query_protein.at[index,'DTS_core']=False\n",
    "    if len(mutations)>0:\n",
    "        rocklin_query_protein.at[index,'mutations']=mutations\n",
    "    rocklin_query_protein.at[index,'Nham']=len(mutations)\n",
    "    if len(mutations)==1 and len(row['name'].split('_'))>2:\n",
    "        rocklin_query_protein.at[index,'redundant_single']=True\n",
    "\n",
    "real_fyn_singles=rocklin_query_protein.loc[rocklin_query_protein['Nham']==1].loc[rocklin_query_protein['core_mutant']==True].loc[rocklin_query_protein['DTS_core']==True].loc[rocklin_query_protein['redundant_single']!=True]\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "rocklin_singles = real_fyn_singles.copy()\n",
    "for index, row in rocklin_singles.iterrows():\n",
    "    rocklin_singles.at[index,'mutations']=row.mutations[0][:1]+str(int(row.mutations[0][1:-1])+1)+row.mutations[0][-1]\n",
    "\n",
    "merged = firstorderterms[['id', 'mean_kcal_mol', 'std_kcal_mol']].merge(rocklin_singles[['mutations', 'deltaG', 'deltaG_95CI']], left_on='id', right_on='mutations')\n",
    "merged.set_index('id', inplace=True)\n",
    "del merged['mutations']\n",
    "merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a5dbeba9-7f22-4bdb-a10c-753f06b50228",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 140x160 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from scipy.stats import pearsonr\n",
    "\n",
    "df = merged.sort_values('mean_kcal_mol').copy()\n",
    "\n",
    "g = sns.FacetGrid(data=df, aspect=2)\n",
    "g.map(plt.errorbar, 'mean_kcal_mol', 'deltaG', 'deltaG_95CI','std_kcal_mol', fmt='o', elinewidth=0.5, capsize=2, capthick=0.5, markersize=2, color='#B4D689')\n",
    "g.fig.set_size_inches(1.4,1.6)\n",
    "g.tick_params(axis='both', which='major', labelsize=6)\n",
    "\n",
    "x = df[['mean_kcal_mol']].to_numpy()\n",
    "y = df[['deltaG']].to_numpy()\n",
    "\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(x, y)\n",
    "\n",
    "y_pred = model.predict(x)\n",
    "r2 = r2_score(y,y_pred)\n",
    "r,p = pearsonr(y.ravel(),x.ravel())\n",
    "\n",
    "plt.plot(x, y_pred, color='Gray', linewidth=1, linestyle='--')\n",
    "plt.xlabel(r'Inferred ${ΔΔG_{f}}$ \\ kcal mol$^-$$^1$', fontsize=7)\n",
    "plt.ylabel('Tsuboyama et al.\\nΔG \\ kcal mol$^-$$^1$', fontsize=7)\n",
    "plt.title('R = '+str(round(r,2))+'\\np-val = '+'{:0.2e}'.format(p), fontsize=7, color='red',loc='right',y=1)\n",
    "plt.xticks(np.arange(-0.5,2.5,0.5))\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "#plt.savefig('FYN_inferred_from_2ndordermodel_ddG_Tsuboyama.svg', dpi=300, transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b3e72c1-6c9b-4480-8e23-e2eb73c2c7d3",
   "metadata": {},
   "source": [
    "## Panel b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c135d76b-238a-4d0e-b8cf-0ec7ec4eacc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 140x160 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib.patches import Rectangle\n",
    "import numpy as np\n",
    "\n",
    "coefs = pd.read_csv(path+'Fig3/CI-2A/CI-2A_mochi_order2/task_1/weights/weights_Folding.txt',\n",
    "                       sep='\\t')\n",
    "coefs.rename(columns={'mean_kcal/mol':'mean_kcal_mol', 'std_kcal/mol':'std_kcal_mol', 'ci95_kcal/mol':'ci95_kcal_mol'}, inplace=True)\n",
    "for index, row in coefs.iterrows():\n",
    "    coefs.at[index,'order']=len(row.id.split('_'))\n",
    "#coefs=coefs.set_index('id')\n",
    "\n",
    "\n",
    "firstorderterms=coefs.loc[coefs['order']==1].copy()\n",
    "\n",
    "for index, row in firstorderterms.iterrows():\n",
    "    firstorderterms.at[index,'mut_from']=row.id[0:-1]\n",
    "    firstorderterms.at[index,'mut_to']=row.id[-1]\n",
    "\n",
    "real_pos = [2,16,18,24,26,48,53]\n",
    "mutated_positions = []\n",
    "\n",
    "for index, row in firstorderterms.iterrows():\n",
    "    if row.id !='WT' and row.mut_from not in mutated_positions:\n",
    "        mutated_positions.append(row.mut_from)\n",
    "\n",
    "coefs_hm=pd.DataFrame(columns=mutated_positions)\n",
    "\n",
    "for index, row in firstorderterms.iterrows():\n",
    "    if row.id !='WT':\n",
    "        coefs_hm.at[row.mut_to,row.mut_from]=row.mean_kcal_mol\n",
    "        \n",
    "        \n",
    "coefs_sparse_model = pd.read_csv(path+'Fig3/CI-2A/CI-2A_mochi_order2_sparse_regularized/task_4/weights/weights_Folding.txt',\n",
    "                       sep='\\t')\n",
    "coefs_sparse_model.rename(columns={'mean_kcal/mol':'mean_kcal_mol', 'std_kcal/mol':'std_kcal_mol', 'ci95_kcal/mol':'ci95_kcal_mol'}, inplace=True)\n",
    "for index, row in coefs_sparse_model.iterrows():\n",
    "    if row.id_ref == 'WT':\n",
    "        coefs_sparse_model.at[index,'order']=0\n",
    "    else:\n",
    "        coefs_sparse_model.at[index,'order']=len(row.id.split('_'))\n",
    "coefs_sparse_model=coefs_sparse_model.set_index('id')\n",
    "#coefs_sparse_model=coefs_sparse_model.loc[coefs_sparse_model['order']==1].copy()\n",
    "\n",
    "for index, row in coefs_sparse_model.iterrows():\n",
    "    coefs_sparse_model.at[index,'mut_from']=row.id_ref[0:-1]\n",
    "    coefs_sparse_model.at[index,'mut_to']=row.id_ref[-1]\n",
    "    \n",
    "coefs_hm_sparse_model = pd.DataFrame(columns=mutated_positions)\n",
    "\n",
    "for index, row in coefs_sparse_model.iterrows():\n",
    "    if row.id_ref !='WT' and row.order==1:\n",
    "        coefs_hm_sparse_model.at[row.mut_to,row.mut_from]='*'\n",
    "coefs_hm_sparse_model=coefs_hm_sparse_model.fillna('')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(1.4,1.6))\n",
    "\n",
    "#ax=sns.heatmap(coefs_hm.fillna(0), cmap='coolwarm', vmin=-1*max(coefs_hm.fillna(0).max()), vmax=max(coefs_hm.fillna(0).max()),cbar_kws={\"orientation\": \"horizontal\", \"pad\": 0.02, 'location':'bottom'}, xticklabels=True, yticklabels=True)\n",
    "singles_boundary=2.1\n",
    "ax=sns.heatmap(coefs_hm.fillna(0), cmap='coolwarm', vmin=-1*singles_boundary, vmax=singles_boundary,cbar_kws={\"orientation\": \"horizontal\", \"pad\": 0.03, 'location':'bottom'}, xticklabels=True, yticklabels=True,\n",
    "               annot=coefs_hm_sparse_model.to_numpy().tolist(), fmt= '', annot_kws={'size':4})\n",
    "ax.figure.axes[-1].set_xticks(ticks=np.arange(-2,3,1),labels=np.arange(-2,3,1), fontsize=7)\n",
    "ax.figure.axes[-1].set_xlabel(r'Inferred ${ΔΔG_{f}}$ \\ kcal mol$^-$$^1$', rotation=0, labelpad=5, fontsize=7)\n",
    "plt.tick_params(axis='both', which='both', labelsize=7, labelbottom = False, bottom=False, top = True, labeltop=True)\n",
    "plt.yticks(fontsize=6)\n",
    "plt.xticks(fontsize=6, rotation=45)\n",
    "plt.xlabel('WT residue',  fontsize=7)\n",
    "ax.xaxis.set_label_position('top') \n",
    "plt.ylabel('Substitution AA', fontsize=7)\n",
    "ax.spines[['right','top','left','bottom']].set_visible(True)\n",
    "#plt.title('Background-averaged\\nmutational effects',fontsize=18)\n",
    "for spine in ax.figure.axes[-1].spines.values():\n",
    "    spine.set(visible=True,lw=1,edgecolor='black')\n",
    "\n",
    "for i in mutated_positions:\n",
    "    for j in mutated_positions:\n",
    "        if i[0]==j[0]:\n",
    "            col=coefs_hm.columns.get_loc(i)\n",
    "            row=coefs_hm.index.get_loc(j[0])\n",
    "            ax.add_patch(Rectangle((col, row), 1, 1, fill=True,edgecolor='black', lw=0.0001, facecolor='black'))\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "#plt.savefig('CI-2A_Mochi_1storderterms_2ndordermodel_ddGs.svg', dpi=300, transparent=True, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9f60920a-49dc-4bec-8193-64250470b349",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_kcal_mol</th>\n",
       "      <th>std_kcal_mol</th>\n",
       "      <th>deltaG</th>\n",
       "      <th>deltaG_95CI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>V12F</th>\n",
       "      <td>0.133974</td>\n",
       "      <td>0.074684</td>\n",
       "      <td>1.505453</td>\n",
       "      <td>0.073157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V12I</th>\n",
       "      <td>0.269149</td>\n",
       "      <td>0.179249</td>\n",
       "      <td>1.780312</td>\n",
       "      <td>0.060700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V12L</th>\n",
       "      <td>-0.335258</td>\n",
       "      <td>0.110445</td>\n",
       "      <td>1.801952</td>\n",
       "      <td>0.086684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V12M</th>\n",
       "      <td>0.231983</td>\n",
       "      <td>0.220112</td>\n",
       "      <td>1.585533</td>\n",
       "      <td>0.100806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I19F</th>\n",
       "      <td>1.808068</td>\n",
       "      <td>0.372398</td>\n",
       "      <td>0.767560</td>\n",
       "      <td>0.119241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I19L</th>\n",
       "      <td>0.455885</td>\n",
       "      <td>0.063182</td>\n",
       "      <td>1.511328</td>\n",
       "      <td>0.175352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I19M</th>\n",
       "      <td>1.491126</td>\n",
       "      <td>0.243979</td>\n",
       "      <td>0.637037</td>\n",
       "      <td>0.128425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I19V</th>\n",
       "      <td>0.432845</td>\n",
       "      <td>0.133775</td>\n",
       "      <td>1.638543</td>\n",
       "      <td>0.071042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I28F</th>\n",
       "      <td>-0.987668</td>\n",
       "      <td>0.224421</td>\n",
       "      <td>1.771296</td>\n",
       "      <td>0.177923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I28L</th>\n",
       "      <td>0.075089</td>\n",
       "      <td>0.145794</td>\n",
       "      <td>1.712761</td>\n",
       "      <td>0.082846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I28M</th>\n",
       "      <td>0.875926</td>\n",
       "      <td>0.181363</td>\n",
       "      <td>1.449813</td>\n",
       "      <td>0.109521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I28V</th>\n",
       "      <td>-0.112968</td>\n",
       "      <td>0.112524</td>\n",
       "      <td>1.607436</td>\n",
       "      <td>0.152373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V46F</th>\n",
       "      <td>1.266201</td>\n",
       "      <td>0.232334</td>\n",
       "      <td>1.177557</td>\n",
       "      <td>0.122903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V46I</th>\n",
       "      <td>-0.183632</td>\n",
       "      <td>0.170460</td>\n",
       "      <td>1.491220</td>\n",
       "      <td>0.136666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V46L</th>\n",
       "      <td>0.594392</td>\n",
       "      <td>0.169453</td>\n",
       "      <td>1.103029</td>\n",
       "      <td>0.146951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V46M</th>\n",
       "      <td>1.226350</td>\n",
       "      <td>0.202044</td>\n",
       "      <td>1.175384</td>\n",
       "      <td>0.144679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L48F</th>\n",
       "      <td>0.564289</td>\n",
       "      <td>0.173516</td>\n",
       "      <td>1.519302</td>\n",
       "      <td>0.145351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L48I</th>\n",
       "      <td>0.238656</td>\n",
       "      <td>0.188776</td>\n",
       "      <td>1.582368</td>\n",
       "      <td>0.152717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L48M</th>\n",
       "      <td>0.476012</td>\n",
       "      <td>0.113702</td>\n",
       "      <td>1.124811</td>\n",
       "      <td>0.245936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L48V</th>\n",
       "      <td>0.449634</td>\n",
       "      <td>0.087323</td>\n",
       "      <td>1.619043</td>\n",
       "      <td>0.054752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V50F</th>\n",
       "      <td>0.583863</td>\n",
       "      <td>0.156822</td>\n",
       "      <td>1.675262</td>\n",
       "      <td>0.109099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V50I</th>\n",
       "      <td>-0.210463</td>\n",
       "      <td>0.123336</td>\n",
       "      <td>1.839516</td>\n",
       "      <td>0.171456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V50L</th>\n",
       "      <td>0.716509</td>\n",
       "      <td>0.161256</td>\n",
       "      <td>1.382698</td>\n",
       "      <td>0.105840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V50M</th>\n",
       "      <td>1.109058</td>\n",
       "      <td>0.118321</td>\n",
       "      <td>1.111024</td>\n",
       "      <td>0.136085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I56F</th>\n",
       "      <td>1.293330</td>\n",
       "      <td>0.361584</td>\n",
       "      <td>0.676508</td>\n",
       "      <td>0.175573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I56L</th>\n",
       "      <td>0.547887</td>\n",
       "      <td>0.158463</td>\n",
       "      <td>1.412561</td>\n",
       "      <td>0.126211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I56M</th>\n",
       "      <td>1.615319</td>\n",
       "      <td>0.221473</td>\n",
       "      <td>0.206316</td>\n",
       "      <td>0.063551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I56V</th>\n",
       "      <td>-0.703971</td>\n",
       "      <td>0.105241</td>\n",
       "      <td>1.819194</td>\n",
       "      <td>0.118815</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mean_kcal_mol  std_kcal_mol    deltaG  deltaG_95CI\n",
       "id                                                      \n",
       "V12F       0.133974      0.074684  1.505453     0.073157\n",
       "V12I       0.269149      0.179249  1.780312     0.060700\n",
       "V12L      -0.335258      0.110445  1.801952     0.086684\n",
       "V12M       0.231983      0.220112  1.585533     0.100806\n",
       "I19F       1.808068      0.372398  0.767560     0.119241\n",
       "I19L       0.455885      0.063182  1.511328     0.175352\n",
       "I19M       1.491126      0.243979  0.637037     0.128425\n",
       "I19V       0.432845      0.133775  1.638543     0.071042\n",
       "I28F      -0.987668      0.224421  1.771296     0.177923\n",
       "I28L       0.075089      0.145794  1.712761     0.082846\n",
       "I28M       0.875926      0.181363  1.449813     0.109521\n",
       "I28V      -0.112968      0.112524  1.607436     0.152373\n",
       "V46F       1.266201      0.232334  1.177557     0.122903\n",
       "V46I      -0.183632      0.170460  1.491220     0.136666\n",
       "V46L       0.594392      0.169453  1.103029     0.146951\n",
       "V46M       1.226350      0.202044  1.175384     0.144679\n",
       "L48F       0.564289      0.173516  1.519302     0.145351\n",
       "L48I       0.238656      0.188776  1.582368     0.152717\n",
       "L48M       0.476012      0.113702  1.124811     0.245936\n",
       "L48V       0.449634      0.087323  1.619043     0.054752\n",
       "V50F       0.583863      0.156822  1.675262     0.109099\n",
       "V50I      -0.210463      0.123336  1.839516     0.171456\n",
       "V50L       0.716509      0.161256  1.382698     0.105840\n",
       "V50M       1.109058      0.118321  1.111024     0.136085\n",
       "I56F       1.293330      0.361584  0.676508     0.175573\n",
       "I56L       0.547887      0.158463  1.412561     0.126211\n",
       "I56M       1.615319      0.221473  0.206316     0.063551\n",
       "I56V      -0.703971      0.105241  1.819194     0.118815"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from Bio.Seq import Seq\n",
    "\n",
    "query_protein='P01053'\n",
    "pdb='3CI2'\n",
    "DTS = ['F', 'L', 'I', 'V', 'M']\n",
    "\n",
    "for index, row in rocklin[rocklin['name'].str.startswith(pdb)].iterrows():\n",
    "    if len(row['name'].split('_'))>1:\n",
    "        if row['name'].split('_')[1]=='scramble':\n",
    "            rocklin.at[index,'scramble']=True\n",
    "\n",
    "query_protein_wt = str(Seq(P01053).translate())\n",
    "rocklin_query_protein = rocklin[rocklin['name'].str.startswith(pdb)].loc[rocklin['scramble']!=True].copy()\n",
    "\n",
    "for index, row in rocklin_query_protein.iterrows():\n",
    "    mutations=[]\n",
    "    core_mutant=[]\n",
    "    iterator=0\n",
    "    for wt_res, var_res in zip(query_protein_wt, str(Seq(row.dna_seq).translate())[4:-5]):\n",
    "        if wt_res != var_res:\n",
    "            mutations.append(wt_res+str(iterator)+var_res)\n",
    "            if iterator in cores[query_protein]:\n",
    "                core_mutant.append(True)\n",
    "            else:\n",
    "                core_mutant.append(False)\n",
    "        iterator+=1\n",
    "    if all(i==True for i in core_mutant)==True:\n",
    "        rocklin_query_protein.at[index,'core_mutant']=True\n",
    "    else:\n",
    "        rocklin_query_protein.at[index,'core_mutant']=False\n",
    "    var_core=[]\n",
    "    for core_pos in cores[query_protein]:\n",
    "        var_core.append(str(Seq(row.dna_seq).translate())[4:-5][core_pos])\n",
    "    rocklin_query_protein.at[index,'core']=''.join(var_core)\n",
    "    if set(var_core) <= set(DTS):\n",
    "        rocklin_query_protein.at[index,'DTS_core']=True\n",
    "    else:\n",
    "        rocklin_query_protein.at[index,'DTS_core']=False\n",
    "    if len(mutations)>0:\n",
    "        rocklin_query_protein.at[index,'mutations']=mutations\n",
    "    rocklin_query_protein.at[index,'Nham']=len(mutations)\n",
    "    if len(mutations)==1 and len(row['name'].split('_'))>2:\n",
    "        rocklin_query_protein.at[index,'redundant_single']=True\n",
    "    else:\n",
    "        rocklin_query_protein.at[index,'redundant_single']=False\n",
    "\n",
    "real_fyn_singles=rocklin_query_protein.loc[rocklin_query_protein['Nham']==1].loc[rocklin_query_protein['core_mutant']==True].loc[rocklin_query_protein['DTS_core']==True].loc[rocklin_query_protein['redundant_single']!=True]\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "rocklin_singles = real_fyn_singles.copy()\n",
    "for index, row in rocklin_singles.iterrows():\n",
    "    rocklin_singles.at[index,'mutations']=row.mutations[0][:1]+str(int(row.mutations[0][1:-1])+1)+row.mutations[0][-1]\n",
    "\n",
    "merged = firstorderterms[['id', 'mean_kcal_mol', 'std_kcal_mol']].merge(rocklin_singles[['mutations', 'deltaG', 'deltaG_95CI']], left_on='id', right_on='mutations')\n",
    "merged.set_index('id', inplace=True)\n",
    "del merged['mutations']\n",
    "merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f9c7e3a8-1b6d-48fd-8f2f-dbdc69c0ca60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 140x160 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from scipy.stats import pearsonr\n",
    "\n",
    "df = merged.sort_values('mean_kcal_mol').copy()\n",
    "\n",
    "g = sns.FacetGrid(data=df, aspect=2)\n",
    "g.map(plt.errorbar, 'mean_kcal_mol', 'deltaG', 'deltaG_95CI','std_kcal_mol', fmt='o', elinewidth=0.5, capsize=2, capthick=0.5, markersize=2, color='#F6DCC9')\n",
    "g.fig.set_size_inches(1.4,1.6)\n",
    "g.tick_params(axis='both', which='major', labelsize=6)\n",
    "\n",
    "x = df[['mean_kcal_mol']].to_numpy()\n",
    "y = df[['deltaG']].to_numpy()\n",
    "\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(x, y)\n",
    "\n",
    "y_pred = model.predict(x)\n",
    "r2 = r2_score(y,y_pred)\n",
    "r,p = pearsonr(y.ravel(),x.ravel())\n",
    "\n",
    "plt.plot(x, y_pred, color='Gray', linewidth=1, linestyle='--')\n",
    "plt.xlabel('Inferred ΔΔG / kcal mol$^-$$^1$', fontsize=7)\n",
    "plt.ylabel('Tsuboyama et al.\\nΔG / kcal mol$^-$$^1$', fontsize=7)\n",
    "plt.title('R = '+str(round(r,2))+'\\np-val = '+'{:0.2e}'.format(p), fontsize=7,color='red',loc='right',y=1)\n",
    "\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "#plt.savefig('CI-2A_inferred_from_2ndordermodel_ddG_Tsuboyama.svg', dpi=300, transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdb52aac-80d2-44ec-8d38-2baeb85c5317",
   "metadata": {},
   "source": [
    "## Panel c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1dec32bc-c287-4157-966f-39dc8f50ec01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.patches import Rectangle\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "plt.rcParams['font.family'] = 'Arial'\n",
    "\n",
    "coefs = pd.read_csv(path+'Fig3/FYN-SH3/FYN_mochi_order2/task_1/weights/weights_Folding.txt',\n",
    "                       sep='\\t')\n",
    "coefs.rename(columns={'mean_kcal/mol':'mean_kcal_mol', 'std_kcal/mol':'std_kcal_mol', 'ci95_kcal/mol':'ci95_kcal_mol'}, inplace=True)\n",
    "for index, row in coefs.iterrows():\n",
    "    if row.id_ref == 'WT':\n",
    "        coefs.at[index,'order']=0\n",
    "    else:\n",
    "        coefs.at[index,'order']=len(row.id.split('_'))\n",
    "\n",
    "secondorderterms=coefs.loc[coefs['order']==2].copy()\n",
    "\n",
    "wt ='FLFFIIV'\n",
    "mut_pos_name = [2,16,18,24,26,48,53]\n",
    "\n",
    "\n",
    "secondorderterms['mutation1']='a'\n",
    "secondorderterms['mutation2']='a'\n",
    "for index,row in secondorderterms.iterrows():\n",
    "    secondorderterms.at[index,'mutation1']=row.id.split('_')[0]\n",
    "    secondorderterms.at[index,'mutation2']=row.id.split('_')[1]\n",
    "\n",
    "single_mutations=[]\n",
    "for index, row in secondorderterms.iterrows():\n",
    "    if row.mutation1 not in single_mutations:\n",
    "        single_mutations.append(row.mutation1)\n",
    "for index, row in secondorderterms.iterrows():\n",
    "    if row.mutation2 not in single_mutations:\n",
    "        single_mutations.append(row.mutation2)    \n",
    "\n",
    "pairwise_secondorderterms=pd.DataFrame(columns=single_mutations)\n",
    "\n",
    "for index, row in secondorderterms.iterrows():\n",
    "    pairwise_secondorderterms.at[row.mutation1,row.mutation2]=row.mean_kcal_mol\n",
    "\n",
    "for index, row in secondorderterms.iterrows():\n",
    "    pairwise_secondorderterms.at[row.mutation2,row.mutation1]=row.mean_kcal_mol\n",
    "\n",
    "pairwise_secondorderterms=pairwise_secondorderterms.fillna(0)\n",
    "pairwise_secondorderterms=pairwise_secondorderterms.reindex(single_mutations)\n",
    "\n",
    "fig, ax=plt.subplots(figsize=(3,3))\n",
    "for mutation_nr, mutation in enumerate(mutated_positions[1:]):\n",
    "    plt.text(-4.2, 2.5+4*mutation_nr, mutation, fontsize=8)\n",
    "for mutation_nr, mutation in enumerate(mutated_positions[:-1]):\n",
    "    plt.text(1+4*mutation_nr, 27.2, mutation, fontsize=8)\n",
    "\n",
    "\n",
    "# Create a custom mask\n",
    "mask = np.zeros_like(pairwise_secondorderterms.iloc[4:,:-4], dtype=bool)\n",
    "for i in range(len(pairwise_secondorderterms.iloc[4:,:-4].index)):\n",
    "    for j in range(i+1, len(pairwise_secondorderterms.iloc[4:,:-4].index)):  # Only mask rows and columns in the upper diagonal\n",
    "        if (i // 4) < (j // 4):  # Mask entire quadrants in the upper diagonal\n",
    "            mask[i, j] = True\n",
    "\n",
    "\n",
    "coefs_sparse_model = pd.read_csv(path+'/Fig3/FYN-SH3/FYN_mochi_order2_sparse_regularized/task_4/weights/weights_Folding.txt',\n",
    "                       sep='\\t')\n",
    "coefs_sparse_model.rename(columns={'mean_kcal/mol':'mean_kcal_mol', 'std_kcal/mol':'std_kcal_mol', 'ci95_kcal/mol':'ci95_kcal_mol'}, inplace=True)\n",
    "for index, row in coefs_sparse_model.iterrows():\n",
    "    coefs_sparse_model.at[index,'order']=len(row.id.split('_'))\n",
    "coefs_sparse_model=coefs_sparse_model.set_index('id')\n",
    "\n",
    "sparse_regularized_selected = pd.DataFrame(index=pairwise_secondorderterms.index, columns=pairwise_secondorderterms.columns)\n",
    "for coupling in list(coefs_sparse_model.loc[coefs_sparse_model['order']==2].index):\n",
    "    mutations=coupling.split('_')\n",
    "    sparse_regularized_selected.at[mutations[0],mutations[1]]='*'\n",
    "    sparse_regularized_selected.at[mutations[1],mutations[0]]='*'\n",
    "    \n",
    "sparse_regularized_selected=sparse_regularized_selected.fillna('')\n",
    "\n",
    "couplings_boundary=2.1            \n",
    "cbar_ax=fig.add_axes([0.55,0.82,0.3,0.02])\n",
    "hmap=sns.heatmap(pairwise_secondorderterms.iloc[4:,:-4], cmap='coolwarm', center=0, xticklabels=True,yticklabels=True, mask=mask,vmin=-1*couplings_boundary, vmax=couplings_boundary,\n",
    "              cbar_kws={\"orientation\": \"horizontal\", \"pad\": 0.02, 'ticks':list(np.arange(-2,3,1))}, ax=ax, cbar_ax=cbar_ax, annot=sparse_regularized_selected.iloc[4:,:-4].mask(mask).to_numpy().tolist(), fmt= '', annot_kws={'size':4})\n",
    "\n",
    "\n",
    "#ax.figure.axes[-1].set_ylabel('ddG', rotation=270)\n",
    "cbar_ax.set_xlabel('ΔΔΔG kcal mol$^-$$^1$', rotation=0, fontsize=8)\n",
    "cbar_ax.xaxis.set_label_position('top')\n",
    "for spine in cbar_ax.spines.values():\n",
    "    spine.set(visible=True,lw=1,edgecolor='black')\n",
    "plt.xticks(fontsize=6)\n",
    "plt.yticks(fontsize=6)\n",
    "    \n",
    "labels_x = [item.get_text() for item in ax.get_xticklabels()]\n",
    "for label_nr, label in enumerate(labels_x):\n",
    "    labels_x[label_nr]=label[-1]\n",
    "ax.set_xticklabels(labels_x, rotation=0,fontsize=6)\n",
    "labels_y = [item.get_text() for item in ax.get_yticklabels()]\n",
    "for label_nr, label in enumerate(labels_y):\n",
    "    labels_y[label_nr]=label[-1]\n",
    "ax.set_yticklabels(labels_y, rotation=0,fontsize=6)\n",
    "\n",
    "for i in range(0, len(pairwise_secondorderterms.iloc[4:,:-4].index), 4):\n",
    "    for j in range(0, len(pairwise_secondorderterms.iloc[4:,:-4].index), 4):\n",
    "        if not np.any(mask[i:i+4, j:j+4]):\n",
    "            rect = Rectangle((j, i), 4, 4, linewidth=1, edgecolor='black', facecolor='none')\n",
    "            ax.add_patch(rect)\n",
    "\n",
    "ax.axhline(y=len(pairwise_secondorderterms.iloc[4:,:-4].index), color='k',linewidth=2)\n",
    "ax.axhline(y=0,xmin=0,xmax=0.16, color='k',linewidth=2)\n",
    "ax.axvline(x=0, color='k',linewidth=2)\n",
    "ax.axvline(x=len(pairwise_secondorderterms.iloc[4:,:-4].index),ymin=0,ymax=0.16, color='k',linewidth=2)\n",
    "\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "#plt.savefig('FYN_SH3_2or_pairwisecoup.svg', dpi=300, transparent=True, bbox_inches = \"tight\")\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02964d56-3527-4daa-ae13-b3ac5b1cd598",
   "metadata": {},
   "source": [
    "## Panel d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bf3e5dc0-b8aa-4b61-ae7c-c695eabe3f47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.patches import Rectangle\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['font.family'] = 'Arial'\n",
    "\n",
    "coefs = pd.read_csv(path+'Fig3/CI-2A/CI-2A_mochi_order2/task_1/weights/weights_Folding.txt',\n",
    "                       sep='\\t')\n",
    "coefs.rename(columns={'mean_kcal/mol':'mean_kcal_mol', 'std_kcal/mol':'std_kcal_mol', 'ci95_kcal/mol':'ci95_kcal_mol'}, inplace=True)\n",
    "for index, row in coefs.iterrows():\n",
    "    coefs.at[index,'order']=len(row.id.split('_'))\n",
    "\n",
    "secondorderterms=coefs.loc[coefs['order']==2].copy()\n",
    "\n",
    "wt ='FLFFIIV'\n",
    "mut_pos_name = [2,16,18,24,26,48,53]\n",
    "\n",
    "\n",
    "secondorderterms['mutation1']='a'\n",
    "secondorderterms['mutation2']='a'\n",
    "for index,row in secondorderterms.iterrows():\n",
    "    secondorderterms.at[index,'mutation1']=row.id.split('_')[0]\n",
    "    secondorderterms.at[index,'mutation2']=row.id.split('_')[1]\n",
    "\n",
    "single_mutations=[]\n",
    "for index, row in secondorderterms.iterrows():\n",
    "    if row.mutation1 not in single_mutations:\n",
    "        single_mutations.append(row.mutation1)\n",
    "for index, row in secondorderterms.iterrows():\n",
    "    if row.mutation2 not in single_mutations:\n",
    "        single_mutations.append(row.mutation2)    \n",
    "\n",
    "pairwise_secondorderterms=pd.DataFrame(columns=single_mutations)\n",
    "\n",
    "for index, row in secondorderterms.iterrows():\n",
    "    pairwise_secondorderterms.at[row.mutation1,row.mutation2]=row.mean_kcal_mol\n",
    "\n",
    "for index, row in secondorderterms.iterrows():\n",
    "    pairwise_secondorderterms.at[row.mutation2,row.mutation1]=row.mean_kcal_mol\n",
    "\n",
    "pairwise_secondorderterms=pairwise_secondorderterms.fillna(0)\n",
    "pairwise_secondorderterms=pairwise_secondorderterms.reindex(single_mutations)\n",
    "\n",
    "fig, ax=plt.subplots(figsize=(3,3))\n",
    "for mutation_nr, mutation in enumerate(mutated_positions[1:]):\n",
    "    plt.text(-4.2, 2.5+4*mutation_nr, mutation, fontsize=8)\n",
    "for mutation_nr, mutation in enumerate(mutated_positions[:-1]):\n",
    "    plt.text(1+4*mutation_nr, 27.2, mutation, fontsize=8)\n",
    "\n",
    "# Create a custom mask\n",
    "mask = np.zeros_like(pairwise_secondorderterms.iloc[4:,:-4], dtype=bool)\n",
    "for i in range(len(pairwise_secondorderterms.iloc[4:,:-4].index)):\n",
    "    for j in range(i+1, len(pairwise_secondorderterms.iloc[4:,:-4].index)):  # Only mask rows and columns in the upper diagonal\n",
    "        if (i // 4) < (j // 4):  # Mask entire quadrants in the upper diagonal\n",
    "            mask[i, j] = True\n",
    "\n",
    "coefs_sparse_model = pd.read_csv(path+'Fig3/CI-2A/CI-2A_mochi_order2_sparse_regularized/task_4/weights/weights_Folding.txt',\n",
    "                       sep='\\t')\n",
    "coefs_sparse_model.rename(columns={'mean_kcal/mol':'mean_kcal_mol', 'std_kcal/mol':'std_kcal_mol', 'ci95_kcal/mol':'ci95_kcal_mol'}, inplace=True)\n",
    "for index, row in coefs_sparse_model.iterrows():\n",
    "    coefs_sparse_model.at[index,'order']=len(row.id.split('_'))\n",
    "coefs_sparse_model=coefs_sparse_model.set_index('id')\n",
    "\n",
    "sparse_regularized_selected = pd.DataFrame(index=pairwise_secondorderterms.index, columns=pairwise_secondorderterms.columns)\n",
    "for coupling in list(coefs_sparse_model.loc[coefs_sparse_model['order']==2].index):\n",
    "    mutations=coupling.split('_')\n",
    "    sparse_regularized_selected.at[mutations[0],mutations[1]]='*'\n",
    "    sparse_regularized_selected.at[mutations[1],mutations[0]]='*'\n",
    "    \n",
    "sparse_regularized_selected=sparse_regularized_selected.fillna('')\n",
    "#sparse_regularized_selected=sparse_regularized_selected.replace('*','')\n",
    "\n",
    "            \n",
    "cbar_ax=fig.add_axes([0.55,0.82,0.3,0.02])\n",
    "hmap=sns.heatmap(pairwise_secondorderterms.iloc[4:,:-4], cmap='coolwarm', center=0, xticklabels=True,yticklabels=True, mask=mask,vmin=-1*couplings_boundary, vmax=couplings_boundary,\n",
    "              cbar_kws={\"orientation\": \"horizontal\", \"pad\": 0.02, 'ticks':list(np.arange(-2,3,1))}, ax=ax, cbar_ax=cbar_ax, annot=sparse_regularized_selected.iloc[4:,:-4].mask(mask).to_numpy().tolist(), fmt= '', annot_kws={'size':4})\n",
    "\n",
    "\n",
    "#ax.figure.axes[-1].set_ylabel('ddG', rotation=270)\n",
    "cbar_ax.set_xlabel('ΔΔΔG kcal mol$^-$$^1$', rotation=0, fontsize=8)\n",
    "cbar_ax.xaxis.set_label_position('top')\n",
    "for spine in cbar_ax.spines.values():\n",
    "    spine.set(visible=True,lw=1,edgecolor='black')\n",
    "plt.xticks(fontsize=6)\n",
    "plt.yticks(fontsize=6)\n",
    "    \n",
    "labels_x = [item.get_text() for item in ax.get_xticklabels()]\n",
    "for label_nr, label in enumerate(labels_x):\n",
    "    labels_x[label_nr]=label[-1]\n",
    "ax.set_xticklabels(labels_x, rotation=0,fontsize=6)\n",
    "labels_y = [item.get_text() for item in ax.get_yticklabels()]\n",
    "for label_nr, label in enumerate(labels_y):\n",
    "    labels_y[label_nr]=label[-1]\n",
    "ax.set_yticklabels(labels_y, rotation=0,fontsize=6)\n",
    "\n",
    "for i in range(0, len(pairwise_secondorderterms.iloc[4:,:-4].index), 4):\n",
    "    for j in range(0, len(pairwise_secondorderterms.iloc[4:,:-4].index), 4):\n",
    "        if not np.any(mask[i:i+4, j:j+4]):\n",
    "            rect = Rectangle((j, i), 4, 4, linewidth=1, edgecolor='black', facecolor='none')\n",
    "            ax.add_patch(rect)\n",
    "\n",
    "ax.axhline(y=len(pairwise_secondorderterms.iloc[4:,:-4].index), color='k',linewidth=2)\n",
    "ax.axhline(y=0,xmin=0,xmax=0.16, color='k',linewidth=2)\n",
    "ax.axvline(x=0, color='k',linewidth=2)\n",
    "ax.axvline(x=len(pairwise_secondorderterms.iloc[4:,:-4].index),ymin=0,ymax=0.16, color='k',linewidth=2)\n",
    "\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "#plt.savefig('CI-2A_2or_pairwisecoup.svg', dpi=300, transparent=True, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "545bc5da-f81d-4270-8a53-227ad4b0e35a",
   "metadata": {},
   "source": [
    "## Panel e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a00846d2-ea47-4bd1-a75c-e542c43897d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from seaborn import kdeplot\n",
    "\n",
    "predicted_phenotypes = pd.read_csv(path+'Fig3/FYN-SH3/FYN_mochi_order2/task_1/predictions/predicted_phenotypes_supp.txt',\n",
    "                                  sep='\\t', lineterminator='\\n', low_memory=False)\n",
    "\n",
    "for index, row in predicted_phenotypes.iterrows():\n",
    "    core=[]\n",
    "    for i in cores['P06241']:\n",
    "        core.append(row.aa_seq[i])\n",
    "    predicted_phenotypes.at[index,'core'] = ''.join(core)\n",
    "\n",
    "RT=303*0.001987\n",
    "predicted_phenotypes['mean_additive_trait']=predicted_phenotypes[['fold_1_additive_trait0','fold_2_additive_trait0','fold_3_additive_trait0','fold_4_additive_trait0','fold_5_additive_trait0','fold_6_additive_trait0','fold_7_additive_trait0','fold_8_additive_trait0','fold_9_additive_trait0','fold_10_additive_trait0']].mean(axis=1)\n",
    "predicted_phenotypes['std_additive_trait']=predicted_phenotypes[['fold_1_additive_trait0','fold_2_additive_trait0','fold_3_additive_trait0','fold_4_additive_trait0','fold_5_additive_trait0','fold_6_additive_trait0','fold_7_additive_trait0','fold_8_additive_trait0','fold_9_additive_trait0','fold_10_additive_trait0']].std(axis=1)\n",
    "\n",
    "predicted_phenotypes['mean']=predicted_phenotypes[['fold_1','fold_2','fold_3','fold_4','fold_5','fold_6','fold_7','fold_8','fold_9','fold_10']].mean(axis=1)\n",
    "predicted_phenotypes['std']=predicted_phenotypes[['fold_1','fold_2','fold_3','fold_4','fold_5','fold_6','fold_7','fold_8','fold_9','fold_10']].std(axis=1)\n",
    "\n",
    "predicted_phenotypes['mean_dG']= predicted_phenotypes['mean_additive_trait']*RT\n",
    "predicted_phenotypes['std_dG']= predicted_phenotypes['std_additive_trait']*RT\n",
    "\n",
    "# Normalize data in the -1 to 0 range\n",
    "\n",
    "def normalize(values, bounds):\n",
    "    return [bounds['desired']['lower'] + (x - bounds['actual']['lower']) * (bounds['desired']['upper'] - bounds['desired']['lower']) / (bounds['actual']['upper'] - bounds['actual']['lower']) for x in values]\n",
    "def normalize_errors(values, bounds):\n",
    "    return [x * (bounds['desired']['upper'] - bounds['desired']['lower']) / (bounds['actual']['upper'] - bounds['actual']['lower']) for x in values]\n",
    "\n",
    "predicted_phenotypes_scaled = predicted_phenotypes.copy()\n",
    "\n",
    "reference=predicted_phenotypes.loc[predicted_phenotypes['WT']==True].iloc[0]['core']\n",
    "\n",
    "fig = plt.figure(figsize=(0,0))\n",
    "lines = kdeplot(data=predicted_phenotypes,x='fitness',linewidth=0).get_lines()\n",
    "for line in lines:\n",
    "    x, y = line.get_data()\n",
    "    mode=(x[np.argmax(y)])\n",
    "plt.close()\n",
    "\n",
    "predicted_phenotypes_scaled['fitness']=normalize(list(predicted_phenotypes_scaled['fitness']),{'actual': {'lower': mode, 'upper': predicted_phenotypes_scaled.loc[predicted_phenotypes_scaled['core']==reference]['fitness'].iloc[0]}, 'desired': {'lower': -1, 'upper': 0}})\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(0,0))\n",
    "lines = kdeplot(data=predicted_phenotypes,x='mean',linewidth=0).get_lines()\n",
    "for line in lines:\n",
    "    x, y = line.get_data()\n",
    "    mode=(x[np.argmax(y)])\n",
    "plt.close()\n",
    "\n",
    "predicted_phenotypes_scaled['mean']=normalize(list(predicted_phenotypes_scaled['mean']),{'actual': {'lower': mode, 'upper': predicted_phenotypes.loc[predicted_phenotypes['aa_seq']==str(Seq(P06241).translate())].iloc[0]['mean']}, 'desired': {'lower': -1, 'upper': 0}})\n",
    "predicted_phenotypes_scaled['std'] = normalize_errors(list(predicted_phenotypes_scaled['std']),{'actual': {'lower': mode, 'upper':predicted_phenotypes.loc[predicted_phenotypes['aa_seq']==str(Seq(P06241).translate())].iloc[0]['mean']}, 'desired': {'lower': -1, 'upper': 0}})\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3c80b66d-7e3c-4863-8747-318a81dc40c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 78125/78125 [00:01<00:00, 54337.34it/s]\n"
     ]
    }
   ],
   "source": [
    "# Define all variants separated by 1 mutation\n",
    "\n",
    "from tqdm import tqdm\n",
    "from itertools import product\n",
    "\n",
    "DTS = ['F','L','I','M','V']\n",
    "a=list(product(DTS,repeat=7))\n",
    "\n",
    "\n",
    "\n",
    "dod={}\n",
    "for variant in tqdm(a, total=len(a)):\n",
    "    iterator=0\n",
    "    positerator=0\n",
    "    singles=[]\n",
    "    for i in product(variant,DTS):\n",
    "        mutant = ''.join(''.join(variant[:positerator])+str(i[1])+''.join(variant[positerator+1:]))\n",
    "        if (iterator/5).is_integer() == True and iterator !=0:\n",
    "            positerator+=1\n",
    "        iterator+=1\n",
    "        if mutant != ''.join(variant):\n",
    "            singles.append(mutant)\n",
    "    dod[''.join(variant)]=singles\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d64f443f-0759-478e-847e-4555ee635ed4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1 import AxesGrid\n",
    "\n",
    "def shiftedColorMap(cmap, start=0, midpoint=0.5, stop=1, name='shiftedcmap'):\n",
    "    '''\n",
    "    Function to offset the \"center\" of a colormap. Useful for\n",
    "    data with a negative min and positive max and you want the\n",
    "    middle of the colormap's dynamic range to be at zero.\n",
    "\n",
    "    Input\n",
    "    -----\n",
    "      cmap : The matplotlib colormap to be altered\n",
    "      start : Offset from lowest point in the colormap's range.\n",
    "          Defaults to 0.0 (no lower offset). Should be between\n",
    "          0.0 and `midpoint`.\n",
    "      midpoint : The new center of the colormap. Defaults to \n",
    "          0.5 (no shift). Should be between 0.0 and 1.0. In\n",
    "          general, this should be  1 - vmax / (vmax + abs(vmin))\n",
    "          For example if your data range from -15.0 to +5.0 and\n",
    "          you want the center of the colormap at 0.0, `midpoint`\n",
    "          should be set to  1 - 5/(5 + 15)) or 0.75\n",
    "      stop : Offset from highest point in the colormap's range.\n",
    "          Defaults to 1.0 (no upper offset). Should be between\n",
    "          `midpoint` and 1.0.\n",
    "    '''\n",
    "    cdict = {\n",
    "        'red': [],\n",
    "        'green': [],\n",
    "        'blue': [],\n",
    "        'alpha': []\n",
    "    }\n",
    "\n",
    "    # regular index to compute the colors\n",
    "    reg_index = np.linspace(start, stop, 257)\n",
    "\n",
    "    # shifted index to match the data\n",
    "    shift_index = np.hstack([\n",
    "        np.linspace(0.0, midpoint, 128, endpoint=False), \n",
    "        np.linspace(midpoint, 1.0, 129, endpoint=True)\n",
    "    ])\n",
    "\n",
    "    for ri, si in zip(reg_index, shift_index):\n",
    "        r, g, b, a = cmap(ri)\n",
    "\n",
    "        cdict['red'].append((si, r, r))\n",
    "        cdict['green'].append((si, g, g))\n",
    "        cdict['blue'].append((si, b, b))\n",
    "        cdict['alpha'].append((si, a, a))\n",
    "\n",
    "    newcmap = matplotlib.colors.LinearSegmentedColormap(name, cdict)\n",
    "    plt.register_cmap(cmap=newcmap)\n",
    "\n",
    "    return newcmap\n",
    "iterator_cmap = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5fbdb96b-7acb-405c-be43-061e62bd83d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 230x250 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import networkx as nx\n",
    "from itertools import count\n",
    "from seaborn import kdeplot\n",
    "from itertools import groupby\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "protein='P06241'\n",
    "\n",
    "protein_variants_scaled = predicted_phenotypes_scaled.sort_values('mean', ascending=False)\n",
    "\n",
    "lines = kdeplot(data=protein_variants_scaled, x='mean', ax=ax).get_lines()\n",
    "    \n",
    "for line in lines:\n",
    "    x, y = line.get_data()\n",
    "    mode=x[np.argmax(y)]\n",
    "\n",
    "G = nx.from_dict_of_lists(dod)\n",
    "G_val = nx.from_dict_of_lists(dod)\n",
    "\n",
    "pos={}\n",
    "for index, row in protein_variants_scaled.iterrows():\n",
    "    pos[row.core] = (row.Nham_aa, row['mean'])\n",
    "\n",
    "fig, (ax0,axviable,ax1,ax2) = plt.subplots(4,1,gridspec_kw={'height_ratios':[2,2,1,12],'hspace': 0.15},figsize=(2.3,2.5))\n",
    "plt.close()   \n",
    "fig, (ax0,axviable,ax1,ax2) = plt.subplots(4,1,gridspec_kw={'height_ratios':[2,2,1,12],'hspace': 0.15},figsize=(2.3,2.5),sharex=ax1)\n",
    "\n",
    "\n",
    "fig=sns.violinplot(x=\"Nham_aa\", y=\"mean\", data=protein_variants_scaled, inner=\"point\", ax=ax2, color='#B4D689', linewidth=0.3)\n",
    "plt.axhline(y=-0.5, linestyle='--', color='gray', linewidth=0.5)\n",
    "\n",
    "remove=[i for i in dod.keys() if i not in pos.keys()]\n",
    "\n",
    "G.remove_nodes_from(remove)\n",
    "\n",
    "fitness_values=[]\n",
    "for i in pos:\n",
    "    fitness_values.append(pos[i][1])\n",
    "groups = []\n",
    "for _, g in groupby(fitness_values, lambda x: (x-1)//0.05):\n",
    "    groups.append(list(g))      # Store group iterator as a list\n",
    "\n",
    "fitness_attr={}\n",
    "for node in G.nodes:\n",
    "    iterator=0\n",
    "    for bin in groups:\n",
    "        if pos[node][1] in bin:\n",
    "            fitness_attr[node]=iterator\n",
    "            break\n",
    "        iterator+=1\n",
    "\n",
    "nx.set_node_attributes(G, fitness_attr, 'mean')\n",
    "groups = set(nx.get_node_attributes(G,'mean').values())\n",
    "mapping = dict(zip(sorted(groups),count()))\n",
    "nodes = G.nodes()\n",
    "colors = [mapping[G.nodes[n]['mean']] for n in nodes]\n",
    "\n",
    "total_viable=0\n",
    "nham_viability=pd.DataFrame(columns=['Number_of_variants','Fraction_50pc_folded'])\n",
    "for nham in range(0,8,1):\n",
    "    nham_viability.at[nham,'protein']=protein\n",
    "    nham_viability.at[nham,'nham']=nham\n",
    "    nham_viability.at[nham,'Number_of_variants']=len(protein_variants_scaled.loc[protein_variants_scaled['Nham_aa']==nham])\n",
    "    nham_viability.at[nham,'Fraction_50pc_folded']=round(100*(len(protein_variants_scaled.loc[protein_variants_scaled['Nham_aa']==nham].loc[protein_variants_scaled['mean']>=-0.5].index)/len(protein_variants_scaled.loc[protein_variants_scaled['Nham_aa']==nham])),1)\n",
    "    nham_viability.at[nham,'Number_of_50pc_folded_variants']=len(protein_variants_scaled.loc[protein_variants_scaled['Nham_aa']==nham].loc[protein_variants_scaled ['mean']>=-0.5].index)\n",
    "    nham_viability.at[nham,'Fraction_75pc_folded']=round(100*(len(protein_variants_scaled.loc[protein_variants_scaled['Nham_aa']==nham].loc[protein_variants_scaled['mean']>=-0.25].index)/len(protein_variants_scaled.loc[protein_variants_scaled['Nham_aa']==nham])),1)\n",
    "    nham_viability.at[nham,'Number_of_75pc_folded_variants']=len(protein_variants_scaled.loc[protein_variants_scaled['Nham_aa']==nham].loc[protein_variants_scaled ['mean']>=-0.25].index)\n",
    "    nham_viability.at[nham,'Fraction_90pc_folded']=round(100*(len(protein_variants_scaled.loc[protein_variants_scaled['Nham_aa']==nham].loc[protein_variants_scaled['mean']>=-0.1].index)/len(protein_variants_scaled.loc[protein_variants_scaled['Nham_aa']==nham])),1)\n",
    "    nham_viability.at[nham,'Number_of_90pc_folded_variants']=len(protein_variants_scaled.loc[protein_variants_scaled['Nham_aa']==nham].loc[protein_variants_scaled ['mean']>=-0.1].index)\n",
    "    total_viable=total_viable+len(protein_variants_scaled.loc[protein_variants_scaled['Nham_aa']==nham].loc[protein_variants_scaled['mean']>=-0.5].index)\n",
    "for index, row in nham_viability.iterrows():\n",
    "    nham_viability.at[index,'Fraction_of_total_50pc_viable_variants']= round(100*row['Number_of_50pc_folded_variants']/total_viable,1)\n",
    "\n",
    "total_core_viabilities = pd.DataFrame(columns=['protein', 'variants_above_01', 'variants_above_025', 'variants_above_05', 'variants_below_05'])\n",
    "total_core_viabilities.at[protein,'variants_above_01']=len(protein_variants_scaled.loc[protein_variants_scaled['fitness']>=-0.1].index)/len(protein_variants_scaled.index)*5**7\n",
    "total_core_viabilities.at[protein,'variants_above_025']=len(protein_variants_scaled.loc[protein_variants_scaled['fitness']>=-0.25].index)/len(protein_variants_scaled.index)*5**7\n",
    "total_core_viabilities.at[protein,'variants_below_05']=5**7-len(protein_variants_scaled.loc[protein_variants_scaled['fitness']>=-0.5].index)/len(protein_variants_scaled.index)*5**7\n",
    "total_core_viabilities.at[protein,'variants_above_05']=len(protein_variants_scaled.loc[protein_variants_scaled['fitness']>=-0.5].index)/len(protein_variants_scaled.index)*5**7\n",
    "\n",
    "\n",
    "    \n",
    "orig_cmap = plt.cm.coolwarm\n",
    "iterator_cmap+=1\n",
    "midpoint=(-0.5 - np.min(protein_variants_scaled['mean']))/ (np.max(protein_variants_scaled['mean']) - np.min(protein_variants_scaled['mean']))\n",
    "shifted_cmap = shiftedColorMap(orig_cmap, midpoint=1-midpoint, name='shifted'+str(iterator_cmap))\n",
    "    \n",
    "nx.draw_networkx(G, pos=pos, with_labels=False, node_size=0, ax=ax2, edge_color='gray', width=0.002)\n",
    "nodes=nx.draw_networkx_nodes(G, pos, nodelist=nodes, node_color=colors, node_size=0.3, cmap=shifted_cmap)\n",
    "ax2.tick_params(left=True, bottom=True, labelleft=True, labelbottom=True)\n",
    "ax2.set_xlabel('Number of amino acid substitutions', fontsize=7)\n",
    "ax2.set_ylabel(\"Abundance\\nfitness\", fontsize=7,rotation='horizontal', ha='right')\n",
    "ax2.spines['top'].set_visible(False)\n",
    "ax2.spines['right'].set_visible(False)\n",
    "ax2.tick_params(axis='both', which='major', labelsize=6)\n",
    "\n",
    "nham_viability['Fraction_50pc_folded'] = pd.to_numeric(nham_viability['Fraction_50pc_folded'], errors='coerce')\n",
    "sns.scatterplot(x=[i-1 for i in nham_viability['nham']],y=[0]*len(nham_viability.index), hue=nham_viability['Fraction_50pc_folded'],palette='coolwarm_r', s=nham_viability['Fraction_50pc_folded']*0.5,ax=ax1)\n",
    "ax1.legend_.remove()\n",
    "ax1.spines[['top','right','left','bottom']].set_visible(False)\n",
    "ax1.tick_params(left=False, bottom=False, labelleft=False, labelbottom=False,top=False, right=False)\n",
    "ax1.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)\n",
    "for index, row in nham_viability.iterrows():\n",
    "    if row.Fraction_50pc_folded == 100.0:\n",
    "        ax2.annotate(100, (row.nham-0.4,0.4), fontsize=6, color='black')\n",
    "    else:\n",
    "        ax2.annotate(row.Fraction_50pc_folded, (row.nham-0.3,0.4), fontsize=6, color='black')\n",
    "ax1.set_ylabel('% variants\\nfitness > -0.5',fontsize=6,rotation='horizontal', ha='right')\n",
    "        \n",
    "ax2.annotate('n='+f\"{len(protein_variants_scaled):,}\", (-0.3,-1.2), fontsize=6)\n",
    "plt.subplots_adjust(hspace=0.05)\n",
    "\n",
    "nham_viability['Number_of_variants'].plot.bar(ax=ax0,fontsize=6,color='gray')\n",
    "ax0.spines[['top','right']].set_visible(False)\n",
    "ax0.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)\n",
    "ax0.set_ylabel('Total count',fontsize=6, rotation='horizontal', ha='right')\n",
    "labels = ax0.get_yticklabels()\n",
    "labels[0] = \"\"\n",
    "ax0.set_yticklabels(labels)\n",
    "\n",
    "nham_viability['Number_of_50pc_folded_variants'].plot.bar(ax=axviable,fontsize=6,color='gray')\n",
    "axviable.spines[['top','right']].set_visible(False)\n",
    "axviable.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)\n",
    "axviable.set_ylabel('Nr. variants\\nfitness > -0.5',fontsize=6,rotation='horizontal', ha='right')\n",
    "\n",
    "ax0.get_yaxis().set_label_coords(-0.2,0)\n",
    "axviable.get_yaxis().set_label_coords(-0.2,0)\n",
    "ax1.get_yaxis().set_label_coords(-0.2,-1)\n",
    "ax2.get_yaxis().set_label_coords(-0.2,0.5)\n",
    "plt.ylim(-1.3,0.6)\n",
    "plt.xlim(-0.5,7.5)\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "#plt.savefig('FYN_full_fitness_landscape.png', dpi=300, transparent=True, bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d402f17d-bb80-473a-97cc-14ba24726813",
   "metadata": {},
   "source": [
    "## Panel f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "38e11259-fbca-4efd-87f6-e119d1e72cd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "from scipy.interpolate import UnivariateSpline, InterpolatedUnivariateSpline\n",
    "import warnings\n",
    "import math\n",
    "\n",
    "# get Tecan machine data\n",
    "data=pd.read_csv(path+\"Fig4/FYN-SH3_prediction_Tecan.txt\", header=0, delim_whitespace=True)\n",
    "\n",
    "# remove columns without data\n",
    "data = data.iloc[:,0:269]\n",
    "\n",
    "final_cycle_col=len(data.columns)-1\n",
    "data2=data\n",
    "data2=data2.drop(data2.index[[0,1]])\n",
    "data2.index=data2.Cycle\n",
    "data2 = data2.drop('Cycle', axis=1)\n",
    "\n",
    "# get well information (randomization file)\n",
    "plasmid_info=pd.read_csv(path+\"Fig4/FYN-SH3_prediction_Tecan_randomization.txt\", header=0, delim_whitespace=True)\n",
    "#plasmid_info=pd.read_csv(path+\"231102_Randomization_FYN_stability_validation.txt\", header=0, delim_whitespace=True)\n",
    "plasmid_info.index=plasmid_info.well\n",
    "plasmid_info = plasmid_info.drop('well',axis=1)\n",
    "data2= pd.concat([data2, plasmid_info], axis=1)\n",
    "#data2 = data2.drop('rand', 1)\n",
    "\n",
    "blanks = data2.loc[data2['plasmid'] == 'blank']\n",
    "blanks = blanks.drop('plasmid', axis=1)\n",
    "blanks = pd.DataFrame(blanks.mean().to_dict(),index=[blanks.index.values[-1]])\n",
    "blanks = blanks.reset_index()\n",
    "del blanks['index']\n",
    "\n",
    "data2 = data2.drop('plasmid', axis=1)\n",
    "\n",
    "data3 = pd.DataFrame(columns=np.arange(1,final_cycle_col+1,1))\n",
    "for index, row in data2.iterrows():\n",
    "    a=list(row-blanks.loc[0])\n",
    "    data3.loc[index]=a\n",
    "\n",
    "\n",
    "data3= pd.concat([data3, plasmid_info], axis=1)   \n",
    "\n",
    "data.index=data.Cycle\n",
    "data = data.drop('Cycle', axis=1)\n",
    "\n",
    "cols=np.arange(1,final_cycle_col+1,1)\n",
    "time=[]\n",
    "for i,j in zip(data.iloc[0],cols):\n",
    "    time.append(int(j)*921.3/3600)\n",
    "\n",
    "to_plot = [i for i in list(plasmid_info['plasmid'].unique()) if i != 'blank']\n",
    "rows = pd.DataFrame(columns=np.arange(1,final_cycle_col+1,1))\n",
    "for i in to_plot:\n",
    "    rows = pd.concat([rows, data3.loc[data3['plasmid'] == i]], axis=0)\n",
    "    \n",
    "plt.figure(figsize=(4,8))\n",
    "plasmids=[]\n",
    "colors=[]\n",
    "for index,row in rows.iterrows():\n",
    "    if row['plasmid'] not in plasmids:\n",
    "        plasmids.append(row['plasmid'])\n",
    "        r = lambda: random.randint(0,255)\n",
    "        colors.append('#%02X%02X%02X' % (r(),r(),r()))\n",
    "    row = row.drop(['plasmid'])\n",
    "    row.index=time\n",
    "    x = time\n",
    "    y = row[:]\n",
    "    y_spl = UnivariateSpline(x,y, s=0.02)\n",
    "    l = final_cycle_col\n",
    "    x_range = np.linspace(x[0],x[l-1],1000)\n",
    "    plt.subplot(211)\n",
    "    plt.plot(x,y, 'o', markersize=3, c=colors[-1], label=plasmids[-1])\n",
    "    plt.plot(x_range, y_spl(x_range), '-', c=colors[-1], label=plasmids[-1])\n",
    "    plt.xlabel('time / h')\n",
    "    plt.ylabel('OD600')\n",
    "    #Derivative  \n",
    "    deriv = y_spl.derivative()(x_range)\n",
    "    #2nd Derivative  \n",
    "    secnd_deriv = y_spl.derivative(n=2)(x_range)\n",
    "    #Plot derivative\n",
    "    plt.subplot(212)\n",
    "    plt.plot(x_range, deriv, c=colors[-1], label=plasmids[-1])\n",
    "    plt.xlabel('time / h')\n",
    "    plt.ylabel('dOD600')\n",
    "    dmax = max(deriv)\n",
    "    if dmax > 0.005:\n",
    "        cp = x_range[np.argmax(deriv)]\n",
    "    else:\n",
    "        cp = np.nan\n",
    "    data3.at[index,'growth_t']=cp\n",
    "    try:\n",
    "        data3.at[index,'growthrate']=math.log(y_spl(x_range[np.argmax(deriv)])/y_spl(x_range)[0])/cp\n",
    "    except ValueError:\n",
    "        data3.at[index,'growthrate']=math.log(y_spl(x_range[np.argmax(deriv)])/(-1*y_spl(x_range)[0]))/cp\n",
    "\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "by_label = dict(zip(labels, handles))\n",
    "plt.legend(by_label.values(), by_label.keys(),bbox_to_anchor = (2, 3.2),ncol=10)\n",
    "plt.show()\n",
    "\n",
    "for index, row in data3.iterrows():\n",
    "    slope = 0\n",
    "    if max(row[:-4]) > 0.1:\n",
    "        b=max(row[:-4])\n",
    "        a = min(i for i in row[:row[row == b].index[0]] if i > 0.05)\n",
    "        odt1=row[row == a].index[0]\n",
    "        if max(row[:row[row == b].index[0]]) < 0.3:\n",
    "            a = max(row[:row[row == b].index[0]])\n",
    "            odt2=row[row == a].index[0]-1\n",
    "        else:\n",
    "            a = min(i for i in row[:row[row == b].index[0]] if i > 0.3)\n",
    "            odt2=row[row == a].index[0]-1\n",
    "        odt1=1\n",
    "        odt2=time.index(30.71)\n",
    "        delta_t=(data.loc['Time'][odt2]-data.loc['Time'][odt1-1])/3600\n",
    "        slope=math.log(row[odt2]/0.05)/delta_t\n",
    "    data3.at[index,'slope']=slope\n",
    "\n",
    "rows2 = pd.DataFrame()\n",
    "growth_t2 = pd.DataFrame()\n",
    "for i in to_plot:\n",
    "    rows = pd.DataFrame()\n",
    "    rows[i] = data3.loc[data3['plasmid'] == i]['slope']\n",
    "    rows=rows.reset_index()\n",
    "    rows2[i]=rows[i]\n",
    "    \n",
    "    growth_t = pd.DataFrame()\n",
    "    growth_t[i] = data3.loc[data3['plasmid'] == i]['growth_t']\n",
    "    growth_t=growth_t.reset_index()\n",
    "    growth_t2[i]=growth_t[i]\n",
    "\n",
    "rows2.plot.box(figsize=(16,6))\n",
    "plt.ylabel('growth rate')\n",
    "plt.xticks(rotation=90)\n",
    "growth_t2.plot.box(figsize=(16,6))\n",
    "plt.ylabel('growth time')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dc53c6a1-322c-4e04-9a90-a461a9125818",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FYN_WT</th>\n",
       "      <th>B7</th>\n",
       "      <th>G7</th>\n",
       "      <th>C7_prev</th>\n",
       "      <th>A7</th>\n",
       "      <th>D10</th>\n",
       "      <th>GRB2_core</th>\n",
       "      <th>E1_prev</th>\n",
       "      <th>C9_prev</th>\n",
       "      <th>D7</th>\n",
       "      <th>E11_prev</th>\n",
       "      <th>B12</th>\n",
       "      <th>C2</th>\n",
       "      <th>G10</th>\n",
       "      <th>A9</th>\n",
       "      <th>D8</th>\n",
       "      <th>C8</th>\n",
       "      <th>D3</th>\n",
       "      <th>C7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.162337</td>\n",
       "      <td>0.074336</td>\n",
       "      <td>0.093548</td>\n",
       "      <td>0.128422</td>\n",
       "      <td>0.159626</td>\n",
       "      <td>0.123776</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.102572</td>\n",
       "      <td>0.067167</td>\n",
       "      <td>0.065802</td>\n",
       "      <td>0.127517</td>\n",
       "      <td>0.061673</td>\n",
       "      <td>0.066507</td>\n",
       "      <td>0.070781</td>\n",
       "      <td>0.134315</td>\n",
       "      <td>0.046070</td>\n",
       "      <td>0.068946</td>\n",
       "      <td>0.071609</td>\n",
       "      <td>0.152887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.170119</td>\n",
       "      <td>0.074612</td>\n",
       "      <td>0.065494</td>\n",
       "      <td>0.196823</td>\n",
       "      <td>0.137877</td>\n",
       "      <td>0.102826</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.121062</td>\n",
       "      <td>0.064298</td>\n",
       "      <td>0.118961</td>\n",
       "      <td>0.118578</td>\n",
       "      <td>0.112264</td>\n",
       "      <td>0.068892</td>\n",
       "      <td>0.064155</td>\n",
       "      <td>0.080053</td>\n",
       "      <td>0.078426</td>\n",
       "      <td>0.054538</td>\n",
       "      <td>0.059339</td>\n",
       "      <td>0.098016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.193397</td>\n",
       "      <td>0.037828</td>\n",
       "      <td>0.071700</td>\n",
       "      <td>0.112425</td>\n",
       "      <td>0.094569</td>\n",
       "      <td>0.062595</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.188495</td>\n",
       "      <td>0.064154</td>\n",
       "      <td>0.067043</td>\n",
       "      <td>0.134203</td>\n",
       "      <td>0.061400</td>\n",
       "      <td>0.064526</td>\n",
       "      <td>0.078666</td>\n",
       "      <td>0.053438</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.107260</td>\n",
       "      <td>0.070609</td>\n",
       "      <td>0.168793</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     FYN_WT        B7        G7   C7_prev        A7       D10  GRB2_core  \\\n",
       "0  0.162337  0.074336  0.093548  0.128422  0.159626  0.123776        NaN   \n",
       "1  0.170119  0.074612  0.065494  0.196823  0.137877  0.102826        NaN   \n",
       "2  0.193397  0.037828  0.071700  0.112425  0.094569  0.062595        NaN   \n",
       "\n",
       "    E1_prev   C9_prev        D7  E11_prev       B12        C2       G10  \\\n",
       "0  0.102572  0.067167  0.065802  0.127517  0.061673  0.066507  0.070781   \n",
       "1  0.121062  0.064298  0.118961  0.118578  0.112264  0.068892  0.064155   \n",
       "2  0.188495  0.064154  0.067043  0.134203  0.061400  0.064526  0.078666   \n",
       "\n",
       "         A9        D8        C8        D3        C7  \n",
       "0  0.134315  0.046070  0.068946  0.071609  0.152887  \n",
       "1  0.080053  0.078426  0.054538  0.059339  0.098016  \n",
       "2  0.053438       NaN  0.107260  0.070609  0.168793  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_tecan=pd.DataFrame()\n",
    "for i in to_plot:\n",
    "    validation_tecan2=pd.DataFrame()\n",
    "    validation_tecan2[i]=data3.loc[data3['plasmid'] == i]['growthrate']\n",
    "    validation_tecan2=validation_tecan2.reset_index()\n",
    "    validation_tecan=pd.concat([validation_tecan, validation_tecan2[i]], axis=1)\n",
    "\n",
    "validation_tecan.columns=to_plot\n",
    "validation_tecan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "88a4d159-89c2-49ff-bd86-ebbd1afe981f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ids = pd.read_csv(path+\"Fig4/FYN-SH3_prediction_Tecan_ids.csv\", sep='\\t', lineterminator='\\n').drop('Unnamed: 0', axis=1)[['well','core','Nham_aa','predicted']]\n",
    "\n",
    "controls = ['pGJJ025', 'pGJJ034']\n",
    "\n",
    "for index, row in ids.iterrows():\n",
    "    ids.at[index,'predicted_fitness']=predicted_phenotypes_scaled.loc[predicted_phenotypes_scaled['core']==row.core].iloc[0]['mean']\n",
    "    ids.at[index,'predicted_fitness_std']=predicted_phenotypes_scaled.loc[predicted_phenotypes_scaled['core']==row.core].iloc[0]['std']\n",
    "    ids.at[index,'Nham_aa']=predicted_phenotypes_scaled.loc[predicted_phenotypes_scaled['core']==row.core].iloc[0]['Nham_aa']\n",
    "\n",
    "ids.head()\n",
    "\n",
    "DMS_tecan = pd.DataFrame(validation_tecan.mean(skipna=True))\n",
    "DMS_tecan = DMS_tecan.rename(columns={0: 'Tecan_Growth_T'})\n",
    "DMS_tecan['Tecan_Growth_T_std']=validation_tecan.std(skipna=True)\n",
    "DMS_tecan = DMS_tecan.reset_index()\n",
    "\n",
    "DMS_tecan = DMS_tecan.merge(ids, left_on=['index'], right_on=['well'], how='left').drop('index', axis=1)\n",
    "\n",
    "DMS_tecan = DMS_tecan.dropna()\n",
    "\n",
    "for index, row in DMS_tecan.iterrows():\n",
    "    if row.predicted == True:\n",
    "        DMS_tecan.at[index,'In training set']=False\n",
    "    else:\n",
    "        DMS_tecan.at[index,'In training set']=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c1c45cd1-747f-4dec-ad7a-36ad09a7fca3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 160x160 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from scipy.stats import pearsonr\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "DMS_tecan=DMS_tecan.rename(columns={'Nham_aa': 'Number of mutations'})\n",
    "DMS_tecan=DMS_tecan.sort_values('predicted_fitness')\n",
    "\n",
    "symbols=['o','X','s','P','D']\n",
    "markers={}\n",
    "for status, symbol in zip([0,4,5,6,7], symbols):\n",
    "    markers[status]=symbol\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(1.6,1.6))\n",
    "\n",
    "plt.errorbar('predicted_fitness', 'Tecan_Growth_T', 'Tecan_Growth_T_std','predicted_fitness_std', elinewidth=0.5, capsize=1, capthick=0.5, markersize=0, linewidth=0,data=DMS_tecan.loc[DMS_tecan['In training set']==True], color=matplotlib.cm.get_cmap('Set2')(1), label='')\n",
    "plt.errorbar('predicted_fitness', 'Tecan_Growth_T', 'Tecan_Growth_T_std','predicted_fitness_std', elinewidth=0.5, capsize=1, capthick=0.5, markersize=0, linewidth=0,data=DMS_tecan.loc[DMS_tecan['In training set']==False], color=matplotlib.cm.get_cmap('Set2')(0), label='')\n",
    "\n",
    "sns.scatterplot(x='predicted_fitness', y='Tecan_Growth_T', data=DMS_tecan, hue='In training set', style='Number of mutations', palette='Set2', ax=ax, s=25, linewidth=0, zorder=10)\n",
    "\n",
    "x = DMS_tecan[['predicted_fitness']].to_numpy()\n",
    "y = DMS_tecan[['Tecan_Growth_T']].to_numpy()\n",
    "\n",
    "ax.annotate('n = '+f\"{len(DMS_tecan.index):,}\", (0,0.02), fontsize=6)\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(x, y)\n",
    "\n",
    "y_pred = model.predict(x)\n",
    "r,p = pearsonr(y.ravel(),y_pred.ravel())\n",
    "\n",
    "\n",
    "x_notintraining = DMS_tecan.loc[DMS_tecan['In training set']==False][['predicted_fitness']].to_numpy()\n",
    "y_notintraining = DMS_tecan.loc[DMS_tecan['In training set']==False][['Tecan_Growth_T']].to_numpy()\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(x_notintraining, y_notintraining)\n",
    "\n",
    "y_pred_notintraining = model.predict(x_notintraining)\n",
    "r_predicted,p_predicted = pearsonr(y_notintraining.ravel(),y_pred_notintraining.ravel())\n",
    "\n",
    "plt.plot(x, y_pred, color='Gray', linewidth=1, linestyle='--')\n",
    "plt.legend(bbox_to_anchor=(1.2,1), fontsize=6,frameon=False, handletextpad=0.1, ncols=1)\n",
    "\n",
    "plt.xlabel('Predicted abundance fitness', fontsize=7)\n",
    "plt.ylabel('Individual measurement\\nGrowth rate', fontsize=7)\n",
    "plt.yticks(ticks=np.arange(0.05,0.25,0.05), fontsize=6)\n",
    "plt.xticks(ticks=np.arange(-1,0.25,0.25),fontsize=6)\n",
    "ax.spines[['right', 'top']].set_visible(False)\n",
    "\n",
    "leg = ax.get_legend()\n",
    "for t in leg.get_texts():\n",
    "    if t.get_text() in ['Number of mutations', 'In training set']:\n",
    "        #t.set_weight('bold')\n",
    "        t.set_fontsize(7)\n",
    "        t.set_ha('left')\n",
    "        t.set_position((-19,0))\n",
    "\n",
    "plt.annotate('R = '+str(round(r,2))+' (all variants, n = '+str(len(x))+')\\np-val = '+'{:0.2e}'.format(p)+' (all variants, n = '+str(len(x))+')\\n\\nR = '+str(round(r_predicted,2))+' (variants not in training set, n = '+str(len(x_notintraining))+')\\np-val = '+'{:0.2e}'.format(p_predicted)+' (variants not in training set, n = '+str(len(x_notintraining))+')',xy=(-1.1,0.24),fontsize=7, annotation_clip=False)\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "#plt.savefig('Fitness_measured_in_permilib_vs_predicted_from_coreDTSlibii.svg', dpi=300, transparent=True, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "af453445-f263-4f7b-aad3-ed1178f35402",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 100x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from scipy.stats import pearsonr\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import ast\n",
    "\n",
    "\n",
    "predicted_vs_measured=DMS_tecan.sort_values('predicted_fitness',ascending=True).copy()\n",
    "predicted_vs_measured=predicted_vs_measured.rename(columns={'Observed': 'In training set'})\n",
    "    \n",
    "fig, ax = plt.subplots(figsize=(1,1))\n",
    "\n",
    "sns.scatterplot(x='predicted_fitness', y='Tecan_Growth_T', data=predicted_vs_measured, hue='In training set', palette=['#BB202E','black'], ax=ax, s=0, markers=markers, edgecolor='k', linewidth=0.1)\n",
    "plt.errorbar('predicted_fitness', 'Tecan_Growth_T', 'Tecan_Growth_T_std','predicted_fitness_std', elinewidth=0.1, capsize=0.3, capthick=0.1, markersize=0, linewidth=0,data=predicted_vs_measured.loc[predicted_vs_measured['In training set']==False], color='#BB202E', label='')\n",
    "plt.errorbar('predicted_fitness', 'Tecan_Growth_T', 'Tecan_Growth_T_std','predicted_fitness_std', elinewidth=0.1, capsize=0.3, capthick=0.1, markersize=0, linewidth=0,data=predicted_vs_measured.loc[predicted_vs_measured['In training set']==True], color='black', label='')\n",
    "\n",
    "\n",
    "x = predicted_vs_measured[['predicted_fitness']].to_numpy()\n",
    "y = predicted_vs_measured[['Tecan_Growth_T']].to_numpy()\n",
    "\n",
    "#ax.annotate('n = '+f\"{len(predicted_vs_measured.loc[predicted_vs_measured['permi_lib_gr'].notnull()].index):,}\", (-0.3,-1.05), fontsize=6)\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(x, y)\n",
    "\n",
    "y_pred = model.predict(x)\n",
    "r,p = pearsonr(y.ravel(),y_pred.ravel())\n",
    "\n",
    "plt.plot(x, y_pred, color='Gray', linewidth=1, linestyle='--')\n",
    "plt.text(-0.45,0.38,'All variants | n = '+str(len(x))+'\\nR = '+str(round(r,2))+ ' | p-val = '+'{:0.2e}'.format(p), fontsize=7, color='black', horizontalalignment='center')\n",
    "bbox_to_anchor=(0.4,1.3)\n",
    "legend0=plt.legend(bbox_to_anchor=bbox_to_anchor,fontsize=6,frameon=False, handletextpad=0.1, title='In training set')\n",
    "plt.xlabel('Abundance fitness\\n(predicted from core energy model)', fontsize=7)\n",
    "plt.ylabel('Individual measurement\\nGrowth rate', fontsize=7)\n",
    "plt.yticks(ticks=np.arange(0.05,0.25,0.05), fontsize=6)\n",
    "ax.set_xticks(np.arange(-1,0.5,0.5))\n",
    "ax.set_xticklabels(np.arange(-1,0.5,0.5), fontsize=6)\n",
    "ax.spines[['right', 'top']].set_visible(False)\n",
    "\n",
    "def label_point(x, y, val1, val2, ax, colors):\n",
    "    a = pd.concat({'x': x, 'y': y, 'val1': val1, 'val2': val2}, axis=1)\n",
    "    col = a.iloc[0]['x']\n",
    "    counter=0\n",
    "    for index, row in a.iterrows():\n",
    "        if row.val2 == False:\n",
    "            ax.text(row['x'], row['y'], str(int(row['val1'])), fontsize=6, color=colors[0], ha='center', va='center')\n",
    "        else:\n",
    "            ax.text(row['x'], row['y'], str(int(row['val1'])), fontsize=6, color=colors[1], ha='center', va='center')\n",
    "\n",
    "#colors=list(sns.color_palette('ocean_r',2))\n",
    "colors=['#8B0000','black']\n",
    "\n",
    "\n",
    "label_point(pd.Series(x.ravel()).loc[pd.Series(y.ravel()).notnull()], pd.Series(y.ravel()).loc[pd.Series(y.ravel()).notnull()], predicted_vs_measured.reset_index()['Number of mutations'], predicted_vs_measured.reset_index()['In training set'], plt.gca(), colors)\n",
    "\n",
    "legend_labels=[]\n",
    "for i in legend0.get_texts():\n",
    "    x = predicted_vs_measured.loc[predicted_vs_measured['In training set']==ast.literal_eval(i.get_text())][['predicted_fitness']].to_numpy()\n",
    "    y = predicted_vs_measured.loc[predicted_vs_measured['In training set']==ast.literal_eval(i.get_text())][['Tecan_Growth_T']].to_numpy()\n",
    "    model.fit(x, y)\n",
    "\n",
    "    y_pred = model.predict(x)\n",
    "    r,p = pearsonr(y.ravel(),y_pred.ravel())\n",
    "    \n",
    "    legend_labels.append(i.get_text()+' | n = '+str(len(x))+'\\nR = '+str(round(r,2))+ ' | p-val = '+'{:0.2e}'.format(p))\n",
    "    #legend_labels.append(i.get_text())\n",
    "\n",
    "handles, labels= ax.get_legend_handles_labels()\n",
    "labels=legend_labels\n",
    "legend=ax.legend(handles,labels,bbox_to_anchor=(1.3,2), fontsize=6,frameon=False, handletextpad=0.1, title='In training set')\n",
    "legend.get_title().set_fontsize('7')\n",
    "\n",
    "\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "#plt.savefig('Fitness_measured_in_permilib_vs_predicted_from_coreDTSlib.svg', dpi=300, transparent=True, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1ed7cb1-0add-4939-bce7-022661dce26e",
   "metadata": {},
   "source": [
    "## Panel g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1e05908d-c884-4867-b4f3-06347772fa36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correct indels in suppressor sequences\n",
    "\n",
    "import ast\n",
    "import pandas as pd\n",
    "\n",
    "all_permissivity_seqs=pd.read_csv(path+'Fig6/Suppressor_library_allseqs.txt', sep='\\t')\n",
    "\n",
    "for index, row in all_permissivity_seqs.iterrows():\n",
    "    if row.mutations.startswith('[')==True:\n",
    "        mutations = ast.literal_eval(row.mutations)\n",
    "        deletions=[]\n",
    "        for mutation in mutations:\n",
    "            if mutation[-1]=='-':\n",
    "                deletions.append(mutation[1:-1])\n",
    "        if len(deletions)>0:\n",
    "            all_permissivity_seqs.at[index,'deletions']=','.join(deletions)\n",
    "        full_variant=row.variant\n",
    "        for deletion in deletions:\n",
    "            full_variant = full_variant[:int(deletion)-1]+'-'+full_variant[int(deletion)-1:]\n",
    "        all_permissivity_seqs.at[index,'full_variant']=full_variant\n",
    "        all_permissivity_seqs.at[index,'full_variant_len']=len(full_variant)\n",
    "\n",
    "\n",
    "permi_variants = pd.read_csv(path+'Fig6/FYN_suppressor_fitness_replicates.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b94c8ce5-167d-4fe0-be82-e19298835acf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from seaborn import kdeplot\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def normalize(values, bounds):\n",
    "    return [bounds['desired']['lower'] + (x - bounds['actual']['lower']) * (bounds['desired']['upper'] - bounds['desired']['lower']) / (bounds['actual']['upper'] - bounds['actual']['lower']) for x in values]\n",
    "\n",
    "permi_variants_scaled = permi_variants.copy()\n",
    "\n",
    "arbitraryWT='FLFFVIV'\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "my_kde = kdeplot(data=permi_variants.loc[permi_variants['fitness']<-0.5], x='fitness', ax=ax)\n",
    "\n",
    "lines = my_kde.get_lines()\n",
    "plt.close()\n",
    "for line in lines:\n",
    "    x, y = line.get_data()\n",
    "    mode=(x[np.argmax(y)])\n",
    "    ax.axvline(x[np.argmax(y)], ls='--')\n",
    "\n",
    "permi_variants_scaled['fitness']=normalize(list(permi_variants_scaled['fitness']),{'actual': {'lower': mode, 'upper': permi_variants_scaled.loc[permi_variants_scaled['Nham_aa']==0]['fitness'].iloc[0]}, 'desired': {'lower': -1, 'upper': 0}})\n",
    "permi_variants_scaled['fitness1_uncorr']=normalize(list(permi_variants_scaled['fitness1_uncorr']),{'actual': {'lower': mode, 'upper': permi_variants_scaled.loc[permi_variants_scaled['Nham_aa']==0]['fitness1_uncorr'].iloc[0]}, 'desired': {'lower': -1, 'upper': 0}})\n",
    "permi_variants_scaled['fitness2_uncorr']=normalize(list(permi_variants_scaled['fitness2_uncorr']),{'actual': {'lower': mode, 'upper': permi_variants_scaled.loc[permi_variants_scaled['Nham_aa']==0]['fitness2_uncorr'].iloc[0]}, 'desired': {'lower': -1, 'upper': 0}})\n",
    "permi_variants_scaled['fitness3_uncorr']=normalize(list(permi_variants_scaled['fitness3_uncorr']),{'actual': {'lower': mode, 'upper': permi_variants_scaled.loc[permi_variants_scaled['Nham_aa']==0]['fitness3_uncorr'].iloc[0]}, 'desired': {'lower': -1, 'upper': 0}})\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "my_kde = kdeplot(data=permi_variants_scaled, x='fitness', ax=ax)\n",
    "\n",
    "lines = my_kde.get_lines()\n",
    "\n",
    "for line in lines:\n",
    "    x, y = line.get_data()\n",
    "\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fc65c651-d270-4fe2-81a0-113a9de5b63a",
   "metadata": {},
   "outputs": [],
   "source": [
    "WTs_bearing_natural_DTS_core_queries=pd.read_csv(path+'Fig6/Structural_homologs_carrying_detrimental_cores_in_FYN.txt',sep='\\t')\n",
    "\n",
    "for index, row in permi_variants_scaled.iterrows():\n",
    "    if row.aa_seq in list(WTs_bearing_natural_DTS_core_queries['DSSP_boundaries']) or row.aa_seq in list(WTs_bearing_natural_DTS_core_queries['Pro_mutant_1']) or row.aa_seq in list(WTs_bearing_natural_DTS_core_queries['Pro_mutant_2']) or row.aa_seq in list(WTs_bearing_natural_DTS_core_queries['Pro_mutant_3']):\n",
    "        permi_variants_scaled.at[index,'other_natural_SH3s']=True\n",
    "        if row.aa_seq in list(WTs_bearing_natural_DTS_core_queries['DSSP_boundaries']):\n",
    "            permi_variants_scaled.at[index,'Pro_mutant']=False\n",
    "        else:\n",
    "            permi_variants_scaled.at[index,'Pro_mutant']=True\n",
    "\n",
    "other_natural_SH3s = permi_variants_scaled.loc[permi_variants_scaled['other_natural_SH3s']==True].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "df40b92d-1717-4f51-9f06-48613443e6a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find core and hamming distances\n",
    "\n",
    "fyn_core_pos = [2,16,18,24,26,48,53]\n",
    "fyn_wt_core = 'FLFFIIV'\n",
    "fyn_permi_variants = permi_variants_scaled.loc[permi_variants_scaled['other_natural_SH3s']!=True].copy()\n",
    "fyn_wt_seq = permi_variants_scaled.loc[permi_variants_scaled['Nham_aa']==0].iloc[0]['aa_seq']\n",
    "for index, row in fyn_permi_variants.iterrows():\n",
    "    fyn_permi_variants.at[index,'full_variant']=all_permissivity_seqs.loc[all_permissivity_seqs['variant']==row.aa_seq].iloc[0]['full_variant']\n",
    "    core=[]\n",
    "    for i in fyn_core_pos:\n",
    "        core.append(all_permissivity_seqs.loc[all_permissivity_seqs['variant']==row.aa_seq].iloc[0]['full_variant'][i])\n",
    "    fyn_permi_variants.at[index,'core'] = ''.join(core)\n",
    "    fyn_permi_variants.at[index, 'Nham_core'] = sum(1 for a,b in zip(core, fyn_wt_core) if a!=b)\n",
    "    fyn_permi_variants.at[index, 'Nham_aa'] = sum(1 for a,b in zip(all_permissivity_seqs.loc[all_permissivity_seqs['variant']==row.aa_seq].iloc[0]['full_variant'], fyn_wt_seq) if a!=b)\n",
    "    if len(row.aa_seq) != len(permi_variants.loc[permi_variants['Nham_aa']==0].iloc[0]['aa_seq']):\n",
    "        fyn_permi_variants.at[index,'indel']=True\n",
    "    fyn_permi_variants.at[index,'len']=len(row.aa_seq)\n",
    "\n",
    "for index, row in fyn_permi_variants.iterrows():\n",
    "    mutations=[]\n",
    "    iterator=1\n",
    "    for wt, mut in zip(fyn_wt_seq, row.full_variant):\n",
    "        if wt != mut:\n",
    "            mutations.append(wt+str(iterator)+mut)\n",
    "        iterator+=1\n",
    "    fyn_permi_variants.at[index,'all_mutations']=','.join(mutations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8d69cc96-d06d-41c4-8ffa-e43fe9eceb37",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "for index, row in predicted_phenotypes_scaled.iterrows():\n",
    "    try:\n",
    "        predicted_phenotypes_scaled.at[index,'permi_lib_fitness'] = fyn_permi_variants.loc[fyn_permi_variants['aa_seq']==row.aa_seq].iloc[0]['fitness']\n",
    "        predicted_phenotypes_scaled.at[index,'permi_lib_sigma']   = fyn_permi_variants.loc[fyn_permi_variants['aa_seq']==row.aa_seq].iloc[0]['sigma']\n",
    "        predicted_phenotypes_scaled.at[index,'permi_lib_gr']      = fyn_permi_variants.loc[fyn_permi_variants['aa_seq']==row.aa_seq].iloc[0]['growthrate']\n",
    "    except IndexError:\n",
    "        continue\n",
    "        \n",
    "for index, row in predicted_phenotypes_scaled.loc[predicted_phenotypes_scaled['permi_lib_gr'].notnull()].iterrows():\n",
    "    if math.isnan(row.fitness) == True:\n",
    "        predicted_phenotypes_scaled.at[index,'Observed']=False\n",
    "    else:\n",
    "        predicted_phenotypes_scaled.at[index,'Observed']=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "08eb9e4c-dd66-4d2c-9343-41e14d6cb025",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 100x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from scipy.stats import pearsonr\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import ast\n",
    "\n",
    "\n",
    "predicted_vs_measured=predicted_phenotypes_scaled.sort_values('mean',ascending=True).copy()\n",
    "predicted_vs_measured=predicted_vs_measured.rename(columns={'Observed': 'In training set'})\n",
    "    \n",
    "fig, ax = plt.subplots(figsize=(1,1))\n",
    "\n",
    "sns.scatterplot(x='mean', y='permi_lib_fitness', data=predicted_vs_measured.loc[predicted_vs_measured['permi_lib_gr'].notnull()], hue='In training set', palette=['#BB202E','black'], ax=ax, s=0, markers=markers, edgecolor='k', linewidth=0.1)\n",
    "plt.errorbar('mean', 'permi_lib_fitness', 'permi_lib_sigma','std', elinewidth=0.1, capsize=1, capthick=0.3, markersize=0.1, linewidth=0,data=predicted_vs_measured.loc[predicted_vs_measured['permi_lib_gr'].notnull()].loc[predicted_vs_measured['In training set']==False], color='#BB202E', label='')\n",
    "plt.errorbar('mean', 'permi_lib_fitness', 'permi_lib_sigma','std', elinewidth=0.1, capsize=1, capthick=0.3, markersize=0.1, linewidth=0,data=predicted_vs_measured.loc[predicted_vs_measured['permi_lib_gr'].notnull()].loc[predicted_vs_measured['In training set']==True], color='black', label='')\n",
    "\n",
    "\n",
    "x = predicted_vs_measured.loc[predicted_vs_measured['permi_lib_gr'].notnull()][['mean']].to_numpy()\n",
    "y = predicted_vs_measured.loc[predicted_vs_measured['permi_lib_gr'].notnull()][['permi_lib_fitness']].to_numpy()\n",
    "\n",
    "#ax.annotate('n = '+f\"{len(predicted_vs_measured.loc[predicted_vs_measured['permi_lib_gr'].notnull()].index):,}\", (-0.3,-1.05), fontsize=6)\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(x, y)\n",
    "\n",
    "y_pred = model.predict(x)\n",
    "r,p = pearsonr(y.ravel(),y_pred.ravel())\n",
    "\n",
    "plt.plot(x, y_pred, color='Gray', linewidth=1, linestyle='--')\n",
    "plt.text(-0.45,1.4,'All variants | n = '+str(len(x))+'\\nR = '+str(round(r,2))+ ' | p-val = '+'{:0.2e}'.format(p), fontsize=7, color='black', horizontalalignment='center')\n",
    "bbox_to_anchor=(0.4,1.3)\n",
    "legend0=plt.legend(bbox_to_anchor=bbox_to_anchor,fontsize=6,frameon=False, handletextpad=0.1, title='In training set')\n",
    "plt.xlabel('Abundance fitness\\n(predicted from core energy model)', fontsize=7)\n",
    "plt.ylabel('Abundance fitness\\n(observed in permissivity lib.)', fontsize=7)\n",
    "ax.set_xticks(np.arange(-1,0.5,0.5))\n",
    "ax.set_xticklabels(np.arange(-1,0.5,0.5), fontsize=6)\n",
    "ax.set_yticks(np.arange(-1,0.5,0.5))\n",
    "ax.set_yticklabels(np.arange(-1,0.5,0.5), fontsize=6)\n",
    "ax.spines[['right', 'top']].set_visible(False)\n",
    "\n",
    "def label_point(x, y, val1, val2, ax, colors):\n",
    "    a = pd.concat({'x': x, 'y': y, 'val1': val1, 'val2': val2}, axis=1)\n",
    "    col = a.iloc[0]['x']\n",
    "    counter=0\n",
    "    for index, row in a.iterrows():\n",
    "        if row.val2 == False:\n",
    "            ax.text(row['x'], row['y'], str(int(row['val1'])), fontsize=6, color=colors[0], ha='center', va='center')\n",
    "        else:\n",
    "            ax.text(row['x'], row['y'], str(int(row['val1'])), fontsize=6, color=colors[1], ha='center', va='center')\n",
    "\n",
    "#colors=list(sns.color_palette('ocean_r',2))\n",
    "colors=['#8B0000','black']\n",
    "\n",
    "\n",
    "label_point(pd.Series(x.ravel()).loc[pd.Series(y.ravel()).notnull()], pd.Series(y.ravel()).loc[pd.Series(y.ravel()).notnull()], predicted_vs_measured.loc[predicted_vs_measured['permi_lib_gr'].notnull()].reset_index()['Nham_aa'], predicted_vs_measured.loc[predicted_vs_measured['permi_lib_gr'].notnull()].reset_index()['In training set'], plt.gca(), colors)\n",
    "\n",
    "legend_labels=[]\n",
    "for i in legend0.get_texts():\n",
    "    x = predicted_vs_measured.loc[predicted_vs_measured['permi_lib_gr'].notnull()].loc[predicted_vs_measured['In training set']==ast.literal_eval(i.get_text())][['mean']].to_numpy()\n",
    "    y = predicted_vs_measured.loc[predicted_vs_measured['permi_lib_gr'].notnull()].loc[predicted_vs_measured['In training set']==ast.literal_eval(i.get_text())][['permi_lib_fitness']].to_numpy()\n",
    "    model.fit(x, y)\n",
    "\n",
    "    y_pred = model.predict(x)\n",
    "    r,p = pearsonr(y.ravel(),y_pred.ravel())\n",
    "    \n",
    "    legend_labels.append(i.get_text()+' | n = '+str(len(x))+'\\nR = '+str(round(r,2))+ ' | p-val = '+'{:0.2e}'.format(p))\n",
    "    #legend_labels.append(i.get_text())\n",
    "\n",
    "handles, labels= ax.get_legend_handles_labels()\n",
    "labels=legend_labels\n",
    "legend=ax.legend(handles,labels,bbox_to_anchor=(1.3,2), fontsize=6,frameon=False, handletextpad=0.1, title='In training set')\n",
    "#legend=plt.legend()\n",
    "legend.get_title().set_fontsize('7')\n",
    "legend._legend_box.align = \"center\"\n",
    "\n",
    "\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "#plt.savefig('Fitness_measured_in_permilib_vs_predicted_from_coreDTSlib_simplified.svg', dpi=300, transparent=True, bbox_inches='tight')"
   ]
  }
 ],
 "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}