├── .ipynb_checkpoints
    └── DeepSP_predictor-checkpoint.ipynb
├── Conv1D_regressionSAPpos.json
├── Conv1D_regressionSCMneg.json
├── Conv1D_regressionSCMpos.json
├── Conv1D_regression_SAPpos.h5
├── Conv1D_regression_SCMneg.h5
├── Conv1D_regression_SCMpos.h5
├── DeepSP-app.py
├── DeepSP_input.csv
├── DeepSP_model_train.py
├── DeepSP_predictor.ipynb
├── LICENSE
├── README.md
├── data
    ├── Deep_SAPpos_data.txt
    ├── Deep_SCMneg_data.txt
    ├── Deep_SCMpos_data.txt
    ├── ERR4082227_1482_rank1_imgt_scheme.pdb
    └── ERR4082243_2914_rank1_imgt_scheme.pdb
├── deepsp_predictor.py
└── environment.yml


/.ipynb_checkpoints/DeepSP_predictor-checkpoint.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {
   6 |     "id": "CqYhZfJnSvsH"
   7 |    },
   8 |    "source": [
   9 |     "Install and Import Necessary Libraries"
  10 |    ]
  11 |   },
  12 |   {
  13 |    "cell_type": "code",
  14 |    "execution_count": 1,
  15 |    "metadata": {
  16 |     "colab": {
  17 |      "base_uri": "https://localhost:8080/"
  18 |     },
  19 |     "id": "icCMgpaXYyFN",
  20 |     "outputId": "4fe01a84-464c-4248-8e1a-c1b671051b4c"
  21 |    },
  22 |    "outputs": [
  23 |     {
  24 |      "name": "stdout",
  25 |      "output_type": "stream",
  26 |      "text": [
  27 |       "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
  28 |       "\u001b[0m✨🍰✨ Everything looks OK!\n",
  29 |       "Channels:\n",
  30 |       " - bioconda\n",
  31 |       " - conda-forge\n",
  32 |       "Platform: linux-64\n",
  33 |       "Collecting package metadata (repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
  34 |       "Solving environment: - \b\b\\ \b\b| \b\bdone\n",
  35 |       "\n",
  36 |       "\n",
  37 |       "==> WARNING: A newer version of conda exists. <==\n",
  38 |       "    current version: 23.11.0\n",
  39 |       "    latest version: 24.3.0\n",
  40 |       "\n",
  41 |       "Please update conda by running\n",
  42 |       "\n",
  43 |       "    $ conda update -n base -c conda-forge conda\n",
  44 |       "\n",
  45 |       "\n",
  46 |       "\n",
  47 |       "## Package Plan ##\n",
  48 |       "\n",
  49 |       "  environment location: /usr/local\n",
  50 |       "\n",
  51 |       "  added / updated specs:\n",
  52 |       "    - anarci\n",
  53 |       "\n",
  54 |       "\n",
  55 |       "The following packages will be downloaded:\n",
  56 |       "\n",
  57 |       "    package                    |            build\n",
  58 |       "    ---------------------------|-----------------\n",
  59 |       "    anarci-2021.02.04          |     pyhdfd78af_0         1.1 MB  bioconda\n",
  60 |       "    biopython-1.83             |  py310h2372a71_0         2.6 MB  conda-forge\n",
  61 |       "    ca-certificates-2024.2.2   |       hbcca054_0         152 KB  conda-forge\n",
  62 |       "    certifi-2024.2.2           |     pyhd8ed1ab_0         157 KB  conda-forge\n",
  63 |       "    hmmer-3.4                  |       hdbdd923_1        11.1 MB  bioconda\n",
  64 |       "    libblas-3.9.0              |22_linux64_openblas          14 KB  conda-forge\n",
  65 |       "    libcblas-3.9.0             |22_linux64_openblas          14 KB  conda-forge\n",
  66 |       "    libgfortran-ng-13.2.0      |       h69a702a_6          24 KB  conda-forge\n",
  67 |       "    libgfortran5-13.2.0        |       h43f5ff8_6         1.4 MB  conda-forge\n",
  68 |       "    liblapack-3.9.0            |22_linux64_openblas          14 KB  conda-forge\n",
  69 |       "    libopenblas-0.3.27         |pthreads_h413a1c8_0         5.3 MB  conda-forge\n",
  70 |       "    numpy-1.26.4               |  py310hb13e2d6_0         6.7 MB  conda-forge\n",
  71 |       "    openssl-3.3.0              |       hd590300_0         2.8 MB  conda-forge\n",
  72 |       "    ------------------------------------------------------------\n",
  73 |       "                                           Total:        31.3 MB\n",
  74 |       "\n",
  75 |       "The following NEW packages will be INSTALLED:\n",
  76 |       "\n",
  77 |       "  anarci             bioconda/noarch::anarci-2021.02.04-pyhdfd78af_0 \n",
  78 |       "  biopython          conda-forge/linux-64::biopython-1.83-py310h2372a71_0 \n",
  79 |       "  hmmer              bioconda/linux-64::hmmer-3.4-hdbdd923_1 \n",
  80 |       "  libblas            conda-forge/linux-64::libblas-3.9.0-22_linux64_openblas \n",
  81 |       "  libcblas           conda-forge/linux-64::libcblas-3.9.0-22_linux64_openblas \n",
  82 |       "  libgfortran-ng     conda-forge/linux-64::libgfortran-ng-13.2.0-h69a702a_6 \n",
  83 |       "  libgfortran5       conda-forge/linux-64::libgfortran5-13.2.0-h43f5ff8_6 \n",
  84 |       "  liblapack          conda-forge/linux-64::liblapack-3.9.0-22_linux64_openblas \n",
  85 |       "  libopenblas        conda-forge/linux-64::libopenblas-0.3.27-pthreads_h413a1c8_0 \n",
  86 |       "  numpy              conda-forge/linux-64::numpy-1.26.4-py310hb13e2d6_0 \n",
  87 |       "\n",
  88 |       "The following packages will be UPDATED:\n",
  89 |       "\n",
  90 |       "  ca-certificates                     2023.11.17-hbcca054_0 --> 2024.2.2-hbcca054_0 \n",
  91 |       "  certifi                           2023.11.17-pyhd8ed1ab_0 --> 2024.2.2-pyhd8ed1ab_0 \n",
  92 |       "  openssl                                  3.2.0-hd590300_1 --> 3.3.0-hd590300_0 \n",
  93 |       "\n",
  94 |       "\n",
  95 |       "\n",
  96 |       "Downloading and Extracting Packages:\n",
  97 |       "hmmer-3.4            | 11.1 MB   | :   0% 0/1 [00:00<?, ?it/s]\n",
  98 |       "numpy-1.26.4         | 6.7 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\n",
  99 |       "\n",
 100 |       "libopenblas-0.3.27   | 5.3 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
 101 |       "\n",
 102 |       "\n",
 103 |       "openssl-3.3.0        | 2.8 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\n",
 104 |       "\n",
 105 |       "\n",
 106 |       "\n",
 107 |       "biopython-1.83       | 2.6 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
 108 |       "\n",
 109 |       "\n",
 110 |       "\n",
 111 |       "\n",
 112 |       "libgfortran5-13.2.0  | 1.4 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 113 |       "\n",
 114 |       "\n",
 115 |       "\n",
 116 |       "\n",
 117 |       "\n",
 118 |       "anarci-2021.02.04    | 1.1 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 119 |       "\n",
 120 |       "\n",
 121 |       "\n",
 122 |       "\n",
 123 |       "\n",
 124 |       "\n",
 125 |       "certifi-2024.2.2     | 157 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 126 |       "\n",
 127 |       "\n",
 128 |       "\n",
 129 |       "\n",
 130 |       "\n",
 131 |       "\n",
 132 |       "\n",
 133 |       "ca-certificates-2024 | 152 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 134 |       "\n",
 135 |       "\n",
 136 |       "\n",
 137 |       "\n",
 138 |       "\n",
 139 |       "\n",
 140 |       "\n",
 141 |       "\n",
 142 |       "libgfortran-ng-13.2. | 24 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 143 |       "\n",
 144 |       "\n",
 145 |       "\n",
 146 |       "\n",
 147 |       "\n",
 148 |       "\n",
 149 |       "\n",
 150 |       "\n",
 151 |       "\n",
 152 |       "libblas-3.9.0        | 14 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 153 |       "\n",
 154 |       "\n",
 155 |       "\n",
 156 |       "\n",
 157 |       "\n",
 158 |       "\n",
 159 |       "\n",
 160 |       "\n",
 161 |       "\n",
 162 |       "\n",
 163 |       "liblapack-3.9.0      | 14 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 164 |       "\n",
 165 |       "\n",
 166 |       "\n",
 167 |       "\n",
 168 |       "\n",
 169 |       "\n",
 170 |       "\n",
 171 |       "\n",
 172 |       "\n",
 173 |       "\n",
 174 |       "\n",
 175 |       "hmmer-3.4            | 11.1 MB   | :   0% 0.00140836123365361/1 [00:00<01:20, 80.97s/it]\n",
 176 |       "numpy-1.26.4         | 6.7 MB    | :   0% 0.002337542641177788/1 [00:00<00:59, 60.03s/it]\u001b[A\n",
 177 |       "\n",
 178 |       "libopenblas-0.3.27   | 5.3 MB    | :   0% 0.0029263690607916376/1 [00:00<00:52, 52.42s/it]\u001b[A\u001b[A\n",
 179 |       "\n",
 180 |       "\n",
 181 |       "\n",
 182 |       "biopython-1.83       | 2.6 MB    | :   1% 0.00597188359638319/1 [00:00<00:26, 26.37s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
 183 |       "\n",
 184 |       "\n",
 185 |       "hmmer-3.4            | 11.1 MB   | :  50% 0.5013765991806851/1 [00:00<00:00,  2.78it/s] \n",
 186 |       "numpy-1.26.4         | 6.7 MB    | :  40% 0.40439487692375736/1 [00:00<00:00,  2.02it/s] \u001b[A\n",
 187 |       "\n",
 188 |       "libopenblas-0.3.27   | 5.3 MB    | :  46% 0.45943994254428716/1 [00:00<00:00,  2.21it/s]  \u001b[A\u001b[A\n",
 189 |       "\n",
 190 |       "\n",
 191 |       "\n",
 192 |       "biopython-1.83       | 2.6 MB    | :  75% 0.752457333144282/1 [00:00<00:00,  3.57it/s]  \u001b[A\u001b[A\u001b[A\u001b[A\n",
 193 |       "\n",
 194 |       "\n",
 195 |       "hmmer-3.4            | 11.1 MB   | :  80% 0.7971324582479433/1 [00:00<00:00,  2.71it/s]\n",
 196 |       "numpy-1.26.4         | 6.7 MB    | :  86% 0.8625532345946039/1 [00:00<00:00,  3.03it/s] \u001b[A\n",
 197 |       "\n",
 198 |       "\n",
 199 |       "\n",
 200 |       "\n",
 201 |       "libgfortran5-13.2.0  | 1.4 MB    | :   1% 0.011357082649394437/1 [00:00<00:32, 33.16s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 202 |       "\n",
 203 |       "\n",
 204 |       "\n",
 205 |       "\n",
 206 |       "\n",
 207 |       "anarci-2021.02.04    | 1.1 MB    | :   1% 0.014093787688236882/1 [00:00<00:27, 28.15s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 208 |       "\n",
 209 |       "\n",
 210 |       "\n",
 211 |       "\n",
 212 |       "\n",
 213 |       "\n",
 214 |       "certifi-2024.2.2     | 157 KB    | :  10% 0.10204348557228184/1 [00:00<00:04,  5.03s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 215 |       "\n",
 216 |       "\n",
 217 |       "\n",
 218 |       "\n",
 219 |       "\n",
 220 |       "\n",
 221 |       "\n",
 222 |       "ca-certificates-2024 | 152 KB    | :  11% 0.10540943949765814/1 [00:00<00:04,  4.96s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 223 |       "\n",
 224 |       "\n",
 225 |       "\n",
 226 |       "\n",
 227 |       "\n",
 228 |       "\n",
 229 |       "\n",
 230 |       "\n",
 231 |       "libgfortran-ng-13.2. | 24 KB     | :  68% 0.6775007236488443/1 [00:00<00:00,  1.28it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 232 |       "\n",
 233 |       "\n",
 234 |       "\n",
 235 |       "\n",
 236 |       "\n",
 237 |       "\n",
 238 |       "\n",
 239 |       "\n",
 240 |       "\n",
 241 |       "libblas-3.9.0        | 14 KB     | : 100% 1.0/1 [00:00<00:00,  1.78it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 242 |       "\n",
 243 |       "\n",
 244 |       "\n",
 245 |       "\n",
 246 |       "\n",
 247 |       "\n",
 248 |       "\n",
 249 |       "\n",
 250 |       "\n",
 251 |       "\n",
 252 |       "liblapack-3.9.0      | 14 KB     | : 100% 1.0/1 [00:00<00:00,  1.76it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 253 |       "\n",
 254 |       "\n",
 255 |       "\n",
 256 |       "\n",
 257 |       "\n",
 258 |       "\n",
 259 |       "\n",
 260 |       "\n",
 261 |       "\n",
 262 |       "\n",
 263 |       "\n",
 264 |       "libcblas-3.9.0       | 14 KB     | : 100% 1.0/1 [00:00<00:00,  1.75it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 265 |       "\n",
 266 |       "\n",
 267 |       "openssl-3.3.0        | 2.8 MB    | : 100% 1.0/1 [00:00<00:00,  3.88it/s]               \u001b[A\u001b[A\u001b[A\n",
 268 |       "\n",
 269 |       "libopenblas-0.3.27   | 5.3 MB    | : 100% 1.0/1 [00:01<00:00,  1.54s/it]                \u001b[A\u001b[A\n",
 270 |       "\n",
 271 |       "libopenblas-0.3.27   | 5.3 MB    | : 100% 1.0/1 [00:01<00:00,  1.54s/it]\u001b[A\u001b[A\n",
 272 |       "\n",
 273 |       "\n",
 274 |       "\n",
 275 |       "biopython-1.83       | 2.6 MB    | : 100% 1.0/1 [00:01<00:00,  3.57it/s]              \u001b[A\u001b[A\u001b[A\u001b[A\n",
 276 |       "\n",
 277 |       "\n",
 278 |       "\n",
 279 |       "\n",
 280 |       "libgfortran5-13.2.0  | 1.4 MB    | : 100% 1.0/1 [00:01<00:00,  1.44s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 281 |       "\n",
 282 |       "\n",
 283 |       "\n",
 284 |       "\n",
 285 |       "libgfortran5-13.2.0  | 1.4 MB    | : 100% 1.0/1 [00:01<00:00,  1.44s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 286 |       "\n",
 287 |       "\n",
 288 |       "\n",
 289 |       "\n",
 290 |       "\n",
 291 |       "\n",
 292 |       "certifi-2024.2.2     | 157 KB    | : 100% 1.0/1 [00:01<00:00,  1.48s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 293 |       "\n",
 294 |       "\n",
 295 |       "\n",
 296 |       "\n",
 297 |       "\n",
 298 |       "\n",
 299 |       "certifi-2024.2.2     | 157 KB    | : 100% 1.0/1 [00:01<00:00,  1.48s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 300 |       "\n",
 301 |       "\n",
 302 |       "\n",
 303 |       "\n",
 304 |       "\n",
 305 |       "\n",
 306 |       "\n",
 307 |       "ca-certificates-2024 | 152 KB    | : 100% 1.0/1 [00:01<00:00,  1.52s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 308 |       "\n",
 309 |       "\n",
 310 |       "\n",
 311 |       "\n",
 312 |       "\n",
 313 |       "\n",
 314 |       "\n",
 315 |       "ca-certificates-2024 | 152 KB    | : 100% 1.0/1 [00:01<00:00,  1.52s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 316 |       "\n",
 317 |       "\n",
 318 |       "\n",
 319 |       "\n",
 320 |       "\n",
 321 |       "\n",
 322 |       "\n",
 323 |       "\n",
 324 |       "libgfortran-ng-13.2. | 24 KB     | : 100% 1.0/1 [00:01<00:00,  1.94s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 325 |       "\n",
 326 |       "\n",
 327 |       "\n",
 328 |       "\n",
 329 |       "\n",
 330 |       "\n",
 331 |       "\n",
 332 |       "\n",
 333 |       "libgfortran-ng-13.2. | 24 KB     | : 100% 1.0/1 [00:01<00:00,  1.94s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 334 |       "\n",
 335 |       "\n",
 336 |       "\n",
 337 |       "\n",
 338 |       "\n",
 339 |       "\n",
 340 |       "\n",
 341 |       "\n",
 342 |       "\n",
 343 |       "libblas-3.9.0        | 14 KB     | : 100% 1.0/1 [00:01<00:00,  1.78it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 344 |       "\n",
 345 |       "\n",
 346 |       "\n",
 347 |       "\n",
 348 |       "\n",
 349 |       "anarci-2021.02.04    | 1.1 MB    | : 100% 1.0/1 [00:02<00:00,  2.44s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 350 |       "\n",
 351 |       "\n",
 352 |       "\n",
 353 |       "\n",
 354 |       "\n",
 355 |       "anarci-2021.02.04    | 1.1 MB    | : 100% 1.0/1 [00:02<00:00,  2.44s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 356 |       "\n",
 357 |       "\n",
 358 |       "\n",
 359 |       "\n",
 360 |       "\n",
 361 |       "\n",
 362 |       "\n",
 363 |       "\n",
 364 |       "\n",
 365 |       "\n",
 366 |       "liblapack-3.9.0      | 14 KB     | : 100% 1.0/1 [00:02<00:00,  1.76it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 367 |       "\n",
 368 |       "\n",
 369 |       "\n",
 370 |       "\n",
 371 |       "\n",
 372 |       "\n",
 373 |       "\n",
 374 |       "\n",
 375 |       "\n",
 376 |       "\n",
 377 |       "\n",
 378 |       "libcblas-3.9.0       | 14 KB     | : 100% 1.0/1 [00:02<00:00,  1.75it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 379 |       "                                                                        \n",
 380 |       "                                                                        \u001b[A\n",
 381 |       "\n",
 382 |       "                                                                        \u001b[A\u001b[A\n",
 383 |       "\n",
 384 |       "\n",
 385 |       "                                                                        \u001b[A\u001b[A\u001b[A\n",
 386 |       "\n",
 387 |       "\n",
 388 |       "\n",
 389 |       "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\n",
 390 |       "\n",
 391 |       "\n",
 392 |       "\n",
 393 |       "\n",
 394 |       "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 395 |       "\n",
 396 |       "\n",
 397 |       "\n",
 398 |       "\n",
 399 |       "\n",
 400 |       "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 401 |       "\n",
 402 |       "\n",
 403 |       "\n",
 404 |       "\n",
 405 |       "\n",
 406 |       "\n",
 407 |       "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 408 |       "\n",
 409 |       "\n",
 410 |       "\n",
 411 |       "\n",
 412 |       "\n",
 413 |       "\n",
 414 |       "\n",
 415 |       "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 416 |       "\n",
 417 |       "\n",
 418 |       "\n",
 419 |       "\n",
 420 |       "\n",
 421 |       "\n",
 422 |       "\n",
 423 |       "\n",
 424 |       "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 425 |       "\n",
 426 |       "\n",
 427 |       "\n",
 428 |       "\n",
 429 |       "\n",
 430 |       "\n",
 431 |       "\n",
 432 |       "\n",
 433 |       "\n",
 434 |       "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 435 |       "\n",
 436 |       "\n",
 437 |       "\n",
 438 |       "\n",
 439 |       "\n",
 440 |       "\n",
 441 |       "\n",
 442 |       "\n",
 443 |       "\n",
 444 |       "\n",
 445 |       "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 446 |       "\n",
 447 |       "\n",
 448 |       "\n",
 449 |       "\n",
 450 |       "\n",
 451 |       "\n",
 452 |       "\n",
 453 |       "\n",
 454 |       "\n",
 455 |       "\n",
 456 |       "\n",
 457 |       "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 458 |       "Preparing transaction: - \b\b\\ \b\b| \b\bdone\n",
 459 |       "Verifying transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
 460 |       "Executing transaction: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n"
 461 |      ]
 462 |     }
 463 |    ],
 464 |    "source": [
 465 |     "!pip install -q condacolab\n",
 466 |     "import condacolab\n",
 467 |     "condacolab.install()\n",
 468 |     "\n",
 469 |     "!conda install -c bioconda anarci"
 470 |    ]
 471 |   },
 472 |   {
 473 |    "cell_type": "code",
 474 |    "execution_count": 1,
 475 |    "metadata": {
 476 |     "id": "SOqM10kcxHb9"
 477 |    },
 478 |    "outputs": [],
 479 |    "source": [
 480 |     "# Import libraries\n",
 481 |     "import numpy as np\n",
 482 |     "import pandas as pd\n",
 483 |     "import random\n",
 484 |     "import matplotlib.pyplot as plt\n",
 485 |     "\n",
 486 |     "from numpy.random import seed\n",
 487 |     "\n",
 488 |     "# Import machine learning libraries\n",
 489 |     "import tensorflow as tf\n",
 490 |     "from tensorflow.keras.models import model_from_json\n",
 491 |     "from tensorflow.keras.utils import plot_model\n",
 492 |     "\n",
 493 |     "import keras\n",
 494 |     "from keras.models import model_from_json\n",
 495 |     "from keras.callbacks import ModelCheckpoint\n",
 496 |     "from keras.optimizers import Adam\n",
 497 |     "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
 498 |     "\n",
 499 |     "from Bio import SeqIO\n",
 500 |     "from Bio.Seq import Seq\n",
 501 |     "from Bio.SeqRecord import SeqRecord"
 502 |    ]
 503 |   },
 504 |   {
 505 |    "cell_type": "markdown",
 506 |    "metadata": {
 507 |     "id": "nifzJMA-TkHp"
 508 |    },
 509 |    "source": [
 510 |     "Import dataset"
 511 |    ]
 512 |   },
 513 |   {
 514 |    "cell_type": "code",
 515 |    "execution_count": 4,
 516 |    "metadata": {
 517 |     "colab": {
 518 |      "base_uri": "https://localhost:8080/",
 519 |      "height": 551
 520 |     },
 521 |     "id": "gp2-sVDPSTXh",
 522 |     "outputId": "977ba12a-5f3b-4228-b978-30d9785f1296"
 523 |    },
 524 |    "outputs": [
 525 |     {
 526 |      "data": {
 527 |       "application/vnd.google.colaboratory.intrinsic+json": {
 528 |        "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 16,\n  \"fields\": [\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"mAb1\",\n          \"mAb2\",\n          \"mAb6\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Heavy_Chain\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"EVQLVESGGGLVQPGRSLRLSCAASGFTFDDYAMHWVRQAPGKGLEWVSAITWNSGHIDYADSVEGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCAKVSYLSTASSLDYWGQGTLVTVSS\",\n          \"EVQLVESGGGLVQPGGSLRLSCAASGFTFSDSWIHWVRQAPGKGLEWVAWISPYGGSTYYADSVKGRFTISADTSKNTAYLQMNSLRAEDTAVYYCARRHWPGGFDYWGQGTLVTVSA\",\n          \"QVQLVESGGGVVQPGRSLRLSCAASGFIFSSYAMHWVRQAPGNGLEWVAFMSYDGSNKKYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARDRGIAAGGNYYYYGMDVWGQGTTVTVSS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Light_Chain\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"DIQMTQSPSSLSASVGDRVTITCRASQGIRNYLAWYQQKPGKAPKLLIYAASTLQSGVPSRFSGSGSGTDFTLTISSLQPEDVATYYCQRYNRAPYTFGQGTKVEIK\",\n          \"DIQMTQSPSSLSASVGDRVTITCRASQDVSTAVAWYQQKPGKAPKLLIYSASFLYSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYLYHPATFGQGTKVEIK\",\n          \"EIVLTQSPATLSLSPGERATLSCRASQSVYSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPPFTFGPGTKVDIK\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
 529 |        "type": "dataframe",
 530 |        "variable_name": "dataset"
 531 |       },
 532 |       "text/html": [
 533 |        "\n",
 534 |        "  <div id=\"df-cea68f34-f43e-4e7f-b0b2-a3543261454b\" class=\"colab-df-container\">\n",
 535 |        "    <div>\n",
 536 |        "<style scoped>\n",
 537 |        "    .dataframe tbody tr th:only-of-type {\n",
 538 |        "        vertical-align: middle;\n",
 539 |        "    }\n",
 540 |        "\n",
 541 |        "    .dataframe tbody tr th {\n",
 542 |        "        vertical-align: top;\n",
 543 |        "    }\n",
 544 |        "\n",
 545 |        "    .dataframe thead th {\n",
 546 |        "        text-align: right;\n",
 547 |        "    }\n",
 548 |        "</style>\n",
 549 |        "<table border=\"1\" class=\"dataframe\">\n",
 550 |        "  <thead>\n",
 551 |        "    <tr style=\"text-align: right;\">\n",
 552 |        "      <th></th>\n",
 553 |        "      <th>Name</th>\n",
 554 |        "      <th>Heavy_Chain</th>\n",
 555 |        "      <th>Light_Chain</th>\n",
 556 |        "    </tr>\n",
 557 |        "  </thead>\n",
 558 |        "  <tbody>\n",
 559 |        "    <tr>\n",
 560 |        "      <th>0</th>\n",
 561 |        "      <td>mAb1</td>\n",
 562 |        "      <td>EVQLVESGGGLVQPGRSLRLSCAASGFTFDDYAMHWVRQAPGKGLE...</td>\n",
 563 |        "      <td>DIQMTQSPSSLSASVGDRVTITCRASQGIRNYLAWYQQKPGKAPKL...</td>\n",
 564 |        "    </tr>\n",
 565 |        "    <tr>\n",
 566 |        "      <th>1</th>\n",
 567 |        "      <td>mAb2</td>\n",
 568 |        "      <td>EVQLVESGGGLVQPGGSLRLSCAASGFTFSDSWIHWVRQAPGKGLE...</td>\n",
 569 |        "      <td>DIQMTQSPSSLSASVGDRVTITCRASQDVSTAVAWYQQKPGKAPKL...</td>\n",
 570 |        "    </tr>\n",
 571 |        "    <tr>\n",
 572 |        "      <th>2</th>\n",
 573 |        "      <td>mAb3</td>\n",
 574 |        "      <td>QVQLKQSGPGLVQPSQSLSITCTVSGFSLTNYGVHWVRQSPGKGLE...</td>\n",
 575 |        "      <td>DILLTQSPVILSVSPGERVSFSCRASQSIGTNIHWYQQRTNGSPRL...</td>\n",
 576 |        "    </tr>\n",
 577 |        "    <tr>\n",
 578 |        "      <th>3</th>\n",
 579 |        "      <td>mAb4</td>\n",
 580 |        "      <td>EVQLLESGGGLVQPGGSLRLSCAVSGFTFNSFAMSWVRQAPGKGLE...</td>\n",
 581 |        "      <td>EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRL...</td>\n",
 582 |        "    </tr>\n",
 583 |        "    <tr>\n",
 584 |        "      <th>4</th>\n",
 585 |        "      <td>mAb5</td>\n",
 586 |        "      <td>EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLE...</td>\n",
 587 |        "      <td>EIVLTQSPGTLSLSPGERATLSCRASQSVRGRYLAWYQQKPGQAPR...</td>\n",
 588 |        "    </tr>\n",
 589 |        "    <tr>\n",
 590 |        "      <th>5</th>\n",
 591 |        "      <td>mAb6</td>\n",
 592 |        "      <td>QVQLVESGGGVVQPGRSLRLSCAASGFIFSSYAMHWVRQAPGNGLE...</td>\n",
 593 |        "      <td>EIVLTQSPATLSLSPGERATLSCRASQSVYSYLAWYQQKPGQAPRL...</td>\n",
 594 |        "    </tr>\n",
 595 |        "    <tr>\n",
 596 |        "      <th>6</th>\n",
 597 |        "      <td>mAb7</td>\n",
 598 |        "      <td>EVKLEESGGGLVQPGGSMKLSCVASGFIFSNHWMNWVRQSPEKGLE...</td>\n",
 599 |        "      <td>DILLTQSPAILSVSPGERVSFSCRASQFVGSSIHWYQQRTNGSPRL...</td>\n",
 600 |        "    </tr>\n",
 601 |        "    <tr>\n",
 602 |        "      <th>7</th>\n",
 603 |        "      <td>mAb8</td>\n",
 604 |        "      <td>EVQLVESGGGLVQPGGSLRLSCAVSGYSITSGYSWNWIRQAPGKGL...</td>\n",
 605 |        "      <td>DIQLTQSPSSLSASVGDRVTITCRASQSVDYDGDSYMNWYQQKPGK...</td>\n",
 606 |        "    </tr>\n",
 607 |        "    <tr>\n",
 608 |        "      <th>8</th>\n",
 609 |        "      <td>mAb9</td>\n",
 610 |        "      <td>QVQLQESGPGLVKPSETLSLTCTVSGGSVSSGDYYWTWIRQSPGKG...</td>\n",
 611 |        "      <td>DIQMTQSPSSLSASVGDRVTITCQASQDISNYLNWYQQKPGKAPKL...</td>\n",
 612 |        "    </tr>\n",
 613 |        "    <tr>\n",
 614 |        "      <th>9</th>\n",
 615 |        "      <td>mAb10</td>\n",
 616 |        "      <td>EVQLVESGGGLVQPGGSLRLSCAASGFTFTDYTMDWVRQAPGKGLE...</td>\n",
 617 |        "      <td>DIQMTQSPSSLSASVGDRVTITCKASQDVSIGVAWYQQKPGKAPKL...</td>\n",
 618 |        "    </tr>\n",
 619 |        "    <tr>\n",
 620 |        "      <th>10</th>\n",
 621 |        "      <td>mAb11</td>\n",
 622 |        "      <td>QVQLQESGPGLVRPSQTLSLTCTVSGYSITSDHAWSWVRQPPGRGL...</td>\n",
 623 |        "      <td>DIQMTQSPSSLSASVGDRVTITCRASQDISSYLNWYQQKPGKAPKL...</td>\n",
 624 |        "    </tr>\n",
 625 |        "    <tr>\n",
 626 |        "      <th>11</th>\n",
 627 |        "      <td>mAb12</td>\n",
 628 |        "      <td>QVQLVQSGAEVKKPGASVKVSCKGSGYTFTSYWMHWVRQAPGQRLE...</td>\n",
 629 |        "      <td>DVVMTQSPLSLPVTPGEPASISCRSSQSLAKSYGNTYLSWYLQKPG...</td>\n",
 630 |        "    </tr>\n",
 631 |        "    <tr>\n",
 632 |        "      <th>12</th>\n",
 633 |        "      <td>mAb13</td>\n",
 634 |        "      <td>QLQQSGTVLARPGASVKMSCKASGYSFTRYWMHWIKQRPGQGLEWI...</td>\n",
 635 |        "      <td>QIVSTQSPAIMSASPGEKVTMTCSASSSRSYMQWYQQKPGTSPKRW...</td>\n",
 636 |        "    </tr>\n",
 637 |        "    <tr>\n",
 638 |        "      <th>13</th>\n",
 639 |        "      <td>mAb14</td>\n",
 640 |        "      <td>QVQLVQSGAEVKKPGASVKVSCKASGFNIKDTYIHWVRQAPGQRLE...</td>\n",
 641 |        "      <td>DIQMTQSPSSLSASVGDRVTITCKTSQDINKYMAWYQQTPGKAPRL...</td>\n",
 642 |        "    </tr>\n",
 643 |        "    <tr>\n",
 644 |        "      <th>14</th>\n",
 645 |        "      <td>mAb15</td>\n",
 646 |        "      <td>QVQLVQSGAEVKKPGASVKVSCKASGYTFTSYYIHWVRQAPGQGLE...</td>\n",
 647 |        "      <td>DIQMTQSPSSLSASVGDRVTITCHASQNIYVWLNWYQQKPGKAPKL...</td>\n",
 648 |        "    </tr>\n",
 649 |        "    <tr>\n",
 650 |        "      <th>15</th>\n",
 651 |        "      <td>mAb16</td>\n",
 652 |        "      <td>EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLE...</td>\n",
 653 |        "      <td>DIQMTQSPSSLSASVGDRVTITCRASQYFSSYLAWYQQKPGKAPKL...</td>\n",
 654 |        "    </tr>\n",
 655 |        "  </tbody>\n",
 656 |        "</table>\n",
 657 |        "</div>\n",
 658 |        "    <div class=\"colab-df-buttons\">\n",
 659 |        "\n",
 660 |        "  <div class=\"colab-df-container\">\n",
 661 |        "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cea68f34-f43e-4e7f-b0b2-a3543261454b')\"\n",
 662 |        "            title=\"Convert this dataframe to an interactive table.\"\n",
 663 |        "            style=\"display:none;\">\n",
 664 |        "\n",
 665 |        "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
 666 |        "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
 667 |        "  </svg>\n",
 668 |        "    </button>\n",
 669 |        "\n",
 670 |        "  <style>\n",
 671 |        "    .colab-df-container {\n",
 672 |        "      display:flex;\n",
 673 |        "      gap: 12px;\n",
 674 |        "    }\n",
 675 |        "\n",
 676 |        "    .colab-df-convert {\n",
 677 |        "      background-color: #E8F0FE;\n",
 678 |        "      border: none;\n",
 679 |        "      border-radius: 50%;\n",
 680 |        "      cursor: pointer;\n",
 681 |        "      display: none;\n",
 682 |        "      fill: #1967D2;\n",
 683 |        "      height: 32px;\n",
 684 |        "      padding: 0 0 0 0;\n",
 685 |        "      width: 32px;\n",
 686 |        "    }\n",
 687 |        "\n",
 688 |        "    .colab-df-convert:hover {\n",
 689 |        "      background-color: #E2EBFA;\n",
 690 |        "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
 691 |        "      fill: #174EA6;\n",
 692 |        "    }\n",
 693 |        "\n",
 694 |        "    .colab-df-buttons div {\n",
 695 |        "      margin-bottom: 4px;\n",
 696 |        "    }\n",
 697 |        "\n",
 698 |        "    [theme=dark] .colab-df-convert {\n",
 699 |        "      background-color: #3B4455;\n",
 700 |        "      fill: #D2E3FC;\n",
 701 |        "    }\n",
 702 |        "\n",
 703 |        "    [theme=dark] .colab-df-convert:hover {\n",
 704 |        "      background-color: #434B5C;\n",
 705 |        "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
 706 |        "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
 707 |        "      fill: #FFFFFF;\n",
 708 |        "    }\n",
 709 |        "  </style>\n",
 710 |        "\n",
 711 |        "    <script>\n",
 712 |        "      const buttonEl =\n",
 713 |        "        document.querySelector('#df-cea68f34-f43e-4e7f-b0b2-a3543261454b button.colab-df-convert');\n",
 714 |        "      buttonEl.style.display =\n",
 715 |        "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
 716 |        "\n",
 717 |        "      async function convertToInteractive(key) {\n",
 718 |        "        const element = document.querySelector('#df-cea68f34-f43e-4e7f-b0b2-a3543261454b');\n",
 719 |        "        const dataTable =\n",
 720 |        "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
 721 |        "                                                    [key], {});\n",
 722 |        "        if (!dataTable) return;\n",
 723 |        "\n",
 724 |        "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
 725 |        "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
 726 |        "          + ' to learn more about interactive tables.';\n",
 727 |        "        element.innerHTML = '';\n",
 728 |        "        dataTable['output_type'] = 'display_data';\n",
 729 |        "        await google.colab.output.renderOutput(dataTable, element);\n",
 730 |        "        const docLink = document.createElement('div');\n",
 731 |        "        docLink.innerHTML = docLinkHtml;\n",
 732 |        "        element.appendChild(docLink);\n",
 733 |        "      }\n",
 734 |        "    </script>\n",
 735 |        "  </div>\n",
 736 |        "\n",
 737 |        "\n",
 738 |        "<div id=\"df-4f1a43b4-c3bb-4ee2-a5e6-32a6a02ce911\">\n",
 739 |        "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4f1a43b4-c3bb-4ee2-a5e6-32a6a02ce911')\"\n",
 740 |        "            title=\"Suggest charts\"\n",
 741 |        "            style=\"display:none;\">\n",
 742 |        "\n",
 743 |        "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
 744 |        "     width=\"24px\">\n",
 745 |        "    <g>\n",
 746 |        "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
 747 |        "    </g>\n",
 748 |        "</svg>\n",
 749 |        "  </button>\n",
 750 |        "\n",
 751 |        "<style>\n",
 752 |        "  .colab-df-quickchart {\n",
 753 |        "      --bg-color: #E8F0FE;\n",
 754 |        "      --fill-color: #1967D2;\n",
 755 |        "      --hover-bg-color: #E2EBFA;\n",
 756 |        "      --hover-fill-color: #174EA6;\n",
 757 |        "      --disabled-fill-color: #AAA;\n",
 758 |        "      --disabled-bg-color: #DDD;\n",
 759 |        "  }\n",
 760 |        "\n",
 761 |        "  [theme=dark] .colab-df-quickchart {\n",
 762 |        "      --bg-color: #3B4455;\n",
 763 |        "      --fill-color: #D2E3FC;\n",
 764 |        "      --hover-bg-color: #434B5C;\n",
 765 |        "      --hover-fill-color: #FFFFFF;\n",
 766 |        "      --disabled-bg-color: #3B4455;\n",
 767 |        "      --disabled-fill-color: #666;\n",
 768 |        "  }\n",
 769 |        "\n",
 770 |        "  .colab-df-quickchart {\n",
 771 |        "    background-color: var(--bg-color);\n",
 772 |        "    border: none;\n",
 773 |        "    border-radius: 50%;\n",
 774 |        "    cursor: pointer;\n",
 775 |        "    display: none;\n",
 776 |        "    fill: var(--fill-color);\n",
 777 |        "    height: 32px;\n",
 778 |        "    padding: 0;\n",
 779 |        "    width: 32px;\n",
 780 |        "  }\n",
 781 |        "\n",
 782 |        "  .colab-df-quickchart:hover {\n",
 783 |        "    background-color: var(--hover-bg-color);\n",
 784 |        "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
 785 |        "    fill: var(--button-hover-fill-color);\n",
 786 |        "  }\n",
 787 |        "\n",
 788 |        "  .colab-df-quickchart-complete:disabled,\n",
 789 |        "  .colab-df-quickchart-complete:disabled:hover {\n",
 790 |        "    background-color: var(--disabled-bg-color);\n",
 791 |        "    fill: var(--disabled-fill-color);\n",
 792 |        "    box-shadow: none;\n",
 793 |        "  }\n",
 794 |        "\n",
 795 |        "  .colab-df-spinner {\n",
 796 |        "    border: 2px solid var(--fill-color);\n",
 797 |        "    border-color: transparent;\n",
 798 |        "    border-bottom-color: var(--fill-color);\n",
 799 |        "    animation:\n",
 800 |        "      spin 1s steps(1) infinite;\n",
 801 |        "  }\n",
 802 |        "\n",
 803 |        "  @keyframes spin {\n",
 804 |        "    0% {\n",
 805 |        "      border-color: transparent;\n",
 806 |        "      border-bottom-color: var(--fill-color);\n",
 807 |        "      border-left-color: var(--fill-color);\n",
 808 |        "    }\n",
 809 |        "    20% {\n",
 810 |        "      border-color: transparent;\n",
 811 |        "      border-left-color: var(--fill-color);\n",
 812 |        "      border-top-color: var(--fill-color);\n",
 813 |        "    }\n",
 814 |        "    30% {\n",
 815 |        "      border-color: transparent;\n",
 816 |        "      border-left-color: var(--fill-color);\n",
 817 |        "      border-top-color: var(--fill-color);\n",
 818 |        "      border-right-color: var(--fill-color);\n",
 819 |        "    }\n",
 820 |        "    40% {\n",
 821 |        "      border-color: transparent;\n",
 822 |        "      border-right-color: var(--fill-color);\n",
 823 |        "      border-top-color: var(--fill-color);\n",
 824 |        "    }\n",
 825 |        "    60% {\n",
 826 |        "      border-color: transparent;\n",
 827 |        "      border-right-color: var(--fill-color);\n",
 828 |        "    }\n",
 829 |        "    80% {\n",
 830 |        "      border-color: transparent;\n",
 831 |        "      border-right-color: var(--fill-color);\n",
 832 |        "      border-bottom-color: var(--fill-color);\n",
 833 |        "    }\n",
 834 |        "    90% {\n",
 835 |        "      border-color: transparent;\n",
 836 |        "      border-bottom-color: var(--fill-color);\n",
 837 |        "    }\n",
 838 |        "  }\n",
 839 |        "</style>\n",
 840 |        "\n",
 841 |        "  <script>\n",
 842 |        "    async function quickchart(key) {\n",
 843 |        "      const quickchartButtonEl =\n",
 844 |        "        document.querySelector('#' + key + ' button');\n",
 845 |        "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
 846 |        "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
 847 |        "      try {\n",
 848 |        "        const charts = await google.colab.kernel.invokeFunction(\n",
 849 |        "            'suggestCharts', [key], {});\n",
 850 |        "      } catch (error) {\n",
 851 |        "        console.error('Error during call to suggestCharts:', error);\n",
 852 |        "      }\n",
 853 |        "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
 854 |        "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
 855 |        "    }\n",
 856 |        "    (() => {\n",
 857 |        "      let quickchartButtonEl =\n",
 858 |        "        document.querySelector('#df-4f1a43b4-c3bb-4ee2-a5e6-32a6a02ce911 button');\n",
 859 |        "      quickchartButtonEl.style.display =\n",
 860 |        "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
 861 |        "    })();\n",
 862 |        "  </script>\n",
 863 |        "</div>\n",
 864 |        "    </div>\n",
 865 |        "  </div>\n"
 866 |       ],
 867 |       "text/plain": [
 868 |        "     Name                                        Heavy_Chain  \\\n",
 869 |        "0    mAb1  EVQLVESGGGLVQPGRSLRLSCAASGFTFDDYAMHWVRQAPGKGLE...   \n",
 870 |        "1    mAb2  EVQLVESGGGLVQPGGSLRLSCAASGFTFSDSWIHWVRQAPGKGLE...   \n",
 871 |        "2    mAb3  QVQLKQSGPGLVQPSQSLSITCTVSGFSLTNYGVHWVRQSPGKGLE...   \n",
 872 |        "3    mAb4  EVQLLESGGGLVQPGGSLRLSCAVSGFTFNSFAMSWVRQAPGKGLE...   \n",
 873 |        "4    mAb5  EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLE...   \n",
 874 |        "5    mAb6  QVQLVESGGGVVQPGRSLRLSCAASGFIFSSYAMHWVRQAPGNGLE...   \n",
 875 |        "6    mAb7  EVKLEESGGGLVQPGGSMKLSCVASGFIFSNHWMNWVRQSPEKGLE...   \n",
 876 |        "7    mAb8  EVQLVESGGGLVQPGGSLRLSCAVSGYSITSGYSWNWIRQAPGKGL...   \n",
 877 |        "8    mAb9  QVQLQESGPGLVKPSETLSLTCTVSGGSVSSGDYYWTWIRQSPGKG...   \n",
 878 |        "9   mAb10  EVQLVESGGGLVQPGGSLRLSCAASGFTFTDYTMDWVRQAPGKGLE...   \n",
 879 |        "10  mAb11  QVQLQESGPGLVRPSQTLSLTCTVSGYSITSDHAWSWVRQPPGRGL...   \n",
 880 |        "11  mAb12  QVQLVQSGAEVKKPGASVKVSCKGSGYTFTSYWMHWVRQAPGQRLE...   \n",
 881 |        "12  mAb13  QLQQSGTVLARPGASVKMSCKASGYSFTRYWMHWIKQRPGQGLEWI...   \n",
 882 |        "13  mAb14  QVQLVQSGAEVKKPGASVKVSCKASGFNIKDTYIHWVRQAPGQRLE...   \n",
 883 |        "14  mAb15  QVQLVQSGAEVKKPGASVKVSCKASGYTFTSYYIHWVRQAPGQGLE...   \n",
 884 |        "15  mAb16  EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLE...   \n",
 885 |        "\n",
 886 |        "                                          Light_Chain  \n",
 887 |        "0   DIQMTQSPSSLSASVGDRVTITCRASQGIRNYLAWYQQKPGKAPKL...  \n",
 888 |        "1   DIQMTQSPSSLSASVGDRVTITCRASQDVSTAVAWYQQKPGKAPKL...  \n",
 889 |        "2   DILLTQSPVILSVSPGERVSFSCRASQSIGTNIHWYQQRTNGSPRL...  \n",
 890 |        "3   EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRL...  \n",
 891 |        "4   EIVLTQSPGTLSLSPGERATLSCRASQSVRGRYLAWYQQKPGQAPR...  \n",
 892 |        "5   EIVLTQSPATLSLSPGERATLSCRASQSVYSYLAWYQQKPGQAPRL...  \n",
 893 |        "6   DILLTQSPAILSVSPGERVSFSCRASQFVGSSIHWYQQRTNGSPRL...  \n",
 894 |        "7   DIQLTQSPSSLSASVGDRVTITCRASQSVDYDGDSYMNWYQQKPGK...  \n",
 895 |        "8   DIQMTQSPSSLSASVGDRVTITCQASQDISNYLNWYQQKPGKAPKL...  \n",
 896 |        "9   DIQMTQSPSSLSASVGDRVTITCKASQDVSIGVAWYQQKPGKAPKL...  \n",
 897 |        "10  DIQMTQSPSSLSASVGDRVTITCRASQDISSYLNWYQQKPGKAPKL...  \n",
 898 |        "11  DVVMTQSPLSLPVTPGEPASISCRSSQSLAKSYGNTYLSWYLQKPG...  \n",
 899 |        "12  QIVSTQSPAIMSASPGEKVTMTCSASSSRSYMQWYQQKPGTSPKRW...  \n",
 900 |        "13  DIQMTQSPSSLSASVGDRVTITCKTSQDINKYMAWYQQTPGKAPRL...  \n",
 901 |        "14  DIQMTQSPSSLSASVGDRVTITCHASQNIYVWLNWYQQKPGKAPKL...  \n",
 902 |        "15  DIQMTQSPSSLSASVGDRVTITCRASQYFSSYLAWYQQKPGKAPKL...  "
 903 |       ]
 904 |      },
 905 |      "execution_count": 4,
 906 |      "metadata": {},
 907 |      "output_type": "execute_result"
 908 |     }
 909 |    ],
 910 |    "source": [
 911 |     "dataset = pd.read_csv('DeepSP_input.csv') # replace with your csv file, see format in DeepSP_input.csv file\n",
 912 |     "dataset"
 913 |    ]
 914 |   },
 915 |   {
 916 |    "cell_type": "code",
 917 |    "execution_count": 5,
 918 |    "metadata": {
 919 |     "id": "XWbYjNz-SoTA"
 920 |    },
 921 |    "outputs": [],
 922 |    "source": [
 923 |     "name = dataset['Name'].to_list()\n",
 924 |     "Heavy_seq = dataset['Heavy_Chain'].to_list()\n",
 925 |     "Light_seq = dataset['Light_Chain'].to_list()"
 926 |    ]
 927 |   },
 928 |   {
 929 |    "cell_type": "markdown",
 930 |    "metadata": {
 931 |     "id": "V-qttNLlTuT4"
 932 |    },
 933 |    "source": [
 934 |     "Convert to Fasta File"
 935 |    ]
 936 |   },
 937 |   {
 938 |    "cell_type": "code",
 939 |    "execution_count": 6,
 940 |    "metadata": {
 941 |     "id": "Pt5KeAfZy8gF"
 942 |    },
 943 |    "outputs": [],
 944 |    "source": [
 945 |     "file_out='seq_H.fasta'\n",
 946 |     "\n",
 947 |     "with open(file_out, \"w\") as output_handle:\n",
 948 |     "  for i in range(len(name)):\n",
 949 |     "    seq_name = name[i]\n",
 950 |     "    seq = Heavy_seq[i]\n",
 951 |     "    record = SeqRecord(\n",
 952 |     "    Seq(seq),\n",
 953 |     "    id=seq_name,\n",
 954 |     "    name=\"\",\n",
 955 |     "    description=\"\",\n",
 956 |     "    )\n",
 957 |     "    SeqIO.write(record, output_handle, \"fasta\")\n",
 958 |     "\n",
 959 |     "file_out='seq_L.fasta'\n",
 960 |     "\n",
 961 |     "with open(file_out, \"w\") as output_handle:\n",
 962 |     "  for i in range(len(name)):\n",
 963 |     "    seq_name = name[i]\n",
 964 |     "    seq = Light_seq[i]\n",
 965 |     "    record = SeqRecord(\n",
 966 |     "    Seq(seq),\n",
 967 |     "    id=seq_name,\n",
 968 |     "    name=\"\",\n",
 969 |     "    description=\"\",\n",
 970 |     "    )\n",
 971 |     "    SeqIO.write(record, output_handle, \"fasta\")"
 972 |    ]
 973 |   },
 974 |   {
 975 |    "cell_type": "markdown",
 976 |    "metadata": {
 977 |     "id": "QugBcnYeT1ci"
 978 |    },
 979 |    "source": [
 980 |     "Sequence Alignment  with ANARCI"
 981 |    ]
 982 |   },
 983 |   {
 984 |    "cell_type": "code",
 985 |    "execution_count": 7,
 986 |    "metadata": {
 987 |     "id": "h7DCE-fo16qG"
 988 |    },
 989 |    "outputs": [],
 990 |    "source": [
 991 |     "!ANARCI -i seq_H.fasta -o seq_aligned -s imgt -r heavy --csv\n",
 992 |     "!ANARCI -i seq_L.fasta -o seq_aligned -s imgt -r light --csv"
 993 |    ]
 994 |   },
 995 |   {
 996 |    "cell_type": "code",
 997 |    "execution_count": 8,
 998 |    "metadata": {
 999 |     "id": "uFOunTDOUUhZ"
1000 |    },
1001 |    "outputs": [],
1002 |    "source": [
1003 |     "H_aligned = pd.read_csv('seq_aligned_H.csv')\n",
1004 |     "L_aligned = pd.read_csv('seq_aligned_KL.csv')"
1005 |    ]
1006 |   },
1007 |   {
1008 |    "cell_type": "code",
1009 |    "execution_count": 9,
1010 |    "metadata": {
1011 |     "id": "Vn4-q554Udy_"
1012 |    },
1013 |    "outputs": [],
1014 |    "source": [
1015 |     "# https://github.com/Lailabcode/DeepSCM/blob/main/deepscm-master/seq_preprocessing.py\n",
1016 |     "\n",
1017 |     "def seq_preprocessing():\n",
1018 |     "  infile_H = pd.read_csv('seq_aligned_H.csv')\n",
1019 |     "  infile_L = pd.read_csv('seq_aligned_KL.csv')\n",
1020 |     "  outfile = open('seq_aligned_HL.txt', \"w\")\n",
1021 |     "\n",
1022 |     "  H_inclusion_list = ['1','2','3','4','5','6','7','8','9','10', \\\n",
1023 |     "                    '11','12','13','14','15','16','17','18','19','20', \\\n",
1024 |     "                    '21','22','23','24','25','26','27','28','29','30', \\\n",
1025 |     "                    '31','32','33','34','35','36','37','38','39','40', \\\n",
1026 |     "                    '41','42','43','44','45','46','47','48','49','50', \\\n",
1027 |     "                    '51','52','53','54','55','56','57','58','59','60', \\\n",
1028 |     "                    '61','62','63','64','65','66','67','68','69','70', \\\n",
1029 |     "                    '71','72','73','74','75','76','77','78','79','80', \\\n",
1030 |     "                    '81','82','83','84','85','86','87','88','89','90', \\\n",
1031 |     "                    '91','92','93','94','95','96','97','98','99','100', \\\n",
1032 |     "                    '101','102','103','104','105','106','107','108','109','110', \\\n",
1033 |     "                    '111','111A','111B','111C','111D','111E','111F','111G','111H', \\\n",
1034 |     "                    '112I','112H','112G','112F','112E','112D','112C','112B','112A','112',\\\n",
1035 |     "                    '113','114','115','116','117','118','119','120', \\\n",
1036 |     "                    '121','122','123','124','125','126','127','128']\n",
1037 |     "\n",
1038 |     "  L_inclusion_list = ['1','2','3','4','5','6','7','8','9','10', \\\n",
1039 |     "                    '11','12','13','14','15','16','17','18','19','20', \\\n",
1040 |     "                    '21','22','23','24','25','26','27','28','29','30', \\\n",
1041 |     "                    '31','32','33','34','35','36','37','38','39','40', \\\n",
1042 |     "                    '41','42','43','44','45','46','47','48','49','50', \\\n",
1043 |     "                    '51','52','53','54','55','56','57','58','59','60', \\\n",
1044 |     "                    '61','62','63','64','65','66','67','68','69','70', \\\n",
1045 |     "                    '71','72','73','74','75','76','77','78','79','80', \\\n",
1046 |     "                    '81','82','83','84','85','86','87','88','89','90', \\\n",
1047 |     "                    '91','92','93','94','95','96','97','98','99','100', \\\n",
1048 |     "                    '101','102','103','104','105','106','107','108','109','110', \\\n",
1049 |     "                    '111','112','113','114','115','116','117','118','119','120', \\\n",
1050 |     "                    '121','122','123','124','125','126','127']\n",
1051 |     "\n",
1052 |     "  H_dict = {'1': 0, '2':1, '3':2, '4':3, '5':4, '6':5, '7':6, '8':7, '9':8, '10':9, \\\n",
1053 |     "          '11':10, '12':11, '13':12, '14':13, '15':14, '16':15, '17':16, '18':17, '19':18, '20':19, \\\n",
1054 |     "          '21':20, '22':21, '23':22, '24':23, '25':24, '26':25, '27':26, '28':27, '29':28, '30':29, \\\n",
1055 |     "          '31':30, '32':31, '33':32, '34':33, '35':34, '36':35, '37':36, '38':37, '39':38, '40':39, \\\n",
1056 |     "          '41':40, '42':41, '43':42, '44':43, '45':44, '46':45, '47':46, '48':47, '49':48, '50':49, \\\n",
1057 |     "          '51':50, '52':51, '53':52, '54':53, '55':54, '56':55, '57':56, '58':57, '59':58, '60':59, \\\n",
1058 |     "          '61':60, '62':61, '63':62, '64':63, '65':64, '66':65, '67':66, '68':67, '69':68, '70':69, \\\n",
1059 |     "          '71':70, '72':71, '73':72, '74':73, '75':74, '76':75, '77':76, '78':77, '79':78, '80':79, \\\n",
1060 |     "          '81':80, '82':81, '83':82, '84':83, '85':84, '86':85, '87':86, '88':87, '89':88, '90':89, \\\n",
1061 |     "          '91':90, '92':91, '93':92, '94':93, '95':94, '96':95, '97':96, '98':97, '99':98, '100':99, \\\n",
1062 |     "          '101':100,'102':101,'103':102,'104':103,'105':104,'106':105,'107':106,'108':107,'109':108,'110':109, \\\n",
1063 |     "          '111':110,'111A':111,'111B':112,'111C':113,'111D':114,'111E':115,'111F':116,'111G':117,'111H':118, \\\n",
1064 |     "          '112I':119,'112H':120,'112G':121,'112F':122,'112E':123,'112D':124,'112C':125,'112B':126,'112A':127,'112':128, \\\n",
1065 |     "          '113':129,'114':130,'115':131,'116':132,'117':133,'118':134,'119':135,'120':136, \\\n",
1066 |     "          '121':137,'122':138,'123':139,'124':140,'125':141,'126':142,'127':143,'128':144}\n",
1067 |     "\n",
1068 |     "  L_dict = {'1': 0, '2':1, '3':2, '4':3, '5':4, '6':5, '7':6, '8':7, '9':8, '10':9, \\\n",
1069 |     "          '11':10, '12':11, '13':12, '14':13, '15':14, '16':15, '17':16, '18':17, '19':18, '20':19, \\\n",
1070 |     "          '21':20, '22':21, '23':22, '24':23, '25':24, '26':25, '27':26, '28':27, '29':28, '30':29, \\\n",
1071 |     "          '31':30, '32':31, '33':32, '34':33, '35':34, '36':35, '37':36, '38':37, '39':38, '40':39, \\\n",
1072 |     "          '41':40, '42':41, '43':42, '44':43, '45':44, '46':45, '47':46, '48':47, '49':48, '50':49, \\\n",
1073 |     "          '51':50, '52':51, '53':52, '54':53, '55':54, '56':55, '57':56, '58':57, '59':58, '60':59, \\\n",
1074 |     "          '61':60, '62':61, '63':62, '64':63, '65':64, '66':65, '67':66, '68':67, '69':68, '70':69, \\\n",
1075 |     "          '71':70, '72':71, '73':72, '74':73, '75':74, '76':75, '77':76, '78':77, '79':78, '80':79, \\\n",
1076 |     "          '81':80, '82':81, '83':82, '84':83, '85':84, '86':85, '87':86, '88':87, '89':88, '90':89, \\\n",
1077 |     "          '91':90, '92':91, '93':92, '94':93, '95':94, '96':95, '97':96, '98':97, '99':98, '100':99, \\\n",
1078 |     "          '101':100,'102':101,'103':102,'104':103,'105':104,'106':105,'107':106,'108':107,'109':108,'110':109, \\\n",
1079 |     "          '111':110,'112':111,'113':112,'114':113,'115':114,'116':115,'117':116,'118':117,'119':118,'120':119, \\\n",
1080 |     "          '121':120,'122':121,'123':122,'124':123,'125':124,'126':125,'127':126,'128':127}\n",
1081 |     "\n",
1082 |     "\n",
1083 |     "  N_mAbs = len(infile_H[\"Id\"])\n",
1084 |     "\n",
1085 |     "  for i in range(N_mAbs):\n",
1086 |     "    H_tmp = 145*['-']\n",
1087 |     "    L_tmp = 127*['-']\n",
1088 |     "    for col in infile_H.columns:\n",
1089 |     "        if(col in H_inclusion_list):\n",
1090 |     "            H_tmp[H_dict[col]]=infile_H.iloc[i][col]\n",
1091 |     "    for col in infile_L.columns:\n",
1092 |     "        if(col in L_inclusion_list):\n",
1093 |     "            L_tmp[L_dict[col]]=infile_L.iloc[i][col]\n",
1094 |     "\n",
1095 |     "    aa_string = ''\n",
1096 |     "    for aa in H_tmp+L_tmp:\n",
1097 |     "         aa_string += aa\n",
1098 |     "    outfile.write(infile_H.iloc[i,0]+\" \"+aa_string)\n",
1099 |     "    outfile.write(\"\\n\")\n",
1100 |     "\n",
1101 |     "  outfile.close()\n",
1102 |     "  return\n",
1103 |     "\n",
1104 |     "seq_preprocessing()"
1105 |    ]
1106 |   },
1107 |   {
1108 |    "cell_type": "markdown",
1109 |    "metadata": {
1110 |     "id": "s0SoIZ19Un54"
1111 |    },
1112 |    "source": [
1113 |     "Read Aligned Sequence"
1114 |    ]
1115 |   },
1116 |   {
1117 |    "cell_type": "code",
1118 |    "execution_count": 2,
1119 |    "metadata": {
1120 |     "id": "8maPu9TsUnU0"
1121 |    },
1122 |    "outputs": [],
1123 |    "source": [
1124 |     "def load_input_data(filename):\n",
1125 |     "    name_list=[]\n",
1126 |     "    seq_list=[]\n",
1127 |     "    with open(filename) as datafile:\n",
1128 |     "        for line in datafile:\n",
1129 |     "            line = line.strip().split()\n",
1130 |     "            name_list.append(line[0])\n",
1131 |     "            seq_list.append(line[1])\n",
1132 |     "    return name_list, seq_list"
1133 |    ]
1134 |   },
1135 |   {
1136 |    "cell_type": "code",
1137 |    "execution_count": 11,
1138 |    "metadata": {
1139 |     "id": "vjQU_ae6Usqq"
1140 |    },
1141 |    "outputs": [],
1142 |    "source": [
1143 |     "name_list, seq_list = load_input_data('seq_aligned_HL.txt')\n",
1144 |     "X = seq_list"
1145 |    ]
1146 |   },
1147 |   {
1148 |    "cell_type": "markdown",
1149 |    "metadata": {
1150 |     "id": "KVJcM7emVAqS"
1151 |    },
1152 |    "source": [
1153 |     "One Hot Encoding of Aligned Sequence"
1154 |    ]
1155 |   },
1156 |   {
1157 |    "cell_type": "code",
1158 |    "execution_count": 12,
1159 |    "metadata": {
1160 |     "id": "QTUshyyHVFbI"
1161 |    },
1162 |    "outputs": [],
1163 |    "source": [
1164 |     "def one_hot_encoder(s):\n",
1165 |     "    d = {'A': 0, 'C': 1, 'D': 2, 'E': 3, 'F': 4, 'G': 5, 'H': 6, 'I': 7, 'K': 8, 'L': 9, 'M': 10, 'N': 11, 'P': 12, 'Q': 13, 'R': 14, 'S': 15, 'T': 16, 'V': 17, 'W': 18, 'Y': 19, '-': 20}\n",
1166 |     "\n",
1167 |     "    x = np.zeros((len(d), len(s)))\n",
1168 |     "    x[[d[c] for c in s], range(len(s))] = 1\n",
1169 |     "\n",
1170 |     "    return x"
1171 |    ]
1172 |   },
1173 |   {
1174 |    "cell_type": "code",
1175 |    "execution_count": 13,
1176 |    "metadata": {
1177 |     "id": "v-JnQUxuVPr9"
1178 |    },
1179 |    "outputs": [],
1180 |    "source": [
1181 |     "X = [one_hot_encoder(s=x) for x in X]\n",
1182 |     "X = np.transpose(np.asarray(X), (0, 2, 1))\n",
1183 |     "X = np.asarray(X)"
1184 |    ]
1185 |   },
1186 |   {
1187 |    "cell_type": "markdown",
1188 |    "metadata": {
1189 |     "id": "QrdyGSKQWv0V"
1190 |    },
1191 |    "source": [
1192 |     "Predict DeepSP Predictor"
1193 |    ]
1194 |   },
1195 |   {
1196 |    "cell_type": "code",
1197 |    "execution_count": 14,
1198 |    "metadata": {
1199 |     "colab": {
1200 |      "base_uri": "https://localhost:8080/"
1201 |     },
1202 |     "id": "3Xyrqu5dXwxq",
1203 |     "outputId": "84dede34-3550-4fa5-d28e-c8bc27c8e2e1"
1204 |    },
1205 |    "outputs": [
1206 |     {
1207 |      "name": "stdout",
1208 |      "output_type": "stream",
1209 |      "text": [
1210 |       "1/1 [==============================] - 0s 424ms/step\n",
1211 |       "1/1 [==============================] - 0s 178ms/step\n",
1212 |       "1/1 [==============================] - 0s 181ms/step\n"
1213 |      ]
1214 |     }
1215 |    ],
1216 |    "source": [
1217 |     "# sappos\n",
1218 |     "json_file = open('Conv1D_regressionSAPpos.json', 'r')\n",
1219 |     "loaded_model_json = json_file.read()\n",
1220 |     "json_file.close()\n",
1221 |     "loaded_model = model_from_json(loaded_model_json)\n",
1222 |     "# load weights into model\n",
1223 |     "loaded_model.load_weights(\"Conv1D_regression_SAPpos.h5\")\n",
1224 |     "loaded_model.compile(optimizer='adam', loss='mae', metrics=['mae'])\n",
1225 |     "sap_pos = loaded_model.predict(X)\n",
1226 |     "\n",
1227 |     "# scmneg\n",
1228 |     "json_file = open('Conv1D_regressionSCMneg.json', 'r')\n",
1229 |     "loaded_model_json = json_file.read()\n",
1230 |     "json_file.close()\n",
1231 |     "loaded_model = model_from_json(loaded_model_json)\n",
1232 |     "# load weights into model\n",
1233 |     "loaded_model.load_weights(\"Conv1D_regression_SCMneg.h5\")\n",
1234 |     "loaded_model.compile(optimizer='adam', loss='mae', metrics=['mae'])\n",
1235 |     "scm_neg = loaded_model.predict(X)\n",
1236 |     "\n",
1237 |     "# scmpos\n",
1238 |     "json_file = open('Conv1D_regressionSCMpos.json', 'r')\n",
1239 |     "loaded_model_json = json_file.read()\n",
1240 |     "json_file.close()\n",
1241 |     "loaded_model = model_from_json(loaded_model_json)\n",
1242 |     "# load weights into model\n",
1243 |     "loaded_model.load_weights(\"Conv1D_regression_SCMpos.h5\")\n",
1244 |     "loaded_model.compile(optimizer='adam', loss='mae', metrics=['mae'])\n",
1245 |     "scm_pos = loaded_model.predict(X)"
1246 |    ]
1247 |   },
1248 |   {
1249 |    "cell_type": "code",
1250 |    "execution_count": 15,
1251 |    "metadata": {
1252 |     "colab": {
1253 |      "base_uri": "https://localhost:8080/",
1254 |      "height": 600
1255 |     },
1256 |     "id": "a-FfSETHegbu",
1257 |     "outputId": "5e5c0891-7fe1-46d2-b5b2-2ff93195f0af"
1258 |    },
1259 |    "outputs": [
1260 |     {
1261 |      "data": {
1262 |       "application/vnd.google.colaboratory.intrinsic+json": {
1263 |        "type": "dataframe",
1264 |        "variable_name": "df"
1265 |       },
1266 |       "text/html": [
1267 |        "\n",
1268 |        "  <div id=\"df-75a98ffa-06ce-4a5b-8d7b-9e36e0855d2a\" class=\"colab-df-container\">\n",
1269 |        "    <div>\n",
1270 |        "<style scoped>\n",
1271 |        "    .dataframe tbody tr th:only-of-type {\n",
1272 |        "        vertical-align: middle;\n",
1273 |        "    }\n",
1274 |        "\n",
1275 |        "    .dataframe tbody tr th {\n",
1276 |        "        vertical-align: top;\n",
1277 |        "    }\n",
1278 |        "\n",
1279 |        "    .dataframe thead th {\n",
1280 |        "        text-align: right;\n",
1281 |        "    }\n",
1282 |        "</style>\n",
1283 |        "<table border=\"1\" class=\"dataframe\">\n",
1284 |        "  <thead>\n",
1285 |        "    <tr style=\"text-align: right;\">\n",
1286 |        "      <th></th>\n",
1287 |        "      <th>Name</th>\n",
1288 |        "      <th>SAP_pos_CDRH1</th>\n",
1289 |        "      <th>SAP_pos_CDRH2</th>\n",
1290 |        "      <th>SAP_pos_CDRH3</th>\n",
1291 |        "      <th>SAP_pos_CDRL1</th>\n",
1292 |        "      <th>SAP_pos_CDRL2</th>\n",
1293 |        "      <th>SAP_pos_CDRL3</th>\n",
1294 |        "      <th>SAP_pos_CDR</th>\n",
1295 |        "      <th>SAP_pos_Hv</th>\n",
1296 |        "      <th>SAP_pos_Lv</th>\n",
1297 |        "      <th>...</th>\n",
1298 |        "      <th>SCM_pos_CDRH1</th>\n",
1299 |        "      <th>SCM_pos_CDRH2</th>\n",
1300 |        "      <th>SCM_pos_CDRH3</th>\n",
1301 |        "      <th>SCM_pos_CDRL1</th>\n",
1302 |        "      <th>SCM_pos_CDRL2</th>\n",
1303 |        "      <th>SCM_pos_CDRL3</th>\n",
1304 |        "      <th>SCM_pos_CDR</th>\n",
1305 |        "      <th>SCM_pos_Hv</th>\n",
1306 |        "      <th>SCM_pos_Lv</th>\n",
1307 |        "      <th>SCM_pos_Fv</th>\n",
1308 |        "    </tr>\n",
1309 |        "  </thead>\n",
1310 |        "  <tbody>\n",
1311 |        "    <tr>\n",
1312 |        "      <th>0</th>\n",
1313 |        "      <td>mAb1</td>\n",
1314 |        "      <td>2.134783</td>\n",
1315 |        "      <td>2.524245</td>\n",
1316 |        "      <td>14.445071</td>\n",
1317 |        "      <td>1.904740</td>\n",
1318 |        "      <td>3.589351</td>\n",
1319 |        "      <td>3.172182</td>\n",
1320 |        "      <td>27.496794</td>\n",
1321 |        "      <td>58.417648</td>\n",
1322 |        "      <td>30.517651</td>\n",
1323 |        "      <td>...</td>\n",
1324 |        "      <td>3.183182</td>\n",
1325 |        "      <td>19.583357</td>\n",
1326 |        "      <td>29.513483</td>\n",
1327 |        "      <td>116.769501</td>\n",
1328 |        "      <td>41.759361</td>\n",
1329 |        "      <td>55.548267</td>\n",
1330 |        "      <td>263.838928</td>\n",
1331 |        "      <td>907.113037</td>\n",
1332 |        "      <td>1219.444458</td>\n",
1333 |        "      <td>2109.088623</td>\n",
1334 |        "    </tr>\n",
1335 |        "    <tr>\n",
1336 |        "      <th>1</th>\n",
1337 |        "      <td>mAb2</td>\n",
1338 |        "      <td>1.844576</td>\n",
1339 |        "      <td>4.339117</td>\n",
1340 |        "      <td>8.942592</td>\n",
1341 |        "      <td>1.613968</td>\n",
1342 |        "      <td>10.417940</td>\n",
1343 |        "      <td>9.945271</td>\n",
1344 |        "      <td>38.409042</td>\n",
1345 |        "      <td>58.486862</td>\n",
1346 |        "      <td>44.568775</td>\n",
1347 |        "      <td>...</td>\n",
1348 |        "      <td>27.889265</td>\n",
1349 |        "      <td>22.455563</td>\n",
1350 |        "      <td>154.704300</td>\n",
1351 |        "      <td>23.503654</td>\n",
1352 |        "      <td>37.771931</td>\n",
1353 |        "      <td>90.647194</td>\n",
1354 |        "      <td>360.761566</td>\n",
1355 |        "      <td>1224.561279</td>\n",
1356 |        "      <td>1132.755249</td>\n",
1357 |        "      <td>2335.686523</td>\n",
1358 |        "    </tr>\n",
1359 |        "    <tr>\n",
1360 |        "      <th>2</th>\n",
1361 |        "      <td>mAb3</td>\n",
1362 |        "      <td>2.809425</td>\n",
1363 |        "      <td>1.746096</td>\n",
1364 |        "      <td>20.808647</td>\n",
1365 |        "      <td>0.617971</td>\n",
1366 |        "      <td>3.460975</td>\n",
1367 |        "      <td>5.031782</td>\n",
1368 |        "      <td>34.877113</td>\n",
1369 |        "      <td>63.382252</td>\n",
1370 |        "      <td>44.274189</td>\n",
1371 |        "      <td>...</td>\n",
1372 |        "      <td>71.399979</td>\n",
1373 |        "      <td>29.845194</td>\n",
1374 |        "      <td>41.844185</td>\n",
1375 |        "      <td>37.094913</td>\n",
1376 |        "      <td>39.442978</td>\n",
1377 |        "      <td>22.183475</td>\n",
1378 |        "      <td>246.880508</td>\n",
1379 |        "      <td>1165.597656</td>\n",
1380 |        "      <td>830.064209</td>\n",
1381 |        "      <td>1963.784912</td>\n",
1382 |        "    </tr>\n",
1383 |        "    <tr>\n",
1384 |        "      <th>3</th>\n",
1385 |        "      <td>mAb4</td>\n",
1386 |        "      <td>3.139667</td>\n",
1387 |        "      <td>0.300687</td>\n",
1388 |        "      <td>26.260990</td>\n",
1389 |        "      <td>1.863188</td>\n",
1390 |        "      <td>0.188002</td>\n",
1391 |        "      <td>6.295355</td>\n",
1392 |        "      <td>38.342857</td>\n",
1393 |        "      <td>73.466133</td>\n",
1394 |        "      <td>38.451836</td>\n",
1395 |        "      <td>...</td>\n",
1396 |        "      <td>27.552292</td>\n",
1397 |        "      <td>11.260792</td>\n",
1398 |        "      <td>56.283066</td>\n",
1399 |        "      <td>13.800642</td>\n",
1400 |        "      <td>66.964668</td>\n",
1401 |        "      <td>50.259052</td>\n",
1402 |        "      <td>225.270020</td>\n",
1403 |        "      <td>993.915222</td>\n",
1404 |        "      <td>1097.456543</td>\n",
1405 |        "      <td>2073.500244</td>\n",
1406 |        "    </tr>\n",
1407 |        "    <tr>\n",
1408 |        "      <th>4</th>\n",
1409 |        "      <td>mAb5</td>\n",
1410 |        "      <td>2.489059</td>\n",
1411 |        "      <td>0.111882</td>\n",
1412 |        "      <td>15.967413</td>\n",
1413 |        "      <td>2.553848</td>\n",
1414 |        "      <td>0.564827</td>\n",
1415 |        "      <td>1.848455</td>\n",
1416 |        "      <td>23.765207</td>\n",
1417 |        "      <td>61.603027</td>\n",
1418 |        "      <td>33.180843</td>\n",
1419 |        "      <td>...</td>\n",
1420 |        "      <td>42.208309</td>\n",
1421 |        "      <td>10.151648</td>\n",
1422 |        "      <td>77.493065</td>\n",
1423 |        "      <td>142.884079</td>\n",
1424 |        "      <td>81.866493</td>\n",
1425 |        "      <td>61.183517</td>\n",
1426 |        "      <td>415.960480</td>\n",
1427 |        "      <td>1010.628784</td>\n",
1428 |        "      <td>1221.234863</td>\n",
1429 |        "      <td>2209.069336</td>\n",
1430 |        "    </tr>\n",
1431 |        "    <tr>\n",
1432 |        "      <th>5</th>\n",
1433 |        "      <td>mAb6</td>\n",
1434 |        "      <td>9.645024</td>\n",
1435 |        "      <td>2.265561</td>\n",
1436 |        "      <td>23.064241</td>\n",
1437 |        "      <td>6.464199</td>\n",
1438 |        "      <td>0.987525</td>\n",
1439 |        "      <td>8.052277</td>\n",
1440 |        "      <td>49.345421</td>\n",
1441 |        "      <td>61.487507</td>\n",
1442 |        "      <td>48.577274</td>\n",
1443 |        "      <td>...</td>\n",
1444 |        "      <td>45.501659</td>\n",
1445 |        "      <td>18.139217</td>\n",
1446 |        "      <td>136.593094</td>\n",
1447 |        "      <td>18.756721</td>\n",
1448 |        "      <td>71.840881</td>\n",
1449 |        "      <td>66.386726</td>\n",
1450 |        "      <td>359.891602</td>\n",
1451 |        "      <td>1329.421021</td>\n",
1452 |        "      <td>1113.381714</td>\n",
1453 |        "      <td>2426.393555</td>\n",
1454 |        "    </tr>\n",
1455 |        "    <tr>\n",
1456 |        "      <th>6</th>\n",
1457 |        "      <td>mAb7</td>\n",
1458 |        "      <td>8.222817</td>\n",
1459 |        "      <td>2.838049</td>\n",
1460 |        "      <td>11.414651</td>\n",
1461 |        "      <td>4.315411</td>\n",
1462 |        "      <td>2.673587</td>\n",
1463 |        "      <td>5.908565</td>\n",
1464 |        "      <td>36.578148</td>\n",
1465 |        "      <td>50.778774</td>\n",
1466 |        "      <td>44.486637</td>\n",
1467 |        "      <td>...</td>\n",
1468 |        "      <td>52.311821</td>\n",
1469 |        "      <td>74.923622</td>\n",
1470 |        "      <td>82.053070</td>\n",
1471 |        "      <td>72.280479</td>\n",
1472 |        "      <td>45.879509</td>\n",
1473 |        "      <td>88.023949</td>\n",
1474 |        "      <td>428.916351</td>\n",
1475 |        "      <td>1007.111145</td>\n",
1476 |        "      <td>848.104492</td>\n",
1477 |        "      <td>1808.066040</td>\n",
1478 |        "    </tr>\n",
1479 |        "    <tr>\n",
1480 |        "      <th>7</th>\n",
1481 |        "      <td>mAb8</td>\n",
1482 |        "      <td>4.424239</td>\n",
1483 |        "      <td>2.095972</td>\n",
1484 |        "      <td>16.019022</td>\n",
1485 |        "      <td>2.523247</td>\n",
1486 |        "      <td>7.355127</td>\n",
1487 |        "      <td>1.797327</td>\n",
1488 |        "      <td>34.046429</td>\n",
1489 |        "      <td>60.678223</td>\n",
1490 |        "      <td>34.210297</td>\n",
1491 |        "      <td>...</td>\n",
1492 |        "      <td>51.687237</td>\n",
1493 |        "      <td>-3.795643</td>\n",
1494 |        "      <td>91.028336</td>\n",
1495 |        "      <td>14.377379</td>\n",
1496 |        "      <td>23.035267</td>\n",
1497 |        "      <td>32.922684</td>\n",
1498 |        "      <td>198.840347</td>\n",
1499 |        "      <td>1157.940308</td>\n",
1500 |        "      <td>944.083252</td>\n",
1501 |        "      <td>2085.386963</td>\n",
1502 |        "    </tr>\n",
1503 |        "    <tr>\n",
1504 |        "      <th>8</th>\n",
1505 |        "      <td>mAb9</td>\n",
1506 |        "      <td>1.428219</td>\n",
1507 |        "      <td>3.449205</td>\n",
1508 |        "      <td>5.459098</td>\n",
1509 |        "      <td>2.133244</td>\n",
1510 |        "      <td>2.771093</td>\n",
1511 |        "      <td>8.270716</td>\n",
1512 |        "      <td>23.198622</td>\n",
1513 |        "      <td>45.440292</td>\n",
1514 |        "      <td>37.316959</td>\n",
1515 |        "      <td>...</td>\n",
1516 |        "      <td>15.407248</td>\n",
1517 |        "      <td>8.218230</td>\n",
1518 |        "      <td>46.540298</td>\n",
1519 |        "      <td>8.149660</td>\n",
1520 |        "      <td>17.474276</td>\n",
1521 |        "      <td>35.439575</td>\n",
1522 |        "      <td>130.615524</td>\n",
1523 |        "      <td>1107.602173</td>\n",
1524 |        "      <td>954.662292</td>\n",
1525 |        "      <td>2039.475220</td>\n",
1526 |        "    </tr>\n",
1527 |        "    <tr>\n",
1528 |        "      <th>9</th>\n",
1529 |        "      <td>mAb10</td>\n",
1530 |        "      <td>2.341985</td>\n",
1531 |        "      <td>0.196968</td>\n",
1532 |        "      <td>10.534814</td>\n",
1533 |        "      <td>4.143441</td>\n",
1534 |        "      <td>6.528968</td>\n",
1535 |        "      <td>15.280798</td>\n",
1536 |        "      <td>40.839794</td>\n",
1537 |        "      <td>55.972923</td>\n",
1538 |        "      <td>49.084797</td>\n",
1539 |        "      <td>...</td>\n",
1540 |        "      <td>25.994398</td>\n",
1541 |        "      <td>14.516958</td>\n",
1542 |        "      <td>26.370356</td>\n",
1543 |        "      <td>19.893885</td>\n",
1544 |        "      <td>69.955307</td>\n",
1545 |        "      <td>26.277246</td>\n",
1546 |        "      <td>174.261017</td>\n",
1547 |        "      <td>1044.724976</td>\n",
1548 |        "      <td>1139.791870</td>\n",
1549 |        "      <td>2159.286621</td>\n",
1550 |        "    </tr>\n",
1551 |        "    <tr>\n",
1552 |        "      <th>10</th>\n",
1553 |        "      <td>mAb11</td>\n",
1554 |        "      <td>3.050937</td>\n",
1555 |        "      <td>9.065768</td>\n",
1556 |        "      <td>5.594949</td>\n",
1557 |        "      <td>1.300072</td>\n",
1558 |        "      <td>4.010072</td>\n",
1559 |        "      <td>6.862015</td>\n",
1560 |        "      <td>30.163914</td>\n",
1561 |        "      <td>55.924561</td>\n",
1562 |        "      <td>34.403553</td>\n",
1563 |        "      <td>...</td>\n",
1564 |        "      <td>46.803642</td>\n",
1565 |        "      <td>44.563057</td>\n",
1566 |        "      <td>90.800636</td>\n",
1567 |        "      <td>29.547958</td>\n",
1568 |        "      <td>120.159859</td>\n",
1569 |        "      <td>28.398201</td>\n",
1570 |        "      <td>362.141327</td>\n",
1571 |        "      <td>1422.787842</td>\n",
1572 |        "      <td>1272.123779</td>\n",
1573 |        "      <td>2652.440430</td>\n",
1574 |        "    </tr>\n",
1575 |        "    <tr>\n",
1576 |        "      <th>11</th>\n",
1577 |        "      <td>mAb12</td>\n",
1578 |        "      <td>2.903481</td>\n",
1579 |        "      <td>0.377317</td>\n",
1580 |        "      <td>11.468390</td>\n",
1581 |        "      <td>6.613725</td>\n",
1582 |        "      <td>5.520360</td>\n",
1583 |        "      <td>1.477862</td>\n",
1584 |        "      <td>28.122194</td>\n",
1585 |        "      <td>51.992001</td>\n",
1586 |        "      <td>53.377674</td>\n",
1587 |        "      <td>...</td>\n",
1588 |        "      <td>35.554497</td>\n",
1589 |        "      <td>-3.779891</td>\n",
1590 |        "      <td>-0.063098</td>\n",
1591 |        "      <td>125.557152</td>\n",
1592 |        "      <td>43.826054</td>\n",
1593 |        "      <td>73.312820</td>\n",
1594 |        "      <td>267.234039</td>\n",
1595 |        "      <td>969.922485</td>\n",
1596 |        "      <td>1093.797974</td>\n",
1597 |        "      <td>2053.877930</td>\n",
1598 |        "    </tr>\n",
1599 |        "    <tr>\n",
1600 |        "      <th>12</th>\n",
1601 |        "      <td>mAb13</td>\n",
1602 |        "      <td>2.835168</td>\n",
1603 |        "      <td>1.458057</td>\n",
1604 |        "      <td>9.994059</td>\n",
1605 |        "      <td>2.109535</td>\n",
1606 |        "      <td>3.517281</td>\n",
1607 |        "      <td>3.287737</td>\n",
1608 |        "      <td>23.887489</td>\n",
1609 |        "      <td>46.273491</td>\n",
1610 |        "      <td>35.819031</td>\n",
1611 |        "      <td>...</td>\n",
1612 |        "      <td>114.275505</td>\n",
1613 |        "      <td>21.517704</td>\n",
1614 |        "      <td>52.248070</td>\n",
1615 |        "      <td>111.769119</td>\n",
1616 |        "      <td>84.824715</td>\n",
1617 |        "      <td>86.854416</td>\n",
1618 |        "      <td>469.482086</td>\n",
1619 |        "      <td>1137.821533</td>\n",
1620 |        "      <td>1275.564941</td>\n",
1621 |        "      <td>2417.019531</td>\n",
1622 |        "    </tr>\n",
1623 |        "    <tr>\n",
1624 |        "      <th>13</th>\n",
1625 |        "      <td>mAb14</td>\n",
1626 |        "      <td>2.589987</td>\n",
1627 |        "      <td>4.791502</td>\n",
1628 |        "      <td>20.667749</td>\n",
1629 |        "      <td>2.308463</td>\n",
1630 |        "      <td>6.206800</td>\n",
1631 |        "      <td>5.448440</td>\n",
1632 |        "      <td>43.316048</td>\n",
1633 |        "      <td>63.523815</td>\n",
1634 |        "      <td>38.004623</td>\n",
1635 |        "      <td>...</td>\n",
1636 |        "      <td>45.412704</td>\n",
1637 |        "      <td>26.941366</td>\n",
1638 |        "      <td>64.773582</td>\n",
1639 |        "      <td>62.668564</td>\n",
1640 |        "      <td>109.243866</td>\n",
1641 |        "      <td>33.384670</td>\n",
1642 |        "      <td>342.168793</td>\n",
1643 |        "      <td>1134.156250</td>\n",
1644 |        "      <td>1143.960449</td>\n",
1645 |        "      <td>2254.151367</td>\n",
1646 |        "    </tr>\n",
1647 |        "    <tr>\n",
1648 |        "      <th>14</th>\n",
1649 |        "      <td>mAb15</td>\n",
1650 |        "      <td>2.769219</td>\n",
1651 |        "      <td>3.568675</td>\n",
1652 |        "      <td>14.486679</td>\n",
1653 |        "      <td>9.599416</td>\n",
1654 |        "      <td>2.980665</td>\n",
1655 |        "      <td>5.314492</td>\n",
1656 |        "      <td>38.965103</td>\n",
1657 |        "      <td>48.768085</td>\n",
1658 |        "      <td>39.433372</td>\n",
1659 |        "      <td>...</td>\n",
1660 |        "      <td>52.217686</td>\n",
1661 |        "      <td>33.024086</td>\n",
1662 |        "      <td>68.119850</td>\n",
1663 |        "      <td>48.751259</td>\n",
1664 |        "      <td>103.548256</td>\n",
1665 |        "      <td>25.522581</td>\n",
1666 |        "      <td>326.298615</td>\n",
1667 |        "      <td>1186.510864</td>\n",
1668 |        "      <td>1266.150513</td>\n",
1669 |        "      <td>2435.450439</td>\n",
1670 |        "    </tr>\n",
1671 |        "    <tr>\n",
1672 |        "      <th>15</th>\n",
1673 |        "      <td>mAb16</td>\n",
1674 |        "      <td>2.071724</td>\n",
1675 |        "      <td>7.665909</td>\n",
1676 |        "      <td>19.876446</td>\n",
1677 |        "      <td>4.267005</td>\n",
1678 |        "      <td>3.184840</td>\n",
1679 |        "      <td>6.350702</td>\n",
1680 |        "      <td>44.845722</td>\n",
1681 |        "      <td>68.781349</td>\n",
1682 |        "      <td>37.064419</td>\n",
1683 |        "      <td>...</td>\n",
1684 |        "      <td>33.740913</td>\n",
1685 |        "      <td>22.686165</td>\n",
1686 |        "      <td>151.189835</td>\n",
1687 |        "      <td>41.309410</td>\n",
1688 |        "      <td>68.023109</td>\n",
1689 |        "      <td>32.488457</td>\n",
1690 |        "      <td>351.102264</td>\n",
1691 |        "      <td>1164.186035</td>\n",
1692 |        "      <td>1198.958496</td>\n",
1693 |        "      <td>2334.452393</td>\n",
1694 |        "    </tr>\n",
1695 |        "  </tbody>\n",
1696 |        "</table>\n",
1697 |        "<p>16 rows × 31 columns</p>\n",
1698 |        "</div>\n",
1699 |        "    <div class=\"colab-df-buttons\">\n",
1700 |        "\n",
1701 |        "  <div class=\"colab-df-container\">\n",
1702 |        "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-75a98ffa-06ce-4a5b-8d7b-9e36e0855d2a')\"\n",
1703 |        "            title=\"Convert this dataframe to an interactive table.\"\n",
1704 |        "            style=\"display:none;\">\n",
1705 |        "\n",
1706 |        "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
1707 |        "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
1708 |        "  </svg>\n",
1709 |        "    </button>\n",
1710 |        "\n",
1711 |        "  <style>\n",
1712 |        "    .colab-df-container {\n",
1713 |        "      display:flex;\n",
1714 |        "      gap: 12px;\n",
1715 |        "    }\n",
1716 |        "\n",
1717 |        "    .colab-df-convert {\n",
1718 |        "      background-color: #E8F0FE;\n",
1719 |        "      border: none;\n",
1720 |        "      border-radius: 50%;\n",
1721 |        "      cursor: pointer;\n",
1722 |        "      display: none;\n",
1723 |        "      fill: #1967D2;\n",
1724 |        "      height: 32px;\n",
1725 |        "      padding: 0 0 0 0;\n",
1726 |        "      width: 32px;\n",
1727 |        "    }\n",
1728 |        "\n",
1729 |        "    .colab-df-convert:hover {\n",
1730 |        "      background-color: #E2EBFA;\n",
1731 |        "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1732 |        "      fill: #174EA6;\n",
1733 |        "    }\n",
1734 |        "\n",
1735 |        "    .colab-df-buttons div {\n",
1736 |        "      margin-bottom: 4px;\n",
1737 |        "    }\n",
1738 |        "\n",
1739 |        "    [theme=dark] .colab-df-convert {\n",
1740 |        "      background-color: #3B4455;\n",
1741 |        "      fill: #D2E3FC;\n",
1742 |        "    }\n",
1743 |        "\n",
1744 |        "    [theme=dark] .colab-df-convert:hover {\n",
1745 |        "      background-color: #434B5C;\n",
1746 |        "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
1747 |        "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
1748 |        "      fill: #FFFFFF;\n",
1749 |        "    }\n",
1750 |        "  </style>\n",
1751 |        "\n",
1752 |        "    <script>\n",
1753 |        "      const buttonEl =\n",
1754 |        "        document.querySelector('#df-75a98ffa-06ce-4a5b-8d7b-9e36e0855d2a button.colab-df-convert');\n",
1755 |        "      buttonEl.style.display =\n",
1756 |        "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1757 |        "\n",
1758 |        "      async function convertToInteractive(key) {\n",
1759 |        "        const element = document.querySelector('#df-75a98ffa-06ce-4a5b-8d7b-9e36e0855d2a');\n",
1760 |        "        const dataTable =\n",
1761 |        "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
1762 |        "                                                    [key], {});\n",
1763 |        "        if (!dataTable) return;\n",
1764 |        "\n",
1765 |        "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
1766 |        "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
1767 |        "          + ' to learn more about interactive tables.';\n",
1768 |        "        element.innerHTML = '';\n",
1769 |        "        dataTable['output_type'] = 'display_data';\n",
1770 |        "        await google.colab.output.renderOutput(dataTable, element);\n",
1771 |        "        const docLink = document.createElement('div');\n",
1772 |        "        docLink.innerHTML = docLinkHtml;\n",
1773 |        "        element.appendChild(docLink);\n",
1774 |        "      }\n",
1775 |        "    </script>\n",
1776 |        "  </div>\n",
1777 |        "\n",
1778 |        "\n",
1779 |        "<div id=\"df-44ad0e36-8122-4442-a0ea-f622510002c2\">\n",
1780 |        "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-44ad0e36-8122-4442-a0ea-f622510002c2')\"\n",
1781 |        "            title=\"Suggest charts\"\n",
1782 |        "            style=\"display:none;\">\n",
1783 |        "\n",
1784 |        "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
1785 |        "     width=\"24px\">\n",
1786 |        "    <g>\n",
1787 |        "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
1788 |        "    </g>\n",
1789 |        "</svg>\n",
1790 |        "  </button>\n",
1791 |        "\n",
1792 |        "<style>\n",
1793 |        "  .colab-df-quickchart {\n",
1794 |        "      --bg-color: #E8F0FE;\n",
1795 |        "      --fill-color: #1967D2;\n",
1796 |        "      --hover-bg-color: #E2EBFA;\n",
1797 |        "      --hover-fill-color: #174EA6;\n",
1798 |        "      --disabled-fill-color: #AAA;\n",
1799 |        "      --disabled-bg-color: #DDD;\n",
1800 |        "  }\n",
1801 |        "\n",
1802 |        "  [theme=dark] .colab-df-quickchart {\n",
1803 |        "      --bg-color: #3B4455;\n",
1804 |        "      --fill-color: #D2E3FC;\n",
1805 |        "      --hover-bg-color: #434B5C;\n",
1806 |        "      --hover-fill-color: #FFFFFF;\n",
1807 |        "      --disabled-bg-color: #3B4455;\n",
1808 |        "      --disabled-fill-color: #666;\n",
1809 |        "  }\n",
1810 |        "\n",
1811 |        "  .colab-df-quickchart {\n",
1812 |        "    background-color: var(--bg-color);\n",
1813 |        "    border: none;\n",
1814 |        "    border-radius: 50%;\n",
1815 |        "    cursor: pointer;\n",
1816 |        "    display: none;\n",
1817 |        "    fill: var(--fill-color);\n",
1818 |        "    height: 32px;\n",
1819 |        "    padding: 0;\n",
1820 |        "    width: 32px;\n",
1821 |        "  }\n",
1822 |        "\n",
1823 |        "  .colab-df-quickchart:hover {\n",
1824 |        "    background-color: var(--hover-bg-color);\n",
1825 |        "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1826 |        "    fill: var(--button-hover-fill-color);\n",
1827 |        "  }\n",
1828 |        "\n",
1829 |        "  .colab-df-quickchart-complete:disabled,\n",
1830 |        "  .colab-df-quickchart-complete:disabled:hover {\n",
1831 |        "    background-color: var(--disabled-bg-color);\n",
1832 |        "    fill: var(--disabled-fill-color);\n",
1833 |        "    box-shadow: none;\n",
1834 |        "  }\n",
1835 |        "\n",
1836 |        "  .colab-df-spinner {\n",
1837 |        "    border: 2px solid var(--fill-color);\n",
1838 |        "    border-color: transparent;\n",
1839 |        "    border-bottom-color: var(--fill-color);\n",
1840 |        "    animation:\n",
1841 |        "      spin 1s steps(1) infinite;\n",
1842 |        "  }\n",
1843 |        "\n",
1844 |        "  @keyframes spin {\n",
1845 |        "    0% {\n",
1846 |        "      border-color: transparent;\n",
1847 |        "      border-bottom-color: var(--fill-color);\n",
1848 |        "      border-left-color: var(--fill-color);\n",
1849 |        "    }\n",
1850 |        "    20% {\n",
1851 |        "      border-color: transparent;\n",
1852 |        "      border-left-color: var(--fill-color);\n",
1853 |        "      border-top-color: var(--fill-color);\n",
1854 |        "    }\n",
1855 |        "    30% {\n",
1856 |        "      border-color: transparent;\n",
1857 |        "      border-left-color: var(--fill-color);\n",
1858 |        "      border-top-color: var(--fill-color);\n",
1859 |        "      border-right-color: var(--fill-color);\n",
1860 |        "    }\n",
1861 |        "    40% {\n",
1862 |        "      border-color: transparent;\n",
1863 |        "      border-right-color: var(--fill-color);\n",
1864 |        "      border-top-color: var(--fill-color);\n",
1865 |        "    }\n",
1866 |        "    60% {\n",
1867 |        "      border-color: transparent;\n",
1868 |        "      border-right-color: var(--fill-color);\n",
1869 |        "    }\n",
1870 |        "    80% {\n",
1871 |        "      border-color: transparent;\n",
1872 |        "      border-right-color: var(--fill-color);\n",
1873 |        "      border-bottom-color: var(--fill-color);\n",
1874 |        "    }\n",
1875 |        "    90% {\n",
1876 |        "      border-color: transparent;\n",
1877 |        "      border-bottom-color: var(--fill-color);\n",
1878 |        "    }\n",
1879 |        "  }\n",
1880 |        "</style>\n",
1881 |        "\n",
1882 |        "  <script>\n",
1883 |        "    async function quickchart(key) {\n",
1884 |        "      const quickchartButtonEl =\n",
1885 |        "        document.querySelector('#' + key + ' button');\n",
1886 |        "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
1887 |        "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
1888 |        "      try {\n",
1889 |        "        const charts = await google.colab.kernel.invokeFunction(\n",
1890 |        "            'suggestCharts', [key], {});\n",
1891 |        "      } catch (error) {\n",
1892 |        "        console.error('Error during call to suggestCharts:', error);\n",
1893 |        "      }\n",
1894 |        "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
1895 |        "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
1896 |        "    }\n",
1897 |        "    (() => {\n",
1898 |        "      let quickchartButtonEl =\n",
1899 |        "        document.querySelector('#df-44ad0e36-8122-4442-a0ea-f622510002c2 button');\n",
1900 |        "      quickchartButtonEl.style.display =\n",
1901 |        "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1902 |        "    })();\n",
1903 |        "  </script>\n",
1904 |        "</div>\n",
1905 |        "    </div>\n",
1906 |        "  </div>\n"
1907 |       ],
1908 |       "text/plain": [
1909 |        "     Name  SAP_pos_CDRH1  SAP_pos_CDRH2  SAP_pos_CDRH3  SAP_pos_CDRL1  \\\n",
1910 |        "0    mAb1       2.134783       2.524245      14.445071       1.904740   \n",
1911 |        "1    mAb2       1.844576       4.339117       8.942592       1.613968   \n",
1912 |        "2    mAb3       2.809425       1.746096      20.808647       0.617971   \n",
1913 |        "3    mAb4       3.139667       0.300687      26.260990       1.863188   \n",
1914 |        "4    mAb5       2.489059       0.111882      15.967413       2.553848   \n",
1915 |        "5    mAb6       9.645024       2.265561      23.064241       6.464199   \n",
1916 |        "6    mAb7       8.222817       2.838049      11.414651       4.315411   \n",
1917 |        "7    mAb8       4.424239       2.095972      16.019022       2.523247   \n",
1918 |        "8    mAb9       1.428219       3.449205       5.459098       2.133244   \n",
1919 |        "9   mAb10       2.341985       0.196968      10.534814       4.143441   \n",
1920 |        "10  mAb11       3.050937       9.065768       5.594949       1.300072   \n",
1921 |        "11  mAb12       2.903481       0.377317      11.468390       6.613725   \n",
1922 |        "12  mAb13       2.835168       1.458057       9.994059       2.109535   \n",
1923 |        "13  mAb14       2.589987       4.791502      20.667749       2.308463   \n",
1924 |        "14  mAb15       2.769219       3.568675      14.486679       9.599416   \n",
1925 |        "15  mAb16       2.071724       7.665909      19.876446       4.267005   \n",
1926 |        "\n",
1927 |        "    SAP_pos_CDRL2  SAP_pos_CDRL3  SAP_pos_CDR  SAP_pos_Hv  SAP_pos_Lv  ...  \\\n",
1928 |        "0        3.589351       3.172182    27.496794   58.417648   30.517651  ...   \n",
1929 |        "1       10.417940       9.945271    38.409042   58.486862   44.568775  ...   \n",
1930 |        "2        3.460975       5.031782    34.877113   63.382252   44.274189  ...   \n",
1931 |        "3        0.188002       6.295355    38.342857   73.466133   38.451836  ...   \n",
1932 |        "4        0.564827       1.848455    23.765207   61.603027   33.180843  ...   \n",
1933 |        "5        0.987525       8.052277    49.345421   61.487507   48.577274  ...   \n",
1934 |        "6        2.673587       5.908565    36.578148   50.778774   44.486637  ...   \n",
1935 |        "7        7.355127       1.797327    34.046429   60.678223   34.210297  ...   \n",
1936 |        "8        2.771093       8.270716    23.198622   45.440292   37.316959  ...   \n",
1937 |        "9        6.528968      15.280798    40.839794   55.972923   49.084797  ...   \n",
1938 |        "10       4.010072       6.862015    30.163914   55.924561   34.403553  ...   \n",
1939 |        "11       5.520360       1.477862    28.122194   51.992001   53.377674  ...   \n",
1940 |        "12       3.517281       3.287737    23.887489   46.273491   35.819031  ...   \n",
1941 |        "13       6.206800       5.448440    43.316048   63.523815   38.004623  ...   \n",
1942 |        "14       2.980665       5.314492    38.965103   48.768085   39.433372  ...   \n",
1943 |        "15       3.184840       6.350702    44.845722   68.781349   37.064419  ...   \n",
1944 |        "\n",
1945 |        "    SCM_pos_CDRH1  SCM_pos_CDRH2  SCM_pos_CDRH3  SCM_pos_CDRL1  SCM_pos_CDRL2  \\\n",
1946 |        "0        3.183182      19.583357      29.513483     116.769501      41.759361   \n",
1947 |        "1       27.889265      22.455563     154.704300      23.503654      37.771931   \n",
1948 |        "2       71.399979      29.845194      41.844185      37.094913      39.442978   \n",
1949 |        "3       27.552292      11.260792      56.283066      13.800642      66.964668   \n",
1950 |        "4       42.208309      10.151648      77.493065     142.884079      81.866493   \n",
1951 |        "5       45.501659      18.139217     136.593094      18.756721      71.840881   \n",
1952 |        "6       52.311821      74.923622      82.053070      72.280479      45.879509   \n",
1953 |        "7       51.687237      -3.795643      91.028336      14.377379      23.035267   \n",
1954 |        "8       15.407248       8.218230      46.540298       8.149660      17.474276   \n",
1955 |        "9       25.994398      14.516958      26.370356      19.893885      69.955307   \n",
1956 |        "10      46.803642      44.563057      90.800636      29.547958     120.159859   \n",
1957 |        "11      35.554497      -3.779891      -0.063098     125.557152      43.826054   \n",
1958 |        "12     114.275505      21.517704      52.248070     111.769119      84.824715   \n",
1959 |        "13      45.412704      26.941366      64.773582      62.668564     109.243866   \n",
1960 |        "14      52.217686      33.024086      68.119850      48.751259     103.548256   \n",
1961 |        "15      33.740913      22.686165     151.189835      41.309410      68.023109   \n",
1962 |        "\n",
1963 |        "    SCM_pos_CDRL3  SCM_pos_CDR   SCM_pos_Hv   SCM_pos_Lv   SCM_pos_Fv  \n",
1964 |        "0       55.548267   263.838928   907.113037  1219.444458  2109.088623  \n",
1965 |        "1       90.647194   360.761566  1224.561279  1132.755249  2335.686523  \n",
1966 |        "2       22.183475   246.880508  1165.597656   830.064209  1963.784912  \n",
1967 |        "3       50.259052   225.270020   993.915222  1097.456543  2073.500244  \n",
1968 |        "4       61.183517   415.960480  1010.628784  1221.234863  2209.069336  \n",
1969 |        "5       66.386726   359.891602  1329.421021  1113.381714  2426.393555  \n",
1970 |        "6       88.023949   428.916351  1007.111145   848.104492  1808.066040  \n",
1971 |        "7       32.922684   198.840347  1157.940308   944.083252  2085.386963  \n",
1972 |        "8       35.439575   130.615524  1107.602173   954.662292  2039.475220  \n",
1973 |        "9       26.277246   174.261017  1044.724976  1139.791870  2159.286621  \n",
1974 |        "10      28.398201   362.141327  1422.787842  1272.123779  2652.440430  \n",
1975 |        "11      73.312820   267.234039   969.922485  1093.797974  2053.877930  \n",
1976 |        "12      86.854416   469.482086  1137.821533  1275.564941  2417.019531  \n",
1977 |        "13      33.384670   342.168793  1134.156250  1143.960449  2254.151367  \n",
1978 |        "14      25.522581   326.298615  1186.510864  1266.150513  2435.450439  \n",
1979 |        "15      32.488457   351.102264  1164.186035  1198.958496  2334.452393  \n",
1980 |        "\n",
1981 |        "[16 rows x 31 columns]"
1982 |       ]
1983 |      },
1984 |      "execution_count": 15,
1985 |      "metadata": {},
1986 |      "output_type": "execute_result"
1987 |     }
1988 |    ],
1989 |    "source": [
1990 |     "features = ['Name', 'SAP_pos_CDRH1','SAP_pos_CDRH2','SAP_pos_CDRH3','SAP_pos_CDRL1','SAP_pos_CDRL2','SAP_pos_CDRL3','SAP_pos_CDR','SAP_pos_Hv','SAP_pos_Lv','SAP_pos_Fv',\n",
1991 |     "          'SCM_neg_CDRH1','SCM_neg_CDRH2','SCM_neg_CDRH3','SCM_neg_CDRL1','SCM_neg_CDRL2','SCM_neg_CDRL3','SCM_neg_CDR','SCM_neg_Hv','SCM_neg_Lv','SCM_neg_Fv',\n",
1992 |     "          'SCM_pos_CDRH1','SCM_pos_CDRH2','SCM_pos_CDRH3','SCM_pos_CDRL1','SCM_pos_CDRL2','SCM_pos_CDRL3','SCM_pos_CDR','SCM_pos_Hv','SCM_pos_Lv','SCM_pos_Fv']\n",
1993 |     "df = pd.concat([pd.DataFrame(name_list), pd.DataFrame(sap_pos),  pd.DataFrame(scm_neg), pd.DataFrame(scm_pos)], ignore_index=True, axis=1,); df.columns = features\n",
1994 |     "df.to_csv('DeepSP_descriptors.csv', index=False)\n",
1995 |     "df"
1996 |    ]
1997 |   },
1998 |   {
1999 |    "cell_type": "code",
2000 |    "execution_count": 15,
2001 |    "metadata": {
2002 |     "id": "R0Gi5_po05Ct"
2003 |    },
2004 |    "outputs": [],
2005 |    "source": []
2006 |   }
2007 |  ],
2008 |  "metadata": {
2009 |   "colab": {
2010 |    "provenance": []
2011 |   },
2012 |   "kernelspec": {
2013 |    "display_name": "Python 3 (ipykernel)",
2014 |    "language": "python",
2015 |    "name": "python3"
2016 |   },
2017 |   "language_info": {
2018 |    "codemirror_mode": {
2019 |     "name": "ipython",
2020 |     "version": 3
2021 |    },
2022 |    "file_extension": ".py",
2023 |    "mimetype": "text/x-python",
2024 |    "name": "python",
2025 |    "nbconvert_exporter": "python",
2026 |    "pygments_lexer": "ipython3",
2027 |    "version": "3.9.13"
2028 |   }
2029 |  },
2030 |  "nbformat": 4,
2031 |  "nbformat_minor": 1
2032 | }
2033 | 


--------------------------------------------------------------------------------
/Conv1D_regressionSAPpos.json:
--------------------------------------------------------------------------------
1 | {"class_name": "Sequential", "config": {"name": "model_conv1D", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, 272, 21], "dtype": "float32", "sparse": false, "ragged": false, "name": "input_1"}}, {"class_name": "Conv1D", "config": {"name": "Conv1D_1", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": [5], "strides": [1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization", "trainable": true, "dtype": "float32", "axis": [2], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}}, {"class_name": "Dropout", "config": {"name": "dropout", "trainable": true, "dtype": "float32", "rate": 0.3, "noise_shape": null, "seed": null}}, {"class_name": "Conv1D", "config": {"name": "Conv1D_2", "trainable": true, "dtype": "float32", "filters": 96, "kernel_size": [4], "strides": [1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization_1", "trainable": true, "dtype": "float32", "axis": [2], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}}, {"class_name": "Conv1D", "config": {"name": "Conv1D_3", "trainable": true, "dtype": "float32", "filters": 32, "kernel_size": [5], "strides": [1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization_2", "trainable": true, "dtype": "float32", "axis": [2], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}}, {"class_name": "MaxPooling1D", "config": {"name": "MaxPooling1D", "trainable": true, "dtype": "float32", "strides": [2], "pool_size": [2], "padding": "valid", "data_format": "channels_last"}}, {"class_name": "Flatten", "config": {"name": "flatten", "trainable": true, "dtype": "float32", "data_format": "channels_last"}}, {"class_name": "Dense", "config": {"name": "Dense_1", "trainable": true, "dtype": "float32", "units": 112, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dense", "config": {"name": "Dense_2", "trainable": true, "dtype": "float32", "units": 48, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dense", "config": {"name": "Dense_3", "trainable": true, "dtype": "float32", "units": 10, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}, "keras_version": "2.11.0", "backend": "tensorflow"}


--------------------------------------------------------------------------------
/Conv1D_regressionSCMneg.json:
--------------------------------------------------------------------------------
1 | {"class_name": "Sequential", "config": {"name": "model_conv1D", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, 272, 21], "dtype": "float32", "sparse": false, "ragged": false, "name": "input_3"}}, {"class_name": "Conv1D", "config": {"name": "Conv1D_1", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": [5], "strides": [1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization_6", "trainable": true, "dtype": "float32", "axis": [2], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}}, {"class_name": "Dropout", "config": {"name": "dropout_4", "trainable": true, "dtype": "float32", "rate": 0.1, "noise_shape": null, "seed": null}}, {"class_name": "Conv1D", "config": {"name": "Conv1D_2", "trainable": true, "dtype": "float32", "filters": 112, "kernel_size": [4], "strides": [1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization_7", "trainable": true, "dtype": "float32", "axis": [2], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}}, {"class_name": "Conv1D", "config": {"name": "Conv1D_3", "trainable": true, "dtype": "float32", "filters": 64, "kernel_size": [4], "strides": [1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization_8", "trainable": true, "dtype": "float32", "axis": [2], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}}, {"class_name": "MaxPooling1D", "config": {"name": "MaxPooling1D", "trainable": true, "dtype": "float32", "strides": [2], "pool_size": [2], "padding": "valid", "data_format": "channels_last"}}, {"class_name": "Flatten", "config": {"name": "flatten_2", "trainable": true, "dtype": "float32", "data_format": "channels_last"}}, {"class_name": "Dense", "config": {"name": "Dense_1", "trainable": true, "dtype": "float32", "units": 128, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dense", "config": {"name": "Dense_2", "trainable": true, "dtype": "float32", "units": 10, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}, "keras_version": "2.11.0", "backend": "tensorflow"}


--------------------------------------------------------------------------------
/Conv1D_regressionSCMpos.json:
--------------------------------------------------------------------------------
1 | {"class_name": "Sequential", "config": {"name": "model_conv1D", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, 272, 21], "dtype": "float32", "sparse": false, "ragged": false, "name": "input_2"}}, {"class_name": "Conv1D", "config": {"name": "Conv1D_1", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": [4], "strides": [1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization_3", "trainable": true, "dtype": "float32", "axis": [2], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}}, {"class_name": "Dropout", "config": {"name": "dropout_1", "trainable": true, "dtype": "float32", "rate": 0.4, "noise_shape": null, "seed": null}}, {"class_name": "Conv1D", "config": {"name": "Conv1D_2", "trainable": true, "dtype": "float32", "filters": 112, "kernel_size": [4], "strides": [1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization_4", "trainable": true, "dtype": "float32", "axis": [2], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}}, {"class_name": "Dropout", "config": {"name": "dropout_2", "trainable": true, "dtype": "float32", "rate": 0.4, "noise_shape": null, "seed": null}}, {"class_name": "Conv1D", "config": {"name": "Conv1D_3", "trainable": true, "dtype": "float32", "filters": 144, "kernel_size": [5], "strides": [1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization_5", "trainable": true, "dtype": "float32", "axis": [2], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}}, {"class_name": "Dropout", "config": {"name": "dropout_3", "trainable": true, "dtype": "float32", "rate": 0.0, "noise_shape": null, "seed": null}}, {"class_name": "MaxPooling1D", "config": {"name": "MaxPooling1D", "trainable": true, "dtype": "float32", "strides": [2], "pool_size": [2], "padding": "valid", "data_format": "channels_last"}}, {"class_name": "Flatten", "config": {"name": "flatten_1", "trainable": true, "dtype": "float32", "data_format": "channels_last"}}, {"class_name": "Dense", "config": {"name": "Dense_1", "trainable": true, "dtype": "float32", "units": 128, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dense", "config": {"name": "Dense_2", "trainable": true, "dtype": "float32", "units": 10, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}, "keras_version": "2.11.0", "backend": "tensorflow"}


--------------------------------------------------------------------------------
/Conv1D_regression_SAPpos.h5:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/Lailabcode/DeepSP/4ba0118fa8e95873daf4a4dcef52beca08b67ab5/Conv1D_regression_SAPpos.h5


--------------------------------------------------------------------------------
/Conv1D_regression_SCMneg.h5:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/Lailabcode/DeepSP/4ba0118fa8e95873daf4a4dcef52beca08b67ab5/Conv1D_regression_SCMneg.h5


--------------------------------------------------------------------------------
/Conv1D_regression_SCMpos.h5:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/Lailabcode/DeepSP/4ba0118fa8e95873daf4a4dcef52beca08b67ab5/Conv1D_regression_SCMpos.h5


--------------------------------------------------------------------------------
/DeepSP-app.py:
--------------------------------------------------------------------------------
  1 | # -*- coding: utf-8 -*-
  2 | """
  3 | Created on Tue Aug 22 17:32:38 2023
  4 | 
  5 | @author: plai3
  6 | """
  7 | 
  8 | import streamlit as st
  9 | import numpy as np
 10 | import pandas as pd
 11 | 
 12 | from keras.models import model_from_json
 13 | 
 14 | from Bio import SeqIO
 15 | from io import StringIO
 16 | from anarci import anarci
 17 | 
 18 | def one_hot_encoder(s):
 19 |     d = {'A': 0, 'C': 1, 'D': 2, 'E': 3, 'F': 4, 'G': 5, 'H': 6, 'I': 7, 'K': 8, 'L': 9, 'M': 10, 'N': 11, 'P': 12, 'Q': 13, 'R': 14, 'S': 15, 'T': 16, 'V': 17, 'W': 18, 'Y': 19, '-': 20}
 20 | 
 21 |     x = np.zeros((len(d), len(s)))
 22 |     x[[d[c] for c in s], range(len(s))] = 1
 23 | 
 24 |     return x
 25 | 
 26 | H_inclusion_list = ['1','2','3','4','5','6','7','8','9','10', \
 27 |                     '11','12','13','14','15','16','17','18','19','20', \
 28 |                     '21','22','23','24','25','26','27','28','29','30', \
 29 |                     '31','32','33','34','35','36','37','38','39','40', \
 30 |                     '41','42','43','44','45','46','47','48','49','50', \
 31 |                     '51','52','53','54','55','56','57','58','59','60', \
 32 |                     '61','62','63','64','65','66','67','68','69','70', \
 33 |                     '71','72','73','74','75','76','77','78','79','80', \
 34 |                     '81','82','83','84','85','86','87','88','89','90', \
 35 |                     '91','92','93','94','95','96','97','98','99','100', \
 36 |                     '101','102','103','104','105','106','107','108','109','110', \
 37 |                     '111','111A','111B','111C','111D','111E','111F','111G','111H', \
 38 |                     '112I','112H','112G','112F','112E','112D','112C','112B','112A','112',\
 39 |                     '113','114','115','116','117','118','119','120', \
 40 |                     '121','122','123','124','125','126','127','128']
 41 | 
 42 | L_inclusion_list = ['1','2','3','4','5','6','7','8','9','10', \
 43 |                     '11','12','13','14','15','16','17','18','19','20', \
 44 |                     '21','22','23','24','25','26','27','28','29','30', \
 45 |                     '31','32','33','34','35','36','37','38','39','40', \
 46 |                     '41','42','43','44','45','46','47','48','49','50', \
 47 |                     '51','52','53','54','55','56','57','58','59','60', \
 48 |                     '61','62','63','64','65','66','67','68','69','70', \
 49 |                     '71','72','73','74','75','76','77','78','79','80', \
 50 |                     '81','82','83','84','85','86','87','88','89','90', \
 51 |                     '91','92','93','94','95','96','97','98','99','100', \
 52 |                     '101','102','103','104','105','106','107','108','109','110', \
 53 |                     '111','112','113','114','115','116','117','118','119','120', \
 54 |                     '121','122','123','124','125','126','127']
 55 | 
 56 | H_dict = {'1': 0, '2':1, '3':2, '4':3, '5':4, '6':5, '7':6, '8':7, '9':8, '10':9, \
 57 |           '11':10, '12':11, '13':12, '14':13, '15':14, '16':15, '17':16, '18':17, '19':18, '20':19, \
 58 |           '21':20, '22':21, '23':22, '24':23, '25':24, '26':25, '27':26, '28':27, '29':28, '30':29, \
 59 |           '31':30, '32':31, '33':32, '34':33, '35':34, '36':35, '37':36, '38':37, '39':38, '40':39, \
 60 |           '41':40, '42':41, '43':42, '44':43, '45':44, '46':45, '47':46, '48':47, '49':48, '50':49, \
 61 |           '51':50, '52':51, '53':52, '54':53, '55':54, '56':55, '57':56, '58':57, '59':58, '60':59, \
 62 |           '61':60, '62':61, '63':62, '64':63, '65':64, '66':65, '67':66, '68':67, '69':68, '70':69, \
 63 |           '71':70, '72':71, '73':72, '74':73, '75':74, '76':75, '77':76, '78':77, '79':78, '80':79, \
 64 |           '81':80, '82':81, '83':82, '84':83, '85':84, '86':85, '87':86, '88':87, '89':88, '90':89, \
 65 |           '91':90, '92':91, '93':92, '94':93, '95':94, '96':95, '97':96, '98':97, '99':98, '100':99, \
 66 |           '101':100,'102':101,'103':102,'104':103,'105':104,'106':105,'107':106,'108':107,'109':108,'110':109, \
 67 |           '111':110,'111A':111,'111B':112,'111C':113,'111D':114,'111E':115,'111F':116,'111G':117,'111H':118, \
 68 |           '112I':119,'112H':120,'112G':121,'112F':122,'112E':123,'112D':124,'112C':125,'112B':126,'112A':127,'112':128, \
 69 |           '113':129,'114':130,'115':131,'116':132,'117':133,'118':134,'119':135,'120':136, \
 70 |           '121':137,'122':138,'123':139,'124':140,'125':141,'126':142,'127':143,'128':144}
 71 | 
 72 | L_dict = {'1': 0, '2':1, '3':2, '4':3, '5':4, '6':5, '7':6, '8':7, '9':8, '10':9, \
 73 |           '11':10, '12':11, '13':12, '14':13, '15':14, '16':15, '17':16, '18':17, '19':18, '20':19, \
 74 |           '21':20, '22':21, '23':22, '24':23, '25':24, '26':25, '27':26, '28':27, '29':28, '30':29, \
 75 |           '31':30, '32':31, '33':32, '34':33, '35':34, '36':35, '37':36, '38':37, '39':38, '40':39, \
 76 |           '41':40, '42':41, '43':42, '44':43, '45':44, '46':45, '47':46, '48':47, '49':48, '50':49, \
 77 |           '51':50, '52':51, '53':52, '54':53, '55':54, '56':55, '57':56, '58':57, '59':58, '60':59, \
 78 |           '61':60, '62':61, '63':62, '64':63, '65':64, '66':65, '67':66, '68':67, '69':68, '70':69, \
 79 |           '71':70, '72':71, '73':72, '74':73, '75':74, '76':75, '77':76, '78':77, '79':78, '80':79, \
 80 |           '81':80, '82':81, '83':82, '84':83, '85':84, '86':85, '87':86, '88':87, '89':88, '90':89, \
 81 |           '91':90, '92':91, '93':92, '94':93, '95':94, '96':95, '97':96, '98':97, '99':98, '100':99, \
 82 |           '101':100,'102':101,'103':102,'104':103,'105':104,'106':105,'107':106,'108':107,'109':108,'110':109, \
 83 |           '111':110,'112':111,'113':112,'114':113,'115':114,'116':115,'117':116,'118':117,'119':118,'120':119, \
 84 |           '121':120,'122':121,'123':122,'124':123,'125':124,'126':125,'127':126,'128':127}
 85 | 
 86 | st.set_page_config(
 87 |     page_title="DeepSP App",
 88 |     layout="centered",
 89 | )
 90 | 
 91 | st.title('DeepSP')
 92 | st.header('Deep learning-based antibody structural properties')
 93 | st.subheader('The FASTA file format is H_seq/L_seq (variable regions)')
 94 | 
 95 | st.markdown('''
 96 | ### EXAMPLE:
 97 | \>6p8n
 98 | QVQLVQSGAEVKKPGASVKVSCKASGYTFTGYYMNWVRQAPGQGLEWMGWINPNSGGTNYAQKFQGRVTMTRDTSISTAYMELSRLRSDDTAVYYCARGKNSDYNWDFQHWGQGTLVTVSS/DIVMSQSPSSLAVSVGEKVTMSCKSSQSLLYSSNQKNYLAWYQQKPGQSPKLLIYWASTRESGVPDRFTGSGSGTDFTLTISSVKAEDLAVYYCQQYEMFGGGTKLEIK 
 99 | ''')
100 | 
101 | seq_file = st.file_uploader("#### Upload your FASTA file", type=['fasta'])
102 | if seq_file is not None:
103 |     stringio = StringIO(seq_file.getvalue().decode("utf-8"))
104 |     sequences_H = []
105 |     sequences_L = []
106 |     name_list = []
107 |     for record in SeqIO.parse(stringio, 'fasta'):
108 |         name = str(record.id)
109 |         name_list.append(name)
110 |         sequence = str(record.seq)
111 |         sequence_H, sequence_L = sequence.split('/')
112 |         sequences_H.append((name,sequence_H))
113 |         sequences_L.append((name,sequence_L))
114 |     
115 |     results_H = anarci(sequences_H, scheme="imgt", output=False)
116 |     results_L = anarci(sequences_L, scheme="imgt", output=False)
117 |     numbering_H, alignment_details_H, hit_tables_H = results_H
118 |     numbering_L, alignment_details_L, hit_tables_L = results_L
119 | 
120 |     # Iterate over the sequences
121 |     seq_list = []
122 |     for i in range(len(sequences_H)):    
123 |         if numbering_H[i] is None:
124 |             print('ANARCI did not number', sequences_H[i][0])
125 |         else:
126 |             domain_numbering_H, start_index_H, end_index_H = numbering_H[i][0]
127 |             domain_numbering_L, start_index_L, end_index_L = numbering_L[i][0]
128 |             H_tmp = 145*['-']
129 |             L_tmp = 127*['-']
130 |             for j in range(len(domain_numbering_H)):
131 |                 col_H = str(domain_numbering_H[j][0][0])+domain_numbering_H[j][0][1]
132 |                 col_H = col_H.replace(" ", "")
133 |                 H_tmp[H_dict[col_H]]=domain_numbering_H[j][1]
134 |             for j in range(len(domain_numbering_L)):
135 |                 col_L = str(domain_numbering_L[j][0][0])+domain_numbering_L[j][0][1]
136 |                 col_L = col_L.replace(" ", "")
137 |                 L_tmp[L_dict[col_L]]=domain_numbering_L[j][1]
138 |             aa_string = ''
139 |             for aa in H_tmp+L_tmp:
140 |                 aa_string += aa
141 |             seq_list.append(aa_string)
142 |     
143 |     X = [one_hot_encoder(s=x) for x in seq_list]
144 |     X = np.transpose(np.asarray(X), (0, 2, 1))
145 |     X = np.asarray(X)
146 | 
147 |     #load DeepSAP_pos model
148 |     json_file = open('Conv1D_regressionSAPpos.json', 'r')
149 |     loaded_model_json = json_file.read()
150 |     json_file.close()
151 |     loaded_model = model_from_json(loaded_model_json)
152 | 
153 |     # load weights into the model
154 |     loaded_model.load_weights('Conv1D_regression_SAPpos.h5')
155 |     loaded_model.compile(optimizer='adam', loss='mae', metrics=['mae'])
156 | 
157 |     # predict SAPpos
158 |     y_pred = loaded_model.predict(X)
159 |     df_SAPpos = pd.DataFrame(y_pred, columns=['SAP_pos_CDRH1', 'SAP_pos_CDRH2', 'SAP_pos_CDRH3',
160 |                                                 'SAP_pos_CDRL1', 'SAP_pos_CDRL2', 'SAP_pos_CDRL3',
161 |                                                 'SAP_pos_CDR', 'SAP_pos_Hv', 'SAP_pos_Lv', 'SAP_pos_Fv'])
162 | 
163 |     #load DeepSCM_neg model
164 |     json_file = open('Conv1D_regressionSCMneg.json', 'r')
165 |     loaded_model_json = json_file.read()
166 |     json_file.close()
167 |     loaded_model = model_from_json(loaded_model_json)
168 |     loaded_model.compile(optimizer='adam', loss='mae', metrics=['mae'])
169 | 
170 |     # load weights into the model
171 |     loaded_model.load_weights('Conv1D_regression_SCMneg.h5')
172 |     loaded_model.compile(optimizer='adam', loss='mae', metrics=['mae'])
173 | 
174 |     # predict SCMneg
175 |     y_pred = loaded_model.predict(X)
176 |     df_SCMneg = pd.DataFrame(y_pred, columns=['SCM_neg_CDRH1', 'SCM_neg_CDRH2', 'SCM_neg_CDRH3',
177 |                                                 'SCM_neg_CDRL1', 'SCM_neg_CDRL2', 'SCM_neg_CDRL3',
178 |                                                 'SCM_neg_CDR', 'SCM_neg_Hv', 'SCM_neg_Lv', 'SCM_neg_Fv'])
179 | 
180 | 
181 |     #load DeepSCM_pos model
182 |     json_file = open('Conv1D_regressionSCMpos.json', 'r')
183 |     loaded_model_json = json_file.read()
184 |     json_file.close()
185 |     loaded_model = model_from_json(loaded_model_json)
186 | 
187 |     # load weights into the model
188 |     loaded_model.load_weights('Conv1D_regression_SCMpos.h5')
189 |     loaded_model.compile(optimizer='adam', loss='mae', metrics=['mae'])
190 | 
191 |     # predict SCMpos
192 |     y_pred = loaded_model.predict(X)
193 |     df_SCMpos = pd.DataFrame(y_pred, columns=['SCM_pos_CDRH1', 'SCM_pos_CDRH2', 'SCM_pos_CDRH3',
194 |                                                 'SCM_pos_CDRL1', 'SCM_pos_CDRL2', 'SCM_pos_CDRL3',
195 |                                                 'SCM_pos_CDR', 'SCM_pos_Hv', 'SCM_pos_Lv', 'SCM_pos_Fv'])
196 | 
197 |     df_name = pd.DataFrame(name_list, columns=['ID'])
198 | 
199 |     df_DeepSP = pd.concat([df_name, df_SAPpos, df_SCMneg, df_SCMpos], axis=1)
200 |     st.dataframe(data = df_DeepSP, use_container_width=True, hide_index=True)
201 | 
202 | 


--------------------------------------------------------------------------------
/DeepSP_input.csv:
--------------------------------------------------------------------------------
 1 | Name,Heavy_Chain,Light_Chain
 2 | mAb1,EVQLVESGGGLVQPGRSLRLSCAASGFTFDDYAMHWVRQAPGKGLEWVSAITWNSGHIDYADSVEGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCAKVSYLSTASSLDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQGIRNYLAWYQQKPGKAPKLLIYAASTLQSGVPSRFSGSGSGTDFTLTISSLQPEDVATYYCQRYNRAPYTFGQGTKVEIK
 3 | mAb2,EVQLVESGGGLVQPGGSLRLSCAASGFTFSDSWIHWVRQAPGKGLEWVAWISPYGGSTYYADSVKGRFTISADTSKNTAYLQMNSLRAEDTAVYYCARRHWPGGFDYWGQGTLVTVSA,DIQMTQSPSSLSASVGDRVTITCRASQDVSTAVAWYQQKPGKAPKLLIYSASFLYSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYLYHPATFGQGTKVEIK
 4 | mAb3,QVQLKQSGPGLVQPSQSLSITCTVSGFSLTNYGVHWVRQSPGKGLEWLGVIWSGGNTDYNTPFTSRLSINKDNSKSQVFFKMNSLQSNDTAIYYCARALTYYDYEFAYWGQGTLVTVSA,DILLTQSPVILSVSPGERVSFSCRASQSIGTNIHWYQQRTNGSPRLLIKYASESISGIPSRFSGSGSGTDFTLSINSVESEDIADYYCQQNNNWPTTFGAGTKLELK
 5 | mAb4,EVQLLESGGGLVQPGGSLRLSCAVSGFTFNSFAMSWVRQAPGKGLEWVSAISGSGGGTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYFCAKDKILWFGEPVFDYWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPPTFGQGTKVEIK
 6 | mAb5,EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSGITGSGGSTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCAKDPGTTVIMSWFDPWGQGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVRGRYLAWYQQKPGQAPRLLIYGASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVFYCQQYGSSPRTFGQGTKVEIK
 7 | mAb6,QVQLVESGGGVVQPGRSLRLSCAASGFIFSSYAMHWVRQAPGNGLEWVAFMSYDGSNKKYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARDRGIAAGGNYYYYGMDVWGQGTTVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVYSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPPFTFGPGTKVDIK
 8 | mAb7,EVKLEESGGGLVQPGGSMKLSCVASGFIFSNHWMNWVRQSPEKGLEWVAEIRSKSINSATHYAESVKGRFTISRDDSKSAVYLQMTDLRTEDTGVYYCSRNYYGSTYDYWGQGTTLTVSS,DILLTQSPAILSVSPGERVSFSCRASQFVGSSIHWYQQRTNGSPRLLIKYASESMSGIPSRFSGSGSGTDFTLSINTVESEDIADYYCQQSHSWPFTFGSGTNLEVK
 9 | mAb8,EVQLVESGGGLVQPGGSLRLSCAVSGYSITSGYSWNWIRQAPGKGLEWVASITYDGSTNYNPSVKGRITISRDDSKNTFYLQMNSLRAEDTAVYYCARGSHYFGHWHFAVWGQGTLVTVSS,DIQLTQSPSSLSASVGDRVTITCRASQSVDYDGDSYMNWYQQKPGKAPKLLIYAASYLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSHEDPYTFGQGTKVEIK
10 | mAb9,QVQLQESGPGLVKPSETLSLTCTVSGGSVSSGDYYWTWIRQSPGKGLEWIGHIYYSGNTNYNPSLKSRLTISIDTSKTQFSLKLSSVTAADTAIYYCVRDRVTGAFDIWGQGTMVTVSS,DIQMTQSPSSLSASVGDRVTITCQASQDISNYLNWYQQKPGKAPKLLIYDASNLETGVPSRFSGSGSGTDFTFTISSLQPEDIATYFCQHFDHLPLAFGGGTKVEIK
11 | mAb10,EVQLVESGGGLVQPGGSLRLSCAASGFTFTDYTMDWVRQAPGKGLEWVADVNPNSGGSIYNQRFKGRFTLSVDRSKNTLYLQMNSLRAEDTAVYYCARNLGPSFYFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQDVSIGVAWYQQKPGKAPKLLIYSASYRYTGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYYIYPYTFGQGTKVEIK
12 | mAb11,QVQLQESGPGLVRPSQTLSLTCTVSGYSITSDHAWSWVRQPPGRGLEWIGYISYSGITTYNPSLKSRVTMLRDTSKNQFSLRLSSVTAADTAVYYCARSLARTTAMDYWGQGSLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQDISSYLNWYQQKPGKAPKLLIYYTSRLHSGVPSRFSGSGSGTDFTFTISSLQPEDIATYYCQQGNTLPYTFGQGTKVEIK
13 | mAb12,QVQLVQSGAEVKKPGASVKVSCKGSGYTFTSYWMHWVRQAPGQRLEWIGEIDPSESNTNYNQKFKGRVTLTVDISASTAYMELSSLRSEDTAVYYCARGGYDGWDYAIDYWGQGTLVTVSS,DVVMTQSPLSLPVTPGEPASISCRSSQSLAKSYGNTYLSWYLQKPGQSPQLLIYGISNRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCLQGTHQPYTFGQGTKVEIK
14 | mAb13,QLQQSGTVLARPGASVKMSCKASGYSFTRYWMHWIKQRPGQGLEWIGAIYPGNSDTSYNQKFEGKAKLTAVTSASTAYMELSSLTHEDSAVYYCSRDYGYYFDFWGQGTTLTVSS,QIVSTQSPAIMSASPGEKVTMTCSASSSRSYMQWYQQKPGTSPKRWIYDTSKLASGVPARFSGSGSGTSYSLTISSMEAEDAATYYCHQRSSYTFGGGTKLEIK
15 | mAb14,QVQLVQSGAEVKKPGASVKVSCKASGFNIKDTYIHWVRQAPGQRLEWMGRIDPANGYTKYDPKFQGRVTITADTSASTAYMELSSLRSEDEAVYYCAREGYYGNYGVYAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKTSQDINKYMAWYQQTPGKAPRLLIHYTSALQPGIPSRFSGSGSGRDYTFTISSLQPEDIATYYCLQYDNLWTFGQGTKVEIK
16 | mAb15,QVQLVQSGAEVKKPGASVKVSCKASGYTFTSYYIHWVRQAPGQGLEWIGCIYPGNVNTNYNEKFKDRATLTVDTSISTAYMELSRLRSDDTAVYFCTRSHYGLDWNFDVWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCHASQNIYVWLNWYQQKPGKAPKLLIYKASNLHTGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQGQTYPYTFGGGTKVEIK
17 | mAb16,EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSQISPAGGYTNYADSVKGRFTISADTSKNTAYLQMNSLRAEDTAVYYCARGELPYYRMSKVMDVWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQYFSSYLAWYQQKPGKAPKLLIYGASSRASGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYLGSPPTFGQGTKVEIK
18 | 


--------------------------------------------------------------------------------
/DeepSP_model_train.py:
--------------------------------------------------------------------------------
  1 | # Import libraries
  2 | import numpy as np
  3 | import pandas as pd
  4 | import random
  5 | 
  6 | 
  7 | from numpy.random import seed
  8 | 
  9 | # Import machine learning libraries
 10 | import tensorflow as tf
 11 | import keras
 12 | from keras.models import model_from_json
 13 | from keras.layers import BatchNormalization
 14 | from keras.callbacks import ModelCheckpoint
 15 | from keras.optimizers import Adam
 16 | import keras_tuner as kt
 17 | from sklearn.model_selection import train_test_split
 18 | from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
 19 | 
 20 | 
 21 | np.random.seed(0)
 22 | random.seed(0)
 23 | tf.random.set_seed(0)
 24 | 
 25 | 
 26 | def load_input_data(filename):
 27 |     name_list=[]
 28 |     seq_list=[]
 29 |     score_list=[]
 30 | 
 31 |     with open(filename) as datafile:
 32 |         for line in datafile:
 33 |             line = line.strip().split()
 34 |             name_list.append(line[0])
 35 |             seq_list.append(line[1])
 36 |             score_temp = []
 37 |             for i in range(len(line[2:])):
 38 |                     data = float(line[i+2])
 39 |                     score_temp.append(data)
 40 |             score_list.append(score_temp)
 41 |     return name_list, seq_list, score_list
 42 | 
 43 | def one_hot_encoder(s):
 44 |     d = {'A': 0, 'C': 1, 'D': 2, 'E': 3, 'F': 4, 'G': 5, 'H': 6, 'I': 7, 'K': 8, 'L': 9, 'M': 10, 'N': 11, 'P': 12, 'Q': 13, 'R': 14, 'S': 15, 'T': 16, 'V': 17, 'W': 18, 'Y': 19, '-': 20}
 45 | 
 46 |     x = np.zeros((len(d), len(s)))
 47 |     x[[d[c] for c in s], range(len(s))] = 1
 48 | 
 49 |     return x
 50 | 
 51 | 
 52 | def best_model_SAPpos():    
 53 |     best_model = keras.Sequential(name="model_conv1D")
 54 |             
 55 |     best_model.add(keras.layers.Input(shape=(272,21)))
 56 | 
 57 |     best_model.add(keras.layers.Conv1D(filters=128, kernel_size=5, activation = 'relu', name="Conv1D_1"))
 58 |     best_model.add(BatchNormalization())
 59 |     best_model.add(keras.layers.Dropout(0.3))
 60 | 
 61 |     best_model.add(keras.layers.Conv1D(filters=96, kernel_size=4, activation = 'relu', name="Conv1D_2"))
 62 |     best_model.add(BatchNormalization())
 63 | 
 64 |     best_model.add(keras.layers.Conv1D(filters=32, kernel_size=5, activation = 'relu', name="Conv1D_3"))
 65 |     best_model.add(BatchNormalization())
 66 | 
 67 |     best_model.add(keras.layers.MaxPooling1D(pool_size=2, name="MaxPooling1D"))
 68 |     best_model.add(keras.layers.Flatten())
 69 | 
 70 | 
 71 |     # Input layer and First hidden layer of neural network
 72 |     best_model.add(keras.layers.Dense(units=112, activation = 'relu', name="Dense_1"))
 73 |     best_model.add(keras.layers.Dense(units=48, activation = 'relu', name="Dense_2"))
 74 |     best_model.add(keras.layers.Dense(10, name="Dense_3"))
 75 |     
 76 |     return best_model
 77 | 
 78 | 
 79 | def best_model_SCMpos():    
 80 |     best_model = keras.Sequential(name="model_conv1D")
 81 |             
 82 |     best_model.add(keras.layers.Input(shape=(272,21)))
 83 | 
 84 |     best_model.add(keras.layers.Conv1D(filters=128, kernel_size=4, activation = 'relu', name="Conv1D_1"))
 85 |     best_model.add(BatchNormalization())
 86 |     best_model.add(keras.layers.Dropout(0.4))
 87 | 
 88 |     best_model.add(keras.layers.Conv1D(filters=112, kernel_size=4, activation = 'relu', name="Conv1D_2"))
 89 |     best_model.add(BatchNormalization())
 90 |     best_model.add(keras.layers.Dropout(0.4))
 91 | 
 92 |     best_model.add(keras.layers.Conv1D(filters=144, kernel_size=5, activation = 'relu', name="Conv1D_3"))
 93 |     best_model.add(BatchNormalization())
 94 |     best_model.add(keras.layers.Dropout(0.0))
 95 | 
 96 |     best_model.add(keras.layers.MaxPooling1D(pool_size=2, name="MaxPooling1D"))
 97 |     best_model.add(keras.layers.Flatten())
 98 | 
 99 |     # Input layer and First hidden layer of neural network
100 |     best_model.add(keras.layers.Dense(units=128, activation = 'relu', name="Dense_1"))
101 |     best_model.add(keras.layers.Dense(10, name="Dense_2"))
102 |     
103 |     return best_model
104 | 
105 | 
106 | def best_model_SCMneg():    
107 |     best_model = keras.Sequential(name="model_conv1D")
108 |             
109 |     best_model.add(keras.layers.Input(shape=(272,21)))
110 | 
111 |     best_model.add(keras.layers.Conv1D(filters=128, kernel_size=5, activation = 'relu', name="Conv1D_1"))
112 |     best_model.add(BatchNormalization())
113 |     best_model.add(keras.layers.Dropout(0.1))
114 | 
115 |     best_model.add(keras.layers.Conv1D(filters=112, kernel_size=4, activation = 'relu', name="Conv1D_2"))
116 |     best_model.add(BatchNormalization())
117 | 
118 |     best_model.add(keras.layers.Conv1D(filters=64, kernel_size=4, activation = 'relu', name="Conv1D_3"))
119 |     best_model.add(BatchNormalization())
120 | 
121 |     best_model.add(keras.layers.MaxPooling1D(pool_size=2, name="MaxPooling1D"))
122 |     best_model.add(keras.layers.Flatten())
123 | 
124 |     # Input layer and First hidden layer of neural network
125 |     best_model.add(keras.layers.Dense(units=128, activation = 'relu', name="Dense_1"))
126 |     best_model.add(keras.layers.Dense(10, name="Dense_2"))
127 |     
128 |     return best_model
129 | 
130 | 
131 | #ts = 0.2; bs = 64
132 | filenames = ['Deep_SAPpos_data.txt', 'Deep_SCMpos_data.txt', 'Deep_SCMneg_data.txt']
133 | models = [best_model_SAPpos(), best_model_SCMpos(), best_model_SCMneg()]
134 | l_rates = [0.0001, 0.005, 0.0001]
135 | 
136 | for file, model, l_rate in zip(filenames,models,l_rates):
137 |     prop = file.split('_')[1]
138 | 
139 |     name_list, seq_list, score_list = load_input_data("data/"+file)
140 |     X= seq_list; y= score_list
141 | 
142 |     #Train and compile model with best hyperparameters
143 |     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
144 |     X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=0)
145 | 
146 |     X_train = [one_hot_encoder(s=x) for x in X_train]
147 |     X_train = np.transpose(np.asarray(X_train), (0, 2, 1))
148 |     X_train = np.asarray(X_train)
149 | 
150 |     X_test = [one_hot_encoder(s=x) for x in X_test]
151 |     X_test = np.transpose(np.asarray(X_test), (0, 2, 1))
152 |     X_test = np.asarray(X_test)
153 | 
154 |     X_val = [one_hot_encoder(s=x) for x in X_val]
155 |     X_val = np.transpose(np.asarray(X_val), (0, 2, 1))
156 |     X_val = np.asarray(X_val)
157 | 
158 |     y_train = np.asarray(y_train).reshape((-1, 10))
159 |     y_test = np.asarray(y_test).reshape((-1, 10))
160 |     y_val = np.asarray(y_val).reshape((-1, 10))
161 | 
162 |     optimizer = Adam(learning_rate = l_rate)
163 |     best_model = model
164 |     best_model.compile(optimizer=optimizer, loss='mae', metrics=None)
165 | 
166 |     # Create callback
167 |     filepath = 'Conv1D_regression_'+prop+'.h5'
168 |     checkpoint = ModelCheckpoint(filepath=filepath, monitor='val_loss', verbose=1, save_best_only=True, save_weights_only=True, mode='min')
169 |     callbacks = [checkpoint]
170 |     
171 |     # Fit the CNN to the training set
172 |     history = best_model.fit(x=X_train, y=y_train, shuffle=True, validation_data=(X_val, y_val), epochs=50, callbacks=callbacks, batch_size=64, verbose=2)
173 | 
174 |     # Save the Conv1D architecture to json
175 |     Conv1D_regression_json = best_model.to_json()
176 |     with open("Conv1D_regression"+prop+".json", "w") as json_file:
177 |         json_file.write(Conv1D_regression_json)
178 | 
179 | 
180 |     # Load the Conv1D architecture from json
181 |     pred_model = model_from_json(Conv1D_regression_json)
182 | 
183 |     # Load weights from the best model into Conv1D model
184 |     pred_model.load_weights(filepath)
185 | 
186 |     # Compile the loaded Conv1D model
187 |     pred_model.compile(optimizer=optimizer, metrics=['mae'])
188 | 
189 |     y_pred = pred_model.predict(X_test)
190 | 
191 |     best_val_loss = min(history.history['val_loss'])
192 | 
193 |     # Initialize lists to store baseline MAE and mean scores for each target
194 |     baseline_mae_list = []
195 |     mean_score_list = []
196 | 
197 |     for i in range(y_test.shape[1]):
198 |         # Calculate the baseline MAE for the i-th target
199 |         baseline_prediction = np.full_like(y_test[:, i], np.mean(y_test[:, i]))
200 |         baseline_mae = mean_absolute_error(y_test[:, i], baseline_prediction)
201 |         baseline_mae_list.append(baseline_mae)
202 | 
203 |         # Calculate the mean score for the i-th target
204 |         mean_score = np.mean(y_test[:, i])
205 |         mean_score_list.append(mean_score)
206 | 
207 |     # Initialize lists to store metrics for each target
208 |     mae_list = []
209 |     corr_list = []
210 | 
211 |     for i in range(y_test.shape[1]):
212 |         # Calculate MAE for the i-th target
213 |         mae = mean_absolute_error(y_test[:, i], y_pred[:, i])
214 |         mae_list.append(mae)
215 | 
216 |         # Calculate correlation coefficient (correlation) for the i-th target
217 |         corr = np.corrcoef(y_test[:, i], y_pred[:, i])[0, 1]
218 |         corr_list.append(corr)
219 | 
220 |     reg = ['CDRH1', 'CDRH2', 'CDRH3', 'CDRL1', 'CDRL2', 'CDRL3', 'CDR', 'Hv', 'Lv', 'Fv']
221 | 
222 |     result_dict = {
223 |     "prop": [],
224 |     "Mean_score": [],
225 |     "Baseline_MAE": [],
226 |     "Val_loss": [],
227 |     "MAE": [],
228 |     "R": [],
229 |     }
230 | 
231 |     for r, i, j, k, l in zip(reg, mean_score_list, baseline_mae_list, mae_list, corr_list):
232 |         # Append the corresponding values to the result_dict
233 |         result_dict["prop"].append(prop + r)
234 |         result_dict["Mean_score"].append(i)
235 |         result_dict["Baseline_MAE"].append(j)
236 |         result_dict["Val_loss"].append(best_val_loss)
237 |         result_dict["MAE"].append(k)
238 |         result_dict["R"].append(l)
239 | 
240 |     # Create the DataFrame
241 |     result_df = pd.DataFrame(result_dict)
242 | 
243 |     # Save the DataFrame to CSV
244 |     result_df.to_csv("hyp_metric_" + prop + ".csv", index=False)
245 |     
246 |     his_df = pd.DataFrame(history.history)
247 |     his_df.to_csv("his" + prop + ".csv", index=False)
248 | 
249 | data_frames = []
250 | for file in filenames:
251 |     prop =  file.split('_')[1]
252 |     infile = "hyp_metric_" + prop + ".csv"
253 |     df = pd.read_csv(infile)
254 |     data_frames.append(df)
255 | concatenated_df = pd.concat(data_frames, ignore_index=True)
256 | concatenated_df.to_csv("Final_model_metric.csv", index=False)
257 | 
258 | 
259 | 
260 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2024 Lailabcode
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # DeepSP
 2 | DeepSP is an antibody-specific surrogate model that can generate 30 spatial properties of an antibody solely based on their sequence.
 3 | 
 4 | # How to generate descriptors (features) using DeepSP
 5 | 
 6 | ## Option 1 - Google colab notebook
 7 | - Run
 8 | 1. Prepare your input file according to the format DeepSP_input.csv
 9 | 2. Run the notebook file DeepSP_predictor.ipynb
10 | 3. DeepSP structural properties for sequences inputed, will be populated and saved to a csv file - 'DeepSP_descriptor.csv'.
11 | 
12 | ## Option 2 - Linux environment 
13 | - Set up (bash)- create an environment and install necessary package
14 | 1. conda create -n deepSP python=3.9.13
15 | 2. source activate deepSP
16 | 3. conda install -c bioconda anarci
17 | 4. pip install keras==2.11.0 tensorflow-cpu==2.11.0 scikit-learn==1.0.2 pandas numpy==1.26.4
18 | - Run
19 | 1. Prepare your input file according to the format DeepSP_input.csv
20 | 2. Run the python file deepsp_predictor.py - 'python deepsp_predictor.py'
21 | 3. DeepSP structural properties for sequences inputed, will be obtained and saved to a csv file - 'DeepSP_descriptor.csv'.
22 | 
23 | 
24 | # Citation
25 | 
26 | Kalejaye, L.; Wu, I.-E.; Terry, T.; Lai, P.-K. DeepSP: Deep Learning-Based Spatial Properties to Predict Monoclonal Antibody Stability. *Comput. Struct. Biotechnol. J.* 2024, 23, 2220–2229 (https://doi.org/10.1016/j.csbj.2024.05.029)


--------------------------------------------------------------------------------
/deepsp_predictor.py:
--------------------------------------------------------------------------------
  1 | # Import libraries
  2 | import os
  3 | os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
  4 | os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1' 
  5 | import numpy as np
  6 | import pandas as pd
  7 | import random
  8 | from numpy.random import seed
  9 | 
 10 | # Import machine learning libraries
 11 | import tensorflow as tf
 12 | from tensorflow.keras.models import model_from_json
 13 | 
 14 | import keras
 15 | from keras.models import model_from_json
 16 | 
 17 | from Bio import SeqIO
 18 | from Bio.Seq import Seq
 19 | from Bio.SeqRecord import SeqRecord
 20 | 
 21 | 
 22 | # Import dataset
 23 | dataset = pd.read_csv('DeepSP_input.csv') # replace with your csv file, see format in DeepSP_input.csv file
 24 | name = dataset['Name'].to_list()
 25 | Heavy_seq = dataset['Heavy_Chain'].to_list()
 26 | Light_seq = dataset['Light_Chain'].to_list()
 27 | 
 28 | # Convert to Fasta File
 29 | file_out='seq_H.fasta'
 30 | with open(file_out, "w") as output_handle:
 31 |   for i in range(len(name)):
 32 |     seq_name = name[i]
 33 |     seq = Heavy_seq[i]
 34 |     record = SeqRecord(
 35 |     Seq(seq),
 36 |     id=seq_name,
 37 |     name="",
 38 |     description="",
 39 |     )
 40 |     SeqIO.write(record, output_handle, "fasta")
 41 | 
 42 | file_out='seq_L.fasta'
 43 | with open(file_out, "w") as output_handle:
 44 |   for i in range(len(name)):
 45 |     seq_name = name[i]
 46 |     seq = Light_seq[i]
 47 |     record = SeqRecord(
 48 |     Seq(seq),
 49 |     id=seq_name,
 50 |     name="",
 51 |     description="",
 52 |     )
 53 |     SeqIO.write(record, output_handle, "fasta")
 54 | 
 55 | # sequence alignment with ANARCI
 56 | os.system('ANARCI -i seq_H.fasta -o seq_aligned -s imgt -r heavy --csv')
 57 | os.system('ANARCI -i seq_L.fasta -o seq_aligned -s imgt -r light --csv')
 58 | 
 59 | H_aligned = pd.read_csv('seq_aligned_H.csv')
 60 | L_aligned = pd.read_csv('seq_aligned_KL.csv')
 61 | 
 62 | #sequence preprocessing: source - https://github.com/Lailabcode/DeepSCM/blob/main/deepscm-master/seq_preprocessing.py
 63 | def seq_preprocessing():
 64 |   infile_H = pd.read_csv('seq_aligned_H.csv')
 65 |   infile_L = pd.read_csv('seq_aligned_KL.csv')
 66 |   outfile = open('seq_aligned_HL.txt', "w")
 67 | 
 68 |   H_inclusion_list = ['1','2','3','4','5','6','7','8','9','10', \
 69 |                     '11','12','13','14','15','16','17','18','19','20', \
 70 |                     '21','22','23','24','25','26','27','28','29','30', \
 71 |                     '31','32','33','34','35','36','37','38','39','40', \
 72 |                     '41','42','43','44','45','46','47','48','49','50', \
 73 |                     '51','52','53','54','55','56','57','58','59','60', \
 74 |                     '61','62','63','64','65','66','67','68','69','70', \
 75 |                     '71','72','73','74','75','76','77','78','79','80', \
 76 |                     '81','82','83','84','85','86','87','88','89','90', \
 77 |                     '91','92','93','94','95','96','97','98','99','100', \
 78 |                     '101','102','103','104','105','106','107','108','109','110', \
 79 |                     '111','111A','111B','111C','111D','111E','111F','111G','111H', \
 80 |                     '112I','112H','112G','112F','112E','112D','112C','112B','112A','112',\
 81 |                     '113','114','115','116','117','118','119','120', \
 82 |                     '121','122','123','124','125','126','127','128']
 83 | 
 84 |   L_inclusion_list = ['1','2','3','4','5','6','7','8','9','10', \
 85 |                     '11','12','13','14','15','16','17','18','19','20', \
 86 |                     '21','22','23','24','25','26','27','28','29','30', \
 87 |                     '31','32','33','34','35','36','37','38','39','40', \
 88 |                     '41','42','43','44','45','46','47','48','49','50', \
 89 |                     '51','52','53','54','55','56','57','58','59','60', \
 90 |                     '61','62','63','64','65','66','67','68','69','70', \
 91 |                     '71','72','73','74','75','76','77','78','79','80', \
 92 |                     '81','82','83','84','85','86','87','88','89','90', \
 93 |                     '91','92','93','94','95','96','97','98','99','100', \
 94 |                     '101','102','103','104','105','106','107','108','109','110', \
 95 |                     '111','112','113','114','115','116','117','118','119','120', \
 96 |                     '121','122','123','124','125','126','127']
 97 | 
 98 |   H_dict = {'1': 0, '2':1, '3':2, '4':3, '5':4, '6':5, '7':6, '8':7, '9':8, '10':9, \
 99 |           '11':10, '12':11, '13':12, '14':13, '15':14, '16':15, '17':16, '18':17, '19':18, '20':19, \
100 |           '21':20, '22':21, '23':22, '24':23, '25':24, '26':25, '27':26, '28':27, '29':28, '30':29, \
101 |           '31':30, '32':31, '33':32, '34':33, '35':34, '36':35, '37':36, '38':37, '39':38, '40':39, \
102 |           '41':40, '42':41, '43':42, '44':43, '45':44, '46':45, '47':46, '48':47, '49':48, '50':49, \
103 |           '51':50, '52':51, '53':52, '54':53, '55':54, '56':55, '57':56, '58':57, '59':58, '60':59, \
104 |           '61':60, '62':61, '63':62, '64':63, '65':64, '66':65, '67':66, '68':67, '69':68, '70':69, \
105 |           '71':70, '72':71, '73':72, '74':73, '75':74, '76':75, '77':76, '78':77, '79':78, '80':79, \
106 |           '81':80, '82':81, '83':82, '84':83, '85':84, '86':85, '87':86, '88':87, '89':88, '90':89, \
107 |           '91':90, '92':91, '93':92, '94':93, '95':94, '96':95, '97':96, '98':97, '99':98, '100':99, \
108 |           '101':100,'102':101,'103':102,'104':103,'105':104,'106':105,'107':106,'108':107,'109':108,'110':109, \
109 |           '111':110,'111A':111,'111B':112,'111C':113,'111D':114,'111E':115,'111F':116,'111G':117,'111H':118, \
110 |           '112I':119,'112H':120,'112G':121,'112F':122,'112E':123,'112D':124,'112C':125,'112B':126,'112A':127,'112':128, \
111 |           '113':129,'114':130,'115':131,'116':132,'117':133,'118':134,'119':135,'120':136, \
112 |           '121':137,'122':138,'123':139,'124':140,'125':141,'126':142,'127':143,'128':144}
113 | 
114 |   L_dict = {'1': 0, '2':1, '3':2, '4':3, '5':4, '6':5, '7':6, '8':7, '9':8, '10':9, \
115 |           '11':10, '12':11, '13':12, '14':13, '15':14, '16':15, '17':16, '18':17, '19':18, '20':19, \
116 |           '21':20, '22':21, '23':22, '24':23, '25':24, '26':25, '27':26, '28':27, '29':28, '30':29, \
117 |           '31':30, '32':31, '33':32, '34':33, '35':34, '36':35, '37':36, '38':37, '39':38, '40':39, \
118 |           '41':40, '42':41, '43':42, '44':43, '45':44, '46':45, '47':46, '48':47, '49':48, '50':49, \
119 |           '51':50, '52':51, '53':52, '54':53, '55':54, '56':55, '57':56, '58':57, '59':58, '60':59, \
120 |           '61':60, '62':61, '63':62, '64':63, '65':64, '66':65, '67':66, '68':67, '69':68, '70':69, \
121 |           '71':70, '72':71, '73':72, '74':73, '75':74, '76':75, '77':76, '78':77, '79':78, '80':79, \
122 |           '81':80, '82':81, '83':82, '84':83, '85':84, '86':85, '87':86, '88':87, '89':88, '90':89, \
123 |           '91':90, '92':91, '93':92, '94':93, '95':94, '96':95, '97':96, '98':97, '99':98, '100':99, \
124 |           '101':100,'102':101,'103':102,'104':103,'105':104,'106':105,'107':106,'108':107,'109':108,'110':109, \
125 |           '111':110,'112':111,'113':112,'114':113,'115':114,'116':115,'117':116,'118':117,'119':118,'120':119, \
126 |           '121':120,'122':121,'123':122,'124':123,'125':124,'126':125,'127':126,'128':127}
127 | 
128 | 
129 |   N_mAbs = len(infile_H["Id"])
130 |   
131 |   for i in range(N_mAbs):
132 |     H_tmp = 145*['-']
133 |     L_tmp = 127*['-']
134 |     for col in infile_H.columns:
135 |         if(col in H_inclusion_list):
136 |             H_tmp[H_dict[col]]=infile_H.iloc[i][col]
137 |     for col in infile_L.columns:
138 |         if(col in L_inclusion_list):
139 |             L_tmp[L_dict[col]]=infile_L.iloc[i][col]
140 | 
141 |     aa_string = ''
142 |     for aa in H_tmp+L_tmp:
143 |          aa_string += aa
144 |     outfile.write(infile_H.iloc[i,0]+" "+aa_string)
145 |     outfile.write("\n")
146 | 
147 |   outfile.close()
148 |   return
149 | 
150 | seq_preprocessing()
151 | 
152 | # Read Aligned Sequence
153 | def load_input_data(filename):
154 |     name_list=[]
155 |     seq_list=[]
156 |     with open(filename) as datafile:
157 |         for line in datafile:
158 |             line = line.strip().split()
159 |             name_list.append(line[0])
160 |             seq_list.append(line[1])
161 |     return name_list, seq_list
162 | 
163 | name_list, seq_list = load_input_data('seq_aligned_HL.txt')
164 | X = seq_list
165 | 
166 | # One Hot Encoding of Aligned Sequence
167 | def one_hot_encoder(s):
168 |     d = {'A': 0, 'C': 1, 'D': 2, 'E': 3, 'F': 4, 'G': 5, 'H': 6, 'I': 7, 'K': 8, 'L': 9, 'M': 10, 'N': 11, 'P': 12, 'Q': 13, 'R': 14, 'S': 15, 'T': 16, 'V': 17, 'W': 18, 'Y': 19, '-': 20}
169 | 
170 |     x = np.zeros((len(d), len(s)))
171 |     x[[d[c] for c in s], range(len(s))] = 1
172 | 
173 |     return x
174 | X = [one_hot_encoder(s=x) for x in X]
175 | X = np.transpose(np.asarray(X), (0, 2, 1))
176 | X = np.asarray(X)
177 | 
178 | 
179 | # Predict DeepSP Descriptors
180 | 
181 | # sappos
182 | json_file = open('Conv1D_regressionSAPpos.json', 'r')
183 | loaded_model_json = json_file.read()
184 | json_file.close()
185 | loaded_model = model_from_json(loaded_model_json)
186 | # load weights into model
187 | loaded_model.load_weights("Conv1D_regression_SAPpos.h5")
188 | loaded_model.compile(optimizer='adam', loss='mae', metrics=['mae'])
189 | sap_pos = loaded_model.predict(X)
190 | 
191 | # scmneg
192 | json_file = open('Conv1D_regressionSCMneg.json', 'r')
193 | loaded_model_json = json_file.read()
194 | json_file.close()
195 | loaded_model = model_from_json(loaded_model_json)
196 | # load weights into model
197 | loaded_model.load_weights("Conv1D_regression_SCMneg.h5")
198 | loaded_model.compile(optimizer='adam', loss='mae', metrics=['mae'])
199 | scm_neg = loaded_model.predict(X)
200 | 
201 | # scmpos
202 | json_file = open('Conv1D_regressionSCMpos.json', 'r')
203 | loaded_model_json = json_file.read()
204 | json_file.close()
205 | loaded_model = model_from_json(loaded_model_json)
206 | # load weights into model
207 | loaded_model.load_weights("Conv1D_regression_SCMpos.h5")
208 | loaded_model.compile(optimizer='adam', loss='mae', metrics=['mae'])
209 | scm_pos = loaded_model.predict(X)
210 | 
211 | features = ['Name', 'SAP_pos_CDRH1','SAP_pos_CDRH2','SAP_pos_CDRH3','SAP_pos_CDRL1','SAP_pos_CDRL2','SAP_pos_CDRL3','SAP_pos_CDR','SAP_pos_Hv','SAP_pos_Lv','SAP_pos_Fv',
212 |           'SCM_neg_CDRH1','SCM_neg_CDRH2','SCM_neg_CDRH3','SCM_neg_CDRL1','SCM_neg_CDRL2','SCM_neg_CDRL3','SCM_neg_CDR','SCM_neg_Hv','SCM_neg_Lv','SCM_neg_Fv',
213 |           'SCM_pos_CDRH1','SCM_pos_CDRH2','SCM_pos_CDRH3','SCM_pos_CDRL1','SCM_pos_CDRL2','SCM_pos_CDRL3','SCM_pos_CDR','SCM_pos_Hv','SCM_pos_Lv','SCM_pos_Fv']
214 | df = pd.concat([pd.DataFrame(name_list), pd.DataFrame(sap_pos),  pd.DataFrame(scm_neg), pd.DataFrame(scm_pos)], ignore_index=True, axis=1,); df.columns = features
215 | df.to_csv('DeepSP_descriptors.csv', index=False)


--------------------------------------------------------------------------------
/environment.yml:
--------------------------------------------------------------------------------
 1 | name: web2
 2 | channels:
 3 |   - conda-forge
 4 |   - bioconda
 5 |   - defaults
 6 | dependencies:
 7 |   - anarci=2021.02.04
 8 |   - biopython=1.78
 9 |   - hmmer=3.3.2
10 |   - keras=2.12.0
11 |   - keras-preprocessing=1.1.2
12 |   - numpy=1.23.5
13 |   - numpy-base=1.23.5
14 |   - pandas=2.0.3
15 |   - python=3.11.5
16 |   - tensorflow=2.12.0
17 |   - tensorflow-base=2.12.0
18 |   - tensorflow-estimator=2.12.0
19 | prefix: /home/pklai/anaconda3/envs/web2
20 | 


--------------------------------------------------------------------------------