Merge pull request #238 from volkamerlab/livecoms-review

Base branch for T23-28 revisions

README.md

@@ -192,7 +192,7 @@ It will help measure the impact of the TeachOpenCADD platform and future funding
 - Web services clients:
   [`pypdb`](https://github.com/williamgilpin/pypdb),
   [`chembl_webresource_client`](https://github.com/chembl/chembl_webresource_client),
-  [`requests`](https://docs.python-requests.org/en/master/),
+  [`requests`](https://requests.readthedocs.io/en/latest/),
   [`bravado`](https://bravado.readthedocs.io/en/stable/),
   [`beautifulsoup4`](https://www.crummy.com/software/BeautifulSoup/bs4/doc/)
 - Utilities:

devtools/test_env.yml

@@ -42,6 +42,7 @@ dependencies:
   - pdbfixer
   - tqdm
   - lxml
+  - kissim
   ## CI tests
   # Workaround for https://github.com/computationalmodelling/nbval/issues/153
   - pytest 5.*

teachopencadd/talktorials/T023_what_is_a_kinase/data/kinase_selection_xiong.csv

@@ -0,0 +1,9 @@
+kinase,kinase_klifs,uniprot_id,group
+RET,RET,P07949,TK
+BRAF,BRAF,P15056,TKL
+SRC,SRC,P12931,TK
+S6K,p70S6K,P23443,AGC
+MKNK1,MNK1,Q9BUB5,CAMK
+TTK,TTK,P33981,Other
+PDK,PDK1,O15530,AGC
+PAK3,PAK3,O75914,STE

teachopencadd/talktorials/T023_what_is_a_kinase/data/pipeline_configs.csv

@@ -0,0 +1,4 @@
+variable,default_value,description
+DEMO,1,"Run the notebooks exactly as displayed online (default: 1) or set to 0 and run your own kinase set (as defined in `kinase_selection.csv`)"
+N_STRUCTURES_PER_KINASE,-1,"Run structure-based notebooks on all structures per kinase (default: -1) or a subset of structures (replace -1 with e.g. 3)"
+N_CORES,1,"Run T025 on one (default: 1) or more cores"

teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb

@@ -162,11 +162,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The KLIFS database ([<i>Nucleic Acid Res.</i> (2020), <b>49(D1)</b>, D562-D569](https://doi.org/10.1093/nar/gkaa895), [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)) fetches all kinase structures deposited in the structural database PDB ([<i>Acta Cryst.</i> (2002), <b>D58</b>, 899-907](https://doi.org/10.1107/S0907444902003451), [<i>Structure</i> (2012), <b>20(3)</b>, 391-396](https://doi.org/10.1016/j.str.2012.01.010)) and processes them as follows: All multi-chain structures in the PDB are split into monomers and aligned to each other with a special focus on a pre-defined binding site of $85$ residues (Figure 1). For example, this means that the conserved gatekeeper (GK) residue at KLIFS position $45$ can be easily queried for any of the over $10,000$ monomeric kinase structures in KLIFS. \n",
+    "The KLIFS database ([<i>Nucleic Acid Res.</i> (2020), <b>49(D1)</b>, D562-D569](https://doi.org/10.1093/nar/gkaa895), [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)) fetches all kinase structures deposited in the structural database PDB ([<i>Acta Cryst.</i> (2002), <b>D58</b>, 899-907](https://doi.org/10.1107/S0907444902003451), [<i>Structure</i> (2012), <b>20(3)</b>, 391-396](https://doi.org/10.1016/j.str.2012.01.010)) and processes them as follows: All multi-chain structures in the PDB are split into monomers and aligned to each other with a special focus on a pre-defined binding site of $85$ residues (Figure 3). For example, this means that the conserved gatekeeper (GK) residue at KLIFS position $45$ can be easily queried for any of the over $10,000$ monomeric kinase structures in KLIFS. \n",
     "\n",
     "![KLIFS binding site](https://klifs.net/images/faq/colors.png)\n",
     "\n",
-    "*Figure 1:* \n",
+    "*Figure 3:* \n",
     "Kinase binding site residues as defined by KLIFS.\n",
     "Figure and description taken from: [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)."
    ]
@@ -202,16 +202,16 @@
     "#### Bioactivity data\n",
     "\n",
     "[ChEMBL](https://www.ebi.ac.uk/chembl/) is a well-known bioactivity database, which releases updated versions every now and then.\n",
-    "In September 2021, there are over two million compounds and $14,000$ targets that are stored. In ChEMBL29, there are over $160,000$ measurements on kinases (see Figure 3).\n",
+    "In September 2021, there are over two million compounds and $14,000$ targets that are stored. In ChEMBL29, there are over $160,000$ measurements on kinases (see Figure 4).\n",
     "\n",
     "- `kinodata` GitHub repository: https://github.com/openkinome/kinodata\n",
     "- `kinodata` ChEMBL29 release: https://github.com/openkinome/kinodata/releases/tag/v0.3 (`activities-chembl29_v0.3.zip`)\n",
     "\n",
-    "As with other data types, the coverage of bioactivity data is highly unbalanced among the human kinases, depending on how much research is spent on certain kinases (Figure 3).\n",
+    "As with other data types, the coverage of bioactivity data is highly unbalanced among the human kinases, depending on how much research is spent on certain kinases.\n",
     "\n",
     "![Manning tree with number of ChEMBL activities per kinase (KinMap)](images/kinmap_n_activities_per_kinase.png)\n",
     "\n",
-    "*Figure 3:* \n",
+    "*Figure 4:* \n",
     "Number of ChEMBL29 bioactivities per kinase mapped onto the Manning kinome tree using KinMap. Check the appendix on how to generate this KinMap tree.\n",
     "<!---\n",
     "We are using KLIFS kinase names; some are not recognized by KinMap and were simply dropped!\n",
@@ -247,11 +247,11 @@
     "\n",
     "As described before, kinases are highly conserved, especially in their binding site. This high similarity is a challenge in drug design because ligands may form similar binding modes not only with their designated target (on-target) but also with other targets (off-targets). Such promiscuous binding can cause mild to severe side effects.\n",
     "\n",
-    "Predicting these side effects is non-trivial since some off-targets are not obvious. For example, the EGFR inhibitor Erlotinib shows affinities to other kinases in the highly sequentially-similar TK kinase group. However, it also strongly affects the off-targets GAK, LOK, and SLK, which are in more remote kinase groups (Figure 4). \n",
+    "Predicting these side effects is non-trivial since some off-targets are not obvious. For example, the EGFR inhibitor Erlotinib shows affinities to other kinases in the highly sequentially-similar TK kinase group. However, it also strongly affects the off-targets GAK, LOK, and SLK, which are in more remote kinase groups (Figure 5). \n",
     "\n",
     "![Erlotinib profiling data from Karaman dataset (KinMap)](images/kinmap_erlotinib_karaman.png)\n",
     "\n",
-    "*Figure 4:* \n",
+    "*Figure 5:* \n",
     "Profiling data for EGFR inhibitor Erlobinib from the Karaman _et al._ dataset ([<i>Nature Biotechnology</i> (2008), <b>26</b>, 127-132](https://doi.org/10.1038/nbt1358)) mapped onto the Manning kinome tree using [KinMap](http://www.kinhub.org/kinmap/). Check the appendix of this notebook on how to generate this figure."
    ]
   },
@@ -326,7 +326,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We have collected information about these nine kinases in the CSV file `kinase_selection.csv`:\n",
+    "We have collected information about these nine kinases in the CSV file `T023_what_is_a_kinase/data/kinase_selection.csv`:\n",
     "\n",
     "- `kinase`: Kinase name as used in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629)\n",
     "- `kinase_klifs`: Kinase name as used in the KLIFS database\n",
@@ -335,13 +335,6 @@
     "- `full_kinase_name`: Full kinase name as used in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "_Note_: You can run the kinase similarity __Talktorials T024-T028__ with your own set of kinases. To do so, please update the CSV file with your kinases; the only mandatory columns are `kinase_klifs` and `uniprot_id`."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -494,6 +487,98 @@
     "We will load this dataset in all downstream talktorials to assess kinase similarity from different perspectives."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "_Note_: You can run the kinase similarity __Talktorials T024-T028__ with your own set of kinases. To do so, please update the following files:\n",
+    "\n",
+    "- Update the `T023_what_is_a_kinase/data/kinase_selection.csv` file with your kinases; the only mandatory columns are `kinase_klifs` and `uniprot_id`.\n",
+    "- Update the `T023_what_is_a_kinase/data/pipeline_configs.csv` file with your configurations:\n",
+    "  - Set \"DEMO\" to 0.\n",
+    "  - Choose the number of structures per kinases to be used in T025 (KiSSim) and T026 (IFP). If \"N_STRUCTURES_PER_KINASE\" is set to -1, all structures are used; if set to a number (X), the best X structures are being used for the encoding and comparison (w.r.t. resolution and KLIFS quality score). The latter makes sense for a test run of your data (running the T025 on all structures is time-consuming).\n",
+    "  - If you run the notebooks on all structures (see \"N_STRUCTURES_PER_KINASE\"), we recommend to increase the number of cores to be used in T025 (KiSSim) by redefining \"N_CORES\".\n",
+    "  \n",
+    "Let's take a look at the currently set configurations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>variable</th>\n",
+       "      <th>default_value</th>\n",
+       "      <th>description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DEMO</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Run the notebooks exactly as displayed online (default: 1) or set to 0 and run your own kinase set (as defined in `kinase_selection.csv`)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>N_STRUCTURES_PER_KINASE</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>Run structure-based notebooks on all structures per kinase (default: -1) or a subset of structures (replace -1 with e.g. 3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>N_CORES</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Run T025 on one (default: 1) or more cores</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  variable  default_value  \\\n",
+       "0                     DEMO              1   \n",
+       "1  N_STRUCTURES_PER_KINASE             -1   \n",
+       "2                  N_CORES              1   \n",
+       "\n",
+       "                                                                                                                                 description  \n",
+       "0  Run the notebooks exactly as displayed online (default: 1) or set to 0 and run your own kinase set (as defined in `kinase_selection.csv`)  \n",
+       "1                Run structure-based notebooks on all structures per kinase (default: -1) or a subset of structures (replace -1 with e.g. 3)  \n",
+       "2                                                                                                 Run T025 on one (default: 1) or more cores  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.options.display.max_colwidth = None\n",
+    "configs = pd.read_csv(DATA / \"pipeline_configs.csv\")\n",
+    "configs"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -510,12 +595,16 @@
     "There are some KinMap trees shown in this notebook. The code below generates the KinMap CSV files to be uploaded to KinMap:\n",
     "http://www.kinhub.org/kinmap.\n",
     "\n",
-    "_Note_: PNG downloads do not seem to work anymore, thus download as SVG and convert to PNG in your terminal (Linux) via `convert -density 25 my_kinmap_figure.svg my_kinmap_figure.png` (SVG cannot be included in Jupyter notebooks out-of-the-box)."
+    "_Note_:\n",
+    "1. PNG downloads do not seem to work anymore, thus download as SVG and convert to PNG in your terminal (Linux) via `convert -density 25 my_kinmap_figure.svg my_kinmap_figure.png` (SVG cannot be included in Jupyter notebooks out-of-the-box).\n",
+    "2. If SVG download doesn't render the figure properly, open your favorite text editor and copy paste this into the SVG file: `xmlns:xlink=\"http://www.w3.org/1999/xlink\"`, resulting in something similar to this in the first few lines:\n",
+    "\n",
+    "`<svg id=\"svgCopy\" viewBox=\"0 0 1591 1959\" preserveAspectRatio=\"xMinYMin meet\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" style=\"\"><desc>Created with Snap</desc><defs></defs><g`\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -561,7 +650,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -596,7 +685,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -605,7 +694,7 @@
        "('CDK2', 426)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -634,7 +723,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -678,7 +767,11 @@
     "- Select \"Data type\": Karaman et al., 2018\n",
     "- Select \"Karaman et al., 2018\": Erlotinib\n",
     "- Click \"Add source\"\n",
-    "- Click \"Apply\""
+    "- In **settings**, select \"RoyalBlue\" in **Fill**\n",
+    "- Click \"Apply\"\n",
+    "- Click on the speech bubble on the top right of the kinome tree to disable annotations.\n",
+    "\n",
+    "_Note_: the name of the on/off-targets (EGFR, GAK, LOK, SLK) have been added manually."
    ]
   },
   {
@@ -692,15 +785,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8951db9308d84e54adceabdb750d2439",
+       "model_id": "27a4173bf7504c9a9f529823f56ec466",
        "version_major": 2,
        "version_minor": 0
       },
@@ -739,7 +832,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -748,7 +841,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {
     "tags": [
      "nbsphinx-thumbnail"
@@ -762,7 +855,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }

teachopencadd/talktorials/T024_kinase_similarity_sequence/README.md

@@ -17,6 +17,8 @@ Two similarity measures are implemented:
 
    1. Sequence identity, i.e., the similarity which is based on character-wise discrepancy.
    2. Sequence similarity, i.e., the similarity which is based on a substitution matrix, thus, reflecting similarities between amino acids.
+
+_Note_: We focus on similarities between orthosteric kinase binding sites; similarities to allosteric binding sites are not covered.
 
 
 ### Contents in *Theory*