README

AngelRuizMoreno · Sep 18, 2021 · d806ecd · d806ecd
1 parent cd0453c
commit d806ecd
Show file tree

Hide file tree

Showing 7 changed files with 5,077 additions and 93 deletions.
diff --git a/.ipynb_checkpoints/1.-Molecular_Docking-checkpoint.ipynb b/.ipynb_checkpoints/1.-Molecular_Docking-checkpoint.ipynb
diff --git a/.ipynb_checkpoints/3.-Blind_Docking-checkpoint.ipynb b/.ipynb_checkpoints/3.-Blind_Docking-checkpoint.ipynb
diff --git a/.ipynb_checkpoints/README-checkpoint.ipynb b/.ipynb_checkpoints/README-checkpoint.ipynb
@@ -49,28 +49,28 @@
     "\n",
     "**Jupyter Dock is a set of Jupyter Notebooks for performing molecular docking protocols interactively, as well as visualizing, converting file formats and analyzing the results.** <br><br>\n",
     "\n",
-    "\n",
     "**See Jupyter Docks in action in my personal website: [chem-workflows](https://chem-workflows.com/)** <br><br>\n",
     "\n",
-    "\n",
     "These notebooks are Python 3 compatible. Each protocol and Jupyter notebook has its own test folder for testing and reproducibility evaluation.\n",
     "\n",
+    "For all notebooks, the demonstration includes the use of AutoDock Vina and Ledock. When available, some alternatives are mentioned in the protocol.\n",
+    "\n",
     "The notebooks includes whole protocols for:\n",
     "\n",
     "**1. Molecular Docking**\n",
     "> For any new user, this is a good place to start. Jupyter Docks' main stages for molecular docking, as well as all functions, methods and codes are described here along with brief explanations, hints, and warnings.\n",
     "\n",
     "**2. Virtual Screening**\n",
-    ">\n",
+    "> Interested in docking multiple ligands into a single target site? This is what you require. This protocol covers all steps from ligand preparation to docking pose visualization in the target site of interest.\n",
     "\n",
     "**3. Blind Docking**\n",
-    ">\n",
+    "> Do you want to dock multiple ligands into whole target surface and/or its pockets? This protocol demonstrates the entire process of pocket search and their use as potential organic molecule binding sites.\n",
     "\n",
-    "**4. Reverse Docking (Target fishing)**\n",
-    ">\n",
+    "**4. Reverse Docking / Target fishing)**\n",
+    "> Interested in docking one or a few molecules into a set of proteins to identify the most promising target(s)? This notebook covers all of the steps required to achieve such a goal in a condensed manner, making the process seem like a walk in the park.\n",
     "\n",
     "**5. Docking Analysis**\n",
-    ">\n",
+    "> Have you completed your docking experiments with Jupyter Dock or another approach and want to conduct a rational analysis? You've come to the right place. This notebook summarizes the most common docking analysis techniques, including score comparisons, z-score calculation between softwares, pose clustering, molecular interactions mapping, and more.\n",
     "\n",
     "\n",
     "Question about usage or troubleshooting? Please leave a comment here"
@@ -125,6 +125,22 @@
     "- [Smina](https://sourceforge.net/projects/smina/)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "c8e8a7c1-1402-4edf-8db5-a412a2bf8e20",
+   "metadata": {},
+   "source": [
+    "## Limitations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b0cda790-83fd-46c7-b05b-54faa0bc19c5",
+   "metadata": {},
+   "source": [
+    "## Examples"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "696f484c-7179-4cc7-900f-bbc5b5db3657",

diff --git a/1.-Molecular_Docking.ipynb b/1.-Molecular_Docking.ipynb
@@ -145,13 +145,13 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "ae18d2ff-7ece-48d4-99f1-092e398e58ae",
+   "cell_type": "markdown",
+   "id": "e1e6f4b6-d96a-458b-9b33-3f4846ff3e68",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "#fix_protein(filename='1AZ8_clean.pdb',addHs_pH=7.4,try_renumberResidues=True,output='1AZ8_clean_H.pdb')"
+    "```\n",
+    "fix_protein(filename='1AZ8_clean.pdb',addHs_pH=7.4,try_renumberResidues=True,output='1AZ8_clean_H.pdb')\n",
+    "```"
    ]
   },
   {
@@ -491,31 +491,18 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "39c43cc9-dc39-4a67-bc48-8d8149bda190",
+   "cell_type": "markdown",
+   "id": "4bcf45df-7781-4243-bc89-881693c03875",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\\nmol = obutils.load_molecule_from_file('1AZ8_lig_H.mol2')\\n\\npreparator = MoleculePreparation(merge_hydrogens=True,hydrate=False)\\npreparator.prepare(mol)\\n\\npreparator.write_pdbqt_file('1AZ8_lig_H.pdbqt')\\n\""
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "'''\n",
+    "```\n",
     "mol = obutils.load_molecule_from_file('1AZ8_lig_H.mol2')\n",
     "\n",
     "preparator = MoleculePreparation(merge_hydrogens=True,hydrate=False)\n",
     "preparator.prepare(mol)\n",
     "\n",
     "preparator.write_pdbqt_file('1AZ8_lig_H.pdbqt')\n",
-    "'''"
+    "```"
    ]
   },
   {
@@ -527,29 +514,16 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "7caf04e8-5860-48e2-84e2-3a56aea353c6",
+   "cell_type": "markdown",
+   "id": "f0141f8b-86cf-4450-95d9-93dc58a34a1b",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\\nligand = [m for m in pybel.readfile(filename='1AZ8_lig_H.mol2',format='mol2')][0]\\nout=pybel.Outputfile(filename='1AZ8_lig_H.pdbqt',format='pdbqt',overwrite=True)\\nout.write(ligand)\\nout.close()\\n\""
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "'''\n",
+    "```\n",
     "ligand = [m for m in pybel.readfile(filename='1AZ8_lig_H.mol2',format='mol2')][0]\n",
     "out=pybel.Outputfile(filename='1AZ8_lig_H.pdbqt',format='pdbqt',overwrite=True)\n",
     "out.write(ligand)\n",
     "out.close()\n",
-    "'''"
+    "```"
    ]
   },
   {
@@ -748,26 +722,13 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "f729fb8c-07af-4591-8b08-4225c066c144",
+   "cell_type": "markdown",
+   "id": "800679a2-6785-4c0a-93a2-ac1d396e4c04",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'\\n!../../bin/smina -r 1AZ8_clean_H.pdbqt -l 1AZ8_lig_H.pdbqt --center_x 31.859 --center_y 13.34 --center_z 17.065 --size_x 24.569 --size_y 18.12 --size_z 17.37 --exhaustiveness 8 --num_modes 5\\n'"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "'''\n",
+    "```\n",
     "!../../bin/smina -r 1AZ8_clean_H.pdbqt -l 1AZ8_lig_H.pdbqt --center_x 31.859 --center_y 13.34 --center_z 17.065 --size_x 24.569 --size_y 18.12 --size_z 17.37 --exhaustiveness 8 --num_modes 5\n",
-    "'''"
+    "```"
    ]
   },
   {
@@ -2046,14 +2007,6 @@
     "net = LigNetwork.from_ifp(results_df,lig_suppl[0],kind=\"frame\", frame=0,rotation=270)\n",
     "net.display()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e8f32c97-7ded-4a09-8419-d4e3bf0154f5",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

diff --git a/3.-Blind_Docking.ipynb b/3.-Blind_Docking.ipynb
@@ -2936,14 +2936,6 @@
     "view.zoomTo()\n",
     "view.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d66076e0-28bd-4c43-b532-b8a2554f79c6",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

diff --git a/README.ipynb b/README.ipynb
@@ -49,28 +49,28 @@
     "\n",
     "**Jupyter Dock is a set of Jupyter Notebooks for performing molecular docking protocols interactively, as well as visualizing, converting file formats and analyzing the results.** <br><br>\n",
     "\n",
-    "\n",
     "**See Jupyter Docks in action in my personal website: [chem-workflows](https://chem-workflows.com/)** <br><br>\n",
     "\n",
-    "\n",
     "These notebooks are Python 3 compatible. Each protocol and Jupyter notebook has its own test folder for testing and reproducibility evaluation.\n",
     "\n",
+    "For all notebooks, the demonstration includes the use of AutoDock Vina and Ledock. When available, some alternatives are mentioned in the protocol.\n",
+    "\n",
     "The notebooks includes whole protocols for:\n",
     "\n",
     "**1. Molecular Docking**\n",
     "> For any new user, this is a good place to start. Jupyter Docks' main stages for molecular docking, as well as all functions, methods and codes are described here along with brief explanations, hints, and warnings.\n",
     "\n",
     "**2. Virtual Screening**\n",
-    ">\n",
+    "> Interested in docking multiple ligands into a single target site? This is what you require. This protocol covers all steps from ligand preparation to docking pose visualization in the target site of interest.\n",
     "\n",
     "**3. Blind Docking**\n",
-    ">\n",
+    "> Do you want to dock multiple ligands into whole target surface and/or its pockets? This protocol demonstrates the entire process of pocket search and their use as potential organic molecule binding sites.\n",
     "\n",
-    "**4. Reverse Docking (Target fishing)**\n",
-    ">\n",
+    "**4. Reverse Docking / Target fishing)**\n",
+    "> Interested in docking one or a few molecules into a set of proteins to identify the most promising target(s)? This notebook covers all of the steps required to achieve such a goal in a condensed manner, making the process seem like a walk in the park.\n",
     "\n",
     "**5. Docking Analysis**\n",
-    ">\n",
+    "> Have you completed your docking experiments with Jupyter Dock or another approach and want to conduct a rational analysis? You've come to the right place. This notebook summarizes the most common docking analysis techniques, including score comparisons, z-score calculation between softwares, pose clustering, molecular interactions mapping, and more.\n",
     "\n",
     "\n",
     "Question about usage or troubleshooting? Please leave a comment here"
@@ -125,6 +125,22 @@
     "- [Smina](https://sourceforge.net/projects/smina/)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "c8e8a7c1-1402-4edf-8db5-a412a2bf8e20",
+   "metadata": {},
+   "source": [
+    "## Limitations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b0cda790-83fd-46c7-b05b-54faa0bc19c5",
+   "metadata": {},
+   "source": [
+    "## Examples"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "696f484c-7179-4cc7-900f-bbc5b5db3657",

diff --git a/README.md b/README.md
@@ -25,28 +25,28 @@
 
 **Jupyter Dock is a set of Jupyter Notebooks for performing molecular docking protocols interactively, as well as visualizing, converting file formats and analyzing the results.** <br><br>
 
-
 **See Jupyter Docks in action in my personal website: [chem-workflows](https://chem-workflows.com/)** <br><br>
 
-
 These notebooks are Python 3 compatible. Each protocol and Jupyter notebook has its own test folder for testing and reproducibility evaluation.
 
+For all notebooks, the demonstration includes the use of AutoDock Vina and Ledock. When available, some alternatives are mentioned in the protocol.
+
 The notebooks includes whole protocols for:
 
 **1. Molecular Docking**
 > For any new user, this is a good place to start. Jupyter Docks' main stages for molecular docking, as well as all functions, methods and codes are described here along with brief explanations, hints, and warnings.
 
 **2. Virtual Screening**
->
+> Interested in docking multiple ligands into a single target site? This is what you require. This protocol covers all steps from ligand preparation to docking pose visualization in the target site of interest.
 
 **3. Blind Docking**
->
+> Do you want to dock multiple ligands into whole target surface and/or its pockets? This protocol demonstrates the entire process of pocket search and their use as potential organic molecule binding sites.
 
-**4. Reverse Docking (Target fishing)**
->
+**4. Reverse Docking / Target fishing)**
+> Interested in docking one or a few molecules into a set of proteins to identify the most promising target(s)? This notebook covers all of the steps required to achieve such a goal in a condensed manner, making the process seem like a walk in the park.
 
 **5. Docking Analysis**
->
+> Have you completed your docking experiments with Jupyter Dock or another approach and want to conduct a rational analysis? You've come to the right place. This notebook summarizes the most common docking analysis techniques, including score comparisons, z-score calculation between softwares, pose clustering, molecular interactions mapping, and more.
 
 
 Question about usage or troubleshooting? Please leave a comment here
@@ -88,6 +88,10 @@ Jupyter Dock is reliant on a variety of academic software. The Jupyter Dock.yaml
 - [Meeko](https://pypi.org/project/meeko/)
 - [Smina](https://sourceforge.net/projects/smina/)
 
+## Limitations
+
+## Examples
+
 ## Citation
 
 If you use these notebooks, please credit this repository and the required tools as follows: