8000 Fix typos in T016. by t-kimber · Pull Request #205 · volkamerlab/teachopencadd · GitHub
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Fix typos in T016. #205

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Merged
merged 2 commits into from
Dec 17, 2021
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Fix typos in T016. #205

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a09c49e
fix list.
t-kimber Dec 17, 2021
f612607
Merge branch 'master' of https://github.com/volkamerlab/teachopencadd…
t-kimber Dec 17, 2021
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29 changes: 16 additions & 13 deletions teachopencadd/talktorials/T016_protein_ligand_interactions/talktorial.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -426,6 +426,7 @@
"These fields are divided in `<field>_features` (containing column names) and `<field>_info` (containing the actual records). If we iterate over the object retrieving the correct attribute name with `getattr()`, we can compose a dictionary that can be passed to a `pandas.DataFrame` for nice overviews.\n",
"\n",
"This dictionary is composed of two levels:\n",
"\n",
"- First level is the detected binding sites.\n",
"- For each binding site, we have one more sub-dictionary containing eight lists, one for each specific interaction. \n",
" * Each list will contain the column names in the first row, and the data (if available) in the following."
Expand All @@ -444,12 +445,12 @@
" Parameters\n",
" ----------\n",
" pdb_file :\n",
" The PDB file of the complex.\n",
" The PDB file of the complex.\n",
"\n",
" Returns\n",
" -------\n",
" dict :\n",
" A dictionary of the binding sites and the interactions.\n",
" A dictionary of the binding sites and the interactions.\n",
" \"\"\"\n",
" protlig = PDBComplex()\n",
" protlig.load_pdb(pdb_file) # load the pdb file\n",
Expand All @@ -459,7 +460,7 @@
" # loop over binding sites\n",
" for key, site in sorted(protlig.interaction_sets.items()):\n",
" binding_site = BindingSiteReport(site) # collect data about interactions\n",
" # tuples of *_features and *_info will be converted to pandas data frame\n",
" # tuples of *_features and *_info will be converted to pandas DataFrame\n",
" keys = (\n",
" \"hydrophobic\",\n",
" \"hbond\",\n",
Expand Down Expand Up @@ -590,8 +591,8 @@
"\n",
" Returns\n",
" -------\n",
" DataFrame :\n",
" Data frame with information retrieved from PLIP.\n",
" pd.DataFrame :\n",
" DataFrame with information retrieved from PLIP.\n",
" \"\"\"\n",
"\n",
" # check if interaction type is valid:\n",
Expand Down Expand Up @@ -1134,7 +1135,7 @@
"metadata": {},
"outputs": [],
"source": [
"def show_interactions_3D(\n",
"def show_interactions_3d(\n",
" pdb_id, selected_site_interactions, highlight_interaction_colors=color_map\n",
"):\n",
" \"\"\"\n",
Expand Down Expand Up @@ -1185,7 +1186,7 @@
" res_sele_nc = \" or \".join([f\"({r} and ((_O) or (_N) or (_S)))\" for r in interacting_residues])\n",
" prot_component.add_ball_and_stick(sele=res_sele, colorScheme=\"chainindex\", aspectRatio=1.5)\n",
" prot_component.add_ball_and_stick(sele=res_sele_nc, colorScheme=\"element\", aspectRatio=1.5)\n",
" # Center on binding site\n",
" # Center on ligand\n",
" viewer.center(\"ligand\")\n",
" return viewer"
]
Expand Down Expand Up @@ -1225,8 +1226,8 @@
}
],
"source": [
"viewer_3D = show_interactions_3D(pdb_id, interactions_by_site[selected_site])\n",
"viewer_3D"
"viewer_3d = show_interactions_3d(pdb_id, interactions_by_site[selected_site])\n",
"viewer_3d"
]
},
{
Expand All @@ -1235,7 +1236,7 @@
"metadata": {},
"outputs": [],
"source": [
"viewer_3D.render_image(trim=True, factor=2, transparent=True);"
"viewer_3d.render_image(trim=True, factor=2, transparent=True);"
]
},
{
Expand All @@ -1256,7 +1257,7 @@
}
],
"source": [
"viewer_3D._display_image()"
"viewer_3d._display_image()"
]
},
{
Expand Down Expand Up @@ -1295,7 +1296,7 @@
"source": [
"## Discussion\n",
"\n",
"In this talktorial we have learned about protein-ligand interactions, more specifically in the context of the Protein–Ligand Interaction Profiler, or PLIP for short. We created a data frame to depict the interactions in a table. Furthermore, we made use of the NGL viewer to visualize these interactions in 3D, which require a good amount of web technologies, mainly based around the NGL viewer itself and `ipywidgets` layouts."
"In this talktorial we have learned about protein-ligand interactions, more specifically in the context of the Protein–Ligand Interaction Profiler, or PLIP for short. We created a DataFrame to depict the interactions in a table. Furthermore, we made use of the NGL viewer to visualize these interactions in 3D, which require a good amount of web technologies, mainly based around the NGL viewer itself and `ipywidgets` layouts."
]
},
{
Expand Down Expand Up @@ -1326,7 +1327,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
"version": "3.8.12"
},
"toc-autonumbering": true,
"widgets": {
Expand Down Expand Up @@ -3243,6 +3244,7 @@
"model_name": "ImageModel",
"state": {
"layout": "IPY_MODEL_bbbf0cd53d46462c81a1bd07e952b7f5",
"value": {},
"width": "99%"
}
},
Expand All @@ -3269,6 +3271,7 @@
"model_name": "ImageModel",
"state": {
"layout": "IPY_MODEL_d4418e9c9b254461a3ff7f234d8917ea",
"value": {},
"width": "99%"
}
},
Expand Down
0