I have been using nglview to look at the results from the fragment hotspot maps<\/a> algorithm. This involves visualising a protein\/binding site of interest, as well as three grid maps, which point to areas of favourable interaction for donor, acceptor and apolar fragment-like probes. A key point of this visualisation is being able to control the threshold at which the maps are displayed: higher thresholds indicate stronger interactions.<\/p>\n\n\n\n In the code snippet below, the hotspot results are housed in the folder .\/my_hotspots, which contains a .pdb file for the protein (‘protein.pdb’) and three .ccp4 grids holding the hotspot maps for the 3 probe types (‘acceptor.ccp4’, ‘donor.ccp4’, ‘apolar.ccp4)<\/p>\n\n\n\n The next cell sets up the viewer and controls widgets.<\/p>\n\n\n\n We can now look for ‘warm spots’ within the binding site… The hottest spots show interactions that are likely to drive fragment binding. <\/p>\n\n\n\n The example presented here is the bromodomain BAZ2B (PDB 4RVR, ligand removed). Bromodomains have an almost universally conserved asparagine residue that acts as key pharmacophoric feature. In this structure, this is residue Asn 1944. <\/p>\n\n\n\n NGLview:<\/strong> NGL viewer:<\/strong>from pathlib import Path\nimport nglview\nfrom ipywidgets import interactive, VBox<\/pre>\n\n\n\n
def load_data(base_path):\n \"\"\"\n Loads the protein and hotspot maps\n :param base_path: path to directory holding the results \n of the hotspot calculation\n \"\"\"\n probes = ['donor', 'acceptor', 'apolar']\n\n view = nglview.NGLWidget()\n view.add_component(str(Path(base_path, \"protein.pdb\")))\n for p in probes:\n view.add_component(str(Path(base_path, f\"{p}.ccp4\")))\n\n return view\n\nclass InteractiveHotspot():\n def __init__(self, hotspot_path):\n self.view = load_data(hotspot_path)\n\n def get_component_index(self, component_name):\n \"\"\"\n Given the name of the loaded file (or a partial string),\n returns the component index if loaded\n \"\"\"\n all_components = self.view._ngl_component_names\n component_idx = all_components.index([i for i in all_components \n if component_name in i][0])\n\n return component_idx\n\n def threshold_isosurface(self, probe_name, threshold):\n \"\"\"\n Controls the representation of the map for specific probe\n :param probe_name: string, one of 'donor', 'acceptor', 'apolar'\n :param threshold: threshold value for displaying the hotspot map\n \"\"\"\n colour_dict = {'donor': 'blue',\n 'acceptor': 'red',\n 'apolar': '#FFF176'}\n comp_idx = self.get_component_index(probe_name)\n repr_params = [{'type': 'surface',\n 'params': {'opacity': 0.4,\n 'isolevelType': 'value',\n 'isolevel': threshold,\n 'color': colour_dict[probe_name]}}]\n\n self.view.set_representations(repr_params, component=comp_idx)<\/pre>\n\n\n\nhotspot_result_path = Path(\"my_hotspots\")\nhotspot = InteractiveHotspot(hotspot_result_path)<\/pre>\n\n\n\n
controls = interactive(hotspot.threshold_isosurface, \n probe_name=['donor', 'acceptor', 'apolar'], \n threshold=range(30))\nVBox([hotspot.view, controls])<\/pre>\n\n\n\n
<\/p>\n\n\n\n![](\"https:\/\/lh5.googleusercontent.com\/AVw5bd96A_2N3xu4SdPcGPqqqCOHUx7PITLGtXcF22SbZSGymqKDtvEO652RqKDXWmQ7fRn9AiVHW7wxGWhTgBaWYWGaAyGRH0NUllrhreROFXG4LTYnPh6WztxNPkEalGoGrQlf\")
![](\"https:\/\/lh6.googleusercontent.com\/UfnNybIqRzMQMU2w5Wee-hGb0AHCUqRdweP5VM2AWLt-YjequPQK7_0xSZ1w1LxsTU1gcUQdSN1yeOamaSqBO118RsSbMiaMuDXu7heEiv0jDYTZckunpLD6xNhhEG0dmSOS7WKb\")
hotspot.view.add_ball_and_stick('1944')<\/pre>\n\n\n\n![](\"https:\/\/lh3.googleusercontent.com\/LqZo4AFYZDWZyHIqAsbmN65nKBp9ZgyJXDkhxvnDI7hGHMkxP1xGxF5zBpZfP8o9ml-7SlJkESZ3rS4w19EEOlnaE6X8IgKoK8q2UH-ATTvxCnEFquA21aQ1xiZp18am0uqpaUF3\")
References:<\/h2>\n\n\n\n
Hai Nguyen, David A Case, Alexander S Rose; NGLview – Interactive molecular graphics for Jupyter notebooks, Bioinformatics, , btx789,\u00a0https:\/\/doi.org\/10.1093\/bioinformatics\/btx789<\/a><\/p>\n\n\n\n
AS Rose, AR Bradley, Y Valasatava, JM Duarte, A Prli\u0107 and PW Rose.\u00a0NGL viewer: web-based molecular graphics for large complexes.<\/em>\u00a0Bioinformatics: bty419, 2018.\u00a0doi:10.1093\/bioinformatics\/bty419<\/a><\/p>\n\n\n\n