Hotspot Pharmacophore API¶
The hotspots.hs_pharmacophore module contains classes for the
conversion of Grid objects to pharmacophore models.
The main class of the hotspots.hs_pharmacophore module is:
A Pharmacophore Model can be generated directly from a hotspots.result.Result :
>>> from hotspots.calculation import Runner
>>> r = Runner()
>>> result = r.from_pdb("1hcl")
>>> result.get_pharmacophore_model(identifier="MyFirstPharmacophore")
The Pharmacophore Model can be used in Pharmit or CrossMiner
>>> result.pharmacophore.write("example.cm") # CrossMiner
>>> result.pharmacophore.write("example.json") # Pharmit
- More information about CrossMiner is available:
Korb O, Kuhn B, hert J, Taylor N, Cole J, Groom C, Stahl M “Interactive and Versatile Navigation of Structural Databases” J Med Chem, 2016, 59(9):4257, [DOI: 10.1021/acs.jmedchem.5b01756]
- More information about Pharmit is available:
Jocelyn Sunseri, David Ryan Koes; Pharmit: interactive exploration of chemical space, Nucleic Acids Research, Volume 44, Issue W1, 8 July 2016, Pages W442-W448 [DIO: 10.1093/nar/gkw287]
-
class
hotspots.hs_pharmacophore.PharmacophoreModel(settings, identifier=None, features=None, protein=None, dic=None)[source]¶ A class to handle a Pharmacophore Model
- Parameters
settings (hotspots.hs_pharmacophore.PharmacophoreModel.Settings) – Pharmacophore Model settings
identifier (str) – Model identifier
features (list) – list of :class:hotspots.hs_pharmacophore._PharmacophoreFeatures
protein (ccdc.protein.Protein) – a protein
dic (dict) – key = grid identifier(interaction type), value =
ccdc.utilities.Grid
-
class
Settings(feature_boundary_cutoff=5, max_hbond_dist=5, radius=1.0, vector_on=False, transparency=0.6, excluded_volume=True, binding_site_radius=12)[source]¶ settings available for adjustment
- Parameters
feature_boundary_cutoff (float) – The map score cutoff used to generate islands
max_hbond_dist (float) – Furthest acceptable distance for a hydrogen bonding partner (from polar feature)
radius (float) – Sphere radius
vector_on (bool) – Include interaction vector
transparency (float) – Set transparency of sphere
excluded_volume (bool) – If True, the CrossMiner pharmacophore will contain excluded volume spheres
binding_site_radius (float) – Radius of search for binding site calculation, used for excluded volume
-
static
from_hotspot(result, identifier='id_01', threshold=5, min_island_size=5, settings=None)[source]¶ creates a pharmacophore model from a Fragment Hotspot Map result
(included for completeness, equivalent to hotspots.result.Result.get_pharmacophore())
- Parameters
result (hotspots.result.Result) – a Fragment Hotspot Maps result (or equivalent)
identifier (str) – Pharmacophore Model identifier
threshold (float) – values above this value
settings (hotspots.hs_pharmacophore.PharmacophoreModel.Settings) – settings
- Returns
>>> from hotspots.calculation import Runner >>> from hotspots.hs_pharmacophore import PharmacophoreModel
>>> r = Runner() >>> result = r.from_pdb("1hcl") >>> model = PharmacophoreModel(result, identifier="pharmacophore")
-
static
from_ligands(ligands, identifier, protein=None, settings=None)[source]¶ creates a Pharmacophore Model from a collection of overlaid ligands
- Parameters
ligands (ccdc,molecule.Molecule) – ligands from which the Model is created
identifier (str) – identifier for the Pharmacophore Model
protein (ccdc.protein.Protein) – target system that the model has been created for
settings (hotspots.hs_pharmacophore.PharmacophoreModel.Settings) – Pharmacophore Model settings
- Returns
>>> from ccdc.io import MoleculeReader >>> from hotspots.hs_pharmacophore import PharmacophoreModel
>>> mols = MoleculeReader("ligand_overlay_model.mol2") >>> model = PharmacophoreModel.from_ligands(mols, "ligand_overlay_pharmacophore") >>> # write to .json and search in pharmit >>> model.write("model.json")
-
static
from_pdb(pdb_code, chain, representatives=None, identifier='LigandBasedPharmacophore')[source]¶ creates a Pharmacophore Model from a PDB code.
This method is used for the creation of Ligand-Based pharmacophores. The PDB is searched for protein-ligand complexes of the same UniProt code as the input. These PDB’s are align, the ligands are clustered and density of atom types a given point is assigned to a grid.
- Parameters
pdb_code (str) – single PDB code from the target system
chain (str) – chain of interest
out_dir (str) – path to output directory
representatives – path to .dat file containing previously clustered data (time saver)
identifier (str) – identifier for the Pharmacophore Model
- Returns
>>> from hotspots.hs_pharmacophore import PharmacophoreModel >>> from hotspots.result import Results >>> from hotspots.hs_io import HotspotWriter >>> from ccdc.protein import Protein >>> from pdb_python_api import PDBResult
>>> # get the PDB ligand-based Pharmacophore for CDK2 >>> model = PharmacophoreModel.from_pdb("1hcl")
>>> # the models grid data is stored as PharmacophoreModel.dic >>> # download the PDB file and create a Results >>> PDBResult("1hcl").download(<output_directory>) >>> result = Result(protein=Protein.from_file("<output_directory>/1hcl.pdb"), super_grids=model.dic) >>> with HotspotWriter("<output_directory>") as w: >>> w.write(result)
-
rank_features(max_features=4, feature_threshold=0, force_apolar=True)[source]¶ orders features by score
- Parameters
max_features (int) – maximum number of features returned
feature_threshold (float) – only features above this value are considered
force_apolar – ensures at least one point is apolar
- Returns
list of features
>>> from hotspots.hs_io import HotspotReader
>>> result = HotspotReader("out.zip").read() >>> model = result.get_pharmacophore_model() >>> print(len(model.features)) 38 >>> model.rank_features(max_features=5) >>> print(len(model.features)) 5