def __init__(self, protein=None, n_jobs=-1):
"""NNScore implementation [1]_. Based on Binana descriptors [2]_ and
an ensemble of 20 best scored nerual networks with a hidden layer of
5 nodes. The NNScore predicts binding affinity (pKi/d).
Parameters
----------
protein : oddt.toolkit.Molecule object
Receptor for the scored ligands
n_jobs: int (default=-1)
Number of cores to use for scoring and training. By default (-1)
all cores are allocated.
References
----------
.. [1] Durrant JD, McCammon JA. NNScore 2.0: a neural-network
receptor-ligand scoring function. J Chem Inf Model. 2011;51:
2897-2903. doi:10.1021/ci2003889
.. [2] Durrant JD, McCammon JA. BINANA: a novel algorithm for
ligand-binding characterization. J Mol Graph Model. 2011;29:
888-893. doi:10.1016/j.jmgm.2011.01.004
"""
self.protein = protein
self.n_jobs = n_jobs
model = None
decsriptors = binana_descriptor(protein)
super(nnscore, self).__init__(model, decsriptors,
score_title='nnscore')
def __init__(self, protein=None, n_jobs=-1):
self.protein = protein
self.n_jobs = n_jobs
model = None
decsriptors = binana_descriptor(protein)
super(nnscore, self).__init__(model,
decsriptors,
score_title='nnscore')
def featurise(protein_file, ligand_file):
"""Compute BINANA features for a protein and a ligand and return the results as a dictionary.
Args:
protein_file (str): Name of the .pdb file containing the protein.
ligand_file (str): Name of the .sdf file containing the ligand.
Returns:
result (dict): A dictionary containing the BINANA features for the protein-ligand complex.
"""
protein = next(ob.readfile('pdb', protein_file))
protein.protein = True # DO NOT SKIP THIS or you will be a sad panda
ligand = next(ob.readfile('sdf', ligand_file))
# Build BINANA features
binana_engine = binana.binana_descriptor(protein)
result = {
name: value
for name, value in zip(binana_engine.titles,
binana_engine.build([ligand])[0])
}
return result
def __init__(self, protein = None, n_jobs = -1, **kwargs):
self.protein = protein
self.n_jobs = n_jobs
model = None
decsriptors = binana_descriptor(protein)
super(nnscore,self).__init__(model, decsriptors, score_title='nnscore')