# Initialize a calculator:
normalize = False
params_file = 'published params.csv'
with open(params_file, 'rb') as f:
reader = csv.reader(f)
calc = zenergy.Calculator(reader, normalize = normalize)
print('Calculator created with normalize set to {}, '
+ 'and parameters from {}... '.format(normalize, params_file) \
+ repr(type(calc)))
# Load matrix:
matrix_file = 'identity.csv'
with open(matrix_file, 'rb') as f:
reader = csv.reader(f)
identity = matrices.retrieve_matrix(reader)
print('Retrieved matrix from {}... '.format(matrix_file) \
+ repr(type(identity)))
# The slow part, open the structures:
structure_files = file_dict('structures with 1qd5',
['aligned_(1QD6).pdb',
'aligned_(1QJP).pdb',
'aligned_(1A0S).pdb'])
parser = PDBParser()
with warnings.catch_warnings():
warnings.simplefilter('ignore')
structures = [parser.get_structure(id_, path) \
for id_, path in structure_files.items()]
for filename in (protein + ' bbtm.aln',
protein + ' gonnet.aln'):
alignment = Bio.AlignIO.read(filename, 'clustal')
oracle = AlignmentOracle(alignment, pdb_name = 'chaina')
abridged = dict()
for i in range(len(alignment)):
abridged.update({oracle.data[i].id: (''.join(oracle.sequence(i)))})
abridged_dicts.append(abridged)
bbtm_alignments.update({protein: abridged_dicts[0]})
gonnet_alignments.update({protein: abridged_dicts[1]})
with open('identity.csv', 'rb') as f:
id_matrix = matrices.retrieve_matrix(csv.reader(f))
def check_range(start, end, protein):
# Observe what positions of each protein in the gonnet alignment and the
# bbtm alignment correspond to a given range in the pdb sequence.
# Positions ocrresponding to gaps in the pdb sequence not shown.
print('name then seq distance then bbtm segment then gonnet segment')
pdb_seq_name = 'chaina_' + protein.lower()
for name in bbtm_alignments[protein].keys():
print(name)
print(matrices.compare(bbtm_alignments[protein][name],
bbtm_alignments[protein][pdb_seq_name],
id_matrix))
print(matrices.compare(gonnet_alignments[protein][name],
gonnet_alignments[protein][pdb_seq_name],
id_matrix))
