def test_sameindex():
TESTDATA_1t48 = os.path.join(os.path.dirname(__file__), 'data',
'1t48_995.pdb')
p1t48 = PandasPdb()
p1t48.read_pdb(TESTDATA_1t48)
print(p1t48)
p1t48.df['ATOM'].index = np.zeros(p1t48.df['ATOM'].shape[0], dtype=int)
expect_res = [
'M', 'E', 'M', 'E', 'K', 'E', 'F', 'E', 'Q', 'I', 'D', 'K', 'S', 'G',
'S', 'W', 'A', 'A', 'I', 'Y', 'Q', 'D', 'I', 'R', 'H', 'E', 'A', 'S',
'D', 'F', 'P', 'C', 'R', 'V', 'A', 'K', 'L', 'P', 'K', 'N', 'K', 'N',
'R', 'N', 'R', 'Y', 'R', 'D', 'V', 'S', 'P', 'F', 'D', 'H', 'S', 'R',
'I', 'K', 'L', 'H', 'Q', 'E', 'D', 'N', 'D', 'Y', 'I', 'N', 'A', 'S',
'L', 'I', 'K', 'M', 'E', 'E', 'A', 'Q', 'R', 'S', 'Y', 'I', 'L', 'T',
'Q', 'G', 'P', 'L', 'P', 'N', 'T', 'C', 'G', 'H', 'F', 'W', 'E', 'M',
'V', 'W', 'E', 'Q', 'K', 'S', 'R', 'G', 'V', 'V', 'M', 'L', 'N', 'R',
'V', 'M', 'E', 'K', 'G', 'S', 'L', 'K'
]
transl = p1t48.amino3to1()
expect_chain = ['A' for _ in range(transl.shape[0])]
got_chain = list(transl['chain_id'].values)
got_res = list(transl['residue_name'].values)
assert expect_chain == got_chain
assert expect_res == got_res
def test_pdb_with_insertion_codes():
PDB_2D7T_PATH = os.path.join(os.path.dirname(__file__), 'data', '2d7t.pdb')
ppdb = PandasPdb().read_pdb(PDB_2D7T_PATH)
sequence = ppdb.amino3to1()
assert "".join(sequence[50:60]['residue_name'].values) == 'INPKSGDTNY'
def run_test(prot_dir, protein, data):
results_df = pd.DataFrame(columns=[
'gene_name', 'uniprot_ID', 'permutation risk', 'permutation prot'
])
file_repo = 'SWISS-MODEL_Repository/' + prot_dir + '/swissmodel/'
if os.path.isdir(file_repo):
try:
print(file_repo)
pdb_file = file_repo + str(os.listdir(file_repo)[0])
ppdb = PandasPdb().read_pdb(pdb_file)
df = pd.DataFrame(ppdb.df['ATOM'])
sequence = ppdb.amino3to1()
protein_spec_df = data[data['uniprot_repo'] == prot_dir]
gene_name = protein_spec_df['gene'].values[0]
uniprot_ID = protein_spec_df['uniprot'].values[0]
protein_spec_df = protein_spec_df[[
'mutation', 'effect_size', 'p-value', 'transition'
]]
df_write = "protein_structs/" + gene_name + '.csv'
df.to_csv(df_write, header=None, index=None, sep='\t')
write_to_dir = 'protein_mutation_locs_txts/' + gene_name + '.T2D.txt'
protein_spec_df.to_csv(write_to_dir,
header=None,
index=None,
sep='\t')
protein_df = pd.read_csv(write_to_dir, header=None, sep="\t")
muts_df = tests.make_dataframe(df, protein_df, sequence)
print(muts_df)
if not (muts_df[muts_df['score'] > 0].empty
or muts_df[muts_df['score'] < 0].empty):
risk = tests.get_dist_vec(muts_df, True)
prot = tests.get_dist_vec(muts_df, False)
#mw = tests.mannwhitneyu(risk, prot)
#print(mw.pvalue)
perm = tests.run_permutation(muts_df, df, np.mean(risk),
np.mean(prot), 1000)
print(perm)
new_row = {
'gene_name': gene_name,
'uniprot_ID': uniprot_ID,
"permutation risk": perm[0],
"permutation prot": perm[1]
}
results_df.append(new_row, ignore_index=True)
out_csv = 'parallelized/' + str(protein) + '-pval.csv'
results_df.to_csv(out_csv)
except Exception as e:
print(e)
pass
def test_multichain():
TESTDATA_5mtn = os.path.join(os.path.dirname(__file__),
'data', '5mtn_multichain.pdb')
mtn = PandasPdb()
mtn.read_pdb(TESTDATA_5mtn)
expect_res_a = ['S', 'L', 'E', 'P', 'E', 'P', 'W', 'F', 'F', 'K', 'N', 'L',
'S', 'R', 'K', 'D', 'A', 'E', 'R', 'Q', 'L', 'L', 'A', 'P',
'G', 'N', 'T', 'H', 'G', 'S', 'F', 'L', 'I', 'R', 'E', 'S',
'E', 'S', 'T', 'A', 'G', 'S', 'F', 'S', 'L', 'S', 'V', 'R',
'D', 'F', 'D', 'Q', 'G', 'E', 'V', 'V', 'K', 'H', 'Y', 'K',
'I', 'R', 'N', 'L', 'D', 'N', 'G', 'G', 'F', 'Y', 'I', 'S',
'P', 'R', 'I', 'T', 'F', 'P', 'G', 'L', 'H', 'E', 'L', 'V',
'R', 'H', 'Y', 'T']
expect_res_b = ['S', 'V', 'S', 'S', 'V', 'P', 'T', 'K', 'L', 'E', 'V', 'V',
'A', 'A', 'T', 'P', 'T', 'S', 'L', 'L', 'I', 'S', 'W', 'D',
'A', 'P', 'A', 'V', 'T', 'V', 'V', 'Y', 'Y', 'L', 'I', 'T',
'Y', 'G', 'E', 'T', 'G', 'S', 'P', 'W', 'P', 'G', 'G', 'Q',
'A', 'F', 'E', 'V', 'P', 'G', 'S', 'K', 'S', 'T', 'A', 'T',
'I', 'S', 'G', 'L', 'K', 'P', 'G', 'V', 'D', 'Y', 'T', 'I',
'T', 'V', 'Y', 'A', 'H', 'R', 'S', 'S', 'Y', 'G', 'Y', 'S',
'E', 'N', 'P', 'I', 'S', 'I', 'N', 'Y', 'R', 'T']
transl = mtn.amino3to1()
expect_chain = ['A' for _ in range(88)] + ['B' for _ in range(94)]
got_chain = list(transl['chain_id'].values)
got_res_a = list(transl.loc[transl['chain_id'] == 'A',
'residue_name'].values)
got_res_b = list(transl.loc[transl['chain_id'] == 'B',
'residue_name'].values)
assert expect_chain == got_chain
assert expect_res_a == got_res_a
assert expect_res_b == got_res_b
def get_seq(struc):
structure = PandasPdb().read_pdb(struc)
sequences = structure.amino3to1(
) # cols = ['chain_id', 'residue_name']
seqs = [
''.join(sequences.loc[sequences['chain_id'] == i,
'residue_name'].to_list())
for i in sequences['chain_id'].unique()
]
return seqs[0] if len(seqs) == 1 else seqs
def main(argv):
ppdb = PandasPdb().read_pdb(argv[1])
df = pd.DataFrame(ppdb.df['ATOM'])
sequence = ppdb.amino3to1()
data = pd.read_csv(argv[2], header=None, sep="\t")
num_runs = argv[3]
df = make_dataframe(df, data, sequence)
risk = get_dist_vec(df, True)
prot = get_dist_vec(df, False)
mw = mannwhitneyu(risk, prot)
#print(mw)
perm = run_permutation(df, np.mean(risk), np.mean(prot), num_runs)
def test_defaults():
TESTDATA_1t48 = os.path.join(os.path.dirname(__file__), 'data',
'1t48_995.pdb')
p1t48 = PandasPdb()
p1t48.read_pdb(TESTDATA_1t48)
expect = [
'M', 'E', 'M', 'E', 'K', 'E', 'F', 'E', 'Q', 'I', 'D', 'K', 'S', 'G',
'S', 'W', 'A', 'A', 'I', 'Y', 'Q', 'D', 'I', 'R', 'H', 'E', 'A', 'S',
'D', 'F', 'P', 'C', 'R', 'V', 'A', 'K', 'L', 'P', 'K', 'N', 'K', 'N',
'R', 'N', 'R', 'Y', 'R', 'D', 'V', 'S', 'P', 'F', 'D', 'H', 'S', 'R',
'I', 'K', 'L', 'H', 'Q', 'E', 'D', 'N', 'D', 'Y', 'I', 'N', 'A', 'S',
'L', 'I', 'K', 'M', 'E', 'E', 'A', 'Q', 'R', 'S', 'Y', 'I', 'L', 'T',
'Q', 'G', 'P', 'L', 'P', 'N', 'T', 'C', 'G', 'H', 'F', 'W', 'E', 'M',
'V', 'W', 'E', 'Q', 'K', 'S', 'R', 'G', 'V', 'V', 'M', 'L', 'N', 'R',
'V', 'M', 'E', 'K', 'G', 'S', 'L', 'K'
]
assert expect == list(p1t48.amino3to1().values)
