def test_get_common_features():
yaml_file = load_yaml_file(os.path.join(base_dir,"mdl_dir","project.yaml"))
aligned_dict={}
for protein in yaml_file["protein_list"]:
t = load_random_traj(yaml_file, protein)
aligned_dict[protein] = t.top.to_fasta(chain=0)
f= DihedralFeaturizer()
common_feature_dic,_ = _get_common_features(yaml_file,f, aligned_dict, False)
for protein in yaml_file["protein_list"]:
t = load_random_traj(yaml_file, protein)
assert(len(common_feature_dic[protein])==f.transform(t)[0].shape[1])
return
def test_get_common_features_2():
yaml_file = load_yaml_file(os.path.join(base_dir,"mdl_dir","project.yaml"))
aligned_dict={}
for protein in yaml_file["protein_list"]:
t = load_random_traj(yaml_file, protein)
aligned_dict[protein] = t.top.to_fasta(chain=0)
f= DihedralFeaturizer(types=['phi','psi','chi1'])
common_feature_dic,_ = _get_common_features(yaml_file,f, aligned_dict, False)
assert(len(set([len(common_feature_dic[i]) for i in yaml_file["protein_list"]]))==1)
return
def test_present_for_all_same_seq():
yaml_file = load_yaml_file(os.path.join(base_dir,"mdl_dir","project.yaml"))
aligned_dict={}
for protein in yaml_file["protein_list"]:
t = load_random_traj(yaml_file, protein)
aligned_dict[protein] = t.top.to_fasta(chain=0)
for protein in yaml_file["protein_list"]:
aligned_seq = aligned_dict[protein]
prt_mapping, prt_seq =_map_residue_ind_seq_ind(yaml_file, protein, aligned_seq)
assert(len(_present_for_all(protein, prt_mapping, prt_seq, aligned_dict))==len(prt_seq))
return
def test_get_common_residues():
yaml_file = load_yaml_file(os.path.join(base_dir,"mdl_dir","project.yaml"))
aligned_dict={}
for protein in yaml_file["protein_list"]:
t = load_random_traj(yaml_file, protein)
aligned_dict[protein] = t.top.to_fasta(chain=0)
res_dic,prt_seq = _get_common_residues(yaml_file, aligned_dict)
for protein in yaml_file["protein_list"]:
print(len(res_dic[protein]),t.n_residues)
assert(len(res_dic[protein])==len(t.top.to_fasta(chain=0)))
return
def test_map_residue_seq_with_insert():
yaml_file = load_yaml_file(os.path.join(base_dir,"mdl_dir","project.yaml"))
aligned_dict={}
for protein in yaml_file["protein_list"]:
expected = {}
t = load_random_traj(yaml_file, protein)
expected[protein] = [i.index+3 for i in t.top.residues if i.is_protein]
aligned_dict[protein] = "---"+ t.top.to_fasta(chain=0)
aligned_seq = aligned_dict[protein]
actual,_ =_map_residue_ind_seq_ind(yaml_file, protein, aligned_seq)
assert expected[protein] == list(actual.values())
return
def test_map_residue_seq_with_insert_at_end():
yaml_file = load_yaml_file(os.path.join(base_dir,"mdl_dir","project.yaml"))
aligned_dict={}
for protein in yaml_file["protein_list"]:
expected = {}
t = load_random_traj(yaml_file, protein)
#add an insertion AFTER 10 residues. We expect all but the 10 have
expected[protein] = [i for i in range(t.n_residues) if t.top.residue(i).code is not None]
aligned_dict[protein] = t.top.to_fasta(chain=0)+"---"
aligned_seq = aligned_dict[protein]
actual,_ =_map_residue_ind_seq_ind(yaml_file, protein, aligned_seq)
assert expected[protein] == list(actual.values())
return
def _map_residue_ind_seq_ind(yaml_file, protein, aligned_seq):
trj = load_random_traj(yaml_file, protein)
mapping = {}
seq_index = 0
prt_seq = trj.top.to_fasta(chain=0)
#test to make sure the alignment sequence matches with the protein sequence.
#get rid of _ from the alignment to account for additions/deletions.
assert(prt_seq==''.join([i for i in aligned_seq if i!="-"]))
for i in [i.index for i in trj.top.residues if i.is_protein]:
while True:
if trj.top.residue(i).code == aligned_seq[seq_index]:
mapping[i] = seq_index
seq_index += 1
break
else:
seq_index += 1
continue
return mapping, prt_seq
def _map_residue_ind_seq_ind(yaml_file, protein, aligned_seq, trj=None):
if trj is None:
trj = load_random_traj(yaml_file, protein)
mapping = {}
seq_index = 0
prt_seq = ''.join([i.code for i in trj.top.residues if i.is_protein])
#test to make sure the alignment sequence matches with the protein sequence.
#get rid of _ from the alignment to account for additions/deletions.
assert(prt_seq==''.join([i for i in aligned_seq if i!="-"]))
for i in [i.index for i in trj.top.residues if i.is_protein]:
while True:
if trj.top.residue(i).code == aligned_seq[seq_index]:
mapping[i] = seq_index
seq_index += 1
break
else:
seq_index += 1
continue
return mapping, prt_seq
def test_map_residue_seq_with_two_inserts():
yaml_file = load_yaml_file(os.path.join(base_dir,"mdl_dir","project.yaml"))
aligned_dict={}
for protein in yaml_file["protein_list"]:
expected = {}
t = load_random_traj(yaml_file, protein)
#add an insertion AFTER 10 residues. and then again at 20
expected[protein] = [i for i in range(10) if t.top.residue(i).code is not None] + \
[i+3 for i in range(10, 20) if t.top.residue(i).code is not None]+\
[i+5 for i in range(20, t.n_residues) if t.top.residue(i).code is not None]
prt_code = t.top.to_fasta(chain=0)
aligned_dict[protein] = prt_code[:10]+\
"---"+ \
prt_code[10:20]+\
"--"+ \
prt_code[20:]
aligned_seq = aligned_dict[protein]
actual,_ =_map_residue_ind_seq_ind(yaml_file, protein, aligned_seq)
assert expected[protein] == list(actual.values())
return
def _get_common_features(yaml_file, featurizer, aligned_dict,save_df=True):
"""
Function to get the common features across protein using the common residues.
can optionally save the pandas data to the mdl_dir
:param yaml_file: The protein yaml_file
:param featurizer: featurizer object used.
:param prt_mapping: Mapping of each residue to its sequence
:param aligned_dict : Dictionary of alignments for each protein
:return:
"""
result_dict = {}
df_dict={}
for protein in yaml_file["protein_list"]:
print(protein)
#reset the featurizer
featurizer = clone(featurizer)
trj = load_random_traj(yaml_file, protein)
df = pd.DataFrame(featurizer.describe_features(trj))
prt_mapping, prt_seq = _map_residue_ind_seq_ind(yaml_file, protein,
aligned_dict[protein])
feature_vec =[]
#for every feature
for i in df.iterrows():
#get the index and the feature itself
feature_ind, feature_dict = i
all_res_in_algn = []
mapped_index_list=[]
for aa_ind in feature_dict["resids"]:
aa_code = prt_seq[aa_ind]
#make sure we have the same residue
assert(trj.top.residue(aa_ind).code==aa_code)
#get the mapping for that aa to the main alignment
mapped_index = prt_mapping[aa_ind]
#for every protein in the alignment, check if we have the same residue
#at the same position
all_res_in_algn.append(np.alltrue([aligned_dict[prt][mapped_index]==aa_code
for prt in yaml_file["protein_list"]]))
mapped_index_list.append(mapped_index)
#to account for additions and deletions, we check if the difference between
#the mapping and the actual residue codes is the same.
mapped_index_difference = [x - mapped_index_list[i - 1]
for i, x in enumerate(mapped_index_list) if i > 0]
resid_index_difference = [x - feature_dict["resids"][i - 1]
for i, x in enumerate(feature_dict["resids"]) if i > 0]
if not np.all(mapped_index_difference==resid_index_difference):
all_res_in_algn.append(False)
if np.alltrue(all_res_in_algn):
feature_vec.append(feature_ind)
df_dict[protein] = df.iloc[feature_vec]
result_dict[protein] = feature_vec
if save_df:
new_df = df.iloc[feature_vec]
with enter_protein_mdl_dir(yaml_file, protein):
verbosedump(new_df, os.path.join("feature_descriptor.h5"))
with enter_protein_data_dir(yaml_file, protein):
verbosedump(new_df, os.path.join("sliced_feature_dir",
"feature_descriptor.h5"))
return result_dict, df_dict
def _get_common_features(yaml_file, featurizer, aligned_dict,save_df=True):
"""
Function to get the common features across protein using the common residues.
can optionally save the pandas data to the mdl_dir
:param yaml_file: The protein yaml_file
:param featurizer: featurizer object used.
:param prt_mapping: Mapping of each residue to its sequence
:param aligned_dict : Dictionary of alignments for each protein
:return:
"""
result_dict = {}
df_dict={}
for protein in yaml_file["protein_list"]:
print(protein)
#reset the featurizer
featurizer = clone(featurizer)
trj = load_random_traj(yaml_file, protein)
df = pd.DataFrame(featurizer.describe_features(trj))
prt_mapping, prt_seq = _map_residue_ind_seq_ind(yaml_file, protein,
aligned_dict[protein])
feature_vec =[]
#for every feature
for i in df.iterrows():
#get the index and the feature itself
feature_ind, feature_dict = i
all_res_in_algn = []
mapped_index_list=[]
for aa_ind in feature_dict["resids"]:
aa_code = prt_seq[aa_ind]
#make sure we have the same residue
assert(trj.top.residue(aa_ind).code==aa_code)
#get the mapping for that aa to the main alignment
mapped_index = prt_mapping[aa_ind]
#for every protein in the alignment, check if we have the same residue
#at the same position
all_res_in_algn.append(np.alltrue([aligned_dict[prt][mapped_index]==aa_code
for prt in yaml_file["protein_list"]]))
mapped_index_list.append(mapped_index)
#to account for additions and deletions, we check if the difference between
#the mapping and the actual residue codes is the same.
mapped_index_difference = [x - mapped_index_list[i - 1]
for i, x in enumerate(mapped_index_list) if i > 0]
resid_index_difference = [x - feature_dict["resids"][i - 1]
for i, x in enumerate(feature_dict["resids"]) if i > 0]
if not np.all(mapped_index_difference==resid_index_difference):
all_res_in_algn.append(False)
if np.alltrue(all_res_in_algn):
feature_vec.append(feature_ind)
df_dict[protein] = df.iloc[feature_vec]
result_dict[protein] = feature_vec
if save_df:
new_df = df.iloc[feature_vec]
with enter_protein_mdl_dir(yaml_file, protein):
verbosedump(new_df, os.path.join("feature_descriptor.h5"))
with enter_protein_data_dir(yaml_file, protein):
verbosedump(new_df, os.path.join("sliced_feature_dir",
"feature_descriptor.h5"))
return result_dict, df_dict