def read_PDB_files(pdbids,directory="",show_info=True):
path = Path.cwd() / directory
path.mkdir(parents=True,exist_ok=True)
# with open(pdbids) as input_pdbs:
# pdbids = list(map(lambda x: x.split(","),input_pdbs.read().split("\n")))
# pdbids = list(filter(lambda x: x[0][0] != "#",pdbids))
pdb_structures = dict()
info = dict()
for i in pdbids:
if (path / ("pdb%s.ent" %i.lower())).exists():
f_name = str(path / ("pdb%s.ent" %i.lower()))
elif (path / ("%s.pdb" %i.lower())).exists():
f_name = str(path / ("%s.pdb" %i.lower()))
else:
f_name = pdbl().retrieve_pdb_file(pdb_code=i,pdir=directory,file_format='pdb')
#chains = [i[2*n+1:2*n+3] for n in range(len(i)//2)]
structure = PDBParser(QUIET=True).get_structure(i,f_name)
pdb_structures[i] = structure
num_models = len([j for j in structure.get_models()])
if num_models > 1:
info[i] = num_models
#exit()
#chains = {k:v for k,v in }
# if show_info:
# print("%d pdbs have multiple models:"%len(info))
# for k,v in sorted(info.items()):
# print("%s-(%d)"%(k,v))
# print("Total number of models: %s"%sum(info.values()))
return pdb_structures
def build_matrix(
path: str,
filename: str,
truncate_log: Union[tqdm.tqdm, None] = None) -> BuildMatrixDict:
"""Build the input matrix for one protein.
Args:
path: path of the pdb file.
filename: name of the file (without extension).
truncate_log: tqdm logger
Returns:
Build matrix dictionary
"""
PROTEIN_SEQ_MAX_LEN = 4000
protein_matrix = [[0 for x in range(PROTEIN_SEQ_MAX_LEN)]
for y in range(10)]
protein_structure = PDBParser().get_structure(filename, path)
protein_model = list(protein_structure.get_models())
protein_chains = list(protein_model[0].get_chains())
col = 0
try:
for chain in protein_chains:
protein_residues = list(chain.get_residues())
for residue in protein_residues:
if Polypeptide.is_aa(residue.get_resname(), standard=True):
atoms = list(residue.get_atoms())
x = []
y = []
z = []
for atom in atoms:
vec = atom.get_vector()
x.append(vec.__getitem__(0))
y.append(vec.__getitem__(1))
z.append(vec.__getitem__(2))
# calculate position of residue
x = round(mean(x))
y = round(mean(y))
z = round(mean(z))
# one letter code
code = Polypeptide.three_to_one(residue.get_resname())
aa = amino_acid[code]
protein_matrix[0][col] = aa["code"]
protein_matrix[1][col] = x
protein_matrix[2][col] = y
protein_matrix[3][col] = z
protein_matrix[4][col] = aa["hydropathy"]
protein_matrix[5][col] = aa["hydropathy_index"]
protein_matrix[6][col] = aa["acidity_basicity"]
protein_matrix[7][col] = aa["mass"]
protein_matrix[8][col] = aa["isoelectric_point"]
protein_matrix[9][col] = aa["charge"]
# Even if the current residue is not amino acid we increase the col.
# 0 is save at this position if it is not an amino acid.
col = col + 1
except IndexError:
if truncate_log is not None:
truncate_log.set_description_str(
f"Protein {filename} is truncated.")
# Prepare dict so it can be load to vaex dataframe
dic: BuildMatrixDict = {
"seq": [[]],
"x_pos": [[]],
"y_pos": [[]],
"z_pos": [[]],
"hydropathy": [[]],
"hydropathy_index": [[]],
"acidity_basicity": [[]],
"mass": [[]],
"isoelectric_point": [[]],
"charge": [[]],
}
for i in range(10):
dic[col_name[i]] = pyarrow.array(
[[protein_matrix[i][x] for x in range(PROTEIN_SEQ_MAX_LEN)]])
return dic
