mirror of
https://github.com/jertubiana/ScanNet.git
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162d696777
- Fixed paths - Uniclust 2020 is the default sequence database - Few fixes to allow parallel processing of DSSP/MSAs.
544 lines
20 KiB
Python
544 lines
20 KiB
Python
import os
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import Bio.PDB
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import numpy as np
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import warnings
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from numba import njit, prange
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from scipy.linalg import eigh
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from Bio.PDB import Selection
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from Bio.PDB.ResidueDepth import _read_vertex_array, _get_atom_radius
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import tempfile
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from utilities.paths import structures_folder,path_to_dssp,path_to_msms
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from preprocessing.protein_chemistry import list_atoms,list_atoms_types,VanDerWaalsRadii,atom_mass,atom_type_to_index,atom_to_index,index_to_type,atom_type_mass
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from preprocessing.protein_chemistry import residue_dictionary,hetresidue_field
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from preprocessing import PDBio
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from datetime import datetime
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#%% Functions for parsing PDB files.
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def is_residue(residue):
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try:
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return (residue.get_id()[0] in hetresidue_field) & (residue.resname in residue_dictionary.keys())
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except:
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return False
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def is_heavy_atom(atom):
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# Second condition for Rosetta-generated files.
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try:
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return (atom.get_id() in atom_to_index.keys() )
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except:
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return False
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def is_hydrogen(atom):
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atomid = atom.get_id()
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if len(atomid)>0:
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cond1 = (atomid[0] == 'H')
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cond2a = (atomid[0] in ['*','0','1','2','3','4','5','6','7','8','9'])
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else:
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cond1 = False
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cond2a = False
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if len(atomid)>1:
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cond2b = atomid[1] == 'H'
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else:
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cond2b = False
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return cond1 | (cond2a & cond2b)
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def process_chain(chain):
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sequence = ''
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backbone_coordinates = []
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all_coordinates = []
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all_atoms = []
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all_atom_types = []
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for residue in Selection.unfold_entities(chain, 'R'):
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if is_residue(residue):
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sequence += residue_dictionary[residue.resname]
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residue_atom_coordinates = np.array(
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[atom.get_coord() for atom in residue if is_heavy_atom(atom)])
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residue_atoms = [atom_to_index[atom.get_id()]
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for atom in residue if is_heavy_atom(atom) ]
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residue_atom_type = [atom_type_to_index[atom.get_id()[0]]
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for atom in residue if is_heavy_atom(atom) ]
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residue_backbone_coordinates = []
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for atom in ['N', 'C', 'CA', 'O', 'CB']:
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try:
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residue_backbone_coordinates.append(
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residue_atom_coordinates[residue_atoms.index(atom_to_index[atom])])
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except:
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residue_backbone_coordinates.append(
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np.ones(3, dtype=np.float32) * np.nan)
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backbone_coordinates.append(residue_backbone_coordinates)
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all_coordinates.append(residue_atom_coordinates)
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all_atoms.append(residue_atoms)
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all_atom_types.append(residue_atom_type)
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backbone_coordinates = np.array(backbone_coordinates)
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return sequence, backbone_coordinates, all_coordinates, all_atoms, all_atom_types
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def fill_cbeta(backbone_coordinates):
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"""
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cbeta = dans le plan n,calpha,c. rotation de 2pi/3.
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cbeta - calpha = - (n-calpha) + - (c-calpha)
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cbeta = 3 calpha -n - c
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"""
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n = backbone_coordinates[:, 0]
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c = backbone_coordinates[:, 1]
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calpha = backbone_coordinates[:, 2]
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cbeta = backbone_coordinates[:, 4]
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problematic = np.isnan(cbeta).max(1)
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# print(np.nonzero(problematic)[0])
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cbeta[problematic] = 3 * calpha[problematic] - \
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n[problematic] - c[problematic]
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return backbone_coordinates
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@njit(parallel=True)
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def _get_SideChainCenterofMass(atomic_coordinates, atom_types):
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N = len(atomic_coordinates)
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SideChainCenterofMass = np.zeros((N, 3))
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for n in prange(N):
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atoms = atomic_coordinates[n][4:]
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types = atom_types[n][4:]
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mass = 0
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for u in range(len(atoms)):
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SideChainCenterofMass[n] += atoms[u] * atom_mass[types[u]]
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mass += atom_mass[types[u]]
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SideChainCenterofMass[n] /= mass
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return SideChainCenterofMass
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def get_SideChainCenterofMass(atomic_coordinates, atom_types, cbeta_coordinates=None):
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SideChainCenterofMass = _get_SideChainCenterofMass(
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atomic_coordinates, atom_types)
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if cbeta_coordinates is not None:
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glycins_or_missing_side_chain = np.isnan(SideChainCenterofMass[:, 0])
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SideChainCenterofMass[glycins_or_missing_side_chain,
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:] = cbeta_coordinates[glycins_or_missing_side_chain, :]
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return SideChainCenterofMass
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def get_PDB_indices(chain_obj,return_model=False,return_chain=False):
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list_indices = []
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for residue in Selection.unfold_entities(chain_obj, 'R'):
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if is_residue(residue):
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if return_model & return_chain:
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list_indices.append( (residue.get_full_id()[1],residue.get_full_id()[2],residue.get_id()[1]) )
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elif return_chain:
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list_indices.append( (residue.get_full_id()[2],residue.get_id()[1]) )
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else:
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list_indices.append(residue.get_id()[1])
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list_indices = np.array(list_indices)
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return list_indices
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#%% Functions for finding interfaces residues and other distance-related features.
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@njit(parallel=False)
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def distance_residues(all_coordinates):
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N = len(all_coordinates)
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d = np.zeros((N, N))
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for i in prange(N):
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for j in prange(N):
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if i > j:
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d[i, j] = np.inf
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coord1 = all_coordinates[i]
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coord2 = all_coordinates[j]
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n1 = len(coord1)
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n2 = len(coord2)
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for n1_ in range(n1):
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for n2_ in range(n2):
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d[i, j] = min(
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np.sum((coord1[n1_] - coord2[n2_])**2), d[i, j])
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d = d + d.T
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d = np.sqrt(d)
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return d
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@njit(parallel=False)
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def distance(x1, x2):
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n1 = x1.shape[0]
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n2 = x2.shape[0]
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out = np.zeros((n1, n2))
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for i in range(n1):
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for j in range(n2):
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out[i, j] = np.sqrt(((x1[i] - x2[j])**2).sum())
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return out
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@njit(parallel=False)
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def residues_in_contact(atom_coords1, atom_coords2, atoms1, atoms2, method='VdW', threshold=3.0):
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n1 = len(atoms1)
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n2 = len(atoms2)
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in_contact = False
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for i in range(n1):
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for j in range(n2):
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d = np.sqrt(((atom_coords1[i] - atom_coords2[j])**2).sum())
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if method == 'VdW':
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thresh = VanDerWaalsRadii[atoms1[i]] + \
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VanDerWaalsRadii[atoms2[j]] + threshold
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elif method == 'Heavy':
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thresh = threshold
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if d < thresh:
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in_contact = True
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break
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return in_contact
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def find_interface_pairs(calphapos1, list_coordinates1, list_atoms1, calphapos2, list_coordinates2, list_atoms2, method='VdW3'):
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n1 = len(list_coordinates1)
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n2 = len(list_coordinates2)
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if 'VdW' in method:
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threshold = float(method[3:])
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method_ = 'VdW'
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elif 'Calpha' in method:
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threshold = float(method[6:])
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method_ = 'Calpha'
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elif 'Heavy' in method:
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threshold = float(method[5:])
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method_ = 'Heavy'
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if method_ == 'Calpha':
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contact1, contact2 = np.nonzero(
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distance(calphapos1, calphapos2) < threshold)
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list_contacts = np.array(list(zip(contact1, contact2)))
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else:
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# Precompute the Calpha distances first... Only performs the Natoms1 X Natoms2 computation if the Calpha are close enough.
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maybe_in_contacts = distance(calphapos1, calphapos2) < 15.
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list_contacts = []
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for i in range(n1):
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for j in range(n2):
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if maybe_in_contacts[i, j]:
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if residues_in_contact(list_coordinates1[i], list_coordinates2[j], list_atoms1[i], list_atoms2[j], method=method_, threshold=threshold):
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list_contacts.append((i, j))
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list_contacts = np.array(list_contacts)
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return list_contacts
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def find_nearby_interface_residues(interface_residues, structure):
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if len(interface_residues) > 0:
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calphapos = structure[:, 2, :]
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return np.nonzero(distance(calphapos[interface_residues], calphapos).min(0) < 6.)[0]
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else:
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return np.array([])
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#%% Handcrafted geometrical features.
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def apply_DSSP(chain_obj, pdbparser=None, io=None, path_to_dssp=path_to_dssp):
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if pdbparser is None:
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pdbparser = Bio.PDB.PDBParser() # PDB parser; to read pdb files.
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if io is None:
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# PDB IO. To compute solvent accessibility, need to write a PDB file with only the monomers...
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io = Bio.PDB.PDBIO()
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if isinstance(chain_obj,list):
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pdb_id, model, chain = chain_obj[0].get_full_id()
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letters = ['A','B','C','D','E','F','G','H','I','J','K','L',
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'M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z',
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'a','b','c','d','e','f','g','h','i','j','k','l','m','n',
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'o','p','q','r','s','t','u','v','w','x','y','z',
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'1','2','3','4','5','6','7','8','9','10']
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chain_objs_copy = []
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for n in range(len(chain_obj)):
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chain_obj_copy = chain_obj[n].copy()
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chain_obj_copy.full_id = ('pdb',0,letters[n])
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chain_obj_copy.id = letters[n]
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chain_objs_copy.append(chain_obj_copy)
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io.set_structure(chain_objs_copy[0])
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for n in range(1,len(chain_obj)):
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io.structure[0].add(chain_objs_copy[n])
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else:
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pdb_id, model, chain = chain_obj.get_full_id()
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# chain id = AB for instance. Only for mmCif file, not compatible with pdbIO.
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if len(chain) > 1:
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chain_obj.id = '!'
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io.set_structure(chain_obj)
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chain_obj.id = chain
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for residue in Selection.unfold_entities(io.structure,'R'):
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for atom in residue:
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atom.disordered_flag = 0
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hash = str(datetime.now())[-6:]
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name = 'tmp_' + pdb_id + \
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'_model_%s_chain_%s' % (model, chain) + '_'+hash + '.pdb'
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io.save(name)
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with warnings.catch_warnings(record=True) as w:
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mini_structure = pdbparser.get_structure(name[:-4] # name of structure
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, name)
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mini_model = mini_structure[0]
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dssp = Bio.PDB.DSSP(mini_model,
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name,
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dssp=path_to_dssp)
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os.system('rm %s' % name)
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secondary_structure = ''
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accessible_surface_area = []
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for residue in Selection.unfold_entities(mini_model,'R'):
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if is_residue(residue):
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try:
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secondary_structure += dssp[residue.get_full_id()
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[2:]][2]
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accessible_surface_area.append(
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dssp[residue.get_full_id()[2:]][3])
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if accessible_surface_area[-1] == 'NA':
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accessible_surface_area[-1] = np.nan
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except:
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secondary_structure += '/'
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accessible_surface_area.append(np.nan)
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accessible_surface_area = np.array(
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accessible_surface_area, dtype=np.float32)
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return secondary_structure, accessible_surface_area
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def get_surface(chain, MSMS=path_to_msms, probe_radius=1.5):
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# Replace pdb_to_xyzr
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# Make x,y,z,radius file
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xyz_tmp = tempfile.mktemp()
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with open(xyz_tmp, 'w') as pdb_to_xyzr:
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all_coordinates = []
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all_ids = []
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for residue in Selection.unfold_entities(chain,'R'):
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if is_residue(residue):
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for atom in residue:
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x, y, z = atom.coord
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# If two atoms have identical coordinates, MSMS breaks.
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if not (x, y, z) in all_coordinates:
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radius = _get_atom_radius(atom, rtype='united')
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print('{:6.3f}\t{:6.3f}\t{:6.3f}\t{:1.2f}'.format(x, y, z, radius),
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file=pdb_to_xyzr)
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all_coordinates.append((x, y, z))
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# make surface
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surface_tmp = tempfile.mktemp()
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MSMS = MSMS + " -probe_radius %.2f -if %s -of %s > " + tempfile.mktemp()
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make_surface = MSMS % (probe_radius, xyz_tmp, surface_tmp)
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os.system(make_surface)
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surface_file = surface_tmp + ".vert"
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print(make_surface)
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if not os.path.isfile(surface_file):
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raise RuntimeError("Failed to generate surface file using "
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"command:\n%s" % make_surface)
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# read surface vertices from vertex file
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surface = _read_vertex_array(surface_file)
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return surface
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def get_surface_robust(chain, MSMS=path_to_msms, probe_radius=1.5, ntrials_max=5):
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success = False
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ntrials = 0
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while not (success | (ntrials > ntrials_max)):
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try:
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surface = get_surface(chain, MSMS=MSMS, probe_radius=probe_radius)
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success = True
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except:
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if isinstance(chain,list):
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chainid = chain[0].get_full_id()
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else:
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chainid = chain.get_full_id()
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print('MSMS failed, radius = %.2f, chain id: (%s)' %
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(probe_radius, chainid) )
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probe_radius = probe_radius + 0.01
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success = False
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ntrials += 1
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if ntrials > ntrials_max:
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raise RuntimeError("Failed to generate surface file using "
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"command:\n%s")
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return surface
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def get_surface_normal(surface, interior_points=None, radius=8., max_nn=10):
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npoints = len(surface)
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d = distance(surface, surface)
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normals = []
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for point in range(npoints):
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subset = np.argsort(d[point])[:max_nn] # max_nn closest points.
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subset = subset[d[point][subset] < radius] # within radius.
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relative_locations = surface[subset, :]
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mu = relative_locations.mean(0)
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covariance = np.dot(relative_locations.T, relative_locations) / \
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len(subset) - mu[:, np.newaxis] * mu[np.newaxis, :]
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lam, v = eigh(covariance)
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normals.append(v[:, 0])
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normals = np.array(normals)
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if interior_points is not None:
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closest_points = np.argsort(
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distance(surface, interior_points), axis=1)[:, 1:4]
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sign_dot_product = np.sign(np.sign(
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((surface[:, np.newaxis] - interior_points[closest_points]) * normals[:, np.newaxis, :]).sum(-1)).mean(-1))
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# If negative, must switch signs.
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normals *= sign_dot_product[:, np.newaxis]
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return normals
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def analyze_surface(chain_obj, atom_coordinates,
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MSMS=path_to_msms,
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probe_radius=1.5,
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normal_estimation_radius=8.,
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normal_estimation_nn=10,
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nr_ball=5,
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ntheta_ball=5,
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nphi_ball=5,
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volume_index_ball_radii=[5.0, 8.0, 11.0]
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):
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"""
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Outputs:
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- Volume index (5.0, 8.0, 11.0)
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- Residue Depth (Backbone and Side Chain)
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- Half sphere exposure (up and down) + Coordination number.
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- [ASA]
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- Calcule la surface.
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- Calcule la normale à la surface.
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- Volume index.
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"""
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nResidues = len(atom_coordinates)
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surface = get_surface_robust(
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chain_obj, MSMS=MSMS, probe_radius=probe_radius)
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normals = get_surface_normal(surface, interior_points=np.concatenate(
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atom_coordinates), radius=normal_estimation_radius, max_nn=normal_estimation_nn)
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BackboneDepth, SideChainDepth = ComputeResidueDepth(
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atom_coordinates, surface)
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VolumeIndex = np.zeros([nResidues, len(volume_index_ball_radii)])
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unit_ball_points, unit_ball_weights = get_unit_ball(
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nr_ball, ntheta_ball, nphi_ball)
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for i in range(nResidues):
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VolumeIndex[i, :] = compute_convexity_index(np.array(atom_coordinates[i]), surface, normals, ball_radii=volume_index_ball_radii,
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unit_ball_points=unit_ball_points,
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unit_ball_weights=unit_ball_weights).mean(0)
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return BackboneDepth, SideChainDepth, VolumeIndex
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def is_inside_surface(query_point, surface, normals):
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"""
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inside = -1
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outside = +1
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"""
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closest = np.argmin(distance(query_point, surface), axis=1)
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is_inside = np.sign(
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((-surface[closest] + query_point) * normals[closest]).sum(-1))
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return is_inside
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def get_unit_ball(nr, ntheta, nphi):
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radii = np.arange(nr) / (nr - 1)
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thetas = np.arange(ntheta) / (ntheta - 1) * np.pi
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phis = np.arange(nphi) / (nphi - 1) * 2 * np.pi
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grid_r, grid_theta, grid_phi = np.meshgrid(
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radii, thetas, phis, indexing='ij')
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grid_r = grid_r.flatten()
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grid_theta = grid_theta.flatten()
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|
grid_phi = grid_phi.flatten()
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|
cartesian = np.zeros([nr * ntheta * nphi, 3])
|
|
cartesian[:, 0] = grid_r * np.sin(grid_theta) * np.cos(grid_phi)
|
|
cartesian[:, 1] = grid_r * np.sin(grid_theta) * np.sin(grid_phi)
|
|
cartesian[:, 2] = grid_r * np.cos(grid_theta)
|
|
dV = grid_r * np.sin(grid_theta)
|
|
return cartesian, dV
|
|
|
|
|
|
def compute_convexity_index(query_points, surface, normals, ball_radii=[2.0],
|
|
nr=5,
|
|
ntheta=5,
|
|
nphi=5,
|
|
subset_size=20,
|
|
unit_ball_points=None,
|
|
unit_ball_weights=None
|
|
):
|
|
npoints = len(query_points)
|
|
nradii = len(ball_radii)
|
|
convexity_index = np.zeros([npoints, nradii])
|
|
|
|
if (unit_ball_points is None) | (unit_ball_weights is None):
|
|
unit_ball_points, unit_ball_weights = get_unit_ball(nr, ntheta, nphi)
|
|
|
|
query_distances = distance(query_points, surface)
|
|
|
|
for i in range(npoints):
|
|
# print(i)
|
|
query_point = query_points[i]
|
|
query_distance = query_distances[i]
|
|
subset = np.argsort(query_distance)[:subset_size]
|
|
surface_subset = surface[subset]
|
|
normals_subset = normals[subset]
|
|
for j, ball_radius in enumerate(ball_radii):
|
|
is_inside = is_inside_surface(
|
|
query_point + ball_radius * unit_ball_points, surface_subset, normals_subset)
|
|
convexity_index[i, j] = (
|
|
is_inside * unit_ball_weights).sum() / unit_ball_weights.sum()
|
|
return np.squeeze(convexity_index)
|
|
|
|
|
|
def ComputeResidueDepth(atom_coordinates, surface):
|
|
nResidues = len(atom_coordinates)
|
|
BackboneDepth = []
|
|
SideChainDepth = []
|
|
for i in range(nResidues):
|
|
nAtoms = len(atom_coordinates[i])
|
|
atomDepth = distance(atom_coordinates[i], surface).min(1)
|
|
if nAtoms >= 4:
|
|
BackboneDepth.append(atomDepth[:4].mean())
|
|
SideChainDepth.append(atomDepth[4:].mean())
|
|
else: # Missing atoms.
|
|
BackboneDepth.append(atomDepth.mean())
|
|
SideChainDepth.append(atomDepth.mean())
|
|
BackboneDepth = np.array(BackboneDepth)
|
|
SideChainDepth = np.array(SideChainDepth)
|
|
glycins_or_missing_side_chain = np.isnan(SideChainDepth)
|
|
SideChainDepth[glycins_or_missing_side_chain] = BackboneDepth[glycins_or_missing_side_chain]
|
|
return BackboneDepth, SideChainDepth
|
|
|
|
|
|
def ComputeResidueHSE(backbone_coordinates, radius=13):
|
|
nResidues = backbone_coordinates.shape[0]
|
|
fill_cbeta(backbone_coordinates)
|
|
calphas = backbone_coordinates[:, 2, :]
|
|
cbetas = backbone_coordinates[:, -1, :]
|
|
calpha_distances = distance(calphas, calphas)
|
|
|
|
HalfSphere_up = np.zeros(nResidues, dtype=np.int)
|
|
HalfSphere_down = np.zeros(nResidues, dtype=np.int)
|
|
Coordination = np.zeros(nResidues, dtype=np.int)
|
|
for i in range(nResidues):
|
|
neighbor_residues = (calpha_distances[i] < radius) & (
|
|
calpha_distances[i] > 0)
|
|
in_upper_plane = np.dot(
|
|
calphas[neighbor_residues] - calphas[i][np.newaxis], cbetas[i] - calphas[i]) >= 0
|
|
HalfSphere_up[i] = in_upper_plane.sum()
|
|
Coordination[i] = neighbor_residues.sum()
|
|
HalfSphere_down[i] = Coordination[i] - HalfSphere_up[i]
|
|
HalfSphere_excess_up = (HalfSphere_up - HalfSphere_down) / Coordination
|
|
return HalfSphere_excess_up, Coordination
|
|
|
|
|
|
def make_values_dictionary(resids,values):
|
|
dictionary_values = {}
|
|
values_is_list = isinstance(values,list)
|
|
for l,resid in enumerate(resids):
|
|
key = (int(resid[0]), resid[1], int(resid[2]) )
|
|
if values_is_list:
|
|
dictionary_values[ key ] = [value[l] for value in values]
|
|
else:
|
|
dictionary_values[ key ] = values[l]
|
|
return dictionary_values
|
|
|