def write_superposed_pdbs(self, output_pdb_folder, alignments: dict = None):
"""
Superposes PDBs according to alignment and writes transformed PDBs to files
(View with Pymol)
Parameters
----------
alignments
output_pdb_folder
"""
if alignments is None:
alignments = self.alignment
output_pdb_folder = Path(output_pdb_folder)
if not output_pdb_folder.exists():
output_pdb_folder.mkdir()
reference_name = self.structures[0].name
reference_pdb = pd.parsePDB(
str(self.output_folder / f"cleaned_pdb/{self.structures[0].name}.pdb")
)
core_indices = np.array(
[
i
for i in range(len(alignments[reference_name]))
if -1 not in [alignments[n][i] for n in alignments]
]
)
aln_ref = alignments[reference_name]
ref_coords_core = (
reference_pdb[helper.get_alpha_indices(reference_pdb)]
.getCoords()
.astype(np.float64)[np.array([aln_ref[c] for c in core_indices])]
)
ref_centroid = helper.nb_mean_axis_0(ref_coords_core)
ref_coords_core -= ref_centroid
transformation = pd.Transformation(np.eye(3), -ref_centroid)
reference_pdb = pd.applyTransformation(transformation, reference_pdb)
pd.writePDB(str(output_pdb_folder / f"{reference_name}.pdb"), reference_pdb)
for i in range(1, len(self.structures)):
name = self.structures[i].name
pdb = pd.parsePDB(
str(self.output_folder / f"cleaned_pdb/{self.structures[i].name}.pdb")
)
aln_name = alignments[name]
common_coords_2 = (
pdb[helper.get_alpha_indices(pdb)]
.getCoords()
.astype(np.float64)[np.array([aln_name[c] for c in core_indices])]
)
(
rotation_matrix,
translation_matrix,
) = superposition_functions.svd_superimpose(
ref_coords_core, common_coords_2
)
transformation = pd.Transformation(rotation_matrix.T, translation_matrix)
pdb = pd.applyTransformation(transformation, pdb)
pd.writePDB(str(output_pdb_folder / f"{name}.pdb"), pdb)
def get_dssp_features(protein_dssp):
"""
Extracts DSSP features (assumes DSSP is run already)
Parameters
----------
protein_dssp
protein on which execDSSP has been called
Returns
-------
dict of secondary,
dssp_
NH_O_1_index, NH_O_1_energy
hydrogen bonds; e.g. -3,-1.4 means: if this residue is residue i then N-H of I is h-bonded to C=O of I-3 with an
electrostatic H-bond energy of -1.4 kcal/mol. There are two columns for each type of H-bond, to allow for bifurcated H-bonds.
NH_O_2_index, NH_O_2_energy
O_NH_1_index, O_NH_1_energy
O_NH_2_index, O_NH_2_energy
acc
number of water molecules in contact with this residue *10. or residue water exposed surface in Angstrom^2.
alpha
virtual torsion angle (dihedral angle) defined by the four Cα atoms of residues I-1,I,I+1,I+2. Used to define chirality.
kappa
virtual bond angle (bend angle) defined by the three Cα atoms of residues I-2,I,I+2. Used to define bend (structure code ‘S’).
phi
IUPAC peptide backbone torsion angles.
psi
IUPAC peptide backbone torsion angles.
tco
cosine of angle between C=O of residue I and C=O of residue I-1. For α-helices, TCO is near +1, for β-sheets TCO is near -1.
Ignores:
dssp_bp1, dssp_bp2, and dssp_sheet_label: residue number of first and second bridge partner followed by one letter sheet label
"""
dssp_ignore = ["dssp_bp1", "dssp_bp2", "dssp_sheet_label", "dssp_resnum"]
dssp_labels = [
label
for label in protein_dssp.getDataLabels()
if label.startswith("dssp") and label not in dssp_ignore
]
data = {}
alpha_indices = helper.get_alpha_indices(protein_dssp)
indices = [protein_dssp[x].getData("dssp_resnum") for x in alpha_indices]
assert len(alpha_indices) == len(indices)
for label in dssp_labels + ["secondary"]:
label_to_index = {
i - 1: protein_dssp[x].getData(label)
for i, x in zip(indices, alpha_indices)
}
data[label] = np.array(
[
label_to_index[i] if i in label_to_index else 0
for i in range(len(alpha_indices))
]
)
return data
def write_superposed_pdbs_reference(self, output_pdb_folder, alignments):
"""
Superposes PDBs according to reference structure and writes transformed PDBs to files
(View with Pymol)
Parameters
----------
alignments
output_pdb_folder
"""
reference_name = self.structures[self.reference_structure_index].name
reference_pdb = pd.parsePDB(
str(self.output_folder /
f"cleaned_pdb/{self.structures[self.reference_structure_index].name}.pdb"
))
aln_ref = alignments[reference_name]
reference_coords = (reference_pdb[helper.get_alpha_indices(
reference_pdb)].getCoords().astype(np.float64))
pd.writePDB(str(output_pdb_folder / f"{reference_name}.pdb"),
reference_pdb)
for i in range(1, len(self.structures)):
name = self.structures[i].name
pdb = pd.parsePDB(
str(self.output_folder /
f"cleaned_pdb/{self.structures[i].name}.pdb"))
aln_name = alignments[name]
common_coords_1, common_coords_2 = get_common_coordinates(
reference_coords,
pdb[helper.get_alpha_indices(pdb)].getCoords().astype(
np.float64),
aln_ref,
aln_name,
)
(
rotation_matrix,
translation_matrix,
) = superposition_functions.svd_superimpose(
common_coords_1, common_coords_2)
transformation = pd.Transformation(rotation_matrix.T,
translation_matrix)
pdb = pd.applyTransformation(transformation, pdb)
pd.writePDB(str(output_pdb_folder / f"{name}.pdb"), pdb)
def get_fluctuations(protein: pd.AtomGroup, n_modes: int = 50):
"""
Get atom fluctuations using anisotropic and Gaussian network models with n_modes modes.
Parameters
----------
protein
n_modes
Returns
-------
dict of anm_ca, anm_cb, gnm_ca, gnm_cb
"""
data = {}
beta_indices = helper.get_beta_indices(protein)
alpha_indices = helper.get_alpha_indices(protein)
data["anm_cb"] = get_anm_fluctuations(protein[beta_indices], n_modes)
data["gnm_cb"] = get_gnm_fluctuations(protein[beta_indices], n_modes)
data["anm_ca"] = get_anm_fluctuations(protein[alpha_indices], n_modes)
data["gnm_ca"] = get_gnm_fluctuations(protein[alpha_indices], n_modes)
return data