def __call__(self):
rot = clipper_python.Mat33_double(self.rotation_mobile_to_ref)
trans = clipper_python.Vec3_double(
self.translation_mobile_to_ref[0],
self.translation_mobile_to_ref[1],
self.translation_mobile_to_ref[2],
)
rtop = clipper_python.RTop_orth(
rot,
trans,
)
# Generate the clipper grid
grid = clipper_python.Grid(
self.grid_params[0],
self.grid_params[1],
self.grid_params[2],
)
# Define nxmap from the clipper grid and rotation-translation operator
nxmap = clipper_python.NXmap_float(
grid,
rtop,
)
# Interpolate the Xmap onto the clipper nxmap
clipper_python.interpolate(nxmap, self.xmap.xmap)
return mdc3.types.real_space.MCDNXMap(nxmap)
def output_mean_nxmap(mean_map_np, cell, path, grid_params):
print("Outputting mean map to: {}".format(path))
rot = clipper_python.Mat33_double(np.eye(3))
trans = clipper_python.Vec3_double(0.0, 0.0, 0.0)
rtop = clipper_python.RTop_orth(
rot,
trans,
)
# Generate the clipper grid
grid = clipper_python.Grid(
grid_params[0],
grid_params[1],
grid_params[2],
)
# resolution = template_xmap.hkl_info.resolution
mean_map_nxmap = clipper_python.NXmap_float(
grid,
rtop,
)
mean_map_nxmap.import_numpy(
clipper_python.Coord_grid(0, 0, 0),
mean_map_np,
)
mapout = clipper_python.CCP4MAPfile()
# print(dir(mapout))
mapout.open_write(str(path))
mapout.set_cell(cell)
mapout.export_nxmap_float(mean_map_nxmap)
mapout.close_write()
def subsample_xmap(xmap=None,
ligand_centroid_orth=np.array([0, 0, 0]),
grid_size=10,
grid_step=0.5):
# Get grid dimensions
grid_dimensions = [grid_size, grid_size, grid_size]
# Generate a random grid rotation
grid_rotation = np.random.rand(3) * 2 * np.pi
# Convert grid rotation to euler angle, and that to a rotation matrix
rot_mat_np = euler2mat(grid_rotation[0], grid_rotation[1],
grid_rotation[2], "sxyz")
# Cast to clipper matrix
rot_mat = clipper.Mat33_double(rot_mat_np)
# Generate a scale matrix to get the right grid size
scale_mat = clipper.Mat33_double(grid_step, 0, 0, 0, grid_step, 0, 0, 0,
grid_step)
# Get grid origin from ligand centroid and rotation
# algorithm: rotate grid_dimensions*grid_step vector with random rotation, step to ligand origin,
# then step back halfway along it
vec_orth = np.array(grid_dimensions) * grid_step
grid_diagonal_reshaped_orth = vec_orth.reshape(3, 1)
rotated_grid_diagonal_vector_orth = np.matmul(
rot_mat_np, grid_diagonal_reshaped_orth).reshape(-1)
ligand_centroid_orth_reshaped = ligand_centroid_orth.reshape(-1)
origin_orth = ligand_centroid_orth_reshaped - (
rotated_grid_diagonal_vector_orth / 2)
# Generate the Translation vector as a clipper vector
trans = clipper.Vec3_double(origin_orth[0], origin_orth[1], origin_orth[2])
# Generate the clipper rotation-translation operator
rtop = clipper.RTop_double(rot_mat * scale_mat, trans)
# Generate the clipper grid
grid = clipper.Grid(grid_dimensions[0], grid_dimensions[1],
grid_dimensions[2])
# Define nxmap from the clipper grid and rotation-translation operator
nxmap = clipper.NXmap_float(grid, rtop)
# Interpolate the Xmap onto the clipper nxmap
clipper.interpolate(nxmap, xmap)
# Convert the nxmap to a numpy array
nxmap_np = nxmap.export_numpy()
return nxmap_np
def interpolate_uniform_grid(
xmap,
translation_mobile_to_ref=(0, 0, 0),
rotation_mobile_to_ref=np.eye(3),
):
rot = clipper_python.Mat33_double(rotation_mobile_to_ref)
trans = clipper_python.Vec3_double(
translation_mobile_to_ref[0],
translation_mobile_to_ref[1],
translation_mobile_to_ref[2],
)
rtop = clipper_python.RTop_orth(
rot,
trans,
)
# Generate the clipper grid
grid = clipper_python.Grid(
100,
100,
100,
)
# Define nxmap from the clipper grid and rotation-translation operator
nxmap = clipper_python.NXmap_float(
grid,
rtop,
)
# Interpolate the Xmap onto the clipper nxmap
# print("interpolating!")
clipper_python.interpolate(nxmap, xmap.xmap)
xmap_np = nxmap.export_numpy()
return xmap_np
def interpolate_uniform_grid(
xmap,
translation_mobile_to_ref=(0, 0, 0),
rotation_mobile_to_ref=np.eye(3),
grid_params=[50, 50, 50],
):
# print("translation_mobile_to_ref: {}".format(translation_mobile_to_ref))
rot = clipper_python.Mat33_double(rotation_mobile_to_ref)
trans = clipper_python.Vec3_double(
translation_mobile_to_ref[0],
translation_mobile_to_ref[1],
translation_mobile_to_ref[2],
)
rtop = clipper_python.RTop_orth(
rot,
trans,
)
# Generate the clipper grid
grid = clipper_python.Grid(
grid_params[0],
grid_params[1],
grid_params[2],
)
# Define nxmap from the clipper grid and rotation-translation operator
nxmap = clipper_python.NXmap_float(
grid,
rtop,
)
# Interpolate the Xmap onto the clipper nxmap
clipper_python.interpolate(nxmap, xmap.xmap)
return nxmap
def align_map_to_reference(
dtag,
reference_dtag,
dataset_path,
reference_pdb_path,
output_path,
min_res,
structure_factors="FWT,PHWT",
):
# Load structures
f_ref = PDBFile(reference_pdb_path)
reference_structure = structure_biopython_from_pdb(f_ref)
f_moving = PDBFile(dataset_path["pdb_path"])
moving_structure = structure_biopython_from_pdb(f_moving)
# Load xmap
xmap = mdc3.types.real_space.xmap_from_path(
dataset_path["mtz_path"],
structure_factors,
)
# Get box limits from reference structure
box_limits = np.max(
np.vstack([
atom.coord for atom in reference_structure.structure.get_atoms()
]),
axis=0,
)
print(box_limits)
# Align and Get RTop to moving protein frame from alignment
alignment_moving_to_ref = mdc3.functions.alignment.align(
reference_structure.structure,
moving_structure.structure,
)
alignment_ref_to_moving = mdc3.functions.alignment.align(
moving_structure.structure,
reference_structure.structure,
)
rotation = alignment_moving_to_ref.rotran[0]
translation = alignment_moving_to_ref.rotran[1]
alignment_moving_to_ref.apply(moving_structure.structure)
print("translation: orthogonal to grid")
print(translation)
print("rotation")
print(rotation)
# Interpolate NX map in moving protein frame
grid_params = [int(x) + 5 for x in box_limits]
nxmap = mdc3.types.real_space.interpolate_uniform_grid(
xmap,
translation,
np.transpose(rotation),
grid_params=grid_params,
)
nxmap_data = nxmap.export_numpy()
origin_nxmap = clipper_python.NXmap_float(
clipper_python.Grid(
grid_params[0],
grid_params[1],
grid_params[2],
),
clipper_python.RTop_orth(
clipper_python.Mat33_double(np.eye(3)),
clipper_python.Vec3_double(0, 0, 0),
))
origin_nxmap.import_numpy(
clipper_python.Coord_grid(0, 0, 0),
nxmap_data,
)
# Output to ccp4
# cell = xmap.xmap.cell
cell = clipper_python.Cell(
clipper_python.Cell_descr(
grid_params[0],
grid_params[1],
grid_params[2],
np.pi / 2,
np.pi / 2,
np.pi / 2,
))
mdc3.types.real_space.output_nxmap(
origin_nxmap,
output_path / "{}_origin.ccp4".format(dtag),
cell,
)
mdc3.types.real_space.output_nxmap(
nxmap,
output_path / "{}.ccp4".format(dtag),
cell,
)
# Output aligned pdb
moving_structure.output(output_path / "{}_aligned.pdb".format(dtag))