nterm = 149
cterm = 183
# Obtain coordinates...
## peptide = ["N", "CA", "C", "O"]
peptide = ["N"]
xyzs = pr.atom.extract_xyz(peptide, atom_dict, chain, nterm, cterm)
# Find the good segment...
helixlen = 18
bindex, result = helix.protein_fit_by_length(xyzs, helixlen)
helix.report_params(result.params, title = f"Optimal params:" + \
f"cost = {result.cost}")
print(f"Select residues: [{nterm + bindex}, {nterm + bindex + helixlen})")
xyzs_sel = xyzs[bindex:bindex + helixlen]
# Estimate the helix axis vector...
nv0 = helix.estimate_axis(xyzs_sel)
# Estimate the point that the axis of helix passes through...
pv0 = np.nanmean(xyzs_sel, axis=0)
# Fitting...
## result = helix.protein(xyzs)
params = result.params
# Check...
## helix.check_fit(params, xyzs[bindex:bindex+helixlen], pv0, nv0)
parvals = helix.unpack_params(params)
helix.check_select(parvals, xyzs, pv0, nv0, nterm, bindex, helixlen)
# Select helix 3...
chain = "A"
nterm = 150
cterm = 167
# Obtain coordinates...
xyzs_dict = {}
peptides = ["N", "CA", "C", "O"]
for i in peptides:
xyzs_dict[i] = pr.atom.extract_xyz([i], atom_dict, chain, nterm, cterm)
# Estimate the mean helix axis...
nv0_dict = {}
for i in peptides:
nv0_dict[i] = helix.estimate_axis(xyzs_dict[i])
nv0_array = np.array([v for v in nv0_dict.values()])
nv0 = np.nanmean(nv0_array, axis=0)
# Estimate the mean position that the axis of helix passes through...
pv0_dict = {}
for i in peptides:
pv0_dict[i] = np.nanmean(xyzs_dict[i], axis=0)
pv0_array = np.array([v for v in pv0_dict.values()])
pv0 = np.nanmean(pv0_array, axis=0)
# Fitting...
result = helix.peptide(xyzs_dict)
params = result.params
## params = helix.peptide(xyzs_dict)
except ValueError:
pass
print(f"Fitting {bindex + nterm}...{bindex + nterm + len_helix}")
## # Report
## helix.report_params_helix(result.params, title = f"Optimal params:" + \
## f"cost = {result.cost}")
# Save values...
params_dict[bindex] = [result.params, result.cost]
# [[[ Visualization purpose ]]]
# Estimate the mean helix axis...
nv0_dict = {}
for i in peptides:
nv0_dict[i] = helix.estimate_axis(xyzs_filtered_dict[i])
nv0_array = np.array([v for v in nv0_dict.values()])
nv0 = np.nanmean(nv0_array, axis=0)
# Estimate the mean position that the axis of helix passes through...
pv0_dict = {}
for i in peptides:
pv0_dict[i] = np.nanmean(xyzs_filtered_dict[i], axis=0)
pv0_array = np.array([v for v in pv0_dict.values()])
pv0 = np.nanmean(pv0_array, axis=0)
# Fitting...
params = result.params
# Check...
# Unpack parameters
xyzs_dict = {}
peptides = ["N", "CA", "C", "O"]
for i in peptides:
xyzs_dict[i] = pr.atom.extract_xyz([i], atom_dict, chain, nterm, cterm)
# Find the good segment...
helixlen = 18
bindex, result = helix.peptide_fit_by_length(xyzs_dict, helixlen)
helix.report_params_peptide(result.params, title = f"Optimal params:" + \
f"cost = {result.cost}")
print(f"Select residues: [{nterm + bindex}, {nterm + bindex + helixlen})")
# Estimate the mean helix axis...
nv0_dict = {}
for i in peptides:
nv0_dict[i] = helix.estimate_axis(xyzs_dict[i][bindex:bindex + helixlen])
nv0_array = np.array([v for v in nv0_dict.values()])
nv0 = np.nanmean(nv0_array, axis=0)
# Estimate the mean position that the axis of helix passes through...
pv0_dict = {}
for i in peptides:
pv0_dict[i] = np.nanmean(xyzs_dict[i][bindex:bindex + helixlen], axis=0)
pv0_array = np.array([v for v in pv0_dict.values()])
pv0 = np.nanmean(pv0_array, axis=0)
# Fitting...
params = result.params
# Check...
# Unpack parameters
