def plotdmat(line):
xyzs = np.zeros((len_peptide, 3))
# Unpack parameters
_, pdb, chain, species = line[:4]
betatype = line[10]
# Read coordinates from a PDB file...
fl_pdb = f"{pdb}.pdb"
pdb_path = os.path.join(drc, fl_pdb)
atoms_pdb = pr.atom.read(pdb_path)
# Create a lookup table for this pdb...
atom_dict = pr.atom.create_lookup_table(atoms_pdb)
# Obtain coordinates...
xyzs = pr.atom.extract_xyz(peptide, atom_dict, chain, nterm, cterm)
# Calculate distance matrix...
dmat = pr.distance.calc_dmat(xyzs, xyzs)
fl_dmat = os.path.join(drc_dmat, f"{pdb}.{chain}.dmat")
plot_dmat(dmat, fl_dmat, lbl = {}, lbl_fontsize = 29, width = 10, height = 12, fontsize = 29, linewidth = 2.0, palette = pal, NaN = 0)
return None
def plotdmat(line):
# Unpack parameters
_, pdb, chain, lb_term, ub_term = line[:5]
# Read coordinates from a PDB file...
fl_pdb = f"{pdb}.pdb"
pdb_path = os.path.join(drc, fl_pdb)
atoms_pdb = pr.atom.read(pdb_path)
# Create a lookup table for this pdb...
atom_dict = pr.atom.create_lookup_table(atoms_pdb)
# Obtain the chain to process...
chain_dict = atom_dict[chain]
# Obtain seq string for the current chain...
tar_seq = seq_dict[f"{pdb.lower()}_{chain}"]
# Obtain xyzs...
entry = f"{pdb}_{chain}"
xyzs = pr.atom.extract_xyz_by_seq(backbone, chain_dict, tar_seq, nseqi,
cseqi)
# Calculate distance matrix...
dmat = pr.distance.calc_dmat(xyzs, xyzs)
fl_dmat = os.path.join(drc_dmat, f"{pdb}.{chain}.dmat")
plot_dmat(dmat,
fl_dmat,
lbl={},
palette=pal,
NaN=0,
linewidth=1,
intst_max=100)
return None
## # Draw the rigid framework...
## fl_fwk = 'fwk.dat'
## fwk = pr.utils.read_file(fl_fwk, numerical = True)
## cmd_fwk = []
## for i in range(len(fwk)):
## cmd = []
## b1, e1 = [ k * 4 for k in fwk[i] ]
## cmd.append(f"set object rectangle front from {b1},{e1} to {e1},{b1} fs empty border linecolor rgb 'black'")
## for j in range(i + 1, len(fwk)):
## b2, e2 = [ k * 4 for k in fwk[j] ]
## cmd.append(f"set object rectangle front from {b1},{b2} to {e1},{e2} fs empty linecolor rgb '{color_guideline}'")
## cmd_fwk.extend(cmd)
## cmds_guideline_bottom.extend(cmd_fwk)
## cmds_guideline_top.append(f"set key top left")
plot_dmat(
dmat_full,
fl_rmsd_dmat,
lbl={},
width=10,
height=12,
fontsize=29,
lbl_fontsize=29,
linewidth=2.0,
palette=pal,
intst_max="*",
## cmds_top = cmds_guideline_top,
## cmds_bottom = cmds_guideline_bottom,
)
chain_dict = atom_dict[chain]
# Obtain seq string for the current chain...
tar_seq = seq_dict[f"{pdb.lower()}_{chain}"]
# Obtain xyzs...
entry = f"{pdb}_{chain}"
xyzs = pr.atom.extract_xyz_by_seq(backbone, chain_dict, tar_seq, nseqi,
cseqi)
xyzs[mask_loop == 0] = np.nan
# Calculate distance matrix...
dmat = pr.distance.calc_dmat(xyzs, xyzs)
fl_dmat = os.path.join(drc_dmat, f"{pdb}.{chain}.dmat")
plot_dmat(dmat, fl_dmat, lbl={}, palette=pal, NaN=0)
if pdb == "7dd5": break
if False:
def plotdmat(line):
# Unpack parameters
_, pdb, chain, lb_term, ub_term = line[:5]
# Read coordinates from a PDB file...
fl_pdb = f"{pdb}.pdb"
pdb_path = os.path.join(drc, fl_pdb)
atoms_pdb = pr.atom.read(pdb_path)
# Create a lookup table for this pdb...
# Find the indices of values that are within the range of (dmin, dmax)...
out_range_bool = np.logical_or(dmin > dmat, dmat > dmax)
# Form a submatrix by selecting values within the range...
dmat[out_range_bool] = np.nan
# Apply dmask...
dmat *= dmask
# Put more resi labels...
diaglbl = {}
for resi in range(nterm, cterm, 1):
seqi = pr.fasta.resi_to_seqi(resi, super_seg, nterm)
diaglbl[resi] = [seqi, seqi]
fl_dmat = os.path.join(drc_dmat, f"{pdb}.{chain}.dmat")
plot_dmat(dmat,
fl_dmat,
lbl=labels_TM,
lbl_fontsize=18,
diaglbl=diaglbl,
diaglblfontsize=3,
intst_min=dmin,
intst_max=dmax,
palette=pal,
temp=False,
mode='sparse',
showsparselabel=False,
NaN=0)
# Define a colorscheme...
# Colorscheme is inspired by from this publication (DOI: 10.1093/nar/gkw555) from Zhong Ren
pal = "set palette defined ( 0 'seagreen', 0.1 'white', 0.5 'blue', 1 'navy' )"
# Create labels...
# +1 to make it a right-end closed
## labels = {
## "TM1" : [ 36, 60 + 1],
## "TM2" : [ 69, 97 + 1],
## "TM3" : [104, 136 + 1],
## "TM4" : [146, 171 + 1],
## "TM5" : [189, 221 + 1],
## "TM6" : [369, 397 + 1],
## "TM7" : [406, 427 + 1],
## "H8" : [432, 441 + 1],
## }
## for k, v in labels.items(): labels[k] = [ (i - nterm) * len_res for i in v ]
plot_dmat(
rmsd_dmat,
fl_rmsd_dmat,
## lbl = labels,
width=10,
height=12,
fontsize=29,
lbl_fontsize=29,
linewidth=1.0,
palette=pal,
intst_max="*",
)