File size: 4,867 Bytes
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import torch
import numpy as np
import py3Dmol
import matplotlib.pyplot as plt
import seaborn as sns
from igfold.utils.folding import get_sequence_dict
from igfold.utils.general import exists
from igfold.utils.pdb import get_cdr_range_dict
def show_pdb(
pdb_filename: str,
num_sequences,
bb_sticks=False,
sc_sticks=False,
color="b",
view_size=(500, 500),
):
return show_pdbs(
[pdb_filename],
num_sequences,
bb_sticks=bb_sticks,
sc_sticks=sc_sticks,
color=color,
view_size=view_size,
)
###
# Inspired by ColabFold visualization from https://github.com/sokrypton/ColabFold
###
def show_pdbs(
pdb_filenames,
num_sequences,
bb_sticks=False,
sc_sticks=False,
color="b",
view_size=(800, 800),
):
grid_width = math.ceil(math.sqrt(len(pdb_filenames)))
grid_height = math.ceil(len(pdb_filenames) / grid_width)
view = py3Dmol.view(
js="https://3dmol.org/build/3Dmol.js",
viewergrid=(grid_height, grid_width),
width=view_size[0],
height=view_size[1],
)
for pdb_i, pdb_filename in enumerate(pdb_filenames):
grid_row, grid_col = pdb_i // grid_width, pdb_i % grid_width
view.addModel(
open(pdb_filename, "r").read(),
"pdb",
viewer=(grid_row, grid_col),
)
if color == "b":
view.setStyle({
"cartoon": {
"colorscheme": {
"prop": "b",
"gradient": "roygb",
"min": 1.5,
"max": 0.5,
}
}
})
elif color == "rainbow":
view.setStyle({"cartoon": {"color": "spectrum"}})
elif color == "chain":
for n, chain, color_ in zip(
range(num_sequences),
list("ABCDEFGH"),
[
"lime", "cyan", "magenta", "yellow", "salmon", "white", "blue",
"orange"
],
):
view.setStyle({"chain": chain}, {"cartoon": {"color": color_}})
if sc_sticks:
BB = ["C", "O", "N"]
view.addStyle(
{
"and": [
{
"resn": ["GLY", "PRO"],
"invert": True
},
{
"atom": BB,
"invert": True
},
]
},
{"stick": {
"colorscheme": f"WhiteCarbon",
"radius": 0.2
}},
)
view.addStyle(
{"and": [{
"resn": "GLY"
}, {
"atom": "CA"
}]},
{"sphere": {
"colorscheme": f"WhiteCarbon",
"radius": 0.3
}},
)
view.addStyle(
{"and": [{
"resn": "PRO"
}, {
"atom": ["C", "O"],
"invert": True
}]},
{"stick": {
"colorscheme": f"WhiteCarbon",
"radius": 0.3
}},
)
if bb_sticks:
BB = ["C", "O", "N", "CA"]
view.addStyle(
{"atom": BB},
{"stick": {
"colorscheme": f"WhiteCarbon",
"radius": 0.3
}},
)
view.zoomTo()
return view
def plot_prmsd(
sequences,
prmsd,
out_file=None,
shade_cdr=False,
pdb_file=None,
):
seq_dict = get_sequence_dict(sequences, None)
delims = np.cumsum([len(s) for s in seq_dict.values()]).tolist()
res_rmsd = prmsd.cpu().square().mean(dim=-1).sqrt().squeeze(0)
chain_res_rmsd = np.split(res_rmsd, delims)
if shade_cdr and exists(pdb_file):
heavy_only = len(sequences) == 1 and "H" in sequences
light_only = len(sequences) == 1 and "L" in sequences
cdr_range_dict = get_cdr_range_dict(
pdb_file,
heavy_only=heavy_only,
light_only=light_only,
offset_heavy=False,
)
cdr_ranges = np.split(np.array(list(cdr_range_dict.values())), [3])
plt.figure(figsize=(8, 4))
for i, (chain, rmsd) in enumerate(zip(seq_dict.keys(), chain_res_rmsd)):
plt.subplot(1, len(seq_dict), i + 1)
res_nums = torch.arange(1, len(rmsd) + 1)
sns.lineplot(x=res_nums, y=rmsd)
if shade_cdr and exists(pdb_file):
for r in cdr_ranges[i]:
plt.axvspan(r[0], r[1], color="gray", alpha=0.5)
plt.xlabel("Residue Number")
plt.ylabel("Predicted RMSD (A)")
plt.title(f"Chain {chain}")
plt.tight_layout()
if exists(out_file):
plt.savefig(out_file, dpi=400)
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