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from typing import List
from einops import rearrange
import torch
import numpy as np
from igfold.model.interface import IgFoldInput
from igfold.utils.fasta import get_fasta_chain_dict
from igfold.utils.general import exists
from igfold.utils.pdb import get_atom_coords, save_PDB, write_pdb_bfactor, cdr_indices
def get_sequence_dict(
sequences,
fasta_file,
):
if exists(sequences) and exists(fasta_file):
print("Both sequences and fasta file provided. Using fasta file.")
seq_dict = get_fasta_chain_dict(fasta_file)
elif not exists(sequences) and exists(fasta_file):
seq_dict = get_fasta_chain_dict(fasta_file)
elif exists(sequences):
seq_dict = sequences
else:
exit("Must provide sequences or fasta file.")
return seq_dict
def process_template(
pdb_file,
fasta_file,
ignore_cdrs=None,
ignore_chain=None,
):
temp_coords, temp_mask = None, None
if exists(pdb_file):
temp_coords = get_atom_coords(
pdb_file,
fasta_file=fasta_file,
)
temp_coords = torch.stack(
[
temp_coords['N'], temp_coords['CA'], temp_coords['C'],
temp_coords['CB']
],
dim=1,
).view(-1, 3).unsqueeze(0)
temp_mask = torch.ones(temp_coords.shape[:2]).bool()
temp_mask[temp_coords.isnan().any(-1)] = False
temp_mask[temp_coords.sum(-1) == 0] = False
if exists(ignore_cdrs):
cdr_names = ["h1", "h2", "h3", "l1", "l2", "l3"]
if ignore_cdrs == False:
cdr_names = []
elif isinstance(ignore_cdrs, list):
cdr_names = ignore_cdrs
elif isinstance(ignore_cdrs, str):
cdr_names = [ignore_cdrs]
for cdr in cdr_names:
cdr_range = cdr_indices(pdb_file, cdr)
temp_mask[:, (cdr_range[0] - 1) * 4:(cdr_range[1] + 2) *
4] = False
if exists(ignore_chain) and ignore_chain in ["H", "L"]:
seq_dict = get_fasta_chain_dict(fasta_file)
hlen = len(seq_dict["H"])
if ignore_chain == "H":
temp_mask[:, :hlen * 4] = False
elif ignore_chain == "L":
temp_mask[:, hlen * 4:] = False
return temp_coords, temp_mask
def process_prediction(
model_out,
pdb_file,
fasta_file,
skip_pdb=False,
do_refine=True,
use_openmm=False,
do_renum=False,
):
prmsd = rearrange(
model_out.prmsd,
"b (l a) -> b l a",
a=4,
)
model_out.prmsd = prmsd
if skip_pdb:
return model_out
coords = model_out.coords.squeeze(0).detach()
res_rmsd = prmsd.square().mean(dim=-1).sqrt().squeeze(0)
seq_dict = get_fasta_chain_dict(fasta_file)
full_seq = "".join(list(seq_dict.values()))
chains = list(seq_dict.keys())
delims = np.cumsum([len(s) for s in seq_dict.values()]).tolist()
write_pdb = not do_refine or use_openmm
pdb_string = save_PDB(
pdb_file,
coords,
full_seq,
chains=chains,
atoms=['N', 'CA', 'C', 'CB', 'O'],
error=res_rmsd,
delim=delims,
write_pdb=write_pdb,
)
if do_refine:
if use_openmm:
try:
from igfold.refine.openmm_ref import refine
refine_input = [pdb_file]
except:
exit("OpenMM not installed. Please install OpenMM to use refinement.")
else:
try:
from igfold.refine.pyrosetta_ref import refine
refine_input = [pdb_file, pdb_string]
except:
exit("PyRosetta not installed. Please install PyRosetta to use refinement.")
refine(*refine_input)
if do_renum:
try:
from igfold.utils.abnumber_ import renumber_pdb
except:
exit("AbNumber not installed. Please install AbNumber to use renumbering.")
renumber_pdb(
pdb_file,
pdb_file,
)
write_pdb_bfactor(
pdb_file,
pdb_file,
bfactor=res_rmsd,
)
return model_out
def fold(
antiberty,
models,
pdb_file,
fasta_file=None,
sequences=None,
template_pdb=None,
ignore_cdrs=None,
ignore_chain=None,
skip_pdb=False,
do_refine=True,
use_openmm=False,
do_renum=True,
truncate_sequences=False,
):
seq_dict = get_sequence_dict(
sequences,
fasta_file,
)
if truncate_sequences:
try:
from igfold.utils.abnumber_ import truncate_seq
except:
exit("AbNumber not installed. Please install AbNumber to use truncation.")
seq_dict = {k: truncate_seq(v) for k, v in seq_dict.items()}
if not exists(fasta_file):
fasta_file = pdb_file.replace(".pdb", ".fasta")
with open(fasta_file, "w") as f:
for chain, seq in seq_dict.items():
f.write(">{}\n{}\n".format(
chain,
seq,
))
embeddings, attentions = antiberty.embed(
seq_dict.values(),
return_attention=True,
)
embeddings = [e[1:-1].unsqueeze(0) for e in embeddings]
attentions = [a[:, :, 1:-1, 1:-1].unsqueeze(0) for a in attentions]
temp_coords, temp_mask = process_template(
template_pdb,
fasta_file,
ignore_cdrs=ignore_cdrs,
ignore_chain=ignore_chain,
)
model_in = IgFoldInput(
embeddings=embeddings,
attentions=attentions,
template_coords=temp_coords,
template_mask=temp_mask,
return_embeddings=True,
)
model_outs, scores = [], []
with torch.no_grad():
for i, model in enumerate(models):
model_out = model(model_in)
model_out = model.gradient_refine(model_in, model_out)
scores.append(model_out.prmsd.quantile(0.9))
model_outs.append(model_out)
best_model_i = scores.index(min(scores))
model_out = model_outs[best_model_i]
process_prediction(
model_out,
pdb_file,
fasta_file,
skip_pdb=skip_pdb,
do_refine=do_refine,
use_openmm=use_openmm,
do_renum=do_renum,
)
return model_out
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