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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import json
import os
from colabdesign.mpnn import clear_mem, mk_mpnn_model
from ml_collections import ConfigDict
from pxdbench.tools.biopython_utils import get_interface_residue_id, hotspot_residues
from pxdbench.utils import extract_chain_sequence, seed_everything
def get_pdb_basename(pdb_path: str):
assert pdb_path.endswith(".pdb")
basename = os.path.basename(pdb_path)
return basename[:-4]
def design_monomer(
pdb_dir: str,
pdb_names: list[str],
num_samples: int,
mpnn_cfg: ConfigDict,
if_print=True,
):
"""
Design sequences for monomer proteins using ProteinMPNN.
Args:
pdb_dir (str): Directory containing input PDB files.
pdb_names (list[str]): List of PDB base names (without '.pdb') to process.
num_samples (int): Number of sequences to generate per PDB.
mpnn_cfg (ConfigDict): ProteinMPNN configuration (temperature, weights, etc.).
if_print (bool, optional): Whether to print progress. Defaults to True.
Returns:
list[dict]: List of design results with keys 'name' (PDB name), 'seq_idx' (sequence index),
and 'sequence' (designed amino acid sequence).
"""
clear_mem()
mpnn_model = mk_mpnn_model(
backbone_noise=0.0,
model_name="v_48_020",
weights=mpnn_cfg.weights,
)
final_result = []
for name in pdb_names:
pdb_path = os.path.join(pdb_dir, name + ".pdb")
# Prepare MPNN input
mpnn_model.prep_inputs(
pdb_filename=pdb_path,
chain="A",
)
if if_print:
print(f"{pdb_path} is done")
temperature = mpnn_cfg.temperature
if isinstance(temperature, str):
if temperature == "auto":
temperature = 0.0001 if num_samples > 1 else 0.1
print(
f"Use temperature {temperature} for num_samples being {num_samples}"
)
else:
temperature = float(temperature)
else:
raise ValueError(f"Unknown temperature {temperature}")
# Run MPNN sampling
mpnn_sequences = mpnn_model.sample(
temperature=temperature,
num=num_samples,
batch=1,
)
# Collect sequences
for i, seq in enumerate(mpnn_sequences["seq"]):
final_result.append(
{"name": name, "seq_idx": i, "sequence": seq.split("/")[-1]}
)
if if_print:
print("finished all Sequence Design")
return final_result
def design_binder(
pdb_dir: str,
pdb_names: list[str],
num_samples: int,
binder_chains: list[str],
cond_chains: list[str],
mpnn_cfg: ConfigDict,
if_print=True,
):
"""
Design sequences for binder proteins using ProteinMPNN.
Args:
pdb_dir (str): Directory containing input PDB files.
pdb_names (list[str]): List of PDB base names (without '.pdb') to process.
num_samples (int): Number of sequences to generate per PDB.
binder_chains (list[str]): List of binder chain IDs.
cond_chains (list[str]): List of conditional chain IDs.
mpnn_cfg (ConfigDict): ProteinMPNN configuration (temperature, weights, etc.).
if_print (bool, optional): Whether to print progress. Defaults to True.
Returns:
list[dict]: List of design results with keys 'name' (PDB name), 'seq_idx' (sequence index),
and 'sequence' (designed amino acid sequence).
"""
clear_mem()
mpnn_model = mk_mpnn_model(
backbone_noise=0.0,
model_name="v_48_020",
weights=mpnn_cfg.weights,
)
final_result = []
for name in pdb_names:
pdb_path = os.path.join(pdb_dir, name + ".pdb")
# Prepare MPNN input
if len(binder_chains) > 1:
raise ValueError(f"Only support one-chain binders, but got {binder_chains}")
if mpnn_cfg.fix_interface:
interacting_residues = hotspot_residues(
pdb_path=pdb_path, binder_chain=binder_chains[0]
) # Hardcode, only take the first binder chains
if len(interacting_residues) > 0:
fix_pos = get_interface_residue_id(
interacting_residues=interacting_residues,
binder_chain=binder_chains[0],
)
else:
fix_pos = ",".join(cond_chains)
else:
fix_pos = ",".join(cond_chains)
mpnn_model.prep_inputs(
pdb_filename=pdb_path,
chain=",".join(cond_chains + binder_chains),
fix_pos=fix_pos,
rm_aa=mpnn_cfg.rm_aa,
)
if if_print:
print(f"{pdb_path} is done")
temperature = mpnn_cfg.temperature
if isinstance(temperature, str):
if temperature == "auto":
temperature = 0.0001 if num_samples > 1 else 0.1
print(
f"Use temperature {temperature} for num_samples being {num_samples}"
)
else:
temperature = float(temperature)
else:
raise ValueError(f"Unknown temperature {temperature}")
# Run MPNN sampling
mpnn_sequences = mpnn_model.sample(
temperature=temperature,
num=num_samples,
batch=1,
)
# Collect sequences
for i, seq in enumerate(mpnn_sequences["seq"]):
final_result.append(
{"name": name, "seq_idx": i, "sequence": seq.split("/")[-1]}
)
if if_print:
print("finished all Sequence Design")
return final_result
def get_gt_sequence(pdb_dir: str, pdb_names: list[str], binder_chain="B"):
"""
Get ground truth sequences for binder proteins from PDB files.
Args:
pdb_dir (str): Directory containing input PDB files.
pdb_names (list[str]): List of PDB base names (without '.pdb') to process.
binder_chain (str, optional): Chain ID of the binder protein. Defaults to "B".
Returns:
list[dict]: List of design results with keys 'name' (PDB name), 'seq_idx' (sequence index),
and 'sequence' (ground truth amino acid sequence).
"""
final_result = []
for name in pdb_names:
sequence = []
result = {}
pdb_path = os.path.join(pdb_dir, name + ".pdb")
seq = extract_chain_sequence(pdb_path, chain_id=binder_chain)
sequence.append(seq)
result["name"] = name
result["sequences"] = sequence
final_result.append({"name": name, "seq_idx": 0, "sequence": sequence[0]})
return final_result
def main():
parser = argparse.ArgumentParser(description="ProteinMPNN Sequence Design")
parser.add_argument("--input", type=str, required=True, help="Input JSON file")
parser.add_argument("--output", type=str, required=True, help="Output JSON file")
parser.add_argument("--seed", type=int, default=None)
args = parser.parse_args()
with open(args.input, "r") as f:
input_data = json.load(f)
if args.seed is not None:
seed_everything(args.seed, deterministic=False)
design_type = input_data["design_type"]
try:
if design_type == "monomer":
result = design_monomer(
pdb_dir=input_data["pdb_dir"],
pdb_names=input_data["pdb_names"],
num_samples=input_data["num_samples"],
mpnn_cfg=ConfigDict(input_data["mpnn_cfg"]),
if_print=True,
)
elif design_type == "binder":
result = design_binder(
pdb_dir=input_data["pdb_dir"],
pdb_names=input_data["pdb_names"],
num_samples=input_data["num_samples"],
binder_chains=input_data["binder_chains"],
cond_chains=input_data["cond_chains"],
mpnn_cfg=ConfigDict(input_data["mpnn_cfg"]),
if_print=True,
)
elif design_type == "gt":
result = get_gt_sequence(
pdb_dir=input_data["pdb_dir"],
pdb_names=input_data["pdb_names"],
binder_chain=input_data.get("binder_chain", "B"),
)
else:
raise ValueError(f"Unknown design type: {design_type}")
with open(args.output, "w") as f:
json.dump(result, f)
print(f"Successfully completed {design_type} design")
except Exception as e:
print(f"Error: {str(e)}")
import traceback
traceback.print_exc()
exit(1)
if __name__ == "__main__":
main()
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