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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 logging
import os
import re
from colabdesign import clear_mem, mk_afdesign_model
from colabdesign.shared.utils import copy_dict
from pxdbench.globals import AF2_PARAMS_PATH
from pxdbench.metrics.Kalign import align_and_calculate_rmsd
from pxdbench.permutation import permute_generated_min_complex_rmsd
from pxdbench.tools.af2.af2_utils import add_cyclic_offset, renumber_by_rebuilding
from pxdbench.utils import concat_dict_values, seed_everything
logger = logging.getLogger(__name__)
def predict_binder_structure(
prediction_model,
sequence: str,
design_name: str,
ori_design_pdb: str,
model_indices: list[int],
save_dir: str,
design_chain_layout: str,
):
"""
Predict binder structure using AlphaFold2 and compute structural metrics.
Args:
prediction_model: Initialized ColabDesign AFDesign model instance.
sequence (str): Amino acid sequence of the binder to predict.
design_name (str): Unique identifier for the design (e.g., "pdbname_seq0").
ori_design_pdb: Path to designed pdb.
model_indices (list[int]): List of AlphaFold2 model indices to use (0-4).
save_dir (str): Directory to save predicted PDB files and metrics.
design_chain_layout (str): "cond_first" or "cond_last".
Returns:
dict: Prediction statistics (pLDDT, pTM, i_pTM, etc.) for each model index.
"""
sequence = re.sub(r"[^A-Z]", "", sequence.upper())
prediction_stats = {}
for model_num in model_indices:
output_name = f"{design_name}_model{model_num+1}"
output_pdb = os.path.join(save_dir, f"{output_name}.pdb")
output_stats_json = os.path.join(save_dir, f"{output_name}.json")
if os.path.exists(output_pdb) and os.path.exists(output_stats_json):
print(
f"Found existing {output_pdb} and {output_stats_json}. Will load from them."
)
# load stats
with open(output_stats_json, "r") as f:
stats = json.load(f)
print(f"Loaded {output_stats_json}.")
else:
prediction_model.predict(
seq=sequence, models=[model_num], num_recycles=3, verbose=True
)
metrics = copy_dict(prediction_model.aux["log"])
stats = {
"pLDDT": round(metrics["plddt"], 2),
"pTM": round(metrics["ptm"], 2),
"i_pTM": round(metrics["i_ptm"], 2),
"pAE": round(metrics["pae"], 2),
"i_pAE": round(metrics["i_pae"], 2), # i_pae divdied by 31
"unscaled_i_pAE": round(metrics["i_pae"] * 31, 2), # raw i_pae
}
# save pdb and stats
prediction_model.save_pdb(output_pdb)
# renumber
renumber_by_rebuilding(
ori_design_pdb, output_pdb, output_pdb, ref_layout=design_chain_layout
)
permute_generated_min_complex_rmsd(output_pdb, ori_design_pdb, output_pdb)
with open(output_stats_json, "w") as f:
json.dump(stats, f)
prediction_stats[model_num] = stats
return prediction_stats
def complex_prediction(
input_dir: str,
save_dir: str,
design_pdb_dir: str,
data_list: list[dict],
cond_chain: str,
binder_chain: str,
af2_cfg,
verbose=True,
is_cyclic=False,
):
"""
Run batch prediction for binder complexes using AlphaFold2.
Args:
input_dir (str): Directory containing input PDB files for target structures.
save_dir (str): Directory to save prediction outputs (PDBs, metrics).
design_pdb_dir (str): Directory to save designed pdbs.
data_list (list[dict]): List of design data with keys "name", "sequence", "seq_idx".
cond_chain (str): Chain ID(s) of the target (conditioning) structure(s).
binder_chain (str): Chain ID of the binder to design/predict.
af2_cfg (dict): AlphaFold2 configuration (model indices, multimer usage, etc.).
verbose (bool, optional): Whether to print progress. Defaults to True.
is_cyclic (bool, optional): Whether the binder is cyclic (adds cyclic offset). Defaults to False.
Returns:
list[dict]: Aggregated prediction statistics for each design in data_list.
"""
use_binder_template = af2_cfg["use_binder_template"]
logger.info(f"Input use_binder_template: {use_binder_template}")
clear_mem()
prediction_model = mk_afdesign_model(
protocol="binder",
num_recycles=3,
data_dir=AF2_PARAMS_PATH,
use_multimer=af2_cfg["use_multimer"],
use_initial_guess=af2_cfg["use_initial_guess"],
use_initial_atom_pos=af2_cfg["use_initial_atom_pos"],
)
os.makedirs(save_dir, exist_ok=True)
results = []
for item in data_list:
name = item["name"]
seq = item["sequence"]
seq_idx = item["seq_idx"]
pdb_file = os.path.join(input_dir, f"{name}.pdb")
if not os.path.exists(pdb_file):
print(f"ERROR: {pdb_file} not found")
continue
prediction_model.prep_inputs(
pdb_filename=pdb_file,
chain=cond_chain,
binder_chain=binder_chain,
use_binder_template=use_binder_template,
rm_target_seq=True,
rm_target_sc=False,
rm_template_ic=True,
)
if is_cyclic:
add_cyclic_offset(prediction_model)
design_name = f"{name}_seq{seq_idx}"
ori_design_pdb = os.path.join(design_pdb_dir, name + ".pdb")
stats = predict_binder_structure(
prediction_model,
seq,
design_name,
ori_design_pdb,
af2_cfg["model_ids"],
save_dir,
design_chain_layout="cond_last" if "A" in binder_chain else "cond_first",
)
stat_list = []
for model_id in af2_cfg["model_ids"]:
s = stats[model_id]
# compute predict-design RMSD
pred_complex_pdb = os.path.join(
save_dir, f"{design_name}_model{model_id + 1}.pdb"
)
if os.path.isfile(ori_design_pdb):
complex_rmsd = align_and_calculate_rmsd(
pred_complex_pdb, ori_design_pdb
)
if complex_rmsd is not None:
complex_rmsd = round(complex_rmsd, 2)
else:
complex_rmsd = None
s["af2_complex_pred_design_rmsd"] = complex_rmsd
stat_list.append(s)
stat = concat_dict_values(stat_list)
if verbose:
print(f"{name}-seq{seq_idx}, {stat}")
results.append(stat)
return results
def main():
parser = argparse.ArgumentParser(description="AF2 Binder Complex Prediction")
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)
# args = parser.parse_args()
# model_ids = [int(x) for x in args.model_ids.split(",")]
if args.seed is not None:
seed_everything(args.seed, deterministic=False)
try:
results = complex_prediction(
input_dir=input_data["input_dir"],
save_dir=input_data["save_dir"],
design_pdb_dir=input_data["design_pdb_dir"],
data_list=input_data["data_list"],
cond_chain=input_data["cond_chain"],
binder_chain=input_data["binder_chain"],
af2_cfg=input_data["af2_cfg"],
verbose=True,
is_cyclic=input_data["is_cyclic"],
)
with open(args.output, "w") as f:
json.dump(results, f)
print(f"Successfully completed AF2 binder complex prediction!")
except Exception as e:
print(f"Error: {str(e)}")
import traceback
traceback.print_exc()
exit(1)
if __name__ == "__main__":
main()
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