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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 Binder_align_and_calculate_rmsd
from pxdbench.tools.af2.af2_utils import add_cyclic_offset
from pxdbench.utils import concat_dict_values, seed_everything
logger = logging.getLogger(__name__)
def predict_binder_monomer(
prediction_model,
sequence: str,
design_name: str,
model_indices: list[int],
save_dir: str,
):
"""
Predict monomeric binder structure using AlphaFold2 and compute monomer-specific metrics.
Args:
prediction_model: Initialized ColabDesign AFDesign model instance (hallucination protocol).
sequence (str): Amino acid sequence of the binder monomer.
design_name (str): Unique identifier for the design (e.g., "pdbname_seq0_MONOMER_ONLY").
model_indices (list[int]): List of AlphaFold2 model indices to use (0-4).
save_dir (str): Directory to save predicted PDB files and metrics.
Returns:
dict: Prediction statistics (pLDDT, pTM, pAE) for each model index.
"""
sequence = re.sub(r"[^A-Z]", "", sequence.upper())
prediction_model.set_seq(sequence)
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(models=[model_num], num_recycles=3, verbose=True)
metrics = copy_dict(prediction_model.aux["log"])
stats = {
"pLDDT_MONOMER": round(metrics["plddt"], 3),
"pTM_MONOMER": round(metrics["ptm"], 3),
"pAE_MONOMER": round(metrics["pae"], 3),
}
# save pdb and stats
prediction_model.save_pdb(output_pdb)
with open(output_stats_json, "w") as f:
json.dump(stats, f)
prediction_stats[model_num] = stats
return prediction_stats
def binder_only_prediction(
save_dir,
design_pdb_dir,
data_list,
af2_cfg,
binder_chain="B",
verbose=True,
is_cyclic=False,
):
"""
Run batch prediction for monomeric binders and compute comparative structural metrics.
Workflow:
1. Initializes AlphaFold2 model for monomer hallucination.
2. Processes each design in data_list, optimizing model compilation by reusing length-specific inputs.
3. Predicts monomer structures and computes pLDDT, pTM, pAE.
4. Calculates RMSD between:
- Predicted monomer and predicted complex (if complex PDB exists).
- Predicted monomer and original design template (if template PDB exists).
5. Aggregates metrics across AlphaFold2 models.
Args:
save_dir (str): Directory to save monomer prediction outputs (PDBs, metrics).
design_pdb_dir (str): Directory containing original design template PDBs.
data_list (list[dict]): List of design data with keys "name", "sequence", "seq_idx".
af2_cfg (dict): AlphaFold2 configuration (model indices, multimer usage, etc.).
binder_chain (str, optional): Chain ID of the binder in original design templates. Defaults to "B".
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 monomer prediction metrics for each design in data_list.
"""
clear_mem()
prediction_model = mk_afdesign_model(
protocol="hallucination",
use_templates=False,
initial_guess=False,
use_initial_atom_pos=False,
num_recycles=3,
data_dir=AF2_PARAMS_PATH,
use_multimer=af2_cfg["use_multimer"],
)
os.makedirs(save_dir, exist_ok=True)
results = []
length_prev = -1
for item in data_list:
name = item["name"]
seq = item["sequence"]
seq_idx = item["seq_idx"]
binder_len = len(seq)
# Only compile when the inference length changes
if length_prev != binder_len:
prediction_model.prep_inputs(
length=binder_len,
)
length_prev = binder_len
if is_cyclic:
add_cyclic_offset(prediction_model)
design_name = f"{name}_seq{seq_idx}_MONOMER_ONLY"
design_complex_name = f"{name}_seq{seq_idx}"
stats = predict_binder_monomer(
prediction_model, seq, design_name, af2_cfg["model_ids"], save_dir
)
stat_list = []
for model_id in af2_cfg["model_ids"]:
s = stats[model_id]
pred_binder_pdb = os.path.join(
save_dir, f"{design_name}_model{model_id + 1}.pdb"
)
pred_complex_pdb = os.path.join(
save_dir, f"{design_complex_name}_model{model_id + 1}.pdb"
)
if os.path.isfile(pred_complex_pdb):
bound_unbound_RMSD = round(
Binder_align_and_calculate_rmsd(
pred_binder_pdb, pred_complex_pdb, binder_chain
),
2,
)
else:
bound_unbound_RMSD = None
# compute predict-design RMSD
ori_design_pdb = os.path.join(design_pdb_dir, name + ".pdb")
if os.path.isfile(ori_design_pdb):
binder_rmsd = round(
Binder_align_and_calculate_rmsd(
pred_binder_pdb, ori_design_pdb, binder_chain
),
2,
)
else:
binder_rmsd = None
s["bound_unbound_RMSD"] = bound_unbound_RMSD
s["af2_binder_pred_design_rmsd"] = binder_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 Monomer 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 = binder_only_prediction(
save_dir=input_data["save_dir"],
design_pdb_dir=input_data["design_pdb_dir"],
data_list=input_data["data_list"],
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 only 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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