File size: 2,894 Bytes
d766458 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | # Copyright 2025 ByteDance and/or its affiliates.
#
# 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 os
from typing import Dict, List
from pxdbench.globals import AF2_PARAMS_PATH, _require
from pxdbench.tools.base import BasePredictor
class AF2ComplexPredictor(BasePredictor):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
dir_name = os.path.dirname(__file__)
self.script_path = os.path.join(dir_name, "main_af2_complex.py")
_require(os.path.join(AF2_PARAMS_PATH, "params_model_1.npz"))
_require(os.path.join(AF2_PARAMS_PATH, "params_model_1_ptm.npz"))
def predict(
self,
input_dir: str,
save_dir: str,
design_pdb_dir: str,
data_list: List[Dict],
cond_chain: str,
binder_chain: str,
):
input_data = {
"input_dir": input_dir,
"save_dir": save_dir,
"design_pdb_dir": design_pdb_dir,
"data_list": data_list,
"cond_chain": cond_chain,
"binder_chain": binder_chain,
"af2_cfg": self.cfg.to_dict(),
"is_cyclic": self.cfg.get("is_cyclic", False),
}
output = self.run(input_data)
for idx, item in enumerate(data_list):
item.update(output[idx])
return output
class AF2MonomerPredictor(BasePredictor):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
dir_name = os.path.dirname(__file__)
self.script_path = os.path.join(dir_name, "main_af2_monomer.py")
_require(os.path.join(AF2_PARAMS_PATH, "params_model_1.npz"))
_require(os.path.join(AF2_PARAMS_PATH, "params_model_1_ptm.npz"))
def predict(
self,
save_dir: str,
design_pdb_dir: str,
data_list: List[Dict],
binder_chain: str,
):
input_data = {
"save_dir": save_dir,
"design_pdb_dir": design_pdb_dir,
"data_list": data_list,
"binder_chain": binder_chain,
"af2_cfg": self.cfg.to_dict(),
"is_cyclic": self.cfg.get("is_cyclic", False),
}
output = self.run(input_data)
for idx, item in enumerate(data_list):
item.update(output[idx])
return output
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