# Copyright 2024 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. # pylint: disable=C0114,C0301 import os from copy import deepcopy from protenix.config.extend_types import GlobalConfigValue, ListValue default_test_configs = { "sampler_configs": { "sampler_type": "uniform", }, "cropping_configs": { "method_weights": [ 0.0, # ContiguousCropping 0.0, # SpatialCropping 1.0, # SpatialInterfaceCropping ], "crop_size": -1, }, "lig_atom_rename": GlobalConfigValue("test_lig_atom_rename"), "shuffle_mols": GlobalConfigValue("test_shuffle_mols"), "shuffle_sym_ids": GlobalConfigValue("test_shuffle_sym_ids"), "constraint": { "enable": False, "fix_seed": False, # True means use use the same contact in each evaluation. }, } default_weighted_pdb_configs = { "sampler_configs": { "sampler_type": "weighted", "beta_dict": { "chain": 0.5, "interface": 1, }, "alpha_dict": { "prot": 3, "nuc": 3, "ligand": 1, }, "force_recompute_weight": True, }, "cropping_configs": { "method_weights": ListValue([0.2, 0.4, 0.4]), "crop_size": GlobalConfigValue("train_crop_size"), }, "sample_weight": 0.5, "limits": -1, "lig_atom_rename": GlobalConfigValue("train_lig_atom_rename"), "shuffle_mols": GlobalConfigValue("train_shuffle_mols"), "shuffle_sym_ids": GlobalConfigValue("train_shuffle_sym_ids"), # If enabled, the training settings for different constraint types, # providing the model a certain proportion of constraints # that meet specific conditions. "constraint": { "enable": False, "fix_seed": False, "pocket": { "prob": 0.0, "size": 1 / 3, "spec_binder_chain": False, "max_distance_range": {"PP": ListValue([6, 20]), "LP": ListValue([6, 20])}, "group": "complex", "distance_type": "center_atom", }, "contact": { "prob": 0.0, "size": 1 / 3, "max_distance_range": { "PP": ListValue([6, 30]), "PL": ListValue([4, 10]), }, "group": "complex", "distance_type": "center_atom", }, "substructure": { "prob": 0.0, "size": 0.8, "mol_type_pairs": { "PP": 15, "PL": 10, "LP": 10, }, "feature_type": "one_hot", "ratios": { "full": [ 0.0, 0.5, 1.0, ], # ratio options of full chain substructure constraint "partial": 0.3, # ratio of partial chain substructure constraint }, "coord_noise_scale": 0.05, "spec_asym_id": False, }, "contact_atom": { "prob": 0.0, "size": 1 / 3, "max_distance_range": { "PP": ListValue([2, 12]), "PL": ListValue([2, 8]), }, "min_distance": -1, "group": "complex", "distance_type": "atom", "feature_type": "continuous", }, }, } DATA_ROOT_DIR = os.environ.get("PROTENIX_DATA_ROOT_DIR", "/af3-dev/release_data/") # Use CCD cache created by scripts/gen_ccd_cache.py priority. (without date in filename) # See: docs/prepare_data.md CCD_COMPONENTS_FILE_PATH = os.path.join(DATA_ROOT_DIR, "components.cif") CCD_COMPONENTS_RDKIT_MOL_FILE_PATH = os.path.join( DATA_ROOT_DIR, "components.cif.rdkit_mol.pkl" ) if (not os.path.exists(CCD_COMPONENTS_FILE_PATH)) or ( not os.path.exists(CCD_COMPONENTS_RDKIT_MOL_FILE_PATH) ): CCD_COMPONENTS_FILE_PATH = os.path.join(DATA_ROOT_DIR, "components.v20240608.cif") CCD_COMPONENTS_RDKIT_MOL_FILE_PATH = os.path.join( DATA_ROOT_DIR, "components.v20240608.cif.rdkit_mol.pkl" ) PDB_CLUSTER_FILE_PATH = os.path.join(DATA_ROOT_DIR, "clusters-by-entity-40.txt") # This is a patch in inference stage for users that do not have root permission. # If you run # ``` # bash inference_demo.sh # ``` # or # ``` # protenix predict --input examples/example.json --out_dir ./output # ```` # The checkpoint and the data cache will be downloaded to the current code directory. if (not os.path.exists(CCD_COMPONENTS_FILE_PATH)) or ( not os.path.exists(CCD_COMPONENTS_RDKIT_MOL_FILE_PATH) ): print("Try to find the ccd cache data in the code directory for inference.") current_file_path = os.path.abspath(__file__) current_directory = os.path.dirname(current_file_path) code_directory = os.path.dirname(current_directory) data_cache_dir = os.path.join(code_directory, "release_data/ccd_cache") CCD_COMPONENTS_FILE_PATH = os.path.join(data_cache_dir, "components.cif") CCD_COMPONENTS_RDKIT_MOL_FILE_PATH = os.path.join( data_cache_dir, "components.cif.rdkit_mol.pkl" ) if (not os.path.exists(CCD_COMPONENTS_FILE_PATH)) or ( not os.path.exists(CCD_COMPONENTS_RDKIT_MOL_FILE_PATH) ): CCD_COMPONENTS_FILE_PATH = os.path.join( data_cache_dir, "components.v20240608.cif" ) CCD_COMPONENTS_RDKIT_MOL_FILE_PATH = os.path.join( data_cache_dir, "components.v20240608.cif.rdkit_mol.pkl" ) data_configs = { "num_dl_workers": 16, "epoch_size": 10000, "train_ref_pos_augment": True, "test_ref_pos_augment": True, "train_sets": ListValue(["weightedPDB_before2109_wopb_nometalc_0925"]), "train_sampler": { "train_sample_weights": ListValue([1.0]), "sampler_type": "weighted", }, "test_sets": ListValue(["recentPDB_1536_sample384_0925"]), "weightedPDB_before2109_wopb_nometalc_0925": { "base_info": { "mmcif_dir": os.path.join(DATA_ROOT_DIR, "mmcif"), "bioassembly_dict_dir": os.path.join(DATA_ROOT_DIR, "mmcif_bioassembly"), "indices_fpath": os.path.join( DATA_ROOT_DIR, "indices/weightedPDB_indices_before_2021-09-30_wo_posebusters_resolution_below_9.csv.gz", ), "pdb_list": "", "random_sample_if_failed": True, "max_n_token": -1, # can be used for removing data with too many tokens. "use_reference_chains_only": False, "exclusion": { # do not sample the data based on ions. "mol_1_type": ListValue(["ions"]), "mol_2_type": ListValue(["ions"]), }, }, **deepcopy(default_weighted_pdb_configs), }, "recentPDB_1536_sample384_0925": { "base_info": { "mmcif_dir": os.path.join(DATA_ROOT_DIR, "mmcif"), "bioassembly_dict_dir": os.path.join( DATA_ROOT_DIR, "recentPDB_bioassembly" ), "indices_fpath": os.path.join( DATA_ROOT_DIR, "indices/recentPDB_low_homology_maxtoken1536.csv" ), "pdb_list": os.path.join( DATA_ROOT_DIR, "indices/recentPDB_low_homology_maxtoken1024_sample384_pdb_id.txt", ), "max_n_token": GlobalConfigValue("test_max_n_token"), # filter data "sort_by_n_token": False, "group_by_pdb_id": True, "find_eval_chain_interface": True, }, **deepcopy(default_test_configs), }, "posebusters_0925": { "base_info": { "mmcif_dir": os.path.join(DATA_ROOT_DIR, "posebusters_mmcif"), "bioassembly_dict_dir": os.path.join( DATA_ROOT_DIR, "posebusters_bioassembly" ), "indices_fpath": os.path.join( DATA_ROOT_DIR, "indices/posebusters_indices_mainchain_interface.csv" ), "pdb_list": "", "find_pocket": True, "find_all_pockets": False, "max_n_token": GlobalConfigValue("test_max_n_token"), # filter data }, **deepcopy(default_test_configs), }, "msa": { "enable": True, "enable_rna_msa": False, "prot": { "pairing_db": "uniref100", "non_pairing_db": "mmseqs_other", "pdb_mmseqs_dir": os.path.join(DATA_ROOT_DIR, "mmcif_msa"), "seq_to_pdb_idx_path": os.path.join(DATA_ROOT_DIR, "seq_to_pdb_index.json"), "indexing_method": "sequence", }, "rna": { "seq_to_pdb_idx_path": "", "rna_msa_dir": "", "indexing_method": "sequence", }, "strategy": "random", "merge_method": "dense_max", "min_size": { "train": 1, "test": 1, }, "max_size": { "train": 16384, "test": 16384, }, "sample_cutoff": { "train": 16384, "test": 16384, }, }, "template": { "enable": False, }, "ccd_components_file": CCD_COMPONENTS_FILE_PATH, "ccd_components_rdkit_mol_file": CCD_COMPONENTS_RDKIT_MOL_FILE_PATH, "pdb_cluster_file": PDB_CLUSTER_FILE_PATH, }