| """ |
| Data interface definitions for ODesign dataset and feature processing. |
| |
| This module defines standardized data structures for features and labels: |
| - OFeatureData: Complete feature set for model input |
| - OLabelData: Ground truth labels for training |
| |
| These interfaces ensure type consistency and provide convenient property |
| accessors for data dimensions. |
| """ |
|
|
| from ._base import * |
| from typing import List |
| from attr import define |
| import attr |
| import numpy as np |
| from src.utils.license_register import register_license |
|
|
|
|
| @define(kw_only=True) |
| @register_license('odesign2025') |
| @auto_type_convert |
| class OFeatureData(DictAccessMixin): |
| """ |
| Complete feature data structure for ODesign model input. |
| |
| This class contains all features extracted from protein/RNA/DNA/ligand structures, |
| including token-level features, atomic features, MSA features, template features, |
| and various masks for training and evaluation. Features are organized by category. |
| |
| Attributes: |
| # Token-level features |
| token_index (IndexType): Token index within the full sequence. |
| Shape: [N_token] |
| |
| residue_index (IndexType): Residue index in original sequence (1-indexed). |
| Shape: [N_token] |
| |
| asym_id (IndexType): Asymmetric unit ID (chain identifier). |
| Shape: [N_token] |
| |
| entity_id (IndexType): Entity ID (molecule type identifier). |
| Same entity ID = same sequence. |
| Shape: [N_token] |
| |
| sym_id (IndexType): Symmetry operation ID. |
| Shape: [N_token] |
| |
| restype (OneHotType): One-hot encoded residue/nucleotide/ligand type. |
| Shape: [N_token, n_residue_types] |
| |
| # Bond features |
| token_bonds (FeatureType): Bond features between token pairs. |
| Encodes bond types and connectivity. |
| Shape: [N_token, N_token, n_bond_features] |
| |
| token_pair_gen_mask (MaskType): Mask for token pairs to generate/evaluate. |
| Shape: [N_token, N_token] |
| |
| token_bond_gen_mask (MaskType): Mask for bond type generation/evaluation. |
| Shape: [N_token, N_token] |
| |
| # Reference atomic features |
| ref_pos (FeatureType): Reference atomic positions from structure. |
| Shape: [N_atom, 3] |
| |
| ref_mask (MaskType): Mask for valid reference atoms (exists in structure). |
| Shape: [N_atom] |
| |
| ref_element (FeatureType): Atomic element encoding. |
| Shape: [N_atom, n_elements] or [N_atom] |
| |
| ref_charge (FeatureType): Atomic partial charge. |
| Shape: [N_atom] |
| |
| ref_atom_name_chars (OneHotType): One-hot encoded atom name characters. |
| Shape: [N_atom, 4, n_char_types] |
| |
| ref_space_uid (IndexType): Spatial unit ID for grouping atoms in same frame. |
| Used for residue-level symmetric atom permutation. |
| Shape: [N_atom] |
| |
| has_frame (MaskType): Mask indicating atoms with valid coordinate frames. |
| Shape: [N_atom] |
| |
| frame_atom_index (IndexType): Indices of atoms defining coordinate frames. |
| For proteins: [N, CA, C] atoms; for nucleotides: [C1', C3', C4'] |
| Shape: [N_frame, 3] |
| |
| # Identity and mapping features |
| atom_to_token_idx (IndexType): Mapping from atom index to token index. |
| Shape: [N_atom] |
| |
| atom_to_tokatom_idx (IndexType): Mapping from atom to token-atom index. |
| Index within atoms of the same token. |
| Shape: [N_atom] |
| |
| is_protein (MaskType): Binary mask indicating protein atoms. |
| Shape: [N_atom] |
| |
| is_ligand (MaskType): Binary mask indicating ligand atoms. |
| Shape: [N_atom] |
| |
| is_dna (MaskType): Binary mask indicating DNA atoms. |
| Shape: [N_atom] |
| |
| is_rna (MaskType): Binary mask indicating RNA atoms. |
| Shape: [N_atom] |
| |
| # Resolution |
| resolution (FeatureType): Structure resolution in Angstroms. |
| Used for filtering confidence loss. Shape: scalar or [1] |
| |
| # Chain permutation features |
| mol_id (IndexType): Molecule ID for each atom. |
| Identifies individual molecules (multiple copies of same entity). |
| Shape: [N_atom] |
| |
| mol_atom_index (IndexType): Atom index within molecule. |
| Shape: [N_atom] |
| |
| entity_mol_id (IndexType): Entity molecule ID for each atom. |
| Shape: [N_atom] |
| |
| masked_asym_ids (List[int], optional): List of asymmetric unit IDs |
| to consider for chain permutation. None means all chains. |
| |
| # Atom permutation features |
| atom_perm_list (list[list[int]]): List of symmetric atom groups. |
| Each group contains atom indices that can be permuted |
| (e.g., symmetric atoms in aromatic rings, carboxyl groups). |
| |
| # Various masks for training and evaluation |
| pae_rep_atom_mask (MaskType): Representative atom mask for PAE computation. |
| Shape: [N_atom] |
| |
| modified_res_mask (MaskType): Mask for modified residues. |
| Shape: [N_token] |
| |
| is_condition_atom (MaskType): Mask for conditioned (fixed) atoms. |
| Atoms not generated by diffusion. |
| Shape: [N_atom] |
| |
| distogram_rep_atom_mask (MaskType): Representative atom mask for distogram. |
| Typically Cβ/Cα for proteins, C4'/C2' for nucleotides. |
| Shape: [N_atom] |
| |
| plddt_m_rep_atom_mask (MaskType): Representative atom mask for pLDDT-m. |
| Shape: [N_atom] |
| |
| bond_mask (MaskType): Mask for valid bonds. |
| Shape: [N_atom, N_atom] or [N_token, N_token] |
| |
| # Constraint features |
| constraint_feature (FeatureType, optional): Constraint features for token pairs. |
| Currently supports distogram constraint, pocket constraint, etc. |
| Shape: [N_token, N_token, n_constraint_features] |
| |
| # MSA features (optional) |
| msa (IndexType, optional): Multiple sequence alignment. |
| Token indices from MSA sequences. |
| Shape: [N_msa, N_token] |
| |
| has_deletion (MaskType, optional): Mask indicating deletions in MSA. |
| Shape: [N_msa, N_token] |
| |
| deletion_value (FeatureType, optional): Deletion values in MSA. |
| Shape: [N_msa, N_token] |
| |
| profile (FeatureType, optional): Sequence profile from MSA. |
| Position-specific scoring matrix. |
| Shape: [N_token, 32] |
| |
| deletion_mean (FeatureType, optional): Mean deletion probability. |
| Shape: [N_token] |
| |
| msa_token_mask (MaskType, optional): Mask for valid MSA tokens. |
| Shape: [N_msa, N_token] |
| |
| prot_pair_num_alignments (torch.Tensor, optional): Number of paired |
| protein alignments. |
| |
| prot_unpair_num_alignments (torch.Tensor, optional): Number of unpaired |
| protein alignments. |
| |
| rna_pair_num_alignments (torch.Tensor, optional): Number of paired |
| RNA alignments. |
| |
| rna_unpair_num_alignments (torch.Tensor, optional): Number of unpaired |
| RNA alignments. |
| |
| # Template features (under development) |
| template_restype (IndexType, optional): Residue types in template structures. |
| Shape: [N_template, N_token] |
| |
| template_all_atom_mask (MaskType, optional): Atom masks in templates. |
| Shape: [N_template, N_token, n_atom_per_token] |
| |
| template_all_atom_positions (FeatureType, optional): Atom positions in templates. |
| Shape: [N_template, N_token, n_atom_per_token, 3] |
| |
| # Hotspot features |
| is_hotspot_residue (FeatureType, optional): Binary mask for hotspot residues. |
| Residues of particular interest (e.g., binding site, catalytic residues). |
| Shape: [N_token] |
| |
| # Cyclic structure features |
| is_cyclic_token (FeatureType, optional): Binary mask for cyclic peptide tokens. |
| Shape: [N_token] |
| |
| Properties: |
| num_asym: Number of asymmetric units (chains). |
| num_token: Number of tokens. |
| num_atom: Number of atoms. |
| num_msa: Number of MSA sequences. |
| default_num_msa: Default number of MSA sequences for padding (1). |
| default_num_templ: Default number of templates for padding (4). |
| default_num_pocket: Default number of pocket residues (30). |
| """ |
| |
| token_index: IndexType |
| residue_index: IndexType |
| asym_id: IndexType |
| entity_id: IndexType |
| sym_id: IndexType |
| restype: OneHotType |
|
|
| |
| token_bonds: FeatureType |
| token_pair_gen_mask: MaskType |
| token_bond_gen_mask: MaskType |
|
|
| |
| ref_pos: FeatureType |
| ref_mask: MaskType |
| ref_element: FeatureType |
| ref_charge: FeatureType |
| ref_atom_name_chars: OneHotType |
| ref_space_uid: IndexType |
| has_frame: MaskType |
| frame_atom_index: IndexType |
|
|
| |
| atom_to_token_idx: IndexType |
| atom_to_tokatom_idx: IndexType |
| is_protein: MaskType |
| is_ligand: MaskType |
| is_dna: MaskType |
| is_rna: MaskType |
|
|
| |
| resolution: FeatureType |
|
|
| |
| mol_id: IndexType |
| mol_atom_index: IndexType |
| entity_mol_id: IndexType |
| masked_asym_ids: List[int] | None = None |
|
|
| |
| atom_perm_list: list[list[int]] |
|
|
| |
| pae_rep_atom_mask: MaskType |
| modified_res_mask: MaskType |
| is_condition_atom: MaskType |
| distogram_rep_atom_mask: MaskType |
| plddt_m_rep_atom_mask: MaskType |
| bond_mask: MaskType |
|
|
| |
| constraint_feature: FeatureType | None = None |
|
|
| |
| msa: IndexType | None = None |
| has_deletion: MaskType | None = None |
| deletion_value: FeatureType | None = None |
| profile: FeatureType | None = None |
| deletion_mean: FeatureType | None = None |
| msa_token_mask: MaskType | None = None |
| prot_pair_num_alignments: torch.Tensor | None = None |
| prot_unpair_num_alignments: torch.Tensor | None = None |
| rna_pair_num_alignments: torch.Tensor | None = None |
| rna_unpair_num_alignments: torch.Tensor | None = None |
|
|
| |
| template_restype: IndexType | None = None |
| template_all_atom_mask: MaskType | None = None |
| template_all_atom_positions: FeatureType | None = None |
|
|
| |
| is_hotspot_residue: FeatureType | None = None |
|
|
| |
| is_cyclic_token: FeatureType | None = None |
| |
| @classmethod |
| def from_feature_dict(cls, feature_dict: dict) -> "OFeatureData": |
| """ |
| Create OFeatureData instance from a feature dictionary. |
| |
| This factory method filters the input dictionary to only include |
| fields defined in OFeatureData and validates that all required |
| fields are present. |
| |
| Args: |
| feature_dict (dict): Dictionary containing feature data. |
| Keys should match OFeatureData field names. |
| |
| Returns: |
| OFeatureData: Validated feature data instance. |
| |
| Raises: |
| AssertionError: If required fields are missing. |
| """ |
| all_fields = {field.name for field in attr.fields(cls)} |
| filtered_dict = {k: v for k, v in feature_dict.items() if k in all_fields} |
| |
| def _check_required_fields(filtered_dict): |
| required_fields = { |
| field.name for field in attr.fields(cls) |
| if field.default == attr.NOTHING |
| } |
| missing_fields = required_fields - set(filtered_dict.keys()) |
| assert not missing_fields, ( |
| f"Missing required fields in {cls.__name__}: {missing_fields}" |
| ) |
|
|
| _check_required_fields(filtered_dict) |
| return cls(**filtered_dict) |
| |
| @property |
| def num_asym(self) -> int: |
| """ |
| Get the number of asymmetric units (chains) in the structure. |
| |
| Returns: |
| int: Number of unique chains. |
| """ |
| return len(torch.unique(self.asym_id)) |
| |
| @property |
| def num_token(self) -> int: |
| """ |
| Get the number of tokens in the sequence. |
| |
| Returns: |
| int: Number of tokens (residues/nucleotides/ligand atoms). |
| """ |
| return self.token_index.shape[0] |
| |
| @property |
| def num_atom(self) -> int: |
| """ |
| Get the number of atoms in the structure. |
| |
| Returns: |
| int: Total number of atoms. |
| """ |
| return self.atom_to_token_idx.shape[0] |
| |
| @property |
| def num_msa(self) -> int: |
| """ |
| Get the number of MSA sequences. |
| |
| Returns: |
| int: Number of sequences in multiple sequence alignment. |
| """ |
| return self.msa.shape[0] |
| |
| @property |
| def default_num_msa(self) -> int: |
| """ |
| Get the default number of MSA sequences for padding. |
| |
| Returns: |
| int: Default value (1) for structures without MSA. |
| """ |
| return 1 |
|
|
| @property |
| def default_num_templ(self) -> int: |
| """ |
| Get the default number of template structures for padding. |
| |
| Returns: |
| int: Default value (4) for template feature dimensions. |
| """ |
| return 4 |
|
|
| @property |
| def default_num_pocket(self) -> int: |
| """ |
| Get the default number of pocket residues. |
| |
| Returns: |
| int: Default value (30) for pocket size in ligand binding. |
| """ |
| return 30 |
| |
|
|
| @define(kw_only=True) |
| @register_license('odesign2025') |
| @auto_type_convert |
| class OLabelData(DictAccessMixin): |
| """ |
| Ground truth label data structure for training and evaluation. |
| |
| This class contains all ground truth labels including atomic coordinates, |
| bond types, and metadata needed for loss computation and evaluation metrics. |
| |
| Attributes: |
| coordinate (FeatureType): Ground truth atomic coordinates. |
| Reference positions from experimental structure. |
| Shape: [N_atom, 3] |
| |
| coordinate_mask (MaskType): Mask indicating which atoms have valid coordinates. |
| Used to filter missing or disordered atoms. |
| Shape: [N_atom] |
| |
| token_bond_type_label (FeatureType, optional): Ground truth bond type labels. |
| Encodes bond types for token pairs (no_bond, single, double, triple, aromatic). |
| Shape: [N_token, N_token] or [N_token, N_token, n_bond_types] |
| |
| ligand_bond_mask (MaskType, optional): Mask for ligand bonds. |
| Indicates which bonds should be considered for ligand bond loss. |
| Shape: [N_atom, N_atom] or [N_token, N_token] |
| |
| entity_mol_id (IndexType, optional): Entity molecule ID for each atom. |
| Used for chain permutation during training. |
| Shape: [N_atom] |
| |
| mol_id (IndexType, optional): Molecule ID for each atom. |
| Identifies individual molecule instances. |
| Shape: [N_atom] |
| |
| mol_atom_index (IndexType, optional): Atom index within molecule. |
| Maps atoms to their position within molecule for permutation. |
| Shape: [N_atom] |
| |
| pae_rep_atom_mask (MaskType, optional): Representative atom mask for PAE. |
| Identifies atoms used as representatives for predicted aligned error. |
| Shape: [N_atom] |
| |
| eval_type (np.ndarray, optional): Evaluation type identifier. |
| Specifies the type of evaluation (e.g., interface, binding, folding). |
| |
| cluster_id (np.ndarray, optional): Cluster ID for evaluation. |
| Used to group similar structures in validation. |
| |
| chain_1_mask (MaskType, optional): Mask for chain 1 in interface evaluation. |
| Used for protein-protein interface or ligand binding evaluation. |
| Shape: [N_atom] |
| |
| chain_2_mask (MaskType, optional): Mask for chain 2 in interface evaluation. |
| Used for protein-protein interface or ligand binding evaluation. |
| Shape: [N_atom] |
| """ |
| coordinate: FeatureType |
| coordinate_mask: MaskType |
|
|
| token_bond_type_label: FeatureType | None = None |
| ligand_bond_mask: MaskType | None = None |
|
|
| entity_mol_id: IndexType | None = None |
| mol_id: IndexType | None = None |
| mol_atom_index: IndexType | None = None |
|
|
| pae_rep_atom_mask: MaskType | None = None |
|
|
| eval_type: np.ndarray | None = None |
| cluster_id: np.ndarray | None = None |
| chain_1_mask: MaskType | None = None |
| chain_2_mask: MaskType | None = None |
|
|
| @classmethod |
| def from_label_dict(cls, label_dict: dict) -> "OLabelData": |
| """ |
| Create OLabelData instance from a label dictionary. |
| |
| This factory method filters the input dictionary to only include |
| fields defined in OLabelData and validates that all required |
| fields are present. |
| |
| Args: |
| label_dict (dict): Dictionary containing label data. |
| Keys should match OLabelData field names. |
| |
| Returns: |
| OLabelData: Validated label data instance. |
| |
| Raises: |
| AssertionError: If required fields are missing. |
| """ |
| all_fields = {field.name for field in attr.fields(cls)} |
| filtered_dict = {k: v for k, v in label_dict.items() if k in all_fields} |
| |
| def _check_required_fields(filtered_dict): |
| required_fields = { |
| field.name for field in attr.fields(cls) |
| if field.default == attr.NOTHING |
| } |
| missing_fields = required_fields - set(filtered_dict.keys()) |
| assert not missing_fields, ( |
| f"Missing required fields in {cls.__name__}: {missing_fields}" |
| ) |
|
|
| _check_required_fields(filtered_dict) |
| return cls(**filtered_dict) |
| |
