schrodinger.application.transforms.prepwizard module¶
PrepWizard transforms for preparing protein structures.
- class schrodinger.application.transforms.prepwizard.PrepWizardConfig(*, settings: schrodinger.application.prepwizard.PrepWizardSettings | None = None, force_field: ~typing.Literal['OPLS_2005', 'S-OPLS'] = 'OPLS_2005', preserve_st_titles: bool = False, max_states: int = 1, preprocess: bool = True, assign_bond_orders: bool = True, use_ccd: bool = True, skip_assigned_residues: bool = False, add_missing_inter_residue_bonds: bool = True, add_terminal_oxygens: bool = False, add_hydrogens: bool = False, readd_hydrogens: bool = True, idealize_hydrogen_tf: bool = True, preprocess_delete_far_waters: bool = False, preprocess_watdist: ~typing.Optional[float] = None, delete_far_waters: bool = True, watdist: float = 5.0, delete_nonbridging_waters: bool = False, delwater_hbond_cutoff: int = 0, treat_metals: bool = True, treat_disulfides: bool = True, treat_glycosylation: bool = True, treat_palmitoylation: bool = False, selenomethionines: bool = True, cap_termini: bool = False, cap_termini_min_atoms: int = 200, fillsidechains: bool = False, fillloops: bool = False, custom_fasta_file: ~typing.Optional[~pathlib.Path] = None, annotate_gpcr: bool = False, antibody_cdr_scheme: ~typing.Literal['Chothia', 'Kabat', 'IMGT', 'EnhancedChothia', 'AHo'] = 'Kabat', renumber_ab_residues: bool = False, tcr_cdr_scheme: ~typing.Optional[~typing.Literal['Chothia', 'Kabat', 'IMGT', 'EnhancedChothia', 'AHo']] = 'IMGT', renumber_tcr_residues: bool = False, run_epik: bool = True, include_epik_states: bool = True, epik_pH: float = 7.4, epik_pHt: float = 2.0, xtal: bool = False, run_protassign: bool = True, sample_water_orientation: bool = True, use_propka: bool = True, propka_pH: float = 7.4, pH: float = 7.4, simplified_pH: ~typing.Literal['very_low', 'low', 'neutral', 'high'] = 'neutral', label_pkas: bool = False, minimize_adj_h: bool = False, protassign_number_sequential_cycles: ~typing.Optional[int] = None, protassign_max_cluster_size: ~typing.Optional[int] = None, run_refine: bool = True, rmsd: float = 0.3, fixheavy: bool = False, reference_struct_path: ~typing.Optional[~pathlib.Path] = None, use_PDB_pH: bool = False, force_list: list[str] = <factory>, error_handling: schrodinger.seam.transforms.outcomes.ErrorHandling | None = None)¶
Bases:
BaseModelConfiguration for PrepWizard transforms.
- settings: Annotated[PrepWizardSettings | None, WithJsonSchema({})]¶
- force_field: ForceFieldName¶
- preserve_st_titles: bool¶
- max_states: PositiveInt¶
- preprocess: bool¶
- assign_bond_orders: bool¶
- use_ccd: bool¶
- skip_assigned_residues: bool¶
- add_missing_inter_residue_bonds: bool¶
- add_terminal_oxygens: bool¶
- add_hydrogens: bool¶
- readd_hydrogens: bool¶
- idealize_hydrogen_tf: bool¶
- preprocess_delete_far_waters: bool¶
- preprocess_watdist: NonNegativeFloat | None¶
- delete_far_waters: bool¶
- watdist: NonNegativeFloat¶
- delete_nonbridging_waters: bool¶
- delwater_hbond_cutoff: NonNegativeInt¶
- treat_metals: bool¶
- treat_disulfides: bool¶
- treat_glycosylation: bool¶
- treat_palmitoylation: bool¶
- selenomethionines: bool¶
- cap_termini: bool¶
- cap_termini_min_atoms: NonNegativeInt¶
- fillsidechains: bool¶
- fillloops: bool¶
- custom_fasta_file: FilePath | None¶
- annotate_gpcr: bool¶
- antibody_cdr_scheme: CDRSchemeName¶
- renumber_ab_residues: bool¶
- tcr_cdr_scheme: CDRSchemeName | None¶
- renumber_tcr_residues: bool¶
- run_epik: bool¶
- include_epik_states: bool¶
- epik_pH: PH_TYPE¶
- epik_pHt: NonNegativeFloat¶
- xtal: bool¶
- run_protassign: bool¶
- sample_water_orientation: bool¶
- use_propka: bool¶
- propka_pH: PH_TYPE¶
- pH: PH_TYPE¶
- simplified_pH: SimplifiedPH¶
- label_pkas: bool¶
- minimize_adj_h: bool¶
- protassign_number_sequential_cycles: PositiveInt | None¶
- protassign_max_cluster_size: PositiveInt | None¶
- run_refine: bool¶
- rmsd: PositiveFloat¶
- fixheavy: bool¶
- reference_struct_path: FilePath | None¶
- use_PDB_pH: bool¶
- force_list: list[str]¶
- error_handling: ErrorHandling | None¶
- model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'frozen': True}¶
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- class schrodinger.application.transforms.prepwizard.PrepWizardResult(*, source_id: ~schrodinger.seam.io.sourceid.SourceID, output_structures: list[schrodinger.structure._structure.Structure] = <factory>)¶
Bases:
BaseModelPer-input result from RunPrepWizard.
- model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'frozen': True}¶
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- class schrodinger.application.transforms.prepwizard.RunPrepWizard(settings: PrepWizardSettings | None = None, **kwargs)¶
Bases:
PTransformWithConfigRun PrepWizard and emit a PrepWizardResult per input structure.
- config_class¶
alias of
PrepWizardConfig
- __init__(settings: PrepWizardSettings | None = None, **kwargs)¶
Initialize the transform with configuration.
All keyword arguments are passed to the config_class constructor to create a validated configuration instance stored as self.config.
- Parameters:
_config – An existing configuration model
kwargs – Configuration parameters for the config_class
- Raises:
AttributeError – If config_class is not defined on the subclass
ValueError – If _config is combined with kwargs
ValidationError – If the configuration parameters are invalid
- class schrodinger.application.transforms.prepwizard.ProteinPrep(settings: PrepWizardSettings | None = None, **kwargs)¶
Bases:
PTransformWithConfigPrepare protein structures using PrepWizard.
Example usage:
>>> from schrodinger.seam.io import chemio >>> with beam.Pipeline() as p: ... _ = (p ... | chemio.ReadStructuresFromFile(infile) ... | ProteinPrep(run_epik=False, cap_termini=True) ... | chemio.WriteStructuresToFile('output.mae'))
- config_class¶
alias of
PrepWizardConfig
- __init__(settings: PrepWizardSettings | None = None, **kwargs)¶
Initialize the transform with configuration.
All keyword arguments are passed to the config_class constructor to create a validated configuration instance stored as self.config.
- Parameters:
_config – An existing configuration model
kwargs – Configuration parameters for the config_class
- Raises:
AttributeError – If config_class is not defined on the subclass
ValueError – If _config is combined with kwargs
ValidationError – If the configuration parameters are invalid