Annotations

class emdb.models.annotations.AlphaFoldDBAnnotation(*, id: str, sample_id: str, provenance: str)

Bases: EMDBBaseAnnotation

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class emdb.models.annotations.CathAnnotation(*, id: str, sample_id: str, provenance: str, start: int, end: int)

Bases: EMDBBaseAnnotation

classmethod from_api(data: dict, sample_id: str) → CathAnnotation

Create a CathAnnotation instance from API data.

Parameters:

• sample_id – The sample ID associated with the annotation.

• data – The data returned by the EMDB API.

Returns:

An instance of CathAnnotation.

end: int

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

start: int

class emdb.models.annotations.ChEBIAnnotation(*, id: str, sample_id: str, provenance: str, title: str | None = None)

Bases: EMDBBaseAnnotation

classmethod from_api(data: dict, sample_id: str) → ChEBIAnnotation

Create a ChEBIAnnotation instance from API data.

Parameters:

• sample_id – The sample ID associated with the annotation.

• data – The data returned by the EMDB API.

Returns:

An instance of ChEBIAnnotation.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

title: str | None

class emdb.models.annotations.ChEMBLAnnotation(*, id: str, sample_id: str, provenance: str, title: str | None = None)

Bases: EMDBBaseAnnotation

classmethod from_api(data: dict, sample_id: str) → ChEMBLAnnotation

Create a ChEMBLAnnotation instance from API data.

Parameters:

• sample_id – The sample ID associated with the annotation.

• data – The data returned by the EMDB API.

Returns:

An instance of ChEMBLAnnotation.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

title: str | None

class emdb.models.annotations.ComplexPortalAnnotation(*, id: str, sample_id: str, provenance: str, title: str | None = None, score: float)

Bases: EMDBBaseAnnotation

classmethod from_api(data: dict, sample_id: str) → ComplexPortalAnnotation

Create a ComplexPortalAnnotation instance from API data.

Parameters:

• sample_id – The sample ID associated with the annotation.

• data – The data returned by the EMDB API.

Returns:

An instance of ComplexPortalAnnotation.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

score: float

title: str | None

class emdb.models.annotations.DrugBankAnnotation(*, id: str, sample_id: str, provenance: str, title: str | None = None)

Bases: EMDBBaseAnnotation

classmethod from_api(data: dict, sample_id: str) → DrugBankAnnotation

Create a DrugBankAnnotation instance from API data.

Parameters:

• sample_id – The sample ID associated with the annotation.

• data – The data returned by the EMDB API.

Returns:

An instance of DrugBankAnnotation.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

title: str | None

class emdb.models.annotations.EMDBAnnotations(*, emdb_id: str, macromolecules: List[EMDBMacromoleculeSample] = [], supramolecules: List[EMDBSupramoleculeSample] = [], orcid: List[ORCIDAnnotation] | None = None, empiar: List[EMPIARAnnotation] | None = None, pdb: List[PDBAnnotation] | None = None)

Bases: BaseModel

Model for EMDB annotations. This model is used to store annotations related to an EMDB entry.

classmethod from_api(data: dict, client: EMDB) → EMDBAnnotations

Create an EMDBAnnotations instance from API data.

Parameters:

• data – The data returned by the EMDB API.

• client – The EMDB client instance used to make the API request.

Returns:

An instance of EMDBAnnotations.

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

• self – The BaseModel instance.

• context – The context.

emdb_id: str

empiar: List[EMPIARAnnotation] | None

macromolecules: List[EMDBMacromoleculeSample]

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

orcid: List[ORCIDAnnotation] | None

pdb: List[PDBAnnotation] | None

supramolecules: List[EMDBSupramoleculeSample]

class emdb.models.annotations.EMDBBaseAnnotation(*, id: str, sample_id: str, provenance: str)

Bases: BaseModel

Base model for EMDB annotations. This model is used to represent a generic annotation in EMDB.

classmethod from_api(data: dict, sample_id: str) → EMDBBaseAnnotation

id: str

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

provenance: str

sample_id: str

class emdb.models.annotations.EMDBMacromoleculeSample(*, id: int, type: str, uniprot: List[UniProtAnnotation] = [], pfam: List[PfamAnnotation] = [], interpro: List[InterProAnnotation] = [], gene_ontology: List[GeneOntologyAnnotation] = [], gene_ontology_cell: List[GeneOntologyAnnotation] = [], gene_ontology_process: List[GeneOntologyAnnotation] = [], gene_ontology_function: List[GeneOntologyAnnotation] = [], cath: List[CathAnnotation] = [], chebi: List[ChEBIAnnotation] = [], chembl: List[ChEMBLAnnotation] = [], drugbank: List[DrugBankAnnotation] = [], pdbekb: List[PDBeKbAnnotation] = [], alphafolddb: List[AlphaFoldDBAnnotation] = [], scop2: List[Scop2Annotation] = [])

Bases: BaseModel

Model for macromolecule sample in EMDB annotations. This model is used to represent a macromolecule sample in EMDB annotations.

classmethod from_api(data: dict, mol_id: str) → EMDBMacromoleculeSample

Create an EMDBMacromoleculeSample instance from API data.

Parameters:

• mol_id – Macromolecule ID. The same ID is used in the EMDB sample.

• data – The data returned by the EMDB API.

Returns:

An instance of EMDBMacromoleculeSample.

alphafolddb: List[AlphaFoldDBAnnotation]

cath: List[CathAnnotation]

chebi: List[ChEBIAnnotation]

chembl: List[ChEMBLAnnotation]

drugbank: List[DrugBankAnnotation]

gene_ontology: List[GeneOntologyAnnotation]

gene_ontology_cell: List[GeneOntologyAnnotation]

gene_ontology_function: List[GeneOntologyAnnotation]

gene_ontology_process: List[GeneOntologyAnnotation]

id: int

interpro: List[InterProAnnotation]

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

pdbekb: List[PDBeKbAnnotation]

pfam: List[PfamAnnotation]

scop2: List[Scop2Annotation]

type: str

uniprot: List[UniProtAnnotation]

class emdb.models.annotations.EMDBSupramoleculeSample(*, id: int, type: str, complex_portal: List[ComplexPortalAnnotation] | None = None)

Bases: BaseModel

Model for supramolecule in EMDB annotations. This model is used to represent a supramolecule in EMDB annotations.

classmethod from_api(data: dict, mol_id: str) → EMDBSupramoleculeSample

Create an EMDBSupramolecule instance from API data.

Parameters:

• mol_id – Supramolecule ID. The same ID is used in the EMDB sample.

• data – The data returned by the EMDB API.

Returns:

An instance of EMDBSupramolecule.

complex_portal: List[ComplexPortalAnnotation] | None

id: int

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

type: str

class emdb.models.annotations.EMPIARAnnotation(*, id: str, sample_id: str, provenance: str)

Bases: EMDBBaseAnnotation

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class emdb.models.annotations.GeneOntologyAnnotation(*, id: str, sample_id: str, provenance: str, title: str | None = None, type: str)

Bases: EMDBBaseAnnotation

classmethod from_api(data: dict, sample_id: str) → GeneOntologyAnnotation

Create a GeneOntologyAnnotation instance from API data.

Parameters:

• sample_id – The sample ID associated with the annotation.

• data – The data returned by the EMDB API.

Returns:

An instance of GeneOntologyAnnotation.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

title: str | None

type: str

class emdb.models.annotations.InterProAnnotation(*, id: str, sample_id: str, provenance: str, title: str | None = None, start: int, end: int)

Bases: EMDBBaseAnnotation

classmethod from_api(data: dict, sample_id: str) → InterProAnnotation

Create an InterProAnnotation instance from API data.

Parameters:

• sample_id – The sample ID associated with the annotation.

• data – The data returned by the EMDB API.

Returns:

An instance of InterProAnnotation.

end: int

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

start: int

title: str | None

class emdb.models.annotations.ORCIDAnnotation(*, id: str, sample_id: str, provenance: str, title: str | None = None)

Bases: EMDBBaseAnnotation

classmethod from_api(data: dict, sample_id: str) → ORCIDAnnotation

Create an ORCIDAnnotation instance from API data.

Parameters:

• sample_id – The sample ID associated with the annotation.

• data – The data returned by the EMDB API.

Returns:

An instance of ORCIDAnnotation.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

title: str | None

class emdb.models.annotations.PDBAnnotation(*, id: str, sample_id: str, provenance: str)

Bases: EMDBBaseAnnotation

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class emdb.models.annotations.PDBeKbAnnotation(*, id: str, sample_id: str, provenance: str)

Bases: EMDBBaseAnnotation

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class emdb.models.annotations.PfamAnnotation(*, id: str, sample_id: str, provenance: str, title: str | None = None, start: int, end: int)

Bases: EMDBBaseAnnotation

classmethod from_api(data: dict, sample_id: str) → PfamAnnotation

Create a PfamAnnotation instance from API data.

Parameters:

• sample_id – The sample ID associated with the annotation.

• data – The data returned by the EMDB API.

Returns:

An instance of PfamAnnotation.

end: int

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

start: int

title: str | None

class emdb.models.annotations.Scop2Annotation(*, id: str, sample_id: str, provenance: str)

Bases: EMDBBaseAnnotation

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class emdb.models.annotations.UniProtAnnotation(*, id: str, sample_id: str, provenance: str)

Bases: EMDBBaseAnnotation

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].