fetch

biotite.database.afdb.fetch(ids, format, target_path=None, overwrite=False, verbose=False)

Download predicted protein structures from the AlphaFold DB.

This function requires an internet connection.

Parameters:

idsstr or iterable object of str

A single ID or a list of IDs of the file(s) to be downloaded. They can be either UniProt IDs (e.g. P12345) or AlphaFold DB IDs (e.g. AF-P12345F1).

format{‘pdb’, ‘pdbx’, ‘cif’, ‘mmcif’, ‘bcif’, ‘fasta’}

The format of the files to be downloaded.

target_pathstr, optional

The target directory of the downloaded files. By default, the file content is stored in a file-like object (StringIO or BytesIO, respectively).

overwritebool, optional

If true, existing files will be overwritten. Otherwise the respective file will only be downloaded if the file does not exist yet in the specified target directory or if the file is empty.

verbosebool, optional

If true, the function will output the download progress.

Returns:

filesstr or StringIO or BytesIO or list of (str or StringIO or BytesIO)

The file path(s) to the downloaded files. If a single string (a single ID) was given in ids, a single string is returned. If a list (or other iterable object) was given, a list of strings is returned. If no target_path was given, the file contents are stored in either StringIO or BytesIO objects.

Examples

>>> from pathlib import Path
>>> file = fetch("P12345", "cif", path_to_directory)
>>> print(Path(file).name)
P12345.cif
>>> files = fetch(["P12345", "Q8K9I1"], "cif", path_to_directory)
>>> print([Path(file).name for file in files])
['P12345.cif', 'Q8K9I1.cif']

Gallery

LDDT for predicted structure evaluation

LDDT for predicted structure evaluation