Datasets:
task_categories:
- text-classification
language:
- en
license: other
license_name: cc-by4.0
license_link: LICENSE.md
tags:
- biology
- chemistry
dataset_summary: ML Application Curated Data from The Structural Antibody Database (SAbDab)
pretty_name: The Curated SAbDab
dataset_description: >-
This dataset contains curated data from the SAbDab as of Mar 4, 2026. The
SAbDab is a database of antibody structures including experimental details,
antibody nomenclature, affinity data and sequence annotations. This data is
filtered to include high quality, antigen-bound, and non-redundant antibody
structures only.
acknowledgements: >-
We kindly acknowledge the ProteinMPNN team, RosettaCommons, and the following
institutions: University of California, Los Angeles; University of Maryland;
University of Oregon; University of Michigan; University of Pennsylvania; and
the Wistar Institute
size_categories:
- 10K<n<100K
citation_bibtex: >-
@article{10.1093/nar/gkt1043, author = {Dunbar, James and Krawczyk, Konrad
and Leem, Jinwoo and Baker, Terry and Fuchs, Angelika and Georges, Guy and
Shi, Jiye and Deane, Charlotte M.}, title = {SAbDab: the structural antibody
database}, journal = {Nucleic Acids Research}, volume = {42}, number = {D1},
pages = {D1140-D1146}, year = {2013}, month = {11}, abstract = {Structural
antibody database (SAbDab; http://opig.stats.ox.ac.uk/webapps/sabdab) is an
online resource containing all the publicly available antibody structures
annotated and presented in a consistent fashion. The data are annotated with
several properties including experimental information, gene details, correct
heavy and light chain pairings, antigen details and, where available,
antibody–antigen binding affinity. The user can select structures, according
to these attributes as well as structural properties such as complementarity
determining region loop conformation and variable domain orientation.
Individual structures, datasets and the complete database can be downloaded.},
issn = {0305-1048}, doi = {10.1093/nar/gkt1043}, url =
{https://doi.org/10.1093/nar/gkt1043}, eprint =
{https://academic.oup.com/nar/article-pdf/42/D1/D1140/3538157/gkt1043.pdf}}
citation_apa: >-
James Dunbar, Konrad Krawczyk, Jinwoo Leem, Terry Baker, Angelika Fuchs, Guy
Georges, Jiye Shi, Charlotte M. Deane, SAbDab: the structural antibody
database, Nucleic Acids Research, Volume 42, Issue D1, 1 January 2014, Pages
D1140–D1146, https://doi.org/10.1093/nar/gkt1043
ML Application Curated SAbDab
Quickstart Usage
Install HuggingFace Datasets package
Each subset can be loaded into python using the Huggingface datasets library.
First, from the command line install the datasets library
$ pip install datasets
Optionally set the cache directory, e.g.
$ HF_HOME=${HOME}/.cache/huggingface/
$ export HF_HOME
then, from within python load the datasets library
>>> import datasets
Load model datasets
To load structures from the entire SAbDab dataset, use datasets.load_dataset(...):
>>> dataset_tag = "train"
>>> dataset_models = datasets.load_dataset(
path = "ProteinMPNN/SAbDab_raw",
name = f"{dataset_tag}_models",
data_dir = f"{dataset_tag}")['train']
and the dataset is loaded as a datasets.arrow_dataset.Dataset
>>> dataset_models
Dataset({
features: [
'pdb',
'Hchain',
'Lchain',
'model',
'antigen_chain',
'antigen_type',
'antigen_het_name',
'antigen_name',
'short_header',
'date',
'compound',
'organism',
'heavy_species',
'light_species',
'antigen_species',
'authors',
'resolution',
'method',
'r_free',
'r_factor',
'scfv',
'engineered',
'heavy_subclass',
'light_subclass',
'light_ctype',
'affinity',
'delta_g',
'affinity_method',
'temperature',
'pmid'
],
num_rows: 20701
})
which is a column oriented format that can be accessed directly, converted in to a pandas.DataFrame, or parquet format, e.g.
>>> dataset_models.data.column('pdb')
>>> dataset_models.to_pandas()
>>> dataset_models.to_parquet("dataset.parquet")
Dataset Details
Dataset Description
This dataset contains curated data from the SAbDab as of Mar 4, 2026. The SAbDab is a database of antibody structures including experimental details, antibody nomenclature, affinity data and sequence annotations. This data is filtered to include high quality, antigen-bound, and non-redundant antibody structures only.
Acknowledgements: We kindly acknowledge the ProteinMPNN team, RosettaCommons, and the following institutions: University of California, Los Angeles; University of Maryland; University of Oregon; University of Michigan; University of Pennsylvania; and the Wistar Institute.
License: CC-BY 4.0
Dataset Sources
- Paper: Dunbar, J., Krawczyk, K. et al (2014). Nucleic Acids Res. 42. D1140-D1146
Uses
Screening of antibody-antigen interactions, querying structural features of antibodies, and more
Citation
@article{10.1093/nar/gkt1043, author = {Dunbar, James and Krawczyk, Konrad and Leem, Jinwoo and Baker, Terry and Fuchs, Angelika and Georges, Guy and Shi, Jiye and Deane, Charlotte M.}, title = {SAbDab: the structural antibody database}, journal = {Nucleic Acids Research}, volume = {42}, number = {D1}, pages = {D1140-D1146}, year = {2013}, month = {11}, abstract = {Structural antibody database (SAbDab; http://opig.stats.ox.ac.uk/webapps/sabdab) is an online resource containing all the publicly available antibody structures annotated and presented in a consistent fashion. The data are annotated with several properties including experimental information, gene details, correct heavy and light chain pairings, antigen details and, where available, antibody–antigen binding affinity. The user can select structures, according to these attributes as well as structural properties such as complementarity determining region loop conformation and variable domain orientation. Individual structures, datasets and the complete database can be downloaded.}, issn = {0305-1048}, doi = {10.1093/nar/gkt1043}, url = {https://doi.org/10.1093/nar/gkt1043}, eprint = {https://academic.oup.com/nar/article-pdf/42/D1/D1140/3538157/gkt1043.pdf} }
Dataset Card Authors
Miranda Simpson (miranda13nicoles@gmail.com), Becca Lee (beccalee5@g.ucla.edu), Nathaniel Felbinger (nfelbing@umd.edu), Pratyush Dhal (pdhal@umich.edu), Colby Agostino (colby.agostino@pennmedicine.upenn.edu)