SAbDab: the structural antibody database

By James Dunbar, Konrad Krawczyk, Jinwoo Leem, Terry Baker, Angelika Fuchs, Guy Georges, Jiye Shi, Charlotte M. Deane. Nucleic Acids Res., 2013

704 citations recorded by the source corpus.

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.

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Citations in the corpus

Repertoire Analysis of Antibody CDR-H3 Loops Suggests Affinity Maturation Does Not Typically Result in Rigidification (2017, methodology): “To investigate this hypothesis, we selected ten mature antibodies from our SAbDab set with CDR-H3 loops of length 10, a length for which loop modeling performs well (49, 60).”
Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models for Protein Structures (2022, methodology): “1 Dataset Curation Our new dataset is collected from two sources: SAbDab [12] and the Protein Data Bank (PDB) [10].”
ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction (2021, methodology): “Epitope database constructed from SabDab (timestamp: 04/19/2021) [50]; 5-fold crossvalidation performance is shown.”
RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning (2022, unknown): “SAbDab (90) The Structural Antibody Database”
SAbPred: a structure-based antibody prediction server (2016, methodology): “Templates for the VH and VL framework regions are chosen from SAbDab (23) and orientated with respect to each other using ABangle (24).”
Blind prediction performance of RosettaAntibody 3.0: Grafting, relaxation, kinematic loop modeling, and full CDR optimization (2014, background): “Fortunately, these scenarios were relatively rare, and 42 of the submitted non-H3 CDR loops were predicted correctly (i.e., backbone RMSD (cid”
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design (2021, methodology): “The Structural Antibody Database (SAbDab) consists of 4994 antibody structures renumbered according to the IMGT numbering scheme (Lefranc et al., 2003).”
CoV-AbDab: the Coronavirus Antibody Database (2020, methodology): “It follows that the presence of CDRH3s shown to bind SARS-CoV but that have high sequence identity matches to OAS may be less useful for diagnosing SARS-CoV-2 exposure.”
Improved antibody structure prediction by deep learning of side chain conformations (2021, methodology): “The training dataset for our model was curated from SAbDab, a database of antibody structures from the Protein Data Bank [19].”
A Review of Deep Learning Methods for Antibodies (2020, methodology): “For training, 1462 structures were taken from the Structural Antibody Database (SAbDab), with 5% of loops randomly chosen and set aside for validation, to account for overfitting [31].”
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development (2021, methodology): “(2020), containing 2,409 antibodies processed from SAbDab (Dunbar et al. 2014).”
Five computational developability guidelines for therapeutic antibody profiling (2019, methodology): “We then mined SAbDab (36) to find all of the human, nonengineered, nonredundant (at 100% sequence identity) X-ray crystal structures in the PDB (22).”
Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants (2021, methodology): “Twenty additional antibody-antigen modeling test cases were selected from antibodyantigen complex structures in the SAbDab database [40], screened by resolution (< 3.”