NAStructuralDB : structural database to facilitate computational studies of molecular modeling and recognition of proteins with special focus on antibody–antigen interactions
Open accessPublished version of a preprint
Abstract
Studying the interactions between antibodies and antigens is fundamental to the development of novel therapeutic biologics. Predictions of such interactions start with data collection. Though there exist reliable resources to identify antibody structures in the Protein Data Bank (PDB), such data still requires substantial processing to be usable in predictive tasks. Redundancy in sequences needs to be removed to avoid data leakages between train, test, and validation sets. Descriptors such as surface accessibility, secondary structure, and antibody region information need to be additionally annotated. Information on inter- and intra-molecular contacts, which is crucial to studying paratope/epitope information, needs to be collected. The specialized immunoglobulin format of Nanobodies® requires a separate dataset mirroring that of antibodies, given that their structure contains only a single VHH chain. Because antibody-antigen structures account for a small amount of all protein-protein contacts, having a molecular contact reference from other proteins is also desired. To address these issues, we introduce NAStructuralDB (https://naturalantibody.com/na-structural/), a dataset of processed structures of antibodies, Nanobodies®, proteins, and their complexes with molecular contact information and associated annotations. We use the opportunity of having collected the contact data to provide a reference of binding propensities of different residues across distinct contact types.
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
De novo design of protein structure and function with RFdiffusion
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al. (2023) · Nature · doi:10.1038/s41586-023-06415-8 · Cited by 2,325 · see citing works
Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery
Wong, F., Krishnan, A., Zheng, E. J., Stärk, H., Manson, A. L., Earl, A. M., Jaakkola, T., & Collins, J. J. (2022) · Molecular Systems Biology · doi:10.15252/msb.202211081 · Cited by 284 · see citing works
Tubiana, J., Schneidman-Duhovny, D., & Wolfson, H. J. (2022) · Nature Methods · doi:10.1038/s41592-022-01490-7 · Cited by 274 · see citing works
Comparative analysis of nanobody sequence and structure data
Mitchell, L. S., & Colwell, L. J. (2018) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25497 · Cited by 233 · see citing works
RosettaAntibodyDesign (RAbD): a general framework for computational antibody design
Adolf-Bryfogle, J., Kalyuzhniy, O., Kubitz, M., Weitzner, B. D., Hu, X., Adachi, Y., Schief, W. R., & Dunbrack, R. L. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006112 · Cited by 227 · see citing works
Computational approaches to therapeutic antibody design: established methods and emerging trends
Norman, R. A., Ambrosetti, F., Bonvin, A. M. J. J., Colwell, L. J., Kelm, S., Kumar, S., & Krawczyk, K. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbz095 · Cited by 219 · cited 1× · see citing works
“10,11 There are several classes of antibody-specific binding predictors that can be distinguished based on whether they predict the binding site on individual molecules or on their pairs.”
Self-citationImproving B-cell epitope prediction and its application to global antibody–antigen docking
Krawczyk, K., Liu, X., Baker, T., Shi, J., & Deane, C. M. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu190 · Cited by 174 · see citing works
Jespersen, M. C., Mahajan, S., Peters, B., Nielsen, M., & Marcatili, P. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.00298 · Cited by 148 · see citing works
Krawczyk, K., Baker, T., Shi, J., & Deane, C. M. (2013) · Protein Engineering Design and Selection · doi:10.1093/protein/gzt043 · Cited by 87 · see citing works
Self-citationFragment-based computational design of antibodies targeting structured epitopes
Aguilar Rangel, M., Bedwell, A., Costanzi, E., Taylor, R. J., Russo, R., Bernardes, G. J. L., Ricagno, S., Frydman, J., Vendruscolo, M., & Sormanni, P. (2022) · Science Advances · doi:10.1126/sciadv.abp9540 · Cited by 55 · see citing works
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- Authors
- Chomicz, D., Dudzic, P., Wrobel, S., Gawlowski, T., Demharter, S., Spreafico, R., Minoux, H., Phillips, A., & Krawczyk, K.
- Affiliations
- Natural Antibody
- Wrocław University of Science and Technology (PL)
- Genmab (United States) (US)
- Sanofi (France) (FR)
- Corresponding author
- Krawczyk, Konrad
- ORCID
- Venue
- mAbs
- Publisher
- Informa UK Limited
- Published
- 2026-03-08
- Volume
- 18
- Issue
- 1
- Pages
- 2630438-2630438
- ISSN
- 1942-0862 (ISSN-L)
- 1942-0870
- OpenAlex
- W7134287346
- Cited by
- 4
- References
- 11
- Citations by year
- 2026: 4
- FWCI
- 8.27
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- mAbs (publishedVersion, cc-by-nc)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by-nc)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Development of the Specitope technology for bioinformatics-based prediction of B-cell-specific epitopes, enabling the ‘in silico’ identification of candidates for therapeutic antibodies (FEMP.01.01-IP.01-0319/23-00)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- antigen-antibody reaction, nanobodies, VHH, protein-protein contacts, epitope prediction, antibody binding sites, surface accessibility, biologic therapeutics, protein-protein interaction prediction
- MeSH terms
- Antigen-Antibody Reactions, Epitopes, Antigens, Humans, Models, Molecular, Protein Conformation, Computational Biology, Databases, Protein, Single-Domain Antibodies
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