Cited by 199Monoclonal and Polyclonal Antibodies ResearchEnzyme Structure and Functionvaccines and immunoinformatics approaches

The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.

Thera-SAbDab: the Therapeutic Structural Antibody Database

Open access

Raybould, Matthew I J, Marks, Claire, Lewis, Alan P, Shi, Jiye, Bujotzek, Alexander, Taddese, Bruck, Deane, Charlotte M·2020·Nucleic Acids Research

DOI 10.1093/nar/gkz827PMC PMC6943036

Published version of a preprint

Open this paper in CoChat — free account Open PDF

Abstract

The Therapeutic Structural Antibody Database (Thera-SAbDab; http://opig.stats.ox.ac.uk/webapps/therasabdab) tracks all antibody- and nanobody-related therapeutics recognized by the World Health Organisation (WHO), and identifies any corresponding structures in the Structural Antibody Database (SAbDab) with near-exact or exact variable domain sequence matches. Thera-SAbDab is synchronized with SAbDab to update weekly, reflecting new Protein Data Bank entries and the availability of new sequence data published by the WHO. Each therapeutic summary page lists structural coverage (with links to the appropriate SAbDab entries), alignments showing where any near-matches deviate in sequence, and accompanying metadata, such as intended target and investigated conditions. Thera-SAbDab can be queried by therapeutic name, by a combination of metadata, or by variable domain sequence - returning all therapeutics that are within a specified sequence identity over a specified region of the query. The sequences of all therapeutics listed in Thera-SAbDab (461 unique molecules, as of 5 August 2019) are downloadable as a single file with accompanying metadata.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1093/nar/gkz827

Cites

19 of 19 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The Protein Data Bank
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196
Strategies and challenges for the next generation of antibody–drug conjugates
Beck, A., Goetsch, L., Dumontet, C., & Corvaïa, N. (2017) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.268 · Cited by 2,271 · cited 1×
“These fragments can be expressed in dimeric form to improve avidity, or conjugated with polyethylene glycol (‘pegylated’) for slower clearance (4), with radioisotopes for diagnostic purposes (5), or with radioisotopes or noxious small molecules/peptides for cytotoxicity (6).”
Background
Bispecific antibodies: a mechanistic review of the pipeline
Labrijn, A. F., Janmaat, M. L., Reichert, J. M., & Parren, P. W. H. I. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0028-1 · Cited by 1,303 · cited 1×
“As of June 2019, bispecific mAbs, linked Fabs, linked scFvs and linked single-domain variable fragments have all been assessed in clinical trials (7).”
Background
Standard conformations for the canonical structures of immunoglobulins 1 1Edited by I. A. Wilson
Al‐Lazikani, B., Lesk, A. M., & Chothia, C. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1997.1354 · Cited by 707
SAbDab: the structural antibody database
Dunbar, J., Krawczyk, K., Leem, J., Baker, T., Fuchs, A., Georges, G., Shi, J., & Deane, C. M. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1043 · Cited by 674 · cited 2×
“A significant fraction of these structures contain antibody variable domains, and these are recorded by the Structural Antibody Database (SAbDab (16); 7184 variable domain structures over 3663 PDB entries as of 5 August 2019).”
Background
Nanobodies as therapeutics: big opportunities for small antibodies
Steeland, S., Vandenbroucke, R. E., & Libert, C. (2016) · Drug Discovery Today · doi:10.1016/j.drudis.2016.04.003 · Cited by 476
The Increasingly Human and Profitable Monoclonal Antibody Market
Grilo, A. L., & Mantalaris, A. (2018) · Trends in biotechnology · doi:10.1016/j.tibtech.2018.05.014 · Cited by 468
Antibodies to watch in 2019
Kaplon, H., & Reichert, J. M. (2018) · mAbs · doi:10.1080/19420862.2018.1556465 · Cited by 435 · cited 1×
“Whole monoclonal antibody (mAb) therapies dominate the industry - drugs that mimic natural antibodies by containing two identical variable domain structures with a particular specificity (3).”
Background
ANARCI: antigen receptor numbering and receptor classification
Dunbar, J., & Deane, C. M. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv552 · Cited by 421 · cited 3×
“We harvest sequences as they are released by the WHO, number them with ANARCI (17), and perform a weekly sequence alignment of all therapeutic variable domain sequences to the sequences of known structures stored in SAbDab.”
BackgroundMethodInfluential
Homology modeling in drug discovery: Overview, current applications, and future perspectives
Muhammed, M. T., & Aki, E. (2018) · Chemical Biology & Drug Design · doi:10.1111/cbdd.13388 · Cited by 381 · cited 1×
“It can also assist subsequent development and optimization, as homology models of mutants derived from a known structure are in general more accurate than those for which no close structural partner is available (14).”
Background

Flagged first, then by citations

Show all 19 references

Research works printed but not linked: 3

WHO, 2018, Proposed International Nonproprietary Names (INN) List 120, WHO Drug Information, 32, 559
WHO, 2019, Recommended International Nonproprietary Names (INN) List 81, WHO Drug Information, 33, 59
Poiron, 2010, IMGT/mAb-DB: the IMGT database for therapeutic monoclonal antibodies, JOBIM 2010, 13, 470b

Sorted by citations

Items 101–197 of 197

An antibody developability triaging pipeline exploiting protein language models
Sweet-Jones, J., & Martin, A. C. R. (2025) · mAbs · doi:10.1080/19420862.2025.2472009 · Cited by 6
Profiling antigen-binding affinity of B cell repertoires in tumors by deep learning predicts immune-checkpoint inhibitor treatment outcomes
Song, B., Wang, K., Na, S., Yao, J., Fattah, F. J., Martin, A. L., von Itzstein, M. S., Yang, D. M., Liu, J., Xue, Y., et al. (2025) · Nature Cancer · doi:10.1038/s43018-025-01001-5 · Cited by 5
“We further validated Cmai on the antigen–antibody pairs from the SAbDab database 36 but limited to the antigens not seen in the training data (Fig.”
Characterising nanobody developability to improve therapeutic design using the Therapeutic Nanobody Profiler
Gordon, G. L., Gervásio, J. H. D. B., Souders, C., & Deane, C. M. (2026) · Communications Biology · doi:10.1038/s42003-026-09594-y · Cited by 11
“Clinical-stage data were obtained from TheraSAbDab, including sequences at any trial stage and including bi-and multi-speci ﬁ cs, for which each different variable domain is counted separately.”
Anchor Clustering for million-scale immune repertoire sequencing data
Chang, H., Ashlock, D., Graether, S. P., & Keller, S. M. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05659-z · Cited by 5
“Moreover, with the appearance of the therapeutic structural antibody database, clustering BCR based on their amino acid data would be meaningful in investigating the structural diversity of repertoires and developing applications in immunodiagnostics and immunotherapeutics [19, 20].”
Background
Effect of charged mutation on aggregation of a pentapeptide: Insights from molecular dynamics simulations
Prabakaran, R., Rawat, P., Yasuo, N., Sekijima, M., Kumar, S., & Gromiha, M. M. (2021) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26230 · Cited by 5
Brewpitopes: a pipeline to refine B-cell epitope predictions during public health emergencies
(2023) · doi:10.3389/fimmu.2023.1278534 · Cited by 4
Unlocking the potential of in silico approach in designing antibodies against SARS-CoV-2
Subramaniam, T. R., Mualif, S. A., Chan, W. H., & Halim, K. B. A. (2025) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2025.1533983 · Cited by 4
Systematic Engineering of TROP2-Targeted CAR T-Cell Therapy Overcomes Resistance Pathways in Solid Tumors
Brea, E. J., Baldacci, S., Savage, N. J., Facchinetti, F., Hinchey, C., Chakravarti, S., Mottram, A., Ngo, K. H., Vo, H. V., Leeper, B. A., et al. (2025) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-25-0527 · Cited by 4
“…the anti-BCMA CAR or vacabtagene autoleucel, or the CD19 FMC63 utilized in tisagenle-cleucel (irrelevant control), and scFv derived from sacituzumab or datopotamab or TROP2 VH binders, including biparatopic CAR, which are composed of individual VH linked via a glycine x4 + serine linker (17–19).”
PPAP: A Protein–protein Affinity Predictor Incorporating Interfacial Contact-Aware Attention
Qian, J., Yang, L., Duan, Z., Wang, R., & Qi, Y. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01390 · Cited by 5
Deep learning assessment of nativeness and pairing likelihood for antibody and nanobody design with AbNatiV2
Ramon, A., Frassetto, N., Zhao, H., Xu, X., Greenig, M., Onuoha, S. C., & Sormanni, P. (2026) · mAbs · doi:10.1080/19420862.2026.2646361 · Cited by 5
IMGT/mAb-KG: the knowledge graph for therapeutic monoclonal antibodies
Sanou, G., Manso, T., Todorov, K., Giudicelli, V., Duroux, P., & Κοσσίδα, Σ. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1393839 · Cited by 4
rAbDesFlow: a novel workflow for computational recombinant antibody design for healthcare engineering
Krishnan, S. R., Sharma, D., Nazeer, Y., Bose, M., Rajkumar, T., Jayaraman, G., Madaboosi, N., & Gromiha, M. M. (2024) · Antibody Therapeutics · doi:10.1093/abt/tbae018 · Cited by 4
Single-domain antibody screening by isPLA-seq
Yin, Y., Yan, F., Zhou, R., Li, M., Ma, J., Liu, Z., & Ma, Z. (2021) · Life Science Alliance · doi:10.26508/lsa.202101115 · Cited by 4
BERT2DAb: a pre-trained model for antibody representation based on amino acid sequences and 2D-structure
Luo, X., Tong, F., Zhao, W., Zheng, X., Li, J., Li, J., & Zhao, D. (2023) · mAbs · doi:10.1080/19420862.2023.2285904 · Cited by 4
PoSSuM v.3: A Major Expansion of the PoSSuM Database for Finding Similar Binding Sites of Proteins
Tsuchiya, Y., Yonezawa, T., Yamamori, Y., Inoura, H., Osawa, M., Ikeda, K., & Tomii, K. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01405 · Cited by 4
SPAT: Surface Protein Annotation ToolPreprint
Spinella, J.-F., Thérêt, L., Aubert, L., Audemard, E. O., Boucher, G., Pfammatter, S., Bonneil, É., Bordeleau, M.-È., Thibault, P., Hébert, J., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.07.547075 · Cited by 4
Author response: Computational Engineering of a Therapeutic Antibody to Inhibit Multiple Mutants of HER2 Without Compromising Inhibition of the Canonical HER2
Peled, S., Guez-Haddad, J., Biton, N. Z., Nimrod, G., Fischman, S., Fastman, Y., & Ofran, Y. (2024) · eLife · doi:10.7554/elife.89814.2.sa0 · Cited by 3
Drug-like antibody design against challenging targets with atomic precisionPreprint
Chai Discovery Team, Boitreaud, J., Chen, R. S., Dent, J., Fairweather, L., Geisz, D., Greenig, M., Boyd, N., Jain, J., Johnston, B., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.29.691346 · Cited by 9
“Benchmark VHHs, on the other hand, were selected from the population of clinical-stage nanobodies in Thera-SAbDab [13] based on their therapeutic relevance; since therapeutic VHHs are much rarer than IgGs, we reasoned that this set would likely provide a representative sample of the current…”
AntiBody Sequence Database
Malesys, S., Torchet, R., Saunier, B., & Maillet, N. (2024) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqae171 · Cited by 3
Tuning antibody stability and function by rational designs of framework mutations
Ng, J. C.-F., Chenoweth, A. M., Sciscio, M. L. D., Grandits, M., Cheung, A., Chu, T., McCraw, A. J., Chauhan, J., Liu, Y., Guo, D., et al. (2025) · mAbs · doi:10.1080/19420862.2025.2532117 · Cited by 5
Enhancing polyreactivity prediction of preclinical antibodies through fine-tuned protein language models
Zhou, Y., Tang, H., Wu, C., Zhang, Z., Wei, J., Gong, R., Gunarathne, S. M. S., Xiang, C., & Huang, J. H. (2025) · Journal of Pharmaceutical Analysis · doi:10.1016/j.jpha.2025.101448 · Cited by 3
“Several databases such as AACDB [47], SAbDab [48] and Thera-SAbDab [49] indeed provide a robust data foundation for structure-based methods.”
Review of Antibody Structure Prediction-Based on Artificial Intelligence
Makram, D., Maghraby, F. A., Shaheen, M., & Mabrouk, M. S. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10404-7 · Cited by 4
Fab–Fc and Fab–Fab interactions of variable strength and valency contribute to the high concentration viscosity of IgG 1 antibodies
Heisler, J. C., Hofmann, J. L., Tan, Z., Zarzar, J., Izadi, S., & Carter, P. J. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2526550123 · Cited by 3
A review on recent synthetic routes and computational approaches for antibody drug conjugation developments used in anti-cancer therapy
Debnath, U., Verma, S. K., Patra, J., & Mandal, S. K. (2022) · Journal of Molecular Structure · doi:10.1016/j.molstruc.2022.132524 · Cited by 3
Amino Acids Frequency and Interaction Trends: Comprehensive Analysis of Experimentally Validated Viral Antigen–Antibody Complexes
Pais, R., Nagraj, A. K., Patel, R., Gavade, A., Momin, M., Scheele, J., Seiz, W., & Patil, J. (2025) · Molecular Biotechnology · doi:10.1007/s12033-024-01361-w · Cited by 3
“As of May 9, 2024, the data-base contains 940 well-curated antibodies and nanobodies recognized by the World Health Organization (WHO) [18].”
Missense variants in the TNFA epitopes and their effects on interaction with therapeutic antibodies—in silico analysis
Ahsan, T., & Sajib, A. A. (2022) · Journal of Genetic Engineering and Biotechnology · doi:10.1186/s43141-021-00288-y · Cited by 3
“Amino acid sequences of the heavy and light chains of the mAbs were obtained from the Therapeutic Structural Antibody Database (TheraSAbDab) [36].”
Background
Divining Deamidation and Isomerization in Therapeutic Proteins: Effect of Neighboring ResiduePreprint
Irudayanathan, F. J., Zarzar, J., Lin, J., & Izadi, S. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.26.453885 · Cited by 3
“uk/webapps/newsabdab/sabdab/).[56, 57] We arrived at 3493 structures which were then cleaned, energy minimized and isolated from any other ligands.”
Background
Molecular Surface Descriptors to Predict Antibody DevelopabilityPreprint
Park, E., & Izadi, S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.18.549448 · Cited by 3
Impact of Human Genetic Variation Underlying Resistance to Antigen-Specific ImmunotherapyPreprint
Marone, R., Asllanaj, E., Capoferri, G., Schwede, T. F., Jeker, L. T., & Lepore, R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.02.651174 · Cited by 3
Antibody immunogenicity prediction and optimization with ImmunoSeqPreprint
Huang, Q., He, Y., & Liu, K. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.14.670305 · Cited by 3
Therapeutic antibodies against cancer—A step toward the treatment
Panwar, U., Khan, M. A., Selvaraj, C., & Singh, S. K. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-19200-5.00001-4 · Cited by 2
Pyroglutamation of cell surface proteins CD47 and TRP1 by glutaminyl cyclase modulates therapeutic antibody binding
van Duijn, A. V. D. P. , Winkelman, L. M. T., Lensen, C., van Duivendijk, M., Derksen, I., van Veenendaal, J. M. F., Schaap, G., Sluijter, M., van Groningen, T., van Doorn, R., et al. (2025) · Communications Biology · doi:10.1038/s42003-025-08938-4 · Cited by 2
“Thesepredictedgeneswerecross-referencedtotheThera-SAbDab database to discover clinically relevant antibodies against glutamine or glutamic-acid bearing proteins 33 .”
Addressing Small Data Challenges in Biopharmaceutical Development and Manufacturing: A Mini Review of Multi‐Fidelity Techniques
Golzarijalal, M., Aickelin, U., & Otte, E. A. (2026) · Biotechnology and Bioengineering · doi:10.1002/bit.70213 · Cited by 2
“For instance, the Therapeutic Structural Antibody Database offers biophysical properties of approved mAbs (Raybould et al. 2020).”
In Silico Tools for Predicting Novel Epitopes
Barra, C., Nilsson, J. B., Saksager, A. B., Carri, I., Deleuran, S. N., Alvarez, H. M. G., Høie, M. H., Li, Y., Clifford, J. N., Wan, Y.-T. R., et al. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-3890-3_17 · Cited by 2
FvFold: A model to predict antibody Fv structure using protein language model with residual network and Rosetta minimization
Sherpa, P., Chong, K. T., & Tayara, H. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109128 · Cited by 2
Amino acids characterization based on frequency and interaction analysis in human antigen-antibody complexes from Thera-SAbDab
Pais, R., Nagraj, A. K., Gavade, A., Patel, R., Momin, M., Scheele, J., Seiz, W., & Patil, J. (2025) · Human Antibodies · doi:10.1177/10932607241303614 · Cited by 2
“42 In this study, therapeutic antibody sequences were used to analyze amino acid frequency, dipeptide and tripeptide combinations, and speci ﬁ c physicochemical properties.”
IgG1-b12–HIV-gp120 Interface in Solution: A Computational Study
Martí, D., Alemán, C., Ainsley, J., Ahumada, Ó., & Torras, J. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01143 · Cited by 2
KA-Search: Rapid and exhaustive sequence identity search of known antibodiesPreprint
Olsen, T. H., Abanades, B., Moal, I. H., & Deane, C. M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.01.513855 · Cited by 2
“The same study can now be done on the 804 therapeutics within Thera-SAbDab (28), August 2022, with KA-Search in less than two days compute and little configuration (see Figure S1).”
Background
Isoelectric point, net charge and amino acid analysis of experimentally validated therapeutic antibodies
Nagraj, A. K., Patel, R., Gavade, A., Pais, R., Verma, P., & Patil, J. (2025) · In Silico Pharmacology · doi:10.1007/s40203-025-00356-y · Cited by 1
Exploring Binding Sites on Proteins for Function Prediction Using the PoSSuM Databases
Tomii, K., & Ikeda, K. (2025) · Methods in molecular biology · doi:10.1007/978-1-0716-4662-5_12 · Cited by 1
H3BERTa: A CDR-H3-specific language model for antibody repertoire analysis
Rodella, C., & Lemmin, T. (2026) · Patterns · doi:10.1016/j.patter.2026.101561
Deep learning-guided rational engineering of synergistic PD-1 and LAG-3 blockade for enhanced tumor immunomodulation
Mazhar, S., Koser, T., & Khalid, R. R. (2025) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-025-00702-4 · Cited by 1
Precision engineering of adalimumab fab variants enhances TNF-α neutralization and structural stability
Khan, A., Alzahrani, A. R., Rehman, Z. U., Zaidi, S. H. H., Hai, A., Al-Bazi, M. M., Banjabi, A. A., & Imran, M. (2025) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2025.130475 · Cited by 1
Identification of engineered IMGT Fc variants in IMGT/mAb-DB, a database of therapeutic antibodies and fusion proteins
Manso, T., Sanou, G., Nousias, C., Maalem, I., Boutin, F., Giudicelli, V., Duroux, P., Lefranc, M., & Κοσσίδα, Σ. (2025) · mAbs · doi:10.1080/19420862.2025.2594260 · Cited by 1
HuAbDiffusion: a discrete language diffusion model used for antibody humanization
Liu, D., Hao, X., & Fan, L. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf658 · Cited by 1
“For clearly showing the performance of the constructed model,22 therapeutic antibodies already used for clinical treatment in the SAbDab [14] database were collected as ground truth samples for model evaluation.”
Structure-guided design of antibody CDRs to reduce their reactivity to treatment-emergent anti-drug antibodies
Johansson, M. U., Kerschenmeyer, A., Carella, A., Carnal, S., Schmidt, Y. T., de Felice, A., Mahler, D., Thomas, M., Spiga, F. M., Tietz, J., et al. (2025) · mAbs · doi:10.1080/19420862.2025.2604353 · Cited by 1
Balancing the extremes for antibody developability: hydrophobic and electrostatic germline framework signatures for CDR-loop compensation
Spanke, V. A., Hoerschinger, V. J., Seidler, C. A., Kroell, K. B., Wieser, V., Imhof-Jung, S., Weiche, B., Bujotzek, A., Georges, G. J., & Liedl, K. R. (2026) · mAbs · doi:10.1080/19420862.2026.2627669 · Cited by 1
Norovirus-specific monoclonal antibodies that block histo-blood group antigen binding isolated from healthy donors
Leonard, S. E. W., Powers, O. C., Amlashi, P., von Itzstein, M., Wasdin, P. T., Marinov, T. M., Abu-Shmais, A. A., Georgiev, I. S., & Hansman, G. S. (2026) · Journal of Virology · doi:10.1128/jvi.00497-26 · Cited by 1
Repurposing of therapeutic antibodies against dengue virus envelope protein receptor binding domain
Chaudhuri, D., Majumder, S., Datta, J., & Giri, K. (2024) · Archives of Microbiology · doi:10.1007/s00203-024-04039-8 · Cited by 1
Artificial intelligence in antibody design and development: harnessing the power of computational approaches
Kavousipour, S., Barazesh, M., & Mohammadi, S. (2025) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-025-03429-4 · Cited by 2
SARS-Cov-2-, HIV-1-, Ebola-neutralizing and anti-PD1 clones are predisposedPreprint
Zhang, Y., Xu, Q., Zeng, H., Wang, M., Zhang, Y., Lan, C., Yang, X., Zhu, Y., Chen, Y., Wang, Q., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.13.249086 · Cited by 1
Computational Engineering of a Therapeutic Antibody to Inhibit Multiple Mutants of HER2 Without Compromising Inhibition of the Canonical HER2Preprint
Peled, S., Guez-Haddad, J., Biton, N. Z., Nimrod, G., Fischman, S., Fastman, Y., & Ofran, Y. (2024) · eLife · doi:10.7554/elife.89814.2 · Cited by 1
DYRK1A regulates cancer cell and cancer-associated fibroblast secretomes to foster an immunosuppressive microenvironment in pancreatic cancerPreprint
Pascual-Sabater, S., Varhadi, S., Vona, C. D., Celma, M., Fillat, C., & de la Luna, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.26.690756 · Cited by 1
The Immunogenicity Database Collaborative (IDC): A Standardized, Publicly Available Database for Clinical Immunogenicity Observations and InsightsPreprint
Agnihotri, S., Gonzalez-Nolasco, B., Monian, B., Pattijn, S., Ackaert, C., Wu, P., Kettenberger, H., Tourdot, S., Hickling, T. P., Hu, Z., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.08.692993 · Cited by 1
Prefusion-specific glycoprotein B human antibodies protect against neonatal HSV-2 infectionPreprint
Amlashi, P., 김정렬, Mendis, N. N., Wasdin, P. T., Bass, L. E., Jordaan, G., Abu-Shmais, A. A., Slein, M. D., Johnson, N. V., Bonami, R. H., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.23.707514 · Cited by 2
Interactions of epitopic variants of epidermal growth factor receptor with therapeutic anti- EGFR antibodies
Mahdiyah, S., Ahsan, T., Fatema, K., Shoily, S. S., & Sajib, A. (2022) · Dhaka University Journal of Biological Sciences · doi:10.3329/dujbs.v30i3.59032
“These antibodies are currently used to treat multiple types of cancers(7).”
BackgroundMethod
Multi-tissue architecture of the adaptive immune receptor repertoire in the cynomolgus macaquePreprint
Zhu, Y., Tang, H., Guan, J., Xie, W., Chen, S., Lan, C., Ma, C., Zeng, H., Yang, X., Wang, Q., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-1778626/v1
Probing highly polymorphic proteins by mass spectrometry
Hoek, M. (2022) · Utrecht University Repository (Utrecht University) · doi:10.33540/1535
Computational Engineering of a Therapeutic Antibody to Inhibit Multiple Mutants of HER2 Without Compromising Inhibition of the Canonical HER2Preprint
Peled, S., Guez-Haddad, J., Biton, N. Z., Nimrod, G., Fischman, S., Fastman, Y., & Ofran, Y. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.21.550003
“Naturally occurring genomic variation can cause altered drug responses, ranging from improved binding to complete drug resistance [48].”
Background
The use of bioinformatics tools to improve current Leishmaniasis diagnostic methods: A review.Preprint
Costa, N. T. C., Pereira, A. M. D. S., Silva, C. F., Souza, E., Ferreira, L. F., de Oliveira, B., Hernandes, M. Z., & Pereira, V. R. A. (2023) · doi:10.22541/au.169302552.24062058/v1
Computational Engineering of a Therapeutic Antibody to Inhibit Multiple Mutants of HER2 Without Compromising Inhibition of the Canonical HER2Preprint
Peled, S., Guez-Haddad, J., Biton, N. Z., Nimrod, G., Fischman, S., Fastman, Y., & Ofran, Y. (2024) · eLife · doi:10.7554/elife.89814
Additional file 1 of Anchor Clustering for million-scale immune repertoire sequencing data
Chang, H., Ashlock, D., Graether, S. P., & Keller, S. M. (2024) · Figshare · doi:10.6084/m9.figshare.25067253
eLife Assessment: Computational Engineering of a Therapeutic Antibody to Inhibit Multiple Mutants of HER2 Without Compromising Inhibition of the Canonical HER2
Golemis, E. A. (2024) · eLife · doi:10.7554/elife.89814.2.sa3
Reviewer #1 (Public Review): Computational Engineering of a Therapeutic Antibody to Inhibit Multiple Mutants of HER2 Without Compromising Inhibition of the Canonical HER2
(2024) · eLife · doi:10.7554/elife.89814.2.sa2
Reviewer #2 (Public Review): Computational Engineering of a Therapeutic Antibody to Inhibit Multiple Mutants of HER2 Without Compromising Inhibition of the Canonical HER2
(2024) · eLife · doi:10.7554/elife.89814.2.sa1
In Silico Target Identification Highlights IL12B as a Candidate for Small Molecule Drug DevelopmentPreprint
Tanner, T. J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.03.609261
AACDB: Antigen-Antibody Complex Database — a Comprehensive Database Unlocking Insights into Interaction InterfacePreprint
Zhou, Y., Liu, W., Huang, Z., Gou, Y., Liu, S., Jiang, L., Yang, Y., & Huang, J. H. (2025) · eLife · doi:10.7554/elife.104934.1
Moremi Bio Agent: Application of A Foundation Model and End-to-End Automation in the Design and Validation of Monoclonal Antibodies Targeting Plasmodium falciparum Invasion ComplexPreprint
Akogo, D., Ayensu, J., Sam, N., Hattoh, G., Nyarko, P., Eshun, S., Alhasan, M. A. H., Mensah-Brown, H. E., & Quashie, P. K. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.12.637967 · Cited by 1
Unveiling Interaction Signatures Across Viral Pathogens through VASCO: Viral Antigen-Antibody Structural COmplex datasetPreprint
Wong, K., Subramanian, I., Stevens, E., & Chakraborty, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.11.642737
“Many contributions have already been made by datasets such as SAbDAb[16], Thera-63 SAbDAb[17] and IEDB-3D[18].”
AACDB: Antigen-Antibody Complex Database — a Comprehensive Database Unlocking Insights into Interaction InterfacePreprint
Zhou, Y., Liu, W., Huang, Z., Gou, Y., Liu, S., Jiang, L., Yang, Y., & Huang, J. H. (2025) · eLife · doi:10.7554/elife.104934.2
Behavioural Profiling of Therapeutic Antibodies via Non-equilibrium Interfacial Wave DynamicsPreprint
Thomas, A. A., John, A. N. S., Crames, M., Caramazza, P., Meghdadi, A., Bailey, J., Hermans, R. I., Lee, S., Lam, E., Krishnan, V., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.09.653084
“We tested this prediction on a larger independent cohort: 235 clinical-stage antibodies (Dataset 2) with known approval status and phase-progression history, sourced from Thera-SAbDab [20].”
Influential
Rational design of oxidation-resistant antibodies through local electrostatic modulationPreprint
Lenka, S., Mehta, S. C., Stephens, N. M., Seeger, F., Liang, W., Watkins, A. M., Zarzar, J., Jafari, N., Puno, M. R., Azumaya, C. M., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.29.662139
PInteract: Detecting Aromatic-Involving Motifs in Proteins and Protein-Nucleic Acid Complexes
Li, D., Pucci, F., & Rooman, M. J. (2025) · Biomolecules · doi:10.3390/biom15081204
Identification of Engineered IMGT Fc Variants in IMGT/mAb-DB Therapeutic Antibodies and Fusion proteinsPreprint
Manso, T., Sanou, G., Nousias, C., Maalem, I., Boutin, F., Giudicelli, V., Duroux, P., Lefranc, M., & Κοσσίδα, Σ. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.03.673706
“Various databases, such as Thera-SAbDab (69) and Inxight Drugs (70), provide freely accessible data.”
HyperBind2: Multi-Shot Learning Enables Progressive Improvement in Computational Antibody DiscoveryPreprint
Dell'uomo, D., Satz, A., & Averso, B. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.06.687005
Explore antibody repertoire in the era of AI
Zhang, Y., Wang, H., Liu, C., & Meng, F. (2025) · Acta Biochimica et Biophysica Sinica · doi:10.3724/abbs.2025230
“ABlooper and DeepAb, both of which were trained on the Structural Antibody Database (SAbDab) [162], focus specifically on improving the accuracy of CDR loops, especially CDR3s [164,176,177].”
Additional file 2 of Anchor Clustering for million-scale immune repertoire sequencing data
Chang, H., Ashlock, D., Graether, S. P., & Keller, S. M. (2024) · Figshare · doi:10.6084/m9.figshare.25067312
Additional file 3 of Anchor Clustering for million-scale immune repertoire sequencing data
Chang, H., Ashlock, D., Graether, S. P., & Keller, S. M. (2024) · Figshare · doi:10.6084/m9.figshare.26672895
Therapeutic monoclonal antibodies repurposing in oncology via IMGT/mAb-KG embeddings
Sanou, G., Manso, T., Todorov, K., Giudicelli, V., Duroux, P., & Κοσσίδα, Σ. (2026) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-026-03394-4
Computer-aided antibody discovery for emerging contaminants: bridging molecular immunotechnology and food-safety sustainability
Zhao, J., EL-Aty, A. M. A., Li, P., She, Y., Jin, F., Wang, J., Tan, M., Zheng, L., & Jin, M. (2026) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2026.105673
KyDab – a comprehensive database of antibody discovery selection campaignsPreprint
Zhou, Q., Chomicz, D., Melvin, D. G., Griffiths, M., Yahiya, S., Reece, S. T., Pannérer, M.-M. L., & Krawczyk, K. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.25.713450
“Existing specialised therapeutic antibody databases, such as Thera-SAbDab 7 and IMGT/mAb-KG 8 , focus primarily on the relatively small set of mature, clinically advanced antibodies.”
Explicit representation of germline and non-germline residues improves antibody language modelingPreprint
Kim, J., Blalock, N., Kulkarni, A., Nakamura, K., & Romero, P. A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.06.723387 · Cited by 1
Benchmarking Boltz-2 for Screening of Therapeutic Antibody-Antigen InteractionsPreprint
Fieux-Castagnet, A., Waton, J., Glukhonemykh, A., Snow, E., Ashokkumar, R., Fleming, J., Champagne, D., Devenyns, T., Peluffo, A., & Anagnostopoulos, C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.13.724924
Biparatopic targeting of IL-23 enables dual-epitope engagement and enhanced neutralization potency
Gauntlett, R., Lodge, J., Watson, J., Yates, J. W. T., Cordy, J. M., Sinfield, H., Caruana, R., & Rattray, Z. (2026) · mAbs · doi:10.1080/19420862.2026.2689777
AI-Driven Design Platforms of Next-Generation Antibody Therapeutics
Wang, Y., Saba, A., Ran, Y., Shehzadi, K., Zhang, Q., Jianhua, L., & Yu, M. (2026) · Topics in Current Chemistry · doi:10.1007/s41061-026-00554-y
The immunogenicity database collaborative: a standardized, publicly available database for clinical immunogenicity observations and insights
Agnihotri, S., Gonzalez-Nolasco, B., Monian, B., Pattijn, S., Ackaert, C., Wu, P., Kettenberger, H., Tourdot, S., Hickling, T. P., Hu, Z., et al. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1816949
Beyond affinity: AI-supported developability assessment and multi-objective optimization in antibody development
Jiang, Q., Guan, J., Yu, D., Singh, P., Pelekos, G., Lo, E. C. M., & Wang, J. (2026) · Antibody Therapeutics · doi:10.1093/abt/tbag039
“[ 40] CoV-AbDab Specialized Focused repository for anti-coronavirus antibodies.”
Structure-aware artificial intelligence for next-generation drug discovery: from protein–ligand modeling to generative biomolecular design
Yoon, H., Han, R., Kim, J.-W., & Lee, Y. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag421
“PDBbind v2021 [ 57], SAbDab [ 160] ProAﬃnity-GNN [ 161] 2024 • Predicts aﬃnity by modeling the topological structure of the protein – ligand interface as a graph.”
Therapeutic Monoclonal Antibodies as Advanced Therapies for Companion Animals: Species Adaptation, Fc Biology, Clinical Translation, and Future Platforms
Fu, Y., Guo, Z., & Wang, H. (2026) · Veterinary Sciences · doi:10.3390/vetsci13080778
“Canine and feline therapeutic mAbs with public sequence data in the Thera-SAbDab review snapshot [7].”
Directed Evolution in Codon SpacePreprint
Heuschkel, J., Kingsley, L. J., Reed, J. M., Li, D., Warner, M. J., Pefaur, N. B., & Cramer, S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.03.742557
“A high-confidence benchmark of pre‑optimized antibody coding DNA sequences was curated by starting from Thera‑SAbDab 51 , which tracks WHO‑recognized antibody-and nanobody‑derived therapeutics and provides associated sequence and metadata.”
TRACE: Trustworthy Reasoning for Agentic Chemical Evidence in Antibody-Drug Conjugate Design
Amjad, M., Khan, M. U. S., & Khan, S. U. (2026) · IEEE Access · doi:10.1109/access.2026.3720203
“All six external data sources, including Thera-SAbDab (Antibody Heavy and Light Chain Sequences) [32], 1 RCSB PDB (structural data) [33], 2 ChEMBL (IC50 binding affinity) [34], 3 BindingDB (IC50 validation) [35], 4 PubChem (payload SMILES) [36], 5 RDKit (Cheminformatics Validation) [37] 6 are…”
Local electrostatics governs tryptophan oxidation and enables rational stability engineering in antibodies
Lenka, S., Mehta, S. C., Stephens, N. M., Seeger, F., Liang, W., Watkins, A. M., Zarzar, J., Jafari, N., Puno, M. R., Azumaya, C. M., et al. (2026) · mAbs · doi:10.1080/19420862.2026.2713231
The Evolution of Artificial Intelligence in Antibody Design: From Structure-Based Engineering to Generative Models
Szataniak, I., & Packi, K. (2026) · Antibodies · doi:10.3390/antib15050081
“…with experimentally determined structures, CoV-AbDab integrates structural and functional data for coronavirus-binding antibodies, and AB-Bind provides experimentally validated mutation-induced changes in antibody–antigen binding affinity for benchmarking computational prediction meth-ods [3,7,46].”
Influential
Monoclonal antibody developability from early assay panels: machine learning for formulation and pharmacokinetic risk
Virk, S. S., & Virk, A. S. (2026) · Machine Learning Health · doi:10.1088/3049-477x/aeafbf
kitAb: an automated framework for assay-specific antibody developability modeling and large-scale landscape analysisPreprint
Трофимова, Е., Shaburova, E. V., Антонец, Д., & Ramensky, V. E. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.09.750540
“Therapeutic antibody sequences were downloaded from Thera-SAbDab [13] on 21 June 2026.”
Artificial Intelligence-Driven Nanobody Engineering for Pathogen Recognition: From Functional Epitope Prioritization to Multimodal Foundation Models
Hu, R.-S., Ehsan, M., Luo, W., Zhou, J., Xiao, Y., Wang, H.-X., Li, C.-Y., & Gu, K. (2026) · Biomolecules · doi:10.3390/biom16101479
“Thera-SAbDab [38] links therapeutic Abs and sdAb-derived therapeutics with available sequence and structural information.”
Insulin-binding B cells can arise in early-stage type 1 diabetes individuals in the absence of somatic hypermutation or insulin autoantibody seropositivityPreprint
Bass, L. E., Taylor, T. C., Forchetti, M. V., Scaglione, S., Padgett, M. L., Skellett, A. M., Byers, C. W., Mahajan, A., Brannon, C. T., Wasdin, P. T., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.20.760517

Authors: Raybould, M. I. J., Marks, C., Lewis, A. P., Shi, J., Bujotzek, A., Taddese, B., & Deane, C. M.
Affiliations: University of Oxford (GB)
GlaxoSmithKline (United Kingdom) (GB)
UCB Pharma (United Kingdom) (GB)
Roche Pharma AG (Germany) (DE)
AstraZeneca (United Kingdom) (GB)
Corresponding author: Deane, Charlotte M
ORCID: Raybould, Matthew I J
Marks, Claire
Shi, Jiye
Bujotzek, Alexander
Taddese, Bruck
Deane, Charlotte M
Venue: Nucleic Acids Research
Publisher: Oxford University Press (OUP)
Published: 2019-09-26
Volume: 48
Issue: D1
Pages: D383-D388
ISSN: 0305-1048 (ISSN-L)
1362-4962
DOI: 10.1093/nar/gkz827
PMC: PMC6943036
OpenAlex: W2974982237
Cited by: 199
References: 19
Citations by year: 2026: 29 · 2025: 55 · 2024: 41 · 2023: 27 · 2022: 23 · 2021: 19 · 2020: 4 · 2019: 1
FWCI: 7.77
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Nucleic Acids Research (publishedVersion, cc-by)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Funders: Engineering and Physical Sciences Research Council
Medical Research Council (EP/L016044/1)
GlaxoSmithKline plc.
AstraZeneca plc
F. Hoffmann-La Roche
UCB Celltech
Topics: Life Sciences › Immunology and Microbiology › Immunology › Monoclonal and Polyclonal Antibodies Research
Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
Keywords: therapeutic antibodies, nanobodies, antibody variable domains
MeSH terms: Metadata, Antibodies, Clinical Trials as Topic, Humans, User-Computer Interface, Sequence Alignment, Internet, Databases, Protein

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Thera-SAbDab: the Therapeutic Structural Antibody Database

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks