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The Immune Epitope Database (IEDB): 2018 update
Open accessAbstract
The Immune Epitope Database (IEDB, iedb.org) captures experimental data confined in figures, text and tables of the scientific literature, making it freely available and easily searchable to the public. The scope of the IEDB extends across immune epitope data related to all species studied and includes antibody, T cell, and MHC binding contexts associated with infectious, allergic, autoimmune, and transplant related diseases. Having been publicly accessible for >10 years, the recent focus of the IEDB has been improved query and reporting functionality to meet the needs of our users to access and summarize data that continues to grow in quantity and complexity. Here we present an update on our current efforts and future goals.
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The FAIR Guiding Principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423
PubChem Substance and Compound databases
Kim, S., Thiessen, P. A., Bolton, E. E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B. A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv951 · Cited by 5,436
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Grifoni, A., Weiskopf, D., Ramirez, S. I., Mateus, J., Dan, J. M., Moderbacher, C. R., Rawlings, S. A., Sutherland, A., Premkumar, L., Jadi, R. S., et al. (2020) · Cell · doi:10.1016/j.cell.2020.05.015 · Cited by 3,595
Self-citationBjornevik, K., Cortese, M., Healy, B. C., Kuhle, J., Mina, M. J., Leng, Y., Elledge, S. J., Niebuhr, D. W., Scher, A. I., Munger, K. L., et al. (2022) · Science · doi:10.1126/science.abj8222 · Cited by 2,049
Tarke, A., Coelho, C. H., Zhang, Z., Dan, J. M., Yu, E. D., Methot, N., Bloom, N. I., Goodwin, B., Phillips, E., Mallal, S., et al. (2022) · Cell · doi:10.1016/j.cell.2022.01.015 · Cited by 858
Self-citationTarke, A., Sidney, J., Methot, N., Yu, E. D., Zhang, Y., Dan, J. M., Goodwin, B., Rubiro, P., Sutherland, A., Wang, E., et al. (2021) · Cell Reports Medicine · doi:10.1016/j.xcrm.2021.100355 · Cited by 706
Self-citationTarke, A., Sidney, J., Kidd, C. K., Dan, J. M., Ramirez, S. I., Yu, E. D., Mateus, J., da Silva Antunes, R., Moore, E., Rubiro, P., et al. (2021) · Cell Reports Medicine · doi:10.1016/j.xcrm.2021.100204 · Cited by 540
Self-citationWells, D. K., van Buuren, M. M., Dang, K. K., Hubbard-Lucey, V. M., Sheehan, K. C. F., Campbell, K. M., Lamb, A., Ward, J. P., Sidney, J., Blazquez, A. B., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.015 · Cited by 538
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Mina, M. J., Kula, T., Leng, Y., Li, M., de Vries, R. D., Knip, M., Siljander, H., Rewers, M., Choy, D. F., Wilson, M. S., et al. (2019) · Science · doi:10.1126/science.aay6485 · Cited by 466
O’Donnell, T. J., Rubinsteyn, A., & Laserson, U. (2020) · Cell Systems · doi:10.1016/j.cels.2020.06.010 · Cited by 440
IEDB-AR: immune epitope database-analysis resource in 2019
Dhanda, S. K., Mahajan, S., Paul, S., Yan, Z., Kim, H., Jespersen, M. C., Jurtz, V., Andreatta, M., Greenbaum, J. A., Marcatili, P., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz452 · Cited by 427
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Grifoni, A., Sidney, J., Vita, R., Peters, B., Crotty, S., Weiskopf, D., & Sette, A. (2021) · Cell Host & Microbe · doi:10.1016/j.chom.2021.05.010 · Cited by 314
Self-citationBepiPred ‐3.0: Improved B‐cell epitope prediction using protein language models
Clifford, J. N., Høie, M. H., Deleuran, S., Peters, B., Nielsen, M., & Marcatili, P. (2022) · Protein Science · doi:10.1002/pro.4497 · Cited by 296
Self-citationThe Immune Epitope Database (IEDB): 2024 update
Vita, R., Blazeska, N., Marrama, D., IEDB Curation Team Members, Shackelford, D., Zalman, L., Foos, G., Zarebski, L., Chan, K., Reardon, B., et al. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkae1092 · Cited by 274
Self-citationWoldemeskel, B. A., Garliss, C. C., & Blankson, J. N. (2021) · Journal of Clinical Investigation · doi:10.1172/jci149335 · Cited by 175
Bispecific antibodies targeting mutant RAS neoantigens
Douglass, J., Hsiue, E. H.-C., Mog, B. J., Hwang, M. S., DiNapoli, S. R., Pearlman, A. H., Miller, M. S., Wright, K. M., Azurmendi, P. A., Wang, Q., et al. (2021) · Science Immunology · doi:10.1126/sciimmunol.abd5515 · Cited by 171
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Gao, Y., Gao, Y., Fan, Y., Zhu, C., Wei, Z., Zhou, C., Chuai, G., Chen, Q., Zhang, H., & Liu, Q. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00619-3 · Cited by 155
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Høie, M. H., Gade, F. S., Johansen, J. M., Würtzen, C., Winther, O., Nielsen, M., & Marcatili, P. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1322712 · Cited by 151
(2023) · doi:10.1038/s42256-023-00694-6 · Cited by 108
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Chen, S.-Y., Yue, T., Lei, Q., & Guo, A.-Y. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa796 · Cited by 106
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Hamley, I. W. (2022) · ACS Applied Bio Materials · doi:10.1021/acsabm.1c01238 · Cited by 104
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Chang, L., & Perez, A. (2023) · Angewandte Chemie International Edition · doi:10.1002/anie.202213362 · Cited by 100
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Nunez-Castilla, J., Stebliankin, V., Baral, P., Balbin, C. A., Sobhan, M., Cickovski, T., Mondal, A. M., Narasimhan, G., Chapagain, P., Mathee, K., et al. (2022) · Viruses · doi:10.3390/v14071415 · Cited by 100
Healthy donor T cell responses to common cold coronaviruses and SARS-CoV-2
Woldemeskel, B. A., Kwaa, A. K., Garliss, C. C., Laeyendecker, O., Ray, S. C., & Blankson, J. N. (2020) · Journal of Clinical Investigation · doi:10.1172/jci143120 · Cited by 78
Accurate modeling of peptide-MHC structures with AlphaFold
Mikhaylov, V., Brambley, C. A., Keller, G. L. J., Arbuiso, A. G., Weiss, L. I., Baker, B. M., & Levine, A. J. (2024) · Structure · doi:10.1016/j.str.2023.11.011 · Cited by 72
Borràs, D. M., Verbandt, S., Ausserhofer, M., Sturm, G., Lim, J., Verge, G. A., Vanmeerbeek, I., Laureano, R. S., Govaerts, J., Sprooten, J., et al. (2023) · Cell Discovery · doi:10.1038/s41421-023-00605-4 · Cited by 67
“These efforts culminated into a categorization of predicted antigen-speci fi cities into broad ‘ TCR families ’ based on existing IEDB annotations 71 or annotations from biomedical literature 72 .”
Zinsli, L. V., Stierlin, N., Loessner, M. J., & Schmelcher, M. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.12.024 · Cited by 63
Recent Advances in Studying Toll-like Receptors with the Use of Computational Methods
Bzówka, M., Bagrowska, W., & Góra, A. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00419 · Cited by 34
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Gupta, S., Nerli, S., Kutti Kandy, S., Mersky, G. L., & Sgourakis, N. G. (2023) · Nature Communications · doi:10.1038/s41467-023-42163-z · Cited by 26
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Hashemi, N., Hao, B., Ignatov, M., Paschalidis, I. C., Vakili, P., Vajda, S., & Kozakov, D. (2023) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1207380 · Cited by 25
Jiang, D., Xi, B., Tan, W., Chen, Z., Wei, J., Hu, M., Lu, X., Chen, D., Cai, H., & Du, H. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae547 · Cited by 23
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Gabrielsen, I. S. M., Helgeland, H., Akselsen, H., D. Aass, H. C., Sundaram, A. Y. M., Snowhite, I. V., Pugliese, A., Flåm, S. T., & Lie, B. A. (2019) · PLOS ONE · doi:10.1371/journal.pone.0218858 · Cited by 21
Gibadullin, R., Randall, C. J., Sidney, J., Sette, A., & Gellman, S. H. (2021) · Journal of the American Chemical Society · doi:10.1021/jacs.1c00016 · Cited by 19
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Epitopedia: identifying molecular mimicry between pathogens and known immune epitopes
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Liebhoff, A.-M., Venkataraman, T., Morgenlander, W. R., Na, M., Kula, T., Waugh, K., Morrison, C., Rewers, M., Longman, R., Round, J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45601-8 · Cited by 10
TransHLA: a Hybrid Transformer model for HLA-presented epitope detection
Lu, T., Wang, X., Nie, W., Huo, M., & Li, S. (2025) · GigaScience · doi:10.1093/gigascience/giaf008 · Cited by 10
Mahoney, K. E., Reser, L., Ruiz Cuevas, M. V., Abelin, J. G., Shabanowitz, J., Hunt, D. F., & Malaker, S. A. (2025) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2025.100971 · Cited by 9
Assessing the generalization capabilities of TCR binding predictors via peptide distance analysis
Castorina, L. V., Grazioli, F., Machart, P., Mösch, A., & Errica, F. (2025) · PLOS One · doi:10.1371/journal.pone.0324011 · Cited by 7
Transformers meets neoantigen detection: a systematic literature review
Machaca, V., Goyzueta, V., Cruz, M. G., Sejje, E., Pilco, L. M., López, J., & Túpac, Y. (2024) · Journal of Integrative Bioinformatics · doi:10.1515/jib-2023-0043 · 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
An Exploratory Bioinformatic Investigation of Cats’ Susceptibility to Coronavirus-Deriving Epitopes
(2024) · doi:10.3390/life14030334 · Cited by 2
Gupta, S., Nerli, S., Kandy, S. K., Mersky, G. L., & Sgourakis, N. G. (2023) · doi:10.1101/2023.03.20.533510 · Cited by 2
De novo design of anti-variant COVID-19 vaccine
Goswami, A., Kumar, M., Ullah, S., & Gore, M. M. (2023) · Biology Methods and Protocols · doi:10.1093/biomethods/bpad021 · Cited by 2
Waury, K., Lelieveld, S., Abeln, S., & van den Ham, H.-J. (2025) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1013057 · Cited by 1
Metadata
- Authors
- Vita, R., Mahajan, S., Overton, J. A., Dhanda, S. K., Martini, S., Cantrell, J. R., Wheeler, D. K., Sette, A., & Peters, B.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-10-24
- Volume
- 47
- Issue
- D1
- Pages
- D339-D343
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2898389621
- Cited by
- 2,186
- References
- 2
- FWCI
- 46.54
- Type
- journal article
- Language
- en
- Open access
- gold
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