Observed Antibody Space: A Resource for Data Mining Next-Generation Sequencing of Antibody Repertoires

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Capillary pericytes regulate cerebral blood flow in health and disease

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IgBLAST: an immunoglobulin variable domain sequence analysis tool

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Co-evolution of a broadly neutralizing HIV-1 antibody and founder virus

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IMGT unique numbering for immunoglobulin and T cell receptor variable domains and Ig superfamily V-like domains

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Database Resources of the National Center for Biotechnology Information

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IMGT unique numbering for immunoglobulin and T cell receptor constant domains and Ig superfamily C-like domains

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ImmPort: disseminating data to the public for the future of immunology

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MetaLogo: a heterogeneity-aware sequence logo generator and aligner

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Artificial intelligence for antibody reading comprehension: AntiBERTa

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Antibody–antigen complex modelling in the era of immunoglobulin repertoire sequencing

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Antibody heavy chain CDR3 length-dependent usage of human IGHJ4 and IGHJ6 germline genes

Wang, H., Yan, K., Wang, R., Yang, Y., Shen, Y., Yu, C., & Chen, L. (2021) · Antibody Therapeutics · doi:10.1093/abt/tbab010 · Cited by 16

Conserved heavy/light contacts and germline preferences revealed by a large-scale analysis of natively paired human antibody sequences and structural data

Dudzic, P., Chomicz, D., Bielska, W., Jaszczyszyn, I., Zieliński, M., Janusz, B., Wróbel, S., Pannérer, M.-M. L., Phillips, A., Prabakaran, P., et al. (2025) · Communications Biology · doi:10.1038/s42003-025-08388-y · Cited by 15

Comprehensive B-Cell Immune Repertoire Analysis of Anti-NMDAR Encephalitis and Anti-LGI1 Encephalitis

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Advances in antibody discovery from human BCR repertoires

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A comprehensive overview of recent advances in generative models for antibodies

Meng, F., Zhou, N., Guangchun, H., Liu, R., Zhang, Y., Jing, M., & Hou, Q. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.06.016 · Cited by 17

Applying computational protein design to therapeutic antibody discovery - current state and perspectives

Bielska, W., Jaszczyszyn, I., Dudzic, P., Janusz, B., Chomicz, D., Wrobel, S., Greiff, V., Feehan, R., Adolf‐Bryfogle, J., & Krawczyk, K. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1571371 · Cited by 17

Computationally profiling peptide:MHC recognition by T-cell receptors and T-cell receptor-mimetic antibodies

Raybould, M. I. J., Nissley, D. A., Kumar, S., & Deane, C. M. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1080596 · Cited by 14

Carcino-Evo-Devo, A Theory of the Evolutionary Role of Hereditary Tumors

Kozlov, A. P. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24108611 · Cited by 14

Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances

Gallo, E. (2024) · Molecular Biotechnology · doi:10.1007/s12033-024-01064-2 · Cited by 17

PLAbDab-nano: a database of camelid and shark nanobodies from patents and literature

Gordon, G. L., Greenshields‐Watson, A., Agarwal, P., Wong, A., Boyles, F., Hummer, A. M., Hernandez, A. G. L., & Deane, C. M. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae881 · Cited by 13

ParaAntiProt provides paratope prediction using antibody and protein language models

Kalemati, M., Noroozi, A., Shahbakhsh, A., & Koohi, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-80940-y · Cited by 15

Computational design of therapeutic antibodies with improved developability: efficient traversal of binder landscapes and rescue of escape mutations

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Human-likeness of antibody biologics determined by back-translation and comparison with large antibody variable gene repertoires

Schmitz, S., Soto, C. S., Crowe, J. E., & Meiler, J. (2020) · mAbs · doi:10.1080/19420862.2020.1758291 · Cited by 12

Comparative analysis of CDR3 length-dependent patterns in VHHs

Zhang, L., Yang, T., Zhang, Y., Yan, J., Li, J., Xu, W., Zhu, W., & Wang, X. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1647230 · Cited by 12

Improving antibody optimization ability of generative adversarial network through large language model

Zhao, W., Luo, X., Tong, F., Zheng, X., Li, J., Zhao, G., & Zhao, D. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.11.041 · Cited by 13

Humatch - fast, gene-specific joint humanisation of antibody heavy and light chains

Chinery, L., Jeliazkov, J. R., & Deane, C. M. (2024) · mAbs · doi:10.1080/19420862.2024.2434121 · Cited by 13

Filtering Next-Generation Sequencing of the Ig Gene Repertoire Data Using Antibody Structural Information

Kovaltsuk, A., Krawczyk, K., Kelm, S., Snowden, J., & Deane, C. M. (2018) · The Journal of Immunology · doi:10.4049/jimmunol.1800669 · Cited by 12

Optimizing Antibody Affinity and Developability Using a Framework–CDR Shuffling Approach—Application to an Anti-SARS-CoV-2 Antibody

Gopal, R., Fitzpatrick, E., Pentakota, N., Jayaraman, A., Tharakaraman, K., & Capila, I. (2022) · Viruses · doi:10.3390/v14122694 · Cited by 12

KA-Search, a method for rapid and exhaustive sequence identity search of known antibodies

Olsen, T. H., Abanades, B., Moal, I. H., & Deane, C. M. (2023) · Scientific Reports · doi:10.1038/s41598-023-38108-7 · Cited by 12

NbBench: benchmarking language models for comprehensive nanobody tasks

Zhang, Y., & Tsuda, K. (2025) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ae20ec · Cited by 16

De Novo Antibody Design with SE(3) Diffusion

Cutting, D., Dreyer, F. A., Errington, D., Schneider, C., & Deane, C. M. (2024) · Journal of Computational Biology · doi:10.1089/cmb.2024.0768 · Cited by 22

Deep learning-based design and experimental validation of a medicine-like human antibody library

Rajagopal, N., Choudhary, U., Tsang, K., Martin, K., Karadag, M., Chen, H.-T., Kwon, N.-Y., Mozdzierz, J., Horspool, A. M., Li, L., et al. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf023 · Cited by 12

Data-driven analyses of human antibody variable domain germlines: pairings, sequences and structural features

Seidler, C. A., Spanke, V. A., Gamper, J., Bujotzek, A., Georges, G. J., & Liedl, K. R. (2025) · mAbs · doi:10.1080/19420862.2025.2507950 · Cited by 11

LAP: Liability Antibody Profiler by sequence & structural mapping of natural and therapeutic antibodies

Satława, T., Tarkowski, M., Wróbel, S., Dudzic, P., Gawłowski, T., Klaus, T., Orłowski, M., Kostyn, A., Kumar, S., Buchanan, A. G., et al. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011881 · Cited by 11

The rise of big data: deep sequencing-driven computational methods are transforming the landscape of synthetic antibody design

Gallo, E. (2024) · Journal of Biomedical Science · doi:10.1186/s12929-024-01018-5 · Cited by 13

Large-scale data mining of four billion human antibody variable regions reveals convergence between therapeutic and natural antibodies that constrains search space for biologics drug discovery

Dudzic, P., Chomicz, D., Kończak, J., Satława, T., Janusz, B., Wrobel, S., Gawłowski, T., Jaszczyszyn, I., Bielska, W., Demharter, S., et al. (2024) · mAbs · doi:10.1080/19420862.2024.2361928 · Cited by 10

Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models

Asim, M. N., Asif, T., Hassan, F., & Dengel, A. (2025) · Database · doi:10.1093/database/baaf027 · Cited by 11

Nucleotide context models outperform protein language models for predicting antibody affinity maturation

Johnson, M. M., Sung, K., Haddox, H. K., Vora, A. A., Araki, T., Victora, G. D., Song, Y. S., Fukuyama, J., & Matsen, F. A., IV (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013758 · Cited by 11

Benchmarking antibody clustering methods using sequence, structural, and machine learning similarity measures for antibody discovery applications

Chomicz, D., Kończak, J., Wróbel, S., Satława, T., Dudzic, P., Janusz, B., Tarkowski, M., Deszyński, P., Gawłowski, T., Kostyn, A., et al. (2024) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2024.1352508 · Cited by 9

Data Sharing and Reuse: A Method by the AIRR Community

Corrie, B., Christley, S., Busse, C. E., Cowell, L. G., Neller, K. C. M., Rubelt, F., Schwab, N., & Community, O. B. O. T. A. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2115-8_23 · Cited by 9

Anti–immune complex antibodies are elicited during repeated immunization with HIV Env immunogens

Brown, S., Antanasijevic, A., Sewall, L. M., Garcia, D. M., Ferguson, J., Brouwer, P. J. M., Sanders, R. W., & Ward, A. B. (2025) · Science Immunology · doi:10.1126/sciimmunol.adp5218 · Cited by 8

ANARCII: A Generalised Language Model for Antigen Receptor NumberingPreprint

Greenshields‐Watson, A., Agarwal, P., Robinson, S. A., Williams, B. H., Gordon, G. L., Capel, H., Li, Y., Spoendlin, F. C., Aguilar-Sanjuan, B., Boyles, F., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.16.648720 · Cited by 9

T-SCAPE: T cell immunogenicity scoring via cross-domain aided predictive engine

Kim, J., Jung, N., Lee, J. Y., Cho, N., Noh, J., & Seok, C. (2025) · Science Advances · doi:10.1126/sciadv.adz8759 · Cited by 8

Biological Computation and Compatibility Search in the Possibility Space as the Mechanism of Complexity Increase During Progressive Evolution

Kozlov, A. P. (2022) · Evolutionary Bioinformatics · doi:10.1177/11769343221110654 · Cited by 8

Network Organization of Antibody Interactions in Sequence and Structure Space: the RADARS Model

Prechl, J. (2020) · Antibodies · doi:10.3390/antib9020013 · Cited by 8

Accelerating Antibody Design with Active LearningPreprint

Seo, S.-W., Kwak, M. W., Kang, E., Kim, C., Park, E. Y., Kang, T. H., & Kim, J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.12.507690 · Cited by 8

IL-17A complexes with therapeutic antibodies exhibit distinct size distributions, potentially contributing to clinically observed immunogenicity

Ungan, D., Be, C., Bączyk, P., Mittermeier, S., Lehmann, S., Wiesmann, C., Huber, T., Kolbinger, F., & Rondeau, J. (2025) · mAbs · doi:10.1080/19420862.2025.2575840 · Cited by 7

The human antibody sequence space and structural design of the V, J regions, and CDRH3 with Rosetta

Schmitz, S., Schmitz, E. A., Crowe, J. E., & Meiler, J. (2022) · mAbs · doi:10.1080/19420862.2022.2068212 · Cited by 8

RAPID: A Rep-Seq Dataset Analysis Platform With an Integrated Antibody Database

Zhang, Y., Chen, T., Zeng, H., Yang, X., Xu, Q., Zhang, Y., Chen, Y., Wang, M., Zhu, Y., Lan, C., et al. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.717496 · Cited by 7

Deep Learning in Therapeutic Antibody Development

Shaver, J. M., Smith, J., & Amimeur, T. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1787-8_19 · Cited by 7

AIRRscape: An interactive tool for exploring B-cell receptor repertoires and antibody responses

Waltari, E., Nafees, S., Wong, J., McCutcheon, K. M., & Pak, J. E. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010052 · Cited by 7

Investigating the ability of deep learning-based structure prediction to extrapolate and/or enrich the set of antibody CDR canonical forms

Greenshields-Watson, A., Abanades, B., & Deane, C. M. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1352703 · Cited by 7

An explainable language model for antibody specificity prediction using curated influenza hemagglutinin antibodiesPreprint

Wang, Y., Lv, H., Lei, R., Yeung, Y.-H., Shen, I. R., Choi, D., Teo, Q. W., Tan, T. J. C., Gopal, A. B., Chen, X., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.11.557288 · Cited by 7

Predicting Structural Consequences of Antibody Light Chain N-Glycosylation in AL Amyloidosis

Morgan, G. J., Yung, Z., Spencer, B., Sanchorawala, V., & Prokaeva, T. B. (2024) · Pharmaceuticals · doi:10.3390/ph17111542 · Cited by 6

AntiRef: reference clusters of human antibody sequences

Briney, B. S. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad109 · Cited by 6

Yvis: antibody high-density alignment visualization and analysis platform with an integrated database

Carvalho, M. B., Molina, F., & Felicori, L. F. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz387 · Cited by 6

The Therapeutic Nanobody Profiler: characterising and predicting nanobody developability to improve therapeutic designPreprint

Gordon, G. L., Gervásio, J. H. D. B., Souders, C., & Deane, C. M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.11.669635 · Cited by 6

Pre-training with a rational approach for antibody sequence representation

Gao, X., Cao, C., He, C., & Lai, L. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1468599 · Cited by 9

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

Structural pre-training improves physical accuracy of antibody structure prediction using deep learning.

Kończak, J., Janusz, B., Młokosiewicz, J., Satława, T., Wróbel, S., Dudzic, P., & Krawczyk, K. (2023) · ImmunoInformatics · doi:10.1016/j.immuno.2023.100028 · Cited by 5

Identification of B cell subsets based on antigen receptor sequences using deep learning

Lee, H., Shin, K., Lee, Y., Lee, S., Lee, S., Lee, E., Kim, S. W., Shin, H. Y., Kim, J. H., Chung, J., et al. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1342285 · Cited by 7

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

Echidna: integrated simulations of single-cell immune receptor repertoires and transcriptomes

Han, J., Masserey, S., Shlesinger, D., Kuhn, R., Papadopoulou, C., Agrafiotis, A., Kreiner, V., Dizerens, R., Hong, K., Weber, C. R., et al. (2022) · Bioinformatics Advances · doi:10.1093/bioadv/vbac062 · Cited by 5

Leveraging protein language and structural models for early prediction of antibodies with fast clearancePreprint

Mazrooei, P., O’Neil, D., Izadi, S., Chen, B., & Ramanujan, S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.08.597997 · Cited by 6

Thrifty wide-context models of B cell receptor somatic hypermutationPreprint

Sung, K., Johnson, M. M., Dumm, W., Simon, N., Haddox, H. K., Fukuyama, J., & Matsen, F. A., IV (2025) · eLife · doi:10.7554/elife.105471.1 · Cited by 5

Conformation-aware structure prediction of antigen-recognizing immune proteins

Dreyer, F. A., Ludwiczak, J., Martinkus, K., Abanades, B., Alberstein, R. G., Kessel, P., Rao, P., Lee, J. H., Bonneau, R., Watkins, A. M., et al. (2025) · mAbs · doi:10.1080/19420862.2025.2602217 · Cited by 8

Ab-SELDON: Leveraging Diversity Data for an Efficient Automated Computational Pipeline for Antibody Design

Sampaio, J. V., Santos-Costa, A. H., Albuquerque, A. O., Souza, J. S., Almeida, D. S., Gaieta, E. M., Almeida, M. V., Sartori, G., & da Silva, J. H. M. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01924 · Cited by 4

Application of protein language models for antibody developability prediction

Amini, S., Huang, Y., Julian, M. C., Palmer, C., Sciabola, S., & Wang, Y. C. (2026) · mAbs · doi:10.1080/19420862.2026.2647489 · Cited by 4

Ontogeny of the B Cell Receptor Repertoire and Microbiome in Mice

Gilboa, A., Hope, R., Ben-Simon, S., Polak, P., Koren, O., & Yaari, G. (2022) · The Journal of Immunology · doi:10.4049/jimmunol.2100955 · Cited by 4

Inference of annealed protein fitness landscapes with AnnealDCA

Sesta, L., Pagnani, A., Fernández-de-Cossio-Díaz, J., & Uguzzoni, G. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011812 · Cited by 3

AI in flow cytometry: Current applications and future directions

Yue, A., Brinkman, R. R., Nash, V., Junker, F., Bogdanoski, G., Divekar, A., Tyznik, A. J., Špidlen, J., Kern, W., Pétriz, J., et al. (2025) · Cytometry Part B Clinical Cytometry · doi:10.1002/cyto.b.22255 · Cited by 7

Therapeutic antibody discovery

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Structure and sequence engineering approaches to improve in vivo expression of nucleic acid-delivered antibodies

Helble, M., Chu, J. D., Flowers, K., Trachtman, A. R., Huynh, A., Kim, A., Shupin, N., Hojecki, C. E., Gary, E. N., Solieva, S. O., et al. (2024) · Molecular Therapy · doi:10.1016/j.ymthe.2024.11.030 · Cited by 4

Building Representation Learning Models for Antibody ComprehensionPreprint

Barton, J., Gaspariunas, A., Galson, J. D., & Leem, J. (2023) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a041462 · Cited by 4

Thrifty wide-context models of B cell receptor somatic hypermutationPreprint

Sung, K., Johnson, M. M., Dumm, W., Simon, N., Haddox, H. K., Fukuyama, J., & Matsen, F. A., IV (2025) · eLife · doi:10.7554/elife.105471 · Cited by 4

Navigating the landscape: A comprehensive overview of computational approaches in therapeutic antibody design and analysis

Yadav, A. J., Bhagat, K., Sharma, A., & Padhi, A. K. (2025) · Advances in protein chemistry and structural biology · doi:10.1016/bs.apcsb.2024.10.011 · Cited by 3

An adaptive autoregressive diffusion approach to design active humanized antibodies and nanobodies

Ma, J., Wu, F., Xu, T., Xu, S., Liu, W., Yan, L., Qu, M., Yang, X., Bai, Q., Xiao, J., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01120-9 · Cited by 8

HeavyBuilder: Analysis of High-Throughput of Antibody Heavy Chain Repertoires in the Structural Space

Gervásio, J. H. D. B., Greenshields‐Watson, A., Quast, N. P., Abanades, B., Felicori, L. F., & Deane, C. M. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.169509 · Cited by 3

A curriculum learning approach to training antibody language models

Burbach, S. M., & Briney, B. S. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013473 · Cited by 3

Molecular divergence and convergence of mammalian antibody responses to the influenza virus hemagglutinin stem

Lv, H., Teo, Q. W., Pholcharee, T., Liu, W., Roy, S., Shi, L., Gopal, A. B., Ardagh, M. R., Chen, C., Sun, Y., et al. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2510927122 · Cited by 3

AbGraftBERT: Enhancing Antibody Design Through Self-Grafting and Amino Acid-Based Perturbation of Pre- Trained Language Models

Wang, Z., Li, X.-G., Xie, Y., Wen, Z., Jin, R., Takada, H., & Nagatomi, R. (2024) · IEEE Access · doi:10.1109/access.2024.3416461 · Cited by 3

How well do contextual protein encodings learn structure, function, and evolutionary context?

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ClonoMatch: a tool for identifying homologous immunoglobulin and T-cell receptor sequences in large databases

Jones, T., Day, S. B., Myers, L., Crowe, J. E., & Soto, C. S. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa1028 · Cited by 3

An adaptive autoregressive diffusion approach to design active humanized antibody and nanobodyPreprint

Ma, J., Wu, F., Xu, T., Xu, S., Liu, W., Yan, D., Bai, Q., & Yao, J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.22.619416 · 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

DualGPT-AB: a dual-stage generative optimization framework for therapeutic antibody design

Xie, D., Chen, S., Zeng, X., Lu, D., Jiao, S., Xiao, S., Liu, J., Hao, J., Dai, H., & Peng, J. (2026) · Nature Computational Science · doi:10.1038/s43588-026-00976-0 · Cited by 2

Review: application and opportunities for machine learning and artificial intelligence in preclinical immunogenicity risk assessment

Hickling, T. P., Nielsen, M., Meysman, P., Rose, R. H., & Obrezanova, O. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1720928 · Cited by 2

Structural Modeling of Adaptive Immune Responses to Infection

Lusiany, T., Xu, Z., Saputri, D. S., Ismanto, H. S., Nazlica, S. A., & Standley, D. M. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2609-2_15 · Cited by 2

Data mining antibody sequences for database searching in bottom-up proteomics

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Y ab X nization platform: A monoclonal antibody heterologization server based on rational design and artificial intelligence-assisted computation

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