AbDiver-a tool to explore the natural antibody landscape to aid therapeutic design
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Abstract
MOTIVATION: Rational design of therapeutic antibodies can be improved by harnessing the natural sequence diversity of these molecules. Our understanding of the diversity of antibodies has recently been greatly facilitated through the deposition of hundreds of millions of human antibody sequences in next-generation sequencing (NGS) repositories. Contrasting a query therapeutic antibody sequence to naturally observed diversity in similar antibody sequences from NGS can provide a mutational roadmap for antibody engineers designing biotherapeutics. Because of the sheer scale of the antibody NGS datasets, performing queries across them is computationally challenging. RESULTS: To facilitate harnessing antibody NGS data, we developed AbDiver (http://naturalantibody.com/abdiver), a free portal allowing users to compare their query sequences to those observed in the natural repertoires. AbDiver offers three antibody-specific use-cases: (i) compare a query antibody to positional variability statistics precomputed from multiple independent studies, (ii) retrieve close full variable sequence matches to a query antibody and (iii) retrieve CDR3 or clonotype matches to a query antibody. We applied our system to a set of 742 therapeutic antibodies, demonstrating that for each use-case our system can retrieve relevant results for most sequences. AbDiver facilitates the navigation of vast antibody mutation space for the purpose of rational therapeutic antibody design. AVAILABILITY AND IMPLEMENTATION: AbDiver is freely accessible at http://naturalantibody.com/abdiver. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Some novel tools leverage antibody sequence datasets for comprehensive analysis of antibody sequences.
“Furthermore, some novel tools leverage these datasets for comprehensive analysis of antibody sequences [10,11] (Table 1).”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [10]
AbDiver employs publicly curated B cell receptor NGS datasets and compares designed sequences to natural repertoires.
“For example, AbDiver is a tool that employs the publicly curated B cell receptor NGS datasets and compares designed sequences to natural repertoires [10].”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [10]
The developer of AbDiver identified that it could find suitable profiles for 742 therapeutic antibodies.
“Moreover, the developer identified that it could find suitable profiles for 742 therapeutic antibodies.”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [10]
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Cites
Lefranc, M., Pommie, C., Ruíz, M., Giudicelli, V., Foulquier, E., Truong, L., Contet, V., & Lefranc, G. (2002) · Developmental & Comparative Immunology · doi:10.1016/s0145-305x(02)00039-3 · Cited by 823 · see citing works
Olsen, T. H., Boyles, F., & Deane, C. M. (2022) · Protein Science · doi:10.1002/pro.4205 · Cited by 319 · see citing works
Mason, D. M., Friedensohn, S., Weber, C. R., Jordi, C., Wagner, B., Meng, S. M., Ehling, R. A., Bonati, L., Dahinden, J., Gainza, P., et al. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00699-9 · Cited by 308 · see citing works
Kovaltsuk, A., Leem, J., Kelm, S., Snowden, J., Deane, C. M., & Krawczyk, K. (2018) · The Journal of Immunology · doi:10.4049/jimmunol.1800708 · Cited by 247 · see citing works
Self-citationBioinformatic and Statistical Analysis of Adaptive Immune Repertoires
Greiff, V., Miho, E., Menzel, U., & Reddy, S. T. (2015) · Trends in Immunology · doi:10.1016/j.it.2015.09.006 · Cited by 210 · see citing works
PIRD: pan immune repertoire database
Zhang, W., Wang, L., Liu, K., Wei, X., Yang, K., Du, W., Wang, S., Guo, N., Ma, C., Luo, L., et al. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz614 · Cited by 166 · see citing works
Humanization of antibodies using a machine learning approach on large-scale repertoire data
Marks, C., Hummer, A. M., Chin, M., & Deane, C. M. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab434 · Cited by 124 · see citing works
Benchmarking immunoinformatic tools for the analysis of antibody repertoire sequences
Smakaj, E., Babrak, L. M., Ohlin, M., Shugay, M., Briney, B. S., Tosoni, D., Galli, C., Grobelsek, V., D’Angelo, I., Olson, B. J., et al. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz845 · Cited by 54 · see citing works
The ADC API: A Web API for the Programmatic Query of the AIRR Data Commons
Christley, S., Aguiar, A., Blanck, G., et al. (2020) · Frontiers in Big Data · doi:10.3389/fdata.2020.00022 · Cited by 42 · see citing works
Looking for therapeutic antibodies in next generation sequencing repositories
Krawczyk, K., Raybould, M., Kovaltsuk, A., & Deane, C. M. (2019) · doi:10.1101/572958 · Cited by 29 · see citing works
Self-citationData mining patented antibody sequences
Krawczyk, K., Buchanan, A. G., & Marcatili, P. (2021) · mAbs · doi:10.1080/19420862.2021.1892366 · Cited by 25 · see citing works
Self-citationPetersen, B. M., Ulmer, S. A., Rhodes, E. R., Gutiérrez-González, M., DeKosky, B. J., Sprenger, K. G., & Whitehead, T. A. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.728694 · Cited by 17 · see citing works
Viewing multiple sequence alignments with the JavaScript Sequence Alignment Viewer (JSAV)
Martin, A. C. R. (2014) · F1000Research · doi:10.12688/f1000research.5486.1 · Cited by 15 · see citing works
Venkataramani, S., Ernst, R., Derebe, M. G., Wright, R. T., Kopenhaver, J. S., Jacobs, S. A., Singh, S., & Ganesan, R. (2020) · Scientific Reports · doi:10.1038/s41598-020-66636-z · Cited by 7 · see citing works
Self-citationHuman antibody immune responses are personalized by selective removal of MHC-II peptide epitopesPreprint
Gutiérrez-González, M., Fahad, A. S., Ardito, M. T., Nanaware, P. P., Lu, L., Normandin, E., Madan, B., Tivin, J., Coates, E. E., Henry, A. R., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.15.426750 · Cited by 5 · see citing works
Jones, T., Day, S. B., Myers, L., Crowe, J. E., & Soto, C. S. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa1028 · Cited by 3 · see citing works
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Computational and artificial intelligence-based methods for antibody development
Kim, J., McFee, M., Fang, Q., Abdin, O., & Kim, P. M. (2023) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2022.12.005 · Cited by 178 · see citing works
- Authors
- Młokosiewicz, J., Deszyński, P., Wilman, W., Jaszczyszyn, I., Ganesan, R., Kovaltsuk, A., Leem, J., Galson, J. D., & Krawczyk, K.
- Affiliations
- NaturalAntibody , 71-064 Szczecin, Poland
- Medical University of Warsaw (PL)
- Alector (United States) (US)
- LabGenius (United Kingdom) (GB)
- Alchemab Therapeutics Ltd, N1C 4AX London , UK
- Corresponding author
- Krawczyk, Konrad
- ORCID
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-03-11
- Volume
- 38
- Issue
- 9
- Pages
- 2628-2630
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC9048670
- OpenAlex
- W4220997534
- Cited by
- 19
- References
- 16
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 6 · 2023: 6 · 2022: 2
- FWCI
- 2.42
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- therapeutic antibody design, biologic therapy, CDR3, clonotypes
- MeSH terms
- Antibodies, Humans, Software, High-Throughput Nucleotide Sequencing
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