1
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Chomicz D, Kończak J, Wróbel S, Satława T, Dudzic P, Janusz B, Tarkowski M, Deszyński P, Gawłowski T, Kostyn A, Orłowski M, Klaus T, Schulte L, Martin K, Comeau SR, Krawczyk K. Benchmarking antibody clustering methods using sequence, structural, and machine learning similarity measures for antibody discovery applications. Front Mol Biosci 2024; 11:1352508. [PMID: 38606289 PMCID: PMC11008471 DOI: 10.3389/fmolb.2024.1352508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 02/09/2024] [Indexed: 04/13/2024] Open
Abstract
Antibodies are proteins produced by our immune system that have been harnessed as biotherapeutics. The discovery of antibody-based therapeutics relies on analyzing large volumes of diverse sequences coming from phage display or animal immunizations. Identification of suitable therapeutic candidates is achieved by grouping the sequences by their similarity and subsequent selection of a diverse set of antibodies for further tests. Such groupings are typically created using sequence-similarity measures alone. Maximizing diversity in selected candidates is crucial to reducing the number of tests of molecules with near-identical properties. With the advances in structural modeling and machine learning, antibodies can now be grouped across other diversity dimensions, such as predicted paratopes or three-dimensional structures. Here we benchmarked antibody grouping methods using clonotype, sequence, paratope prediction, structure prediction, and embedding information. The results were benchmarked on two tasks: binder detection and epitope mapping. We demonstrate that on binder detection no method appears to outperform the others, while on epitope mapping, clonotype, paratope, and embedding clusterings are top performers. Most importantly, all the methods propose orthogonal groupings, offering more diverse pools of candidates when using multiple methods than any single method alone. To facilitate exploring the diversity of antibodies using different methods, we have created an online tool-CLAP-available at (clap.naturalantibody.com) that allows users to group, contrast, and visualize antibodies using the different grouping methods.
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Affiliation(s)
| | | | - Sonia Wróbel
- NaturalAntibody, Szczecin, West Pomeranian, Poland
| | | | - Paweł Dudzic
- NaturalAntibody, Szczecin, West Pomeranian, Poland
| | | | | | | | | | | | - Marek Orłowski
- Pure Biologics, Wrocław, Poland
- Department of Biochemistry, Molecular Biology and Biotechnology, Faculty of Chemistry, Wrocław University of Science and Technology, Wrocław, Poland
| | | | - Lukas Schulte
- Global Computational Biology & Digital Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Kyle Martin
- Biotherapeutics Discovery, Boehringer Ingelheim, Biberach, Germany
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2
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Saputri DS, Ismanto HS, Nugraha DK, Xu Z, Horiguchi Y, Sakakibara S, Standley DM. Deciphering the antigen specificities of antibodies by clustering their complementarity determining region sequences. mSystems 2023; 8:e0072223. [PMID: 37975681 PMCID: PMC10734444 DOI: 10.1128/msystems.00722-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 10/06/2023] [Indexed: 11/19/2023] Open
Abstract
IMPORTANCE Determining antigen and epitope specificity is an essential step in the discovery of therapeutic antibodies as well as in the analysis adaptive immune responses to disease or vaccination. Despite extensive efforts, deciphering antigen specificity solely from BCR amino acid sequence remains a challenging task, requiring a combination of experimental and computational approaches. Here, we describe and experimentally validate a simple and straightforward approach for grouping antibodies that share antigen and epitope specificities based on their CDR sequence similarity. This approach allows us to identify the specificities of a large number of antibodies whose antigen targets are unknown, using a small fraction of antibodies with well-annotated binding specificities.
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Affiliation(s)
- Dianita S. Saputri
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
- Graduate School of Medicine, Osaka University, Suita, Japan
| | - Hendra S. Ismanto
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Dendi K. Nugraha
- Department of Molecular Bacteriology, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Zichang Xu
- Immunology Frontier Research Center, Osaka University, Suita, Japan
| | - Yasuhiko Horiguchi
- Graduate School of Medicine, Osaka University, Suita, Japan
- Department of Molecular Bacteriology, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
- Center for Infectious Disease Education and Research, Osaka University, Suita, Japan
| | - Shuhei Sakakibara
- Immunology Frontier Research Center, Osaka University, Suita, Japan
- Graduate School of Medical Safety Management, Jikei University of Health Care Sciences, Osaka, Japan
| | - Daron M. Standley
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
- Graduate School of Medicine, Osaka University, Suita, Japan
- Immunology Frontier Research Center, Osaka University, Suita, Japan
- Center for Infectious Disease Education and Research, Osaka University, Suita, Japan
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3
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Pelissier A, Stratigopoulou M, Donner N, Dimitriadis E, Bende RJ, Guikema JE, Rodriguez Martinez M, van Noesel CJ. Convergent evolution and B-cell recirculation in germinal centers in a human lymph node. Life Sci Alliance 2023; 6:e202301959. [PMID: 37640448 PMCID: PMC10462906 DOI: 10.26508/lsa.202301959] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 07/31/2023] [Accepted: 08/02/2023] [Indexed: 08/31/2023] Open
Abstract
Germinal centers (GCs) play a central role in generating an effective immune response against infectious pathogens, and failures in their regulating mechanisms can lead to the development of autoimmune diseases and cancer. Although previous works study experimental systems of the immune response with mouse models that are immunized with specific antigens, our study focused on a real-life situation, with an ongoing GC response in a human lymph node (LN) involving multiple asynchronized GCs reacting simultaneously to unknown antigens. We combined laser capture microdissection of individual GCs from human LN with next-generation repertoire sequencing to characterize individual GCs as distinct evolutionary spaces. In line with well-characterized GC responses in mice, elicited by immunization with model antigens, we observe a heterogeneous clonal diversity across individual GCs from the same human LN. Still, we identify shared clones in several individual GCs, and phylogenetic tree analysis combined with paratope modeling suggest the re-engagement and rediversification of B-cell clones across GCs and expanded clones exhibiting shared antigen responses across distinct GCs, indicating convergent evolution of the GCs.
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Affiliation(s)
- Aurelien Pelissier
- https://ror.org/02js37d36 IBM Research Europe, Rüschlikon, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Maria Stratigopoulou
- Department of Pathology, Amsterdam University Medical Centers, Location AMC, Lymphoma and Myeloma Center Amsterdam, Amsterdam, Netherlands
| | - Naomi Donner
- Department of Pathology, Amsterdam University Medical Centers, Location AMC, Lymphoma and Myeloma Center Amsterdam, Amsterdam, Netherlands
| | | | - Richard J Bende
- Department of Pathology, Amsterdam University Medical Centers, Location AMC, Lymphoma and Myeloma Center Amsterdam, Amsterdam, Netherlands
| | - Jeroen E Guikema
- Department of Pathology, Amsterdam University Medical Centers, Location AMC, Lymphoma and Myeloma Center Amsterdam, Amsterdam, Netherlands
| | | | - Carel Jm van Noesel
- Department of Pathology, Amsterdam University Medical Centers, Location AMC, Lymphoma and Myeloma Center Amsterdam, Amsterdam, Netherlands
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4
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Spoendlin FC, Abanades B, Raybould MIJ, Wong WK, Georges G, Deane CM. Improved computational epitope profiling using structural models identifies a broader diversity of antibodies that bind to the same epitope. Front Mol Biosci 2023; 10:1237621. [PMID: 37790877 PMCID: PMC10544996 DOI: 10.3389/fmolb.2023.1237621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 08/28/2023] [Indexed: 10/05/2023] Open
Abstract
The function of an antibody is intrinsically linked to the epitope it engages. Clonal clustering methods, based on sequence identity, are commonly used to group antibodies that will bind to the same epitope. However, such methods neglect the fact that antibodies with highly diverse sequences can exhibit similar binding site geometries and engage common epitopes. In a previous study, we described SPACE1, a method that structurally clustered antibodies in order to predict their epitopes. This methodology was limited by the inaccuracies and incomplete coverage of template-based modeling. In addition, it was only benchmarked at the level of domain-consistency on one virus class. Here, we present SPACE2, which uses the latest machine learning-based structure prediction technology combined with a novel clustering protocol, and benchmark it on binding data that have epitope-level resolution. On six diverse sets of antigen-specific antibodies, we demonstrate that SPACE2 accurately clusters antibodies that engage common epitopes and achieves far higher dataset coverage than clonal clustering and SPACE1. Furthermore, we show that the functionally consistent structural clusters identified by SPACE2 are even more diverse in sequence, genetic lineage, and species origin than those found by SPACE1. These results reiterate that structural data improve our ability to identify antibodies that bind to the same epitope, adding information to sequence-based methods, especially in datasets of antibodies from diverse sources. SPACE2 is openly available on GitHub (https://github.com/oxpig/SPACE2).
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Affiliation(s)
- Fabian C. Spoendlin
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Brennan Abanades
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Matthew I. J. Raybould
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Wing Ki Wong
- Large Molecule Research, Roche Pharma Research and Early Development, Roche Innovation Center Munich, Penzberg, Germany
| | - Guy Georges
- Large Molecule Research, Roche Pharma Research and Early Development, Roche Innovation Center Munich, Penzberg, Germany
| | - Charlotte M. Deane
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, United Kingdom
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5
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Olsen TH, Abanades B, Moal IH, Deane CM. KA-Search, a method for rapid and exhaustive sequence identity search of known antibodies. Sci Rep 2023; 13:11612. [PMID: 37463925 DOI: 10.1038/s41598-023-38108-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 07/03/2023] [Indexed: 07/20/2023] Open
Abstract
Antibodies with similar amino acid sequences, especially across their complementarity-determining regions, often share properties. Finding that an antibody of interest has a similar sequence to naturally expressed antibodies in healthy or diseased repertoires is a powerful approach for the prediction of antibody properties, such as immunogenicity or antigen specificity. However, as the number of available antibody sequences is now in the billions and continuing to grow, repertoire mining for similar sequences has become increasingly computationally expensive. Existing approaches are limited by either being low-throughput, non-exhaustive, not antibody specific, or only searching against entire chain sequences. Therefore, there is a need for a specialized tool, optimized for a rapid and exhaustive search of any antibody region against all known antibodies, to better utilize the full breadth of available repertoire sequences. We introduce Known Antibody Search (KA-Search), a tool that allows for the rapid search of billions of antibody variable domains by amino acid sequence identity across either the variable domain, the complementarity-determining regions, or a user defined antibody region. We show KA-Search in operation on the [Formula: see text]2.4 billion antibody sequences available in the OAS database. KA-Search can be used to find the most similar sequences from OAS within 30 minutes and a representative subset of 10 million sequences in less than 9 seconds. We give examples of how KA-Search can be used to obtain new insights about an antibody of interest. KA-Search is freely available at https://github.com/oxpig/kasearch .
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Affiliation(s)
- Tobias H Olsen
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK
| | - Brennan Abanades
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK
| | - Iain H Moal
- GSK Medicines Research Centre, GlaxoSmithKline plc, Stevenage, SG1 2NY, UK
| | - Charlotte M Deane
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK.
- Exscientia plc, Oxford, OX4 4GE, UK.
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6
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Jaszczyszyn I, Bielska W, Gawlowski T, Dudzic P, Satława T, Kończak J, Wilman W, Janusz B, Wróbel S, Chomicz D, Galson JD, Leem J, Kelm S, Krawczyk K. Structural modeling of antibody variable regions using deep learning-progress and perspectives on drug discovery. Front Mol Biosci 2023; 10:1214424. [PMID: 37484529 PMCID: PMC10361724 DOI: 10.3389/fmolb.2023.1214424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Accepted: 06/12/2023] [Indexed: 07/25/2023] Open
Abstract
AlphaFold2 has hallmarked a generational improvement in protein structure prediction. In particular, advances in antibody structure prediction have provided a highly translatable impact on drug discovery. Though AlphaFold2 laid the groundwork for all proteins, antibody-specific applications require adjustments tailored to these molecules, which has resulted in a handful of deep learning antibody structure predictors. Herein, we review the recent advances in antibody structure prediction and relate them to their role in advancing biologics discovery.
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Affiliation(s)
- Igor Jaszczyszyn
- NaturalAntibody, Kraków, Poland
- Medical University of Warsaw, Warsaw, Poland
| | - Weronika Bielska
- NaturalAntibody, Kraków, Poland
- Medical University of Lodz, Lodz, Poland
| | | | | | | | | | | | | | | | | | | | - Jinwoo Leem
- Alchemab Therapeutics Ltd., London, United Kingdom
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7
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Abstract
Deep learning has led to incredible breakthroughs in areas of research, from self-driving vehicles to solutions, to formal mathematical proofs. In the biomedical sciences, however, the revolutionary results seen in other fields are only now beginning to be realized. Vaccine research and development efforts represent an application with high public health significance. Protein structure prediction, immune repertoire analysis, and phylogenetics are three principal areas in which deep learning is poised to provide key advances. Here, we opine on some of the current challenges with deep learning and how they are being addressed. Despite the nascent stage of deep learning applications in immunological studies, there is ample opportunity to utilize this new technology to address the most challenging and burdensome infectious diseases confronting global populations.
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Affiliation(s)
| | - Margaret E Ackerman
- Thayer School of Engineering, Dartmouth College, Hanover, NH, USA; Department of Microbiology and Immunology, Geisel School of Medicine, Hanover, NH, USA.
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8
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Cable J, Saphire EO, Hayday AC, Wiltshire TD, Mousa JJ, Humphreys DP, Breij ECW, Bruhns P, Broketa M, Furuya G, Hauser BM, Mahévas M, Carfi A, Cantaert T, Kwong PD, Tripathi P, Davis JH, Brewis N, Keyt BA, Fennemann FL, Dussupt V, Sivasubramanian A, Kim PM, Rawi R, Richardson E, Leventhal D, Wolters RM, Geuijen CAW, Sleeman MA, Pengo N, Donnellan FR. Antibodies as drugs-a Keystone Symposia report. Ann N Y Acad Sci 2023; 1519:153-166. [PMID: 36382536 PMCID: PMC10103175 DOI: 10.1111/nyas.14915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Therapeutic antibodies have broad indications across diverse disease states, such as oncology, autoimmune diseases, and infectious diseases. New research continues to identify antibodies with therapeutic potential as well as methods to improve upon endogenous antibodies and to design antibodies de novo. On April 27-30, 2022, experts in antibody research across academia and industry met for the Keystone symposium "Antibodies as Drugs" to present the state-of-the-art in antibody therapeutics, repertoires and deep learning, bispecific antibodies, and engineering.
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Affiliation(s)
| | - Erica Ollmann Saphire
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, California, USA.,Department of Medicine, University of California San Diego, La Jolla, California, USA
| | - Adrian C Hayday
- Peter Gorer Department of Immunobiology, King's College London, London, UK.,Cancer Research UK Cancer Immunotherapy Accelerator, London, UK.,Immunosurveillance Laboratory, The Francis Crick Institute, London, UK
| | | | - Jarrod J Mousa
- Department of Infectious Diseases and Center for Vaccines and Immunology, College of Veterinary Medicine, Athens, Georgia, USA.,Department of Biochemistry and Molecular Biology, Franklin College of Arts and Sciences, University of Georgia, Athens, Georgia, USA.,Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | | - Esther C W Breij
- Translational Research and Precision Medicine, Genmab BV, Utrecht, the Netherlands
| | - Pierre Bruhns
- Institut Pasteur, Université de Paris, Unit of Antibodies in Therapy and Pathology, Paris, France
| | - Matteo Broketa
- Institut Pasteur, Université de Paris, Unit of Antibodies in Therapy and Pathology, Paris, France
| | - Genta Furuya
- Department of Preventive Medicine and Department of Pathology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Blake M Hauser
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, Massachusetts, USA
| | - Matthieu Mahévas
- Service de Médecine Interne, Centre de Référence des Cytopénies Auto-immunes de l'adulte, Centre Hospitalier Universitaire Henri-Mondor, Assistance Publique-Hôpitaux de Paris, Université Paris-Est Créteil, Créteil, France
| | - Andrea Carfi
- Moderna Inc., Cambridge, Massachusetts, USA.,Department of Pathology, Miller School of Medicine, University of Miami, Miami, Florida, USA
| | - Tineke Cantaert
- Immunology Unit, Institut Pasteur du Cambodge, The Pasteur Network, Phnom Penh, Cambodia
| | - Peter D Kwong
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Prabhanshu Tripathi
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | | | | | - Bruce A Keyt
- IGM Biosciences, Inc., Mountainview, California, USA
| | | | - Vincent Dussupt
- Emerging Infectious Diseases Branch, U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, Maryland, USA.,Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland, USA
| | | | - Philip M Kim
- Department of Molecular Genetics, Donnelly Centre for Cellular and Biomolecular Research, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Reda Rawi
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Eve Richardson
- Department of Statistics, University of Oxford, Oxford, UK
| | | | - Rachael M Wolters
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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9
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Pennell M, Rodriguez OL, Watson CT, Greiff V. The evolutionary and functional significance of germline immunoglobulin gene variation. Trends Immunol 2023; 44:7-21. [PMID: 36470826 DOI: 10.1016/j.it.2022.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 11/07/2022] [Indexed: 12/04/2022]
Abstract
The recombination between immunoglobulin (IG) gene segments determines an individual's naïve antibody repertoire and, consequently, (auto)antigen recognition. Emerging evidence suggests that mammalian IG germline variation impacts humoral immune responses associated with vaccination, infection, and autoimmunity - from the molecular level of epitope specificity, up to profound changes in the architecture of antibody repertoires. These links between IG germline variants and immunophenotype raise the question on the evolutionary causes and consequences of diversity within IG loci. We discuss why the extreme diversity in IG loci remains a mystery, why resolving this is important for the design of more effective vaccines and therapeutics, and how recent evidence from multiple lines of inquiry may help us do so.
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Affiliation(s)
- Matt Pennell
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA; Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA.
| | - Oscar L Rodriguez
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA
| | - Corey T Watson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.
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10
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Robert PA, Greiff V. Bridging the neutralization gap for unseen antibodies. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00594-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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11
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Jaffe DB, Shahi P, Adams BA, Chrisman AM, Finnegan PM, Raman N, Royall AE, Tsai F, Vollbrecht T, Reyes DS, Hepler NL, McDonnell WJ. Functional antibodies exhibit light chain coherence. Nature 2022; 611:352-357. [PMID: 36289331 PMCID: PMC9607724 DOI: 10.1038/s41586-022-05371-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 09/21/2022] [Indexed: 11/08/2022]
Abstract
The vertebrate adaptive immune system modifies the genome of individual B cells to encode antibodies that bind particular antigens1. In most mammals, antibodies are composed of heavy and light chains that are generated sequentially by recombination of V, D (for heavy chains), J and C gene segments. Each chain contains three complementarity-determining regions (CDR1-CDR3), which contribute to antigen specificity. Certain heavy and light chains are preferred for particular antigens2-22. Here we consider pairs of B cells that share the same heavy chain V gene and CDRH3 amino acid sequence and were isolated from different donors, also known as public clonotypes23,24. We show that for naive antibodies (those not yet adapted to antigens), the probability that they use the same light chain V gene is around 10%, whereas for memory (functional) antibodies, it is around 80%, even if only one cell per clonotype is used. This property of functional antibodies is a phenomenon that we call light chain coherence. We also observe this phenomenon when similar heavy chains recur within a donor. Thus, although naive antibodies seem to recur by chance, the recurrence of functional antibodies reveals surprising constraint and determinism in the processes of V(D)J recombination and immune selection. For most functional antibodies, the heavy chain determines the light chain.
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12
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Wilman W, Wróbel S, Bielska W, Deszynski P, Dudzic P, Jaszczyszyn I, Kaniewski J, Młokosiewicz J, Rouyan A, Satława T, Kumar S, Greiff V, Krawczyk K. Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery. Brief Bioinform 2022; 23:6643456. [PMID: 35830864 PMCID: PMC9294429 DOI: 10.1093/bib/bbac267] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/09/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Antibodies are versatile molecular binders with an established and growing role as therapeutics. Computational approaches to developing and designing these molecules are being increasingly used to complement traditional lab-based processes. Nowadays, in silico methods fill multiple elements of the discovery stage, such as characterizing antibody–antigen interactions and identifying developability liabilities. Recently, computational methods tackling such problems have begun to follow machine learning paradigms, in many cases deep learning specifically. This paradigm shift offers improvements in established areas such as structure or binding prediction and opens up new possibilities such as language-based modeling of antibody repertoires or machine-learning-based generation of novel sequences. In this review, we critically examine the recent developments in (deep) machine learning approaches to therapeutic antibody design with implications for fully computational antibody design.
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13
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Leem J, Mitchell LS, Farmery JH, Barton J, Galson JD. Deciphering the language of antibodies using self-supervised learning. Patterns 2022; 3:100513. [PMID: 35845836 PMCID: PMC9278498 DOI: 10.1016/j.patter.2022.100513] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 03/01/2022] [Accepted: 04/26/2022] [Indexed: 11/17/2022]
Abstract
An individual’s B cell receptor (BCR) repertoire encodes information about past immune responses and potential for future disease protection. Deciphering the information stored in BCR sequence datasets will transform our understanding of disease and enable discovery of novel diagnostics and antibody therapeutics. A key challenge of BCR sequence analysis is the prediction of BCR properties from their amino acid sequence alone. Here, we present an antibody-specific language model, Antibody-specific Bidirectional Encoder Representation from Transformers (AntiBERTa), which provides a contextualized representation of BCR sequences. Following pre-training, we show that AntiBERTa embeddings capture biologically relevant information, generalizable to a range of applications. As a case study, we fine-tune AntiBERTa to predict paratope positions from an antibody sequence, outperforming public tools across multiple metrics. To our knowledge, AntiBERTa is the deepest protein-family-specific language model, providing a rich representation of BCRs. AntiBERTa embeddings are primed for multiple downstream tasks and can improve our understanding of the language of antibodies. AntiBERTa is an antibody-specific transformer model for representation learning AntiBERTa embeddings capture aspects of antibody function Attention maps of AntiBERTa correspond to structural contacts and binding sites AntiBERTa can be fine-tuned for state-of-the-art paratope prediction
Understanding antibody function is critical for deciphering the biology of disease and for the discovery of novel therapeutic antibodies. The challenge is the vast diversity of antibody variants compared with the limited labeled data available. We overcome this challenge by using self-supervised learning to train a large antibody-specific language model, followed by transfer learning, to fine-tune the model for predicting information related to antibody function. We initially demonstrate the success of the model by providing leading results in antibody binding site prediction. The model is amenable to further fine-tuning for diverse applications to improve our understanding of antibody function.
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Affiliation(s)
- Jinwoo Leem
- Alchemab Therapeutics, Ltd., East Side, Office 1.02, Kings Cross, London N1C 4AX, UK
- Corresponding author
| | - Laura S. Mitchell
- Alchemab Therapeutics, Ltd., East Side, Office 1.02, Kings Cross, London N1C 4AX, UK
| | - James H.R. Farmery
- Alchemab Therapeutics, Ltd., East Side, Office 1.02, Kings Cross, London N1C 4AX, UK
| | - Justin Barton
- Alchemab Therapeutics, Ltd., East Side, Office 1.02, Kings Cross, London N1C 4AX, UK
| | - Jacob D. Galson
- Alchemab Therapeutics, Ltd., East Side, Office 1.02, Kings Cross, London N1C 4AX, UK
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14
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Khetan R, Curtis R, Deane CM, Hadsund JT, Kar U, Krawczyk K, Kuroda D, Robinson SA, Sormanni P, Tsumoto K, Warwicker J, Martin ACR. Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics. MAbs 2022; 14:2020082. [PMID: 35104168 PMCID: PMC8812776 DOI: 10.1080/19420862.2021.2020082] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Therapeutic monoclonal antibodies and their derivatives are key components of clinical pipelines in the global biopharmaceutical industry. The availability of large datasets of antibody sequences, structures, and biophysical properties is increasingly enabling the development of predictive models and computational tools for the "developability assessment" of antibody drug candidates. Here, we provide an overview of the antibody informatics tools applicable to the prediction of developability issues such as stability, aggregation, immunogenicity, and chemical degradation. We further evaluate the opportunities and challenges of using biopharmaceutical informatics for drug discovery and optimization. Finally, we discuss the potential of developability guidelines based on in silico metrics that can be used for the assessment of antibody stability and manufacturability.
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Affiliation(s)
- Rahul Khetan
- Manchester Institute of Biotechnology, University of Manchester, Manchester, UK
| | - Robin Curtis
- Manchester Institute of Biotechnology, University of Manchester, Manchester, UK
| | | | | | - Uddipan Kar
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | | | - Daisuke Kuroda
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan.,Medical Device Development and Regulation Research Center, School of Engineering, The University of Tokyo, Tokyo, Japan.,Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan
| | | | - Pietro Sormanni
- Chemistry of Health, Yusuf Hamied Department of Chemistry, University of Cambridge
| | - Kouhei Tsumoto
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan.,Medical Device Development and Regulation Research Center, School of Engineering, The University of Tokyo, Tokyo, Japan.,Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan.,The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Jim Warwicker
- Manchester Institute of Biotechnology, University of Manchester, Manchester, UK
| | - Andrew C R Martin
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London, UK
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15
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Akbar R, Bashour H, Rawat P, Robert PA, Smorodina E, Cotet TS, Flem-Karlsen K, Frank R, Mehta BB, Vu MH, Zengin T, Gutierrez-Marcos J, Lund-Johansen F, Andersen JT, Greiff V. Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies. MAbs 2022; 14:2008790. [PMID: 35293269 PMCID: PMC8928824 DOI: 10.1080/19420862.2021.2008790] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 11/04/2021] [Accepted: 11/17/2021] [Indexed: 12/15/2022] Open
Abstract
Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.
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Affiliation(s)
- Rahmad Akbar
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Habib Bashour
- School of Life Sciences, University of Warwick, Coventry, UK
| | - Puneet Rawat
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - Philippe A. Robert
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Eva Smorodina
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russia
| | | | - Karine Flem-Karlsen
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Department of Pharmacology, University of Oslo and Oslo University Hospital, Norway
| | - Robert Frank
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Brij Bhushan Mehta
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Mai Ha Vu
- Department of Linguistics and Scandinavian Studies, University of Oslo, Norway
| | - Talip Zengin
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Bioinformatics, Mugla Sitki Kocman University, Turkey
| | | | | | - Jan Terje Andersen
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Department of Pharmacology, University of Oslo and Oslo University Hospital, Norway
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
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16
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Robinson SA, Raybould MIJ, Schneider C, Wong WK, Marks C, Deane CM. Epitope profiling using computational structural modelling demonstrated on coronavirus-binding antibodies. PLoS Comput Biol 2021; 17:e1009675. [PMID: 34898603 PMCID: PMC8700021 DOI: 10.1371/journal.pcbi.1009675] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 12/23/2021] [Accepted: 11/22/2021] [Indexed: 12/30/2022] Open
Abstract
Identifying the epitope of an antibody is a key step in understanding its function and its potential as a therapeutic. Sequence-based clonal clustering can identify antibodies with similar epitope complementarity, however, antibodies from markedly different lineages but with similar structures can engage the same epitope. We describe a novel computational method for epitope profiling based on structural modelling and clustering. Using the method, we demonstrate that sequence dissimilar but functionally similar antibodies can be found across the Coronavirus Antibody Database, with high accuracy (92% of antibodies in multiple-occupancy structural clusters bind to consistent domains). Our approach functionally links antibodies with distinct genetic lineages, species origins, and coronavirus specificities. This indicates greater convergence exists in the immune responses to coronaviruses than is suggested by sequence-based approaches. Our results show that applying structural analytics to large class-specific antibody databases will enable high confidence structure-function relationships to be drawn, yielding new opportunities to identify functional convergence hitherto missed by sequence-only analysis. Antibodies are a key component of the immune system that combat pathogens by binding to a defined region of their molecular surface (known as an ‘epitope’). The ability to map which antibodies target the same epitopes is crucial when designing non-competing antibody therapeutics or predicting the influence of pathogen mutation on population immunity. While one can use laboratory experiments to deduce when pairs of antibodies engage the same epitope, such experiments are very expensive and time consuming if used to compare on the order of thousands of antibodies. In this work, we report a new computational algorithm (SPACE) that clusters antibodies that target the same epitope based on their predicted 3D structure, as binding site structure is a property often conserved between binders complementary to the same epitope. Unlike existing antibody epitope profiling tools which assume two antibodies must share a high sequence identity/similar genetic basis to engage the same region, our orthogonal method can detect broader patterns of convergent evolution across binders to different pathogen strains, and between antibodies with different genetic and even species origins.
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MESH Headings
- Amino Acid Sequence
- Animals
- Antibodies, Neutralizing/chemistry
- Antibodies, Neutralizing/genetics
- Antibodies, Viral/chemistry
- Antibodies, Viral/genetics
- Antibodies, Viral/metabolism
- Antibody Specificity
- Antigen-Antibody Complex/chemistry
- Antigen-Antibody Complex/genetics
- Antigen-Antibody Reactions/genetics
- Antigen-Antibody Reactions/immunology
- Antigens, Viral/chemistry
- COVID-19/immunology
- COVID-19/virology
- Computational Biology
- Coronavirus/chemistry
- Coronavirus/genetics
- Coronavirus/immunology
- Databases, Chemical
- Epitope Mapping
- Epitopes, B-Lymphocyte/chemistry
- Epitopes, B-Lymphocyte/genetics
- Humans
- Mice
- Models, Molecular
- Pandemics
- SARS-CoV-2/chemistry
- SARS-CoV-2/genetics
- SARS-CoV-2/immunology
- Single-Domain Antibodies/immunology
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Affiliation(s)
- Sarah A Robinson
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, United Kingdom
| | - Matthew I J Raybould
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, United Kingdom
| | - Constantin Schneider
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, United Kingdom
| | - Wing Ki Wong
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, United Kingdom
| | - Claire Marks
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, United Kingdom
| | - Charlotte M Deane
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, United Kingdom
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17
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Trück J, Eugster A, Barennes P, Tipton CM, Luning Prak ET, Bagnara D, Soto C, Sherkow JS, Payne AS, Lefranc MP, Farmer A, Bostick M, Mariotti-Ferrandiz E. Biological controls for standardization and interpretation of adaptive immune receptor repertoire profiling. eLife 2021; 10:66274. [PMID: 34037521 PMCID: PMC8154019 DOI: 10.7554/elife.66274] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 05/15/2021] [Indexed: 12/15/2022] Open
Abstract
Use of adaptive immune receptor repertoire sequencing (AIRR-seq) has become widespread, providing new insights into the immune system with potential broad clinical and diagnostic applications. However, like many high-throughput technologies, it comes with several problems, and the AIRR Community was established to understand and help solve them. We, the AIRR Community’s Biological Resources Working Group, have surveyed scientists about the need for standards and controls in generating and annotating AIRR-seq data. Here, we review the current status of AIRR-seq, provide the results of our survey, and based on them, offer recommendations for developing AIRR-seq standards and controls, including future work.
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Affiliation(s)
- Johannes Trück
- University Children's Hospital and the Children's Research Center, University of Zurich, Zurich, Switzerland
| | - Anne Eugster
- CRTD Center for Regenerative Therapies Dresden, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Pierre Barennes
- Sorbonne Université U959, Immunology-Immunopathology-Immunotherapy (i3), Paris, France.,AP-HP Hôpital Pitié-Salpêtrière, Biotherapy (CIC-BTi), Paris, France
| | - Christopher M Tipton
- Lowance Center for Human Immunology, Emory University School of Medicine, Atlanta, United States
| | - Eline T Luning Prak
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
| | - Davide Bagnara
- University of Genoa, Department of Experimental Medicine, Genoa, Italy
| | - Cinque Soto
- The Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, United States.,Department of Pediatrics, Vanderbilt University Medical Center, Nashville, United States
| | - Jacob S Sherkow
- College of Law, University of Illinois, Champaign, United States.,Center for Advanced Studies in Biomedical Innovation Law, University of Copenhagen Faculty of Law, Copenhagen, Denmark.,Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana, Illinois, United States
| | - Aimee S Payne
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
| | - Marie-Paule Lefranc
- IMGT, The International ImMunoGeneTics Information System (IMGT), Laboratoire d'ImmunoGénétique Moléculaire (LIGM), Institut de Génétique Humaine (IGH), CNRS, University of Montpellier, Montpellier, France.,Laboratoire d'ImmunoGénétique Moléculaire (LIGM) CNRS, University of Montpellier, Montpellier, France.,Institut de Génétique Humaine (IGH), CNRS, University of Montpellier, Montpellier, France
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18
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Abstract
Animal-derived antibody sources, particularly, transgenic mice that are engineered with human immunoglobulin loci, along with advanced antibody generation technology platforms have facilitated the discoveries of human antibody therapeutics. For example, isolation of antigen-specific B cells, microfluidics, and next-generation sequencing have emerged as powerful tools for identifying and developing monoclonal antibodies (mAbs). These technologies enable not only antibody drug discovery but also lead to the understanding of B cell biology, immune mechanisms and immunogenetics of antibodies. In this perspective article, we discuss the scientific merits of animal immunization combined with advanced methods for antibody generation as compared to animal-free alternatives through in-vitro-generated antibody libraries. The knowledge gained from animal-derived antibodies concerning the recombinational diversity, somatic hypermutation patterns, and physiochemical properties is found more valuable and prerequisite for developing in vitro libraries, as well as artificial intelligence/machine learning methods to discover safe and effective mAbs.
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Affiliation(s)
- Ponraj Prabakaran
- Biologics Research US, Global Large Molecules Research, Sanofi, Framingham, MA, USA
| | - Sambasiva P Rao
- Biologics Research US, Global Large Molecules Research, Sanofi, Framingham, MA, USA
| | - Maria Wendt
- Biologics Research US, Global Large Molecules Research, Sanofi, Framingham, MA, USA
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19
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Raybould MIJ, Marks C, Kovaltsuk A, Lewis AP, Shi J, Deane CM. Public Baseline and shared response structures support the theory of antibody repertoire functional commonality. PLoS Comput Biol 2021; 17:e1008781. [PMID: 33647011 PMCID: PMC7951972 DOI: 10.1371/journal.pcbi.1008781] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 03/11/2021] [Accepted: 02/08/2021] [Indexed: 12/14/2022] Open
Abstract
The naïve antibody/B-cell receptor (BCR) repertoires of different individuals ought to exhibit significant functional commonality, given that most pathogens trigger an effective antibody response to immunodominant epitopes. Sequence-based repertoire analysis has so far offered little evidence for this phenomenon. For example, a recent study estimated the number of shared ('public') antibody clonotypes in circulating baseline repertoires to be around 0.02% across ten unrelated individuals. However, to engage the same epitope, antibodies only require a similar binding site structure and the presence of key paratope interactions, which can occur even when their sequences are dissimilar. Here, we search for evidence of geometric similarity/convergence across human antibody repertoires. We first structurally profile naïve ('baseline') antibody diversity using snapshots from 41 unrelated individuals, predicting all modellable distinct structures within each repertoire. This analysis uncovers a high (much greater than random) degree of structural commonality. For instance, around 3% of distinct structures are common to the ten most diverse individual samples ('Public Baseline' structures). Our approach is the first computational method to find levels of BCR commonality commensurate with epitope immunodominance and could therefore be harnessed to find more genetically distant antibodies with same-epitope complementarity. We then apply the same structural profiling approach to repertoire snapshots from three individuals before and after flu vaccination, detecting a convergent structural drift indicative of recognising similar epitopes ('Public Response' structures). We show that Antibody Model Libraries derived from Public Baseline and Public Response structures represent a powerful geometric basis set of low-immunogenicity candidates exploitable for general or target-focused therapeutic antibody screening.
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Affiliation(s)
- Matthew I. J. Raybould
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Claire Marks
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Aleksandr Kovaltsuk
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Alan P. Lewis
- Data and Computational Sciences, GlaxoSmithKline Research and Development, Stevenage, United Kingdom
| | - Jiye Shi
- Chemistry Department, UCB Pharma, Slough, United Kingdom
| | - Charlotte M. Deane
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, United Kingdom
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20
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Raybould MIJ, Rees AR, Deane CM. Current strategies for detecting functional convergence across B-cell receptor repertoires. MAbs 2021; 13:1996732. [PMID: 34781829 PMCID: PMC8604390 DOI: 10.1080/19420862.2021.1996732] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 10/10/2021] [Accepted: 10/12/2021] [Indexed: 12/11/2022] Open
Abstract
Convergence across B-cell receptor (BCR) and antibody repertoires has become instrumental in prioritizing candidates in recent rapid therapeutic antibody discovery campaigns. It has also increased our understanding of the immune system, providing evidence for the preferential selection of BCRs to particular (immunodominant) epitopes post vaccination/infection. These important implications for both drug discovery and immunology mean that it is essential to consider the optimal way to combine experimental and computational technology when probing BCR repertoires for convergence signatures. Here, we discuss the theoretical basis for observing BCR repertoire functional convergence and explore factors of study design that can impact functional signal. We also review the computational arsenal available to detect antibodies with similar functional properties, highlighting opportunities enabled by recent clustering algorithms that exploit structural similarities between BCRs. Finally, we suggest future areas of development that should increase the power of BCR repertoire functional clustering.
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Affiliation(s)
- Matthew I. J. Raybould
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, UK
| | | | - Charlotte M. Deane
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, UK
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