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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Chang H, Ashlock DA, Graether SP, Keller SM. Anchor Clustering for million-scale immune repertoire sequencing data. BMC Bioinformatics 2024; 25:42. [PMID: 38273275 PMCID: PMC10809746 DOI: 10.1186/s12859-024-05659-z] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 01/16/2024] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND The clustering of immune repertoire data is challenging due to the computational cost associated with a very large number of pairwise sequence comparisons. To overcome this limitation, we developed Anchor Clustering, an unsupervised clustering method designed to identify similar sequences from millions of antigen receptor gene sequences. First, a Point Packing algorithm is used to identify a set of maximally spaced anchor sequences. Then, the genetic distance of the remaining sequences to all anchor sequences is calculated and transformed into distance vectors. Finally, distance vectors are clustered using unsupervised clustering. This process is repeated iteratively until the resulting clusters are small enough so that pairwise distance comparisons can be performed. RESULTS Our results demonstrate that Anchor Clustering is faster than existing pairwise comparison clustering methods while providing similar clustering quality. With its flexible, memory-saving strategy, Anchor Clustering is capable of clustering millions of antigen receptor gene sequences in just a few minutes. CONCLUSIONS This method enables the meta-analysis of immune-repertoire data from different studies and could contribute to a more comprehensive understanding of the immune repertoire data space.
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Affiliation(s)
- Haiyang Chang
- Department of Mathematics and Statistics, University of Guelph, 50 Stone Rd E, Guelph, ON, N1G 2W1, Canada
| | - Daniel A Ashlock
- Department of Mathematics and Statistics, University of Guelph, 50 Stone Rd E, Guelph, ON, N1G 2W1, Canada
| | - Steffen P Graether
- Department of Molecular and Cellular Biology, University of Guelph, 50 Stone Rd E, Guelph, ON, N1G 2W1, Canada
| | - Stefan M Keller
- Department of Pathology, Microbiology and Immunology, School of Veterinary Medicine, University of California Davis, One Shields Avenue, Davis, CA, 95616, USA.
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3
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Levi R, Dvorkin S, Louzoun Y. Shared bias in H chain V-J pairing in naive and memory B cells. Front Immunol 2023; 14:1166116. [PMID: 37790930 PMCID: PMC10543446 DOI: 10.3389/fimmu.2023.1166116] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 08/23/2023] [Indexed: 10/05/2023] Open
Abstract
Introduction H chain rearrangement in B cells is a two-step process where first DH binds JH, and only then VH is joined to the complex. As such, there is no direct rearrangement between VH and JH. Results Nevertheless, we here show that the VHJH combinations frequency in humans deviates from the one expected based on each gene usage frequency. This bias is observed mainly in functional rearrangements, and much less in out-of-frame rearrangements. The bias cannot be explained by preferred binding for DH genes or a preferred reading frame. Preferred VH JH combinations are shared between donors. Discussion These results suggest a common structural mechanism for these biases. Through development, thepreferred VH JH combinations evolve during peripheral selection to become stronger, but less shared. We propose that peripheral Heavy chain VH JH usage is initially shaped by a structural selection before the naive B cellstate, followed by pathogen-induced selection for host specific VH-JH pairs.
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Affiliation(s)
| | | | - Yoram Louzoun
- Department of Mathematics, Bar Ilan University, Ramat Gan, Israel
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4
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Ghanbarpour A, Jiang M, Foster D, Chai Q. Structure-free antibody paratope similarity prediction for in silico epitope binning via protein language models. iScience 2023; 26:106036. [PMID: 36824280 PMCID: PMC9941125 DOI: 10.1016/j.isci.2023.106036] [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] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 11/28/2022] [Accepted: 01/17/2023] [Indexed: 01/26/2023] Open
Abstract
Antibodies are an important group of biological molecules that are used as therapeutics and diagnostic tools. Although millions of antibody sequences are available, identifying their structural and functional similarity and their antigen binding sites remains a challenge at large scale. Here, we present a fast, sequence-based computational method for antibody paratope prediction based on protein language models. The paratope information is then used to measure similarity among antibodies via protein language models. Our computational method enables binning of antibody discovery hits into groups as the function of epitope engagement. We further demonstrate the utility of the method by identifying antibodies targeting highly similar epitopes of the same antigens from a large pool of antibody sequences, using two case studies: SARS CoV2 Receptor Binding Domain (RBD) and Epidermal Growth Factor Receptor (EGFR). Our approach highlights the potential in accelerating antibody discovery by enhancing hit prioritization and diversity selection.
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Affiliation(s)
- Ahmadreza Ghanbarpour
- Biotechnology Discovery Research, Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, CA 92121, USA
| | - Min Jiang
- Advanced Analytics and Data Sciences, Lilly Corporate Center, Indianapolis, IN 46225, USA
| | - Denisa Foster
- Biotechnology Discovery Research, Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, CA 92121, USA
| | - Qing Chai
- Biotechnology Discovery Research, Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, CA 92121, USA,Corresponding author
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5
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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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6
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Pettini E, Medaglini D, Ciabattini A. Profiling the B cell immune response elicited by vaccination against the respiratory virus SARS-CoV-2. Front Immunol 2022; 13:1058748. [PMID: 36505416 PMCID: PMC9729280 DOI: 10.3389/fimmu.2022.1058748] [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: 09/30/2022] [Accepted: 11/04/2022] [Indexed: 11/25/2022] Open
Abstract
B cells play a fundamental role in host defenses against viral infections. Profiling the B cell response elicited by SARS-CoV-2 vaccination, including the generation and persistence of antigen-specific memory B cells, is essential for improving the knowledge of vaccine immune responsiveness, beyond the antibody response. mRNA-based vaccines have shown to induce a robust class-switched memory B cell response that persists overtime and is boosted by further vaccine administration, suggesting that memory B cells are critical in driving a recall response upon re-exposure to SARS-CoV-2 antigens. Here, we focus on the role of the B cell response in the context of SARS-CoV-2 vaccination, offering an overview of the different technologies that can be used to identify spike-specific B cells, characterize their phenotype using machine learning approaches, measure their capacity to reactivate following antigen encounter, and tracking the maturation of the B cell receptor antigenic affinity.
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7
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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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8
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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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9
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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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10
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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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11
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Shevyrev D, Tereshchenko V, Kozlov V. Immune Equilibrium Depends on the Interaction Between Recognition and Presentation Landscapes. Front Immunol 2021; 12:706136. [PMID: 34394106 PMCID: PMC8362327 DOI: 10.3389/fimmu.2021.706136] [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: 05/06/2021] [Accepted: 07/12/2021] [Indexed: 12/15/2022] Open
Abstract
In this review, we described the structure and organization of antigen-recognizing repertoires of B and T cells from the standpoint of modern immunology. We summarized the latest advances in bioinformatics analysis of sequencing data from T and B cell repertoires and also presented contemporary ideas about the mechanisms of clonal diversity formation at different stages of organism development. At the same time, we focused on the importance of the allelic variants of the HLA genes and spectra of presented antigens for the formation of T-cell receptors (TCR) landscapes. The main idea of this review is that immune equilibrium and proper functioning of immunity are highly dependent on the interaction between the recognition and the presentation landscapes of antigens. Certain changes in these landscapes can occur during life, which can affect the protective function of adaptive immunity. We described some mechanisms associated with these changes, for example, the conversion of effector cells into regulatory cells and vice versa due to the trans-differentiation or bystander effect, changes in the clonal organization of the general TCR repertoire due to homeostatic proliferation or aging, and the background for the altered presentation of some antigens due to SNP mutations of MHC, or the alteration of the presenting antigens due to post-translational modifications. The authors suggest that such alterations can lead to an increase in the risk of the development of oncological and autoimmune diseases and influence the sensitivity of the organism to different infectious agents.
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Affiliation(s)
- Daniil Shevyrev
- Laboratory of Clinical Immunopathology, Research Institute for Fundamental and Clinical Immunology, Novosibirsk, Russia
| | - Valeriy Tereshchenko
- Laboratory of Molecular Immunology, Research Institute for Fundamental and Clinical Immunology, Novosibirsk, Russia
| | - Vladimir Kozlov
- Laboratory of Clinical Immunopathology, Research Institute for Fundamental and Clinical Immunology, Novosibirsk, Russia
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12
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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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13
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Ripoll DR, Chaudhury S, Wallqvist A. Using the antibody-antigen binding interface to train image-based deep neural networks for antibody-epitope classification. PLoS Comput Biol 2021; 17:e1008864. [PMID: 33780441 PMCID: PMC8032195 DOI: 10.1371/journal.pcbi.1008864] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [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: 10/02/2020] [Revised: 04/08/2021] [Accepted: 03/10/2021] [Indexed: 12/05/2022] Open
Abstract
High-throughput B-cell sequencing has opened up new avenues for investigating complex mechanisms underlying our adaptive immune response. These technological advances drive data generation and the need to mine and analyze the information contained in these large datasets, in particular the identification of therapeutic antibodies (Abs) or those associated with disease exposure and protection. Here, we describe our efforts to use artificial intelligence (AI)-based image-analyses for prospective classification of Abs based solely on sequence information. We hypothesized that Abs recognizing the same part of an antigen share a limited set of features at the binding interface, and that the binding site regions of these Abs share share common structure and physicochemical property patterns that can serve as a "fingerprint" to recognize uncharacterized Abs. We combined large-scale sequence-based protein-structure predictions to generate ensembles of 3-D Ab models, reduced the Ab binding interface to a 2-D image (fingerprint), used pre-trained convolutional neural networks to extract features, and trained deep neural networks (DNNs) to classify Abs. We evaluated this approach using Ab sequences derived from human HIV and Ebola viral infections to differentiate between two Abs, Abs belonging to specific B-cell family lineages, and Abs with different epitope preferences. In addition, we explored a different type of DNN method to detect one class of Abs from a larger pool of Abs. Testing on Ab sets that had been kept aside during model training, we achieved average prediction accuracies ranging from 71-96% depending on the complexity of the classification task. The high level of accuracies reached during these classification tests suggests that the DNN models were able to learn a series of structural patterns shared by Abs belonging to the same class. The developed methodology provides a means to apply AI-based image recognition techniques to analyze high-throughput B-cell sequencing datasets (repertoires) for Ab classification.
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Affiliation(s)
- Daniel R. Ripoll
- DoD Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Maryland, United States of America
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. (HJF), Bethesda, Maryland, United States of America
| | - Sidhartha Chaudhury
- DoD Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Maryland, United States of America
- Center for Enabling Capabilities, Walter Reed Army Institute of Research, Silver Spring, Maryland, United States of America
| | - Anders Wallqvist
- DoD Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Maryland, United States of America
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14
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Wong WK, Robinson SA, Bujotzek A, Georges G, Lewis AP, Shi J, Snowden J, Taddese B, Deane CM. Ab-Ligity: identifying sequence-dissimilar antibodies that bind to the same epitope. MAbs 2021; 13:1873478. [PMID: 33448242 PMCID: PMC7833755 DOI: 10.1080/19420862.2021.1873478] [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] [Indexed: 12/18/2022] Open
Abstract
Solving the structure of an antibody-antigen complex gives atomic level information of the interactions between an antibody and its antigen, but such structures are expensive and hard to obtain. Alternative experimental sources include epitope mapping and binning experiments, which can be used as a surrogate to identify key interacting residues. However, their resolution is usually not sufficient to identify if two antibodies have identical interactions. Computational approaches to this problem have so far been based on the premise that antibodies with similar sequences behave similarly. Such approaches will fail to identify sequence-distant antibodies that target the same epitope. Here, we present Ab-Ligity, a structure-based similarity measure tailored to antibody-antigen interfaces. Using predicted paratopes on model antibody structures, we assessed its ability to identify those antibodies that target highly similar epitopes. Most antibodies adopting similar binding modes can be identified from sequence similarity alone, using methods such as clonotyping. In the challenging subset of antibodies whose sequences differ significantly, Ab-Ligity is still able to predict antibodies that would bind to highly similar epitopes (precision of 0.95 and recall of 0.69). We compared Ab-Ligity's performance to an existing tool for comparing general protein interfaces, InterComp, and showed improved performance on antibody cases achieved in a substantially reduced time. These results suggest that Ab-Ligity will allow the identification of diverse (sequence-dissimilar) antibodies that bind to the same epitopes from large datasets such as immune repertoires. The tool is available at http://opig.stats.ox.ac.uk/resources.
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Affiliation(s)
- Wing Ki Wong
- Department of Statistics, University of Oxford , Oxford, UK
| | | | - Alexander Bujotzek
- Roche Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich , Penzberg, Germany
| | - Guy Georges
- Roche Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich , Penzberg, Germany
| | - Alan P Lewis
- Data and Computational Sciences, GlaxoSmithKline Research and Development , Stevenage, UK
| | | | | | - Bruck Taddese
- Discovery Sciences, BioPharmaceuticals, R&D, AstraZeneca , Cambridge, UK
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15
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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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16
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Ghraichy M, Galson JD, Kovaltsuk A, von Niederhäusern V, Pachlopnik Schmid J, Recher M, Jauch AJ, Miho E, Kelly DF, Deane CM, Trück J. Maturation of the Human Immunoglobulin Heavy Chain Repertoire With Age. Front Immunol 2020; 11:1734. [PMID: 32849618 PMCID: PMC7424015 DOI: 10.3389/fimmu.2020.01734] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [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: 04/06/2020] [Accepted: 06/29/2020] [Indexed: 01/01/2023] Open
Abstract
B cells play a central role in adaptive immune processes, mainly through the production of antibodies. The maturation of the B cell system with age is poorly studied. We extensively investigated age-related alterations of naïve and antigen-experienced immunoglobulin heavy chain (IgH) repertoires. The most significant changes were observed in the first 10 years of life, and were characterized by altered immunoglobulin gene usage and an increased frequency of mutated antibodies structurally diverging from their germline precursors. Older age was associated with an increased usage of downstream IgH constant region genes and fewer antibodies with self-reactive properties. As mutations accumulated with age, the frequency of germline-encoded self-reactive antibodies decreased, indicating a possible beneficial role of self-reactive B cells in the developing immune system. Our results suggest a continuous process of change through childhood across a broad range of parameters characterizing IgH repertoires and stress the importance of using well-selected, age-appropriate controls in IgH studies.
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Affiliation(s)
- Marie Ghraichy
- Division of Immunology, University Children's Hospital, University of Zurich, Zurich, Switzerland.,Children's Research Center, University of Zurich, Zurich, Switzerland
| | - Jacob D Galson
- Children's Research Center, University of Zurich, Zurich, Switzerland.,Alchemab Therapeutics Ltd, London, United Kingdom
| | | | - Valentin von Niederhäusern
- Division of Immunology, University Children's Hospital, University of Zurich, Zurich, Switzerland.,Children's Research Center, University of Zurich, Zurich, Switzerland
| | - Jana Pachlopnik Schmid
- Division of Immunology, University Children's Hospital, University of Zurich, Zurich, Switzerland.,Children's Research Center, University of Zurich, Zurich, Switzerland
| | - Mike Recher
- Immunodeficiency Laboratory, Department of Biomedicine, University and University Hospital of Basel, Basel, Switzerland
| | - Annaïse J Jauch
- Immunodeficiency Laboratory, Department of Biomedicine, University and University Hospital of Basel, Basel, Switzerland
| | - Enkelejda Miho
- Institute of Medical Engineering and Medical Informatics, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Muttenz, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.,aiNET GmbH, Basel, Switzerland
| | - Dominic F Kelly
- Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford, United Kingdom.,Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Charlotte M Deane
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Johannes Trück
- Division of Immunology, University Children's Hospital, University of Zurich, Zurich, Switzerland.,Children's Research Center, University of Zurich, Zurich, Switzerland
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17
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Teraguchi S, Saputri DS, Llamas-Covarrubias MA, Davila A, Diez D, Nazlica SA, Rozewicki J, Ismanto HS, Wilamowski J, Xie J, Xu Z, Loza-Lopez MDJ, van Eerden FJ, Li S, Standley DM. Methods for sequence and structural analysis of B and T cell receptor repertoires. Comput Struct Biotechnol J 2020; 18:2000-2011. [PMID: 32802272 PMCID: PMC7366105 DOI: 10.1016/j.csbj.2020.07.008] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [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: 04/30/2020] [Revised: 07/08/2020] [Accepted: 07/08/2020] [Indexed: 02/07/2023] Open
Abstract
B cell receptors (BCRs) and T cell receptors (TCRs) make up an essential network of defense molecules that, collectively, can distinguish self from non-self and facilitate destruction of antigen-bearing cells such as pathogens or tumors. The analysis of BCR and TCR repertoires plays an important role in both basic immunology as well as in biotechnology. Because the repertoires are highly diverse, specialized software methods are needed to extract meaningful information from BCR and TCR sequence data. Here, we review recent developments in bioinformatics tools for analysis of BCR and TCR repertoires, with an emphasis on those that incorporate structural features. After describing the recent sequencing technologies for immune receptor repertoires, we survey structural modeling methods for BCR and TCRs, along with methods for clustering such models. We review downstream analyses, including BCR and TCR epitope prediction, antibody-antigen docking and TCR-peptide-MHC Modeling. We also briefly discuss molecular dynamics in this context.
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Affiliation(s)
- Shunsuke Teraguchi
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Dianita S. Saputri
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Mara Anais Llamas-Covarrubias
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
- Departamento de Biología Molecular y Genómica, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Mexico
| | - Ana Davila
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Diego Diez
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Sedat Aybars Nazlica
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - John Rozewicki
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Hendra S. Ismanto
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Jan Wilamowski
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Jiaqi Xie
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Zichang Xu
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | | | - Floris J. van Eerden
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Songling Li
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Daron M. Standley
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
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18
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Cao Y, Su B, Guo X, Sun W, Deng Y, Bao L, Zhu Q, Zhang X, Zheng Y, Geng C, Chai X, He R, Li X, Lv Q, Zhu H, Deng W, Xu Y, Wang Y, Qiao L, Tan Y, Song L, Wang G, Du X, Gao N, Liu J, Xiao J, Su XD, Du Z, Feng Y, Qin C, Qin C, Jin R, Xie XS. Potent Neutralizing Antibodies against SARS-CoV-2 Identified by High-Throughput Single-Cell Sequencing of Convalescent Patients' B Cells. Cell 2020; 182:73-84.e16. [PMID: 32425270 PMCID: PMC7231725 DOI: 10.1016/j.cell.2020.05.025] [Citation(s) in RCA: 904] [Impact Index Per Article: 226.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 05/07/2020] [Accepted: 05/13/2020] [Indexed: 12/28/2022]
Abstract
The COVID-19 pandemic urgently needs therapeutic and prophylactic interventions. Here, we report the rapid identification of SARS-CoV-2-neutralizing antibodies by high-throughput single-cell RNA and VDJ sequencing of antigen-enriched B cells from 60 convalescent patients. From 8,558 antigen-binding IgG1+ clonotypes, 14 potent neutralizing antibodies were identified, with the most potent one, BD-368-2, exhibiting an IC50 of 1.2 and 15 ng/mL against pseudotyped and authentic SARS-CoV-2, respectively. BD-368-2 also displayed strong therapeutic and prophylactic efficacy in SARS-CoV-2-infected hACE2-transgenic mice. Additionally, the 3.8 Å cryo-EM structure of a neutralizing antibody in complex with the spike-ectodomain trimer revealed the antibody’s epitope overlaps with the ACE2 binding site. Moreover, we demonstrated that SARS-CoV-2-neutralizing antibodies could be directly selected based on similarities of their predicted CDR3H structures to those of SARS-CoV-neutralizing antibodies. Altogether, we showed that human neutralizing antibodies could be efficiently discovered by high-throughput single B cell sequencing in response to pandemic infectious diseases.
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Affiliation(s)
- Yunlong Cao
- Beijing Advanced Innovation Center for Genomics (ICG) & Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing 100871, China
| | - Bin Su
- Beijing Youan Hospital, Capital Medical University, Beijing 100069, China
| | - Xianghua Guo
- Beijing Youan Hospital, Capital Medical University, Beijing 100069, China
| | - Wenjie Sun
- Beijing Advanced Innovation Center for Genomics (ICG) & Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing 100871, China
| | - Yongqiang Deng
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing 100071, China
| | - Linlin Bao
- Key Laboratory of Human Disease Comparative Medicine, Chinese Ministry of Health, Beijing Key Laboratory for Animal Models of Emerging and Remerging Infectious Diseases, Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Comparative Medicine Center, Peking Union Medical College, Beijing, China
| | - Qinyu Zhu
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China; Peking-Tsinghua Center for Life Sciences (CLS), Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Xu Zhang
- Singlomics (Beijing DanXu Pharmaceuticals), Beijing 102206, China
| | - Yinghui Zheng
- Beijing Advanced Innovation Center for Genomics (ICG) & Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing 100871, China
| | - Chenyang Geng
- Beijing Advanced Innovation Center for Genomics (ICG) & Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing 100871, China
| | - Xiaoran Chai
- Beijing Advanced Innovation Center for Genomics (ICG) & Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing 100871, China
| | - Runsheng He
- Beijing Advanced Innovation Center for Genomics (ICG) & Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing 100871, China
| | - Xiaofeng Li
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing 100071, China
| | - Qi Lv
- Key Laboratory of Human Disease Comparative Medicine, Chinese Ministry of Health, Beijing Key Laboratory for Animal Models of Emerging and Remerging Infectious Diseases, Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Comparative Medicine Center, Peking Union Medical College, Beijing, China
| | - Hua Zhu
- Key Laboratory of Human Disease Comparative Medicine, Chinese Ministry of Health, Beijing Key Laboratory for Animal Models of Emerging and Remerging Infectious Diseases, Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Comparative Medicine Center, Peking Union Medical College, Beijing, China
| | - Wei Deng
- Key Laboratory of Human Disease Comparative Medicine, Chinese Ministry of Health, Beijing Key Laboratory for Animal Models of Emerging and Remerging Infectious Diseases, Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Comparative Medicine Center, Peking Union Medical College, Beijing, China
| | - Yanfeng Xu
- Key Laboratory of Human Disease Comparative Medicine, Chinese Ministry of Health, Beijing Key Laboratory for Animal Models of Emerging and Remerging Infectious Diseases, Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Comparative Medicine Center, Peking Union Medical College, Beijing, China
| | - Yanjun Wang
- Beijing Youan Hospital, Capital Medical University, Beijing 100069, China
| | - Luxin Qiao
- Beijing Youan Hospital, Capital Medical University, Beijing 100069, China
| | - Yafang Tan
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing 100071, China
| | - Liyang Song
- Beijing Advanced Innovation Center for Genomics (ICG) & Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing 100871, China; State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China
| | - Guopeng Wang
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China
| | - Xiaoxia Du
- Beijing Advanced Innovation Center for Genomics (ICG) & Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing 100871, China; State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China
| | - Ning Gao
- Peking-Tsinghua Center for Life Sciences (CLS), Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China
| | - Jiangning Liu
- Key Laboratory of Human Disease Comparative Medicine, Chinese Ministry of Health, Beijing Key Laboratory for Animal Models of Emerging and Remerging Infectious Diseases, Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Comparative Medicine Center, Peking Union Medical College, Beijing, China
| | - Junyu Xiao
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China; Peking-Tsinghua Center for Life Sciences (CLS), Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Xiao-Dong Su
- Beijing Advanced Innovation Center for Genomics (ICG) & Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing 100871, China; State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China
| | - Zongmin Du
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing 100071, China
| | - Yingmei Feng
- Beijing Youan Hospital, Capital Medical University, Beijing 100069, China
| | - Chuan Qin
- Key Laboratory of Human Disease Comparative Medicine, Chinese Ministry of Health, Beijing Key Laboratory for Animal Models of Emerging and Remerging Infectious Diseases, Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Comparative Medicine Center, Peking Union Medical College, Beijing, China.
| | - Chengfeng Qin
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing 100071, China.
| | - Ronghua Jin
- Beijing Youan Hospital, Capital Medical University, Beijing 100069, China.
| | - X Sunney Xie
- Beijing Advanced Innovation Center for Genomics (ICG) & Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences (CLS), Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; School of Life Sciences, Peking University, Beijing 100871, China.
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19
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Abstract
Antibodies are vital proteins of the immune system that recognize potentially harmful molecules and initiate their removal. Mammals can efficiently create vast numbers of antibodies with different sequences capable of binding to any antigen with high affinity and specificity. Because they can be developed to bind to many disease agents, antibodies can be used as therapeutics. In an organism, after antigen exposure, antibodies specific to that antigen are enriched through clonal selection, expansion, and somatic hypermutation. The antibodies present in an organism therefore report on its immune status, describe its innate ability to deal with harmful substances, and reveal how it has previously responded. Next-generation sequencing technologies are being increasingly used to query the antibody, or B-cell receptor (BCR), sequence repertoire, and the amount of BCR data in public repositories is growing. The Observed Antibody Space database, for example, currently contains over a billion sequences from 68 different studies. Repertoires are available that represent both the naive state (i.e. antigen-inexperienced) and that after immunization. This wealth of data has created opportunities to learn more about our immune system. In this review, we discuss the many ways in which BCR repertoire data have been or could be exploited. We highlight its utility for providing insights into how the naive immune repertoire is generated and how it responds to antigens. We also consider how structural information can be used to enhance these data and may lead to more accurate depictions of the sequence space and to applications in the discovery of new therapeutics.
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Affiliation(s)
- Claire Marks
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Charlotte M Deane
- Department of Statistics, University of Oxford, Oxford, United Kingdom
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