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  1. Article ; Online: Synthetic coevolution reveals adaptive mutational trajectories of neutralizing antibodies and SARS-CoV-2

    Ehling, Roy A. / Minot, Mason / Overath, Max D. / Sheward, Daniel J. / Han, Jiami / Gao, Beichen / Taft, Joseph M. / Pertseva, Margarita / Weber, Cédric R. / Frei, Lester / Bikias, Thomas / Murrell, Ben / Reddy, Sai T.

    bioRxiv

    Abstract: The Covid-19 pandemic showcases a coevolutionary race between the human immune system and SARS-CoV-2, mirroring the Red Queen hypothesis of evolutionary biology. The immune system generates neutralizing antibodies targeting the SARS-CoV-2 spike protein9s ...

    Abstract The Covid-19 pandemic showcases a coevolutionary race between the human immune system and SARS-CoV-2, mirroring the Red Queen hypothesis of evolutionary biology. The immune system generates neutralizing antibodies targeting the SARS-CoV-2 spike protein9s receptor binding domain (RBD), crucial for host cell invasion, while the virus evolves to evade antibody recognition. Here, we establish a synthetic coevolution system combining high-throughput screening of antibody and RBD variant libraries with protein mutagenesis, surface display, and deep sequencing. Additionally, we train a protein language machine learning model that predicts antibody escape to RBD variants. Synthetic coevolution reveals antagonistic and compensatory mutational trajectories of neutralizing antibodies and SARS-CoV-2 variants, enhancing the understanding of this evolutionary conflict.
    Keywords covid19
    Language English
    Publishing date 2024-04-01
    Publisher Cold Spring Harbor Laboratory
    Document type Article ; Online
    DOI 10.1101/2024.03.28.587189
    Database COVID19

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  2. Article: Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain

    Taft, Joseph M. / Weber, Cédric R. / Gao, Beichen / Ehling, Roy A. / Han, Jiami / Frei, Lester / Metcalfe, Sean W. / Overath, Max D. / Yermanos, Alexander / Kelton, William / Reddy, Sai T.

    Cell. 2022 Aug. 25,

    2022  

    Abstract: The continual evolution of SARS-CoV-2 and the emergence of variants that show resistance to vaccines and neutralizing antibodies threaten to prolong the COVID-19 pandemic. Selection and emergence of SARS-CoV-2 variants are driven in part by mutations ... ...

    Abstract The continual evolution of SARS-CoV-2 and the emergence of variants that show resistance to vaccines and neutralizing antibodies threaten to prolong the COVID-19 pandemic. Selection and emergence of SARS-CoV-2 variants are driven in part by mutations within the viral spike protein and in particular the ACE2 receptor-binding domain (RBD), a primary target site for neutralizing antibodies. Here, we develop deep mutational learning (DML), a machine-learning-guided protein engineering technology, which is used to investigate a massive sequence space of combinatorial mutations, representing billions of RBD variants, by accurately predicting their impact on ACE2 binding and antibody escape. A highly diverse landscape of possible SARS-CoV-2 variants is identified that could emerge from a multitude of evolutionary trajectories. DML may be used for predictive profiling on current and prospective variants, including highly mutated variants such as Omicron, thus guiding the development of therapeutic antibody treatments and vaccines for COVID-19.
    Keywords COVID-19 infection ; Severe acute respiratory syndrome coronavirus 2 ; antibodies ; evolution ; landscapes ; therapeutics
    Language English
    Dates of publication 2022-0825
    Publishing place Elsevier Inc.
    Document type Article
    Note Pre-press version
    ZDB-ID 187009-9
    ISSN 1097-4172 ; 0092-8674
    ISSN (online) 1097-4172
    ISSN 0092-8674
    DOI 10.1016/j.cell.2022.08.024
    Database NAL-Catalogue (AGRICOLA)

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  3. Article ; Online: Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain.

    Taft, Joseph M / Weber, Cédric R / Gao, Beichen / Ehling, Roy A / Han, Jiami / Frei, Lester / Metcalfe, Sean W / Overath, Max D / Yermanos, Alexander / Kelton, William / Reddy, Sai T

    Cell

    2022  Volume 185, Issue 21, Page(s) 4008–4022.e14

    Abstract: The continual evolution of SARS-CoV-2 and the emergence of variants that show resistance to vaccines and neutralizing antibodies threaten to prolong the COVID-19 pandemic. Selection and emergence of SARS-CoV-2 variants are driven in part by mutations ... ...

    Abstract The continual evolution of SARS-CoV-2 and the emergence of variants that show resistance to vaccines and neutralizing antibodies threaten to prolong the COVID-19 pandemic. Selection and emergence of SARS-CoV-2 variants are driven in part by mutations within the viral spike protein and in particular the ACE2 receptor-binding domain (RBD), a primary target site for neutralizing antibodies. Here, we develop deep mutational learning (DML), a machine-learning-guided protein engineering technology, which is used to investigate a massive sequence space of combinatorial mutations, representing billions of RBD variants, by accurately predicting their impact on ACE2 binding and antibody escape. A highly diverse landscape of possible SARS-CoV-2 variants is identified that could emerge from a multitude of evolutionary trajectories. DML may be used for predictive profiling on current and prospective variants, including highly mutated variants such as Omicron, thus guiding the development of therapeutic antibody treatments and vaccines for COVID-19.
    MeSH term(s) Angiotensin-Converting Enzyme 2/chemistry ; Angiotensin-Converting Enzyme 2/genetics ; Angiotensin-Converting Enzyme 2/metabolism ; Antibodies, Neutralizing ; Antibodies, Viral ; COVID-19 ; COVID-19 Vaccines ; Humans ; Mutation ; Pandemics ; Protein Binding ; SARS-CoV-2/genetics ; Spike Glycoprotein, Coronavirus/chemistry ; Spike Glycoprotein, Coronavirus/genetics ; Spike Glycoprotein, Coronavirus/metabolism
    Chemical Substances Antibodies, Neutralizing ; Antibodies, Viral ; COVID-19 Vaccines ; Spike Glycoprotein, Coronavirus ; spike protein, SARS-CoV-2 ; Angiotensin-Converting Enzyme 2 (EC 3.4.17.23)
    Language English
    Publishing date 2022-08-31
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 187009-9
    ISSN 1097-4172 ; 0092-8674
    ISSN (online) 1097-4172
    ISSN 0092-8674
    DOI 10.1016/j.cell.2022.08.024
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1167: Show issues Location:
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    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
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  4. Article ; Online: SARS-CoV-2 reactive and neutralizing antibodies discovered by single-cell sequencing of plasma cells and mammalian display.

    Ehling, Roy A / Weber, Cédric R / Mason, Derek M / Friedensohn, Simon / Wagner, Bastian / Bieberich, Florian / Kapetanovic, Edo / Vazquez-Lombardi, Rodrigo / Di Roberto, Raphaël B / Hong, Kai-Lin / Wagner, Camille / Pataia, Michele / Overath, Max D / Sheward, Daniel J / Murrell, Ben / Yermanos, Alexander / Cuny, Andreas P / Savic, Miodrag / Rudolf, Fabian /
    Reddy, Sai T

    Cell reports

    2021  Volume 38, Issue 3, Page(s) 110242

    Abstract: Characterization of COVID-19 antibodies has largely focused on memory B cells; however, it is the antibody-secreting plasma cells that are directly responsible for the production of serum antibodies, which play a critical role in resolving SARS-CoV-2 ... ...

    Abstract Characterization of COVID-19 antibodies has largely focused on memory B cells; however, it is the antibody-secreting plasma cells that are directly responsible for the production of serum antibodies, which play a critical role in resolving SARS-CoV-2 infection. Little is known about the specificity of plasma cells, largely because plasma cells lack surface antibody expression, thereby complicating their screening. Here, we describe a technology pipeline that integrates single-cell antibody repertoire sequencing and mammalian display to interrogate the specificity of plasma cells from 16 convalescent patients. Single-cell sequencing allows us to profile antibody repertoire features and identify expanded clonal lineages. Mammalian display screening is used to reveal that 43 antibodies (of 132 candidates) derived from expanded plasma cell lineages are specific to SARS-CoV-2 antigens, including antibodies with high affinity to the SARS-CoV-2 receptor-binding domain (RBD) that exhibit potent neutralization and broad binding to the RBD of SARS-CoV-2 variants (of concern/interest).
    MeSH term(s) Animals ; Antibodies, Neutralizing/isolation & purification ; Antibodies, Viral/isolation & purification ; COVID-19/immunology ; COVID-19/prevention & control ; Cells, Cultured ; Cohort Studies ; Gene Library ; HEK293 Cells ; High-Throughput Nucleotide Sequencing/methods ; Humans ; Mammals ; Neutralization Tests ; Peptide Library ; Plasma Cells/chemistry ; Plasma Cells/metabolism ; SARS-CoV-2/immunology ; Single-Cell Analysis/methods
    Chemical Substances Antibodies, Neutralizing ; Antibodies, Viral ; Peptide Library
    Language English
    Publishing date 2021-12-22
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2649101-1
    ISSN 2211-1247 ; 2211-1247
    ISSN (online) 2211-1247
    ISSN 2211-1247
    DOI 10.1016/j.celrep.2021.110242
    Database MEDical Literature Analysis and Retrieval System OnLINE

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