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A large-scale systematic survey of SARS-CoV-2 antibodies reveals recurring molecular features. | LitMetric

AI Article Synopsis

  • Researchers have identified and characterized a large collection of approximately 8,000 human antibodies against the SARS-CoV-2 spike protein, sourced from over 200 donors, revealing key insights into the immune response to COVID-19.
  • The study highlights common antibody responses, including specific genetic sequences and mutations, providing a clearer picture of how the human immune system reacts to this virus.
  • Additionally, advancements in deep learning techniques are shown to be effective in predicting antibody specificity, enhancing research for future vaccines and therapies.

Article Abstract

In the past two years, the global research in combating COVID-19 pandemic has led to isolation and characterization of numerous human antibodies to the SARS-CoV-2 spike. This enormous collection of antibodies provides an unprecedented opportunity to study the antibody response to a single antigen. From mining information derived from 88 research publications and 13 patents, we have assembled a dataset of ∼8,000 human antibodies to the SARS-CoV-2 spike from >200 donors. Analysis of antibody targeting of different domains of the spike protein reveals a number of common (public) responses to SARS-CoV-2, exemplified via recurring IGHV/IGK(L)V pairs, CDR H3 sequences, IGHD usage, and somatic hypermutation. We further present a proof-of-concept for prediction of antigen specificity using deep learning to differentiate sequences of antibodies to SARS-CoV-2 spike and to influenza hemagglutinin. Overall, this study not only provides an informative resource for antibody and vaccine research, but fundamentally advances our molecular understanding of public antibody responses to a viral pathogen.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8647650PMC
http://dx.doi.org/10.1101/2021.11.26.470157DOI Listing

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