Scientists from the United States recently characterized a number of antibodies to the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) to identify molecular features of public antibody responses to the SARS-CoV-2 spike protein.
To learn: A large-scale systematic investigation of SARS-CoV-2 antibodies reveals recurring molecular features. Photo credit: Mongkolchon Akesin / Shutterstock
The study is currently available on the preprint server nioRxiv * while the article is being peer-reviewed.
Since the coronavirus disease (COVID-19) pandemic began in 2019, many studies have been conducted to characterize the humoral immune responses triggered by natural SARS-CoV-2 infection and vaccination. Many monoclonal antibodies targeting the SARS-CoV-2 spike protein have been isolated and characterized as an attempt to identify effective therapeutic interventions.
The SARS-CoV-2 spike protein has three main domains, including the receptor binding domain (RBD), the N-terminal domain (NTD), and the S2 domain. Of these domains, the RBD is highly immunogenic and has been considered the primary target for neutralizing antibody development.
A public antibody response defines a group of antigen-specific antibodies isolated from different individuals that share genetic elements and modes of antigen recognition. The most common strategy for studying the public antibody response is to identify antibodies from different individuals that share the same immunoglobulin heavy variable (IGHV) gene and complementarity determining region (CDR) H3 sequences.
In the current study, the scientists carried out a systematic literature search and prepared a large data set of monoclonal anti-SARS-CoV-2 antibodies with donor information. With the data set, they examined the public antibody response to the SARS-CoV-2 spike protein.
The scientists analyzed a total of 88 published articles and 13 patents, and compiled a dataset of more than 8,000 anti-SARS-CoV-2 spike monoclonal antibodies isolated from more than 200 donors.
They analyzed the use of immunoglobulin variables (V) and observed that antibodies targeting RBD, NTD and S2 have different patterns of V gene use. Given the importance of CDR H3 in determining antigen-antibody binding, they determined the convergence of CDR H3 sequences among anti-spike antibodies. The results show that public antibody responses to RBD and S2 depend largely on the CDR H3 sequence, while the antigen binding sites (paratopes) of most anti-NTD antibodies are not dominated by CDR H3.
They identified a number of antibodies with paratopes, consisting mainly of CDR H3 and the light chain. In addition, they observed a high accumulation of an immunoglobulin heavy constant delta (IGHD) gene (IGHD1-26) among anti-S2 antibodies, which were predominantly encoded by the IGHV3-30 gene. About 70% of these antibodies have a CDR H3 of 14 amino acids. Upon further analysis, they found that the IGHD-dependent public antibody response to S2 is mainly driven by the heavy chain and that IGHV3-30 / IGHD1-26 represents a public antibody response to a highly conserved epitope in S2.
By analyzing somatic hypermutation among anti-SARS-CoV-2 antibodies, they identified several recurrent somatic hypermutations, including VH F27V, T28I, and Y58F, in IGHV3-53 / 3-66-encoded public clonotypes. In addition, they identified several new recurring heavy / light chain somatic hypermutations, including VL S29R in a public IGHV1-58 / IGKV3-20 clonotype.
Upon further analysis, they found that antibodies of the public clonotype IGHV1-58 / IGKV3-20 bind to spike RBD. According to the available literature, these antibodies could be induced both by vaccination and by infection with various SARS-CoV-2 variants. In addition, these antibodies are highly efficient at neutralizing various worrying SARS-CoV-2 variants (VOCs).
By analyzing the structure of the antigen-antibody complex, they identified that VL S29R forms a salt bridge with another somatic hypermutation to stabilize the antigen-antibody interaction.
Analysis of the antigen specificity
The scientists used a deep learning model to differentiate between anti-spike and anti-influenza hemagglutinin (HA) antibodies. They trained the model with six CDR sequences (H1, H2, H3, L1, L2, and L3) and used a total of 4,736 anti-spike antibodies and 2,204 anti-influenza HA antibodies for analysis.
The model performed best in distinguishing antigen-specific antibodies when trained on all six CDRs. Similar performance was also achieved when exercising on three heavy chain CDRs (H1, H2, and H3). When the model was trained on three light chain CDRs, the model performed reasonably. This indicates that the molecular information stored in the heavy chain sequence is of most value in determining antigen specificity.
The study identifies various molecular features of public antibody responses to the SARS-CoV-2 spike protein. As noted by the scientists, the study results can be used as a valuable resource to understand the molecular aspects that drive an antibody’s antigen specificity.
bioRxiv publishes preliminary scientific reports that have not been peer reviewed and therefore should not be considered conclusive, that guide clinical practice / health-related behavior or should be treated as established information.