General Trends of the Camelidae Antibody VHHs Domain Dynamics.

Conformational flexibility plays an essential role in antibodies' functional and structural stability. They facilitate and determine the strength of antigen-antibody interactions. Camelidae express an interesting subtype of single-chain antibody, named Heavy Chain only Antibody. They have only one N-terminal Variable domain (VHH) per chain, composed of Frameworks (FRs) and Complementarity Determining regions (CDRs) like their VH and VL counterparts in IgG. Even when expressed independently, VHH domains display excellent solubility and (thermo)stability, which helps them to retain their impressive interaction capabilities. Sequence and structural features of VHH domains contributing to these abilities have already been studied compared to classical antibodies. To have the broadest view and understand the changes in dynamics of these macromolecules, large-scale molecular dynamics simulations for a large number of non-redundant VHH structures have been performed for the first time. This analysis reveals the most prevalent movements in these domains. It reveals the four main classes of VHHs dynamics. Diverse local changes were observed in CDRs with various intensities. Similarly, different types of constraints were observed in CDRs, while FRs close to CDRs were sometimes primarily impacted. This study sheds light on the changes in flexibility in different regions of VHH that may impact their in silico design.

Functional antibodies exhibit light chain coherence.

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 antigens^2-22. 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 clonotypes^23,24. 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.

MG1141A as a Highly Potent Monoclonal Neutralizing Antibody Against SARS-CoV-2 Variants.

Since the coronavirus disease outbreak in 2019, several antibody therapeutics have been developed to treat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Antibody therapeutics are effective in neutralizing the virus and reducing hospitalization in patients with mild and moderate infections. These therapeutics target the spike protein of SARS-CoV-2; however, emerging mutations in this protein reduce their efficiency. In this study, we developed a universal SARS-CoV-2 neutralizing antibody. We generated a humanized monoclonal antibody, MG1141A, against the receptor-binding domain of the spike protein through traditional mouse immunization. We confirmed that MG1141A could effectively neutralize live viruses, with an EC50 of 92 pM, and that it exhibited effective Fc-mediated functions. Additionally, it retained its neutralizing activity against the alpha (UK), beta (South Africa), and gamma (Brazil) variants of SARS-CoV-2. Taken together, our study contributes to the development of a novel antibody therapeutic approach, which can effectively combat emerging SARS-CoV-2 mutations.

Genetic and structural basis for SARS-CoV-2 variant neutralization by a two-antibody cocktail.

Understanding the molecular basis for immune recognition of SARS-CoV-2 spike glycoprotein antigenic sites will inform the development of improved therapeutics. We determined the structures of two human monoclonal antibodies-AZD8895 and AZD1061-which form the basis of the investigational antibody cocktail AZD7442, in complex with the receptor-binding domain (RBD) of SARS-CoV-2 to define the genetic and structural basis of neutralization. AZD8895 forms an 'aromatic cage' at the heavy/light chain interface using germ line-encoded residues in complementarity-determining regions (CDRs) 2 and 3 of the heavy chain and CDRs 1 and 3 of the light chain. These structural features explain why highly similar antibodies (public clonotypes) have been isolated from multiple individuals. AZD1061 has an unusually long LCDR1; the HCDR3 makes interactions with the opposite face of the RBD from that of AZD8895. Using deep mutational scanning and neutralization escape selection experiments, we comprehensively mapped the crucial binding residues of both antibodies and identified positions of concern with regards to virus escape from antibody-mediated neutralization. Both AZD8895 and AZD1061 have strong neutralizing activity against SARS-CoV-2 and variants of concern with antigenic substitutions in the RBD. We conclude that germ line-encoded antibody features enable recognition of the SARS-CoV-2 spike RBD and demonstrate the utility of the cocktail AZD7442 in neutralizing emerging variant viruses.

GIANA allows computationally-efficient TCR clustering and multi-disease repertoire classification by isometric transformation.

Similarity in T-cell receptor (TCR) sequences implies shared antigen specificity between receptors, and could be used to discover novel therapeutic targets. However, existing methods that cluster T-cell receptor sequences by similarity are computationally inefficient, making them impractical to use on the ever-expanding datasets of the immune repertoire. Here, we developed GIANA (Geometric Isometry-based TCR AligNment Algorithm) a computationally efficient tool for this task that provides the same level of clustering specificity as TCRdist at 600 times its speed, and without sacrificing accuracy. GIANA also allows the rapid query of large reference cohorts within minutes. Using GIANA to cluster large-scale TCR datasets provides candidate disease-specific receptors, and provides a new solution to repertoire classification. Querying unseen TCR-seq samples against an existing reference differentiates samples from patients across various cohorts associated with cancer, infectious and autoimmune disease. Our results demonstrate how GIANA could be used as the basis for a TCR-based non-invasive multi-disease diagnostic platform.

Sequence signatures of two public antibody clonotypes that bind SARS-CoV-2 receptor binding domain.

Since the COVID-19 pandemic onset, the antibody response to SARS-CoV-2 has been extensively characterized. Antibodies to the receptor binding domain (RBD) on the spike protein are frequently encoded by IGHV3-53/3-66 with a short complementarity-determining region (CDR) H3. Germline-encoded sequence motifs in heavy chain CDRs H1 and H2 have a major function, but whether any common motifs are present in CDR H3, which is often critical for binding specificity, is not clear. Here, we identify two public clonotypes of IGHV3-53/3-66 RBD antibodies with a 9-residue CDR H3 that pair with different light chains. Distinct sequence motifs on CDR H3 are present in the two public clonotypes that seem to be related to differential light chain pairing. Additionally, we show that Y58F is a common somatic hypermutation that results in increased binding affinity of IGHV3-53/3-66 RBD antibodies with a short CDR H3. These results advance understanding of the antibody response to SARS-CoV-2.

Use of machine learning to identify a T cell response to SARS-CoV-2.

The identification of SARS-CoV-2-specific T cell receptor (TCR) sequences is critical for understanding T cell responses to SARS-CoV-2. Accordingly, we reanalyze publicly available data from SARS-CoV-2-recovered patients who had low-severity disease (n = 17) and SARS-CoV-2 infection-naive (control) individuals (n = 39). Applying a machine learning approach to TCR beta (TRB) repertoire data, we can classify patient/control samples with a training sensitivity, specificity, and accuracy of 88.2%, 100%, and 96.4% and a testing sensitivity, specificity, and accuracy of 82.4%, 97.4%, and 92.9%, respectively. Interestingly, the same machine learning approach cannot separate SARS-CoV-2 recovered from SARS-CoV-2 infection-naive individual samples on the basis of B cell receptor (immunoglobulin heavy chain; IGH) repertoire data, suggesting that the T cell response to SARS-CoV-2 may be more stereotyped and longer lived. Following validation in larger cohorts, our method may be useful in detecting protective immunity acquired through natural infection or in determining the longevity of vaccine-induced immunity.

Structural basis of a shared antibody response to SARS-CoV-2.

Molecular understanding of neutralizing antibody responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could accelerate vaccine design and drug discovery. We analyzed 294 anti-SARS-CoV-2 antibodies and found that immunoglobulin G heavy-chain variable region 3-53 (IGHV3-53) is the most frequently used IGHV gene for targeting the receptor-binding domain (RBD) of the spike protein. Co-crystal structures of two IGHV3-53-neutralizing antibodies with RBD, with or without Fab CR3022, at 2.33- to 3.20-angstrom resolution revealed that the germline-encoded residues dominate recognition of the angiotensin I converting enzyme 2 (ACE2)-binding site. This binding mode limits the IGHV3-53 antibodies to short complementarity-determining region H3 loops but accommodates light-chain diversity. These IGHV3-53 antibodies show minimal affinity maturation and high potency, which is promising for vaccine design. Knowledge of these structural motifs and binding mode should facilitate the design of antigens that elicit this type of neutralizing response.