Conformational flexibility in neutralization of SARS-CoV-2 by naturally elicited anti-SARS-CoV-2 antibodies

Cryo-EM reveals conformational variability in the SARS-CoV-2 spike protein RBD induced by two broadly neutralizing monoclonal antibodies

SARS-CoV-2 spike proteins play a critical role in infection by interacting with the ACE2 receptors. Their receptor-binding domains and N-terminal domains exhibit remarkable flexibility and can adopt various conformations that facilitate receptor engagement. Previous structural studies have reported the RBD of the spike protein in "up", "down", and various intermediate states, as well as its different conformational changes during ACE2 binding. This flexibility also influences its interactions with the neutralizing antibodies, yet its role in the antibody complexes remains understudied. In this study, we used cryo-electron microscopy to investigate the structural properties of two broadly neutralizing monoclonal antibodies, THSC20.HVTR04 and THSC20.HVTR26. These antibodies were isolated from an unvaccinated individual and demonstrated potent neutralization of multiple SARS-CoV-2 variants. Our analysis revealed distinct binding characteristics and conformational changes in the spike RBD upon binding with the monoclonal antibodies. The structural characterization of the spike protein-monoclonal antibody complexes provided valuable insights into the structural variability of the spike protein and the possible mechanisms for antibody-mediated neutralization.

Challenges and compromises: Predicting unbound antibody structures with deep learning

Therapeutic antibodies are manufactured, stored and administered in the free state; this makes understanding the unbound form key to designing and improving development pipelines. Prediction of unbound antibodies is challenging, specifically modelling of the CDRH3 loop, where inaccuracies are potentially worse due to a bias in structural data towards antibody-antigen complexes. This class imbalance provides a challenge for deep learning models trained on this data, potentially limiting generalisation to unbound forms. Here we discuss the importance of unbound structures in antibody development pipelines. We explore how the latest generation of structure predictors can provide new insights and assess how conformational heterogeneity may influence binding kinetics. We hypothesise that generative models may address some of these issues. While prediction of antibodies in complex is essential, we should not ignore the need for progress in modelling the unbound form.

Ultrasensitive detection of intact SARS-CoV-2 particles in complex biofluids using microfluidic affinity capture

Measuring virus in biofluids is complicated by confounding biomolecules coisolated with viral nucleic acids. To address this, we developed an affinity-based microfluidic device for specific capture of intact severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our approach used an engineered angiotensin-converting enzyme 2 to capture intact virus from plasma and other complex biofluids. Our device leverages a staggered herringbone pattern, nanoparticle surface coating, and processing conditions to achieve detection of as few as 3 viral copies per milliliter. We further validated our microfluidic assay on 103 plasma, 36 saliva, and 29 stool samples collected from unique patients with COVID-19, showing SARS-CoV-2 detection in 72% of plasma samples. Longitudinal monitoring in the plasma revealed our device's capacity for ultrasensitive detection of active viral infections over time. Our technology can be adapted to target other viruses using relevant cell entry molecules for affinity capture. This versatility underscores the potential for widespread application in viral load monitoring and disease management.

Improved deep learning prediction of antigen-antibody interactions

Identifying antibodies that neutralize specific antigens is crucial for developing effective immunotherapies, but this task remains challenging for many target antigens. The rise of deep learning-based computational approaches presents a promising avenue to address this challenge. Here, we assess the performance of a deep learning approach through two benchmark tests aimed at predicting antibodies for the receptor-binding domain of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. Three different strategies for constructing input sequence alignments are employed for predicting structural models of antigen-antibody complexes. In our initial testing set, which comprises known experimental structures, these strategies collectively yield a significant top-ranked prediction for 61% of cases and a success rate of 47%. Notably, one strategy that utilizes the sequences of known antigen binders outperforms the other two, achieving a precision of 90% in a subsequent test set of ~1,000 antibodies, balanced between true and control antibodies for the antigen, albeit with a lower recall of 25%. Our results underscore the potential of integrating deep learning methods with single B cell sequencing techniques to enhance the prediction accuracy of antigen-antibody interactions.

Design and Development of an Antigen Test for SARS-CoV-2 Nucleocapsid Protein to Validate the Viral Quality Assurance Panels

The continuing mutability of the SARS-CoV-2 virus can result in failures of diagnostic assays. To address this, we describe a generalizable bioinformatics-to-biology pipeline developed for the calibration and quality assurance of inactivated SARS-CoV-2 variant panels provided to Radical Acceleration of Diagnostics programs (RADx)-radical program awardees. A heuristic genetic analysis based on variant-defining mutations demonstrated the lowest genetic variance in the Nucleocapsid protein (Np)-C-terminal domain (CTD) across all SARS-CoV-2 variants. We then employed the Shannon entropy method on (Np) sequences collected from the major variants, verifying the CTD with lower entropy (less prone to mutations) than other Np regions. Polyclonal and monoclonal antibodies were raised against this target CTD antigen and used to develop an Enzyme-linked immunoassay (ELISA) test for SARS-CoV-2. Blinded Viral Quality Assurance (VQA) panels comprised of UV-inactivated SARS-CoV-2 variants (XBB.1.5, BF.7, BA.1, B.1.617.2, and WA1) and distractor respiratory viruses (CoV 229E, CoV OC43, RSV A2, RSV B, IAV H1N1, and IBV) were assembled by the RADx-rad Diagnostics core and tested using the ELISA described here. The assay tested positive for all variants with high sensitivity (limit of detection: 1.72-8.78 ng/mL) and negative for the distractor virus panel. Epitope mapping for the monoclonal antibodies identified a 20 amino acid antigenic peptide on the Np-CTD that an in-silico program also predicted for the highest antigenicity. This work provides a template for a bioinformatics pipeline to select genetic regions with a low propensity for mutation (low Shannon entropy) to develop robust 'pan-variant' antigen-based assays for viruses prone to high mutational rates.

Neutralizing antibodies to block viral entry and for identification of entry inhibitors

Neutralizing antibodies (NAbs) are naturally produced by our immune system to combat viral infections. Clinically, neutralizing antibodies with potent efficacy and high specificity have been extensively used to prevent and treat a wide variety of viral infections, including Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), Human Immunodeficiency Virus (HIV), Dengue Virus (DENV) and Hepatitis B Virus (HBV). An overwhelmingly large subset of clinically effective NAbs operates by targeting viral envelope proteins to inhibit viral entry into the host cell. Binding of viral envelope protein to the host receptor is a critical rate limiting step triggering a cascade of downstream events, including endocytosis, membrane fusion and pore formation to allow viral entry. In recent years, improved structural knowledge on these processes have allowed researchers to also leverage NAbs as an indispensable tool in guiding discovery of novel antiviral entry inhibitors, providing drug candidates with high efficacy and pan-genus specificity. This review will summarize the latest progresses on the applications of NAbs as effective entry inhibitors and as important tools to develop antiviral therapeutics by high-throughput drug screenings, rational design of peptidic entry inhibitor mimicking NAbs and in silico computational modeling approaches.

Evaluation of the Ability of AlphaFold to Predict the Three-Dimensional Structures of Antibodies and Epitopes

Being able to accurately predict the three-dimensional structure of an Ab can facilitate Ab characterization and epitope prediction, with important diagnostic and clinical implications. In this study, we evaluated the ability of AlphaFold to predict the structures of 222 recently published, high-resolution Fab H and L chain structures of Abs from different species directed against different Ags. We show that although the overall Ab prediction quality is in line with the results of CASP14, regions such as the complementarity-determining regions (CDRs) of the H chain, which are prone to higher variation, are predicted less accurately. Moreover, we discovered that AlphaFold mispredicts the bending angles between the variable and constant domains. To evaluate the ability of AlphaFold to model Ab-Ag interactions based only on sequence, we used AlphaFold-Multimer in combination with ZDOCK to predict the structures of 26 known Ab-Ag complexes. ZDOCK, which was applied on bound components of both the Ab and the Ag, succeeded in assembling 11 complexes, whereas AlphaFold succeeded in predicting only 2 of 26 models, with significant deviations in the docking contacts predicted in the rest of the molecules. Within the 11 complexes that were successfully predicted by ZDOCK, 9 involved short-peptide Ags (18-mer or less), whereas only 2 were complexes of Ab with a full-length protein. Docking of modeled unbound Ab and Ag was unsuccessful. In summary, our study provides important information about the abilities and limitations of using AlphaFold to predict Ab-Ag interactions and suggests areas for possible improvement.

Monkeypox infection elicits strong antibody and B cell response against A35R and H3L antigens

Monkeypox virus (MPXV) resides in two forms; mature and enveloped, and depending on it, distinct proteins are displayed on the viral surface. Here, we expressed two MPXV antigens from the mature, and one from the enveloped form, and tested their reactivity to sera of 11 MPXV recoverees while comparing to sera from recently and past vaccinated individuals. 8 out of 11 recoverees exhibited detectable neutralization levels against Vaccinia Lister. Sera from all recoverees bound strongly to A35R and H3L antigens. Moreover, the responses to A35R were significantly higher within the recoverees compared to both recently and past vaccinated donors. Lastly, A35R- and H3L-specific IgG⁺ B cells ranging from 0.03-0.46% and 0.11-0.36%, respectively, were detected in all recoverees (A35R), and in 9 out of 11 recoverees (H3L). Therefore, A35R and H3L represent MPXV immune targets and could be used in a heat-inactivated serological ELISA for the identification of recent MPXV infection.