An overview of methods for the structural and functional mapping of epitopes recognized by anti-SARS-CoV-2 antibodies

Vaccine process technology-A decade of progress

In the past decade, new approaches to the discovery and development of vaccines have transformed the field. Advances during the COVID-19 pandemic allowed the production of billions of vaccine doses per year using novel platforms such as messenger RNA and viral vectors. Improvements in the analytical toolbox, equipment, and bioprocess technology have made it possible to achieve both unprecedented speed in vaccine development and scale of vaccine manufacturing. Macromolecular structure-function characterization technologies, combined with improved modeling and data analysis, enable quantitative evaluation of vaccine formulations at single-particle resolution and guided design of vaccine drug substances and drug products. These advances play a major role in precise assessment of critical quality attributes of vaccines delivered by newer platforms. Innovations in label-free and immunoassay technologies aid in the characterization of antigenic sites and the development of robust in vitro potency assays. These methods, along with molecular techniques such as next-generation sequencing, will accelerate characterization and release of vaccines delivered by all platforms. Process analytical technologies for real-time monitoring and optimization of process steps enable the implementation of quality-by-design principles and faster release of vaccine products. In the next decade, the field of vaccine discovery and development will continue to advance, bringing together new technologies, methods, and platforms to improve human health.

Epitope mapping of SARS-CoV-2 RBDs by hydroxyl radical protein footprinting reveals the importance of including negative antibody controls

Understanding protein-protein interactions is crucial for drug design and investigating biological processes. Various techniques, such as CryoEM, X-ray spectroscopy, linear epitope mapping, and mass spectrometry-based methods, can be employed to map binding regions on proteins. Commonly used mass spectrometry-based techniques are cross-linking and hydrogen‑deuterium exchange (HDX). Another approach, hydroxyl radical protein footprinting (HRPF), identifies binding residues on proteins but faces challenges due to high initial costs and complex setups. This study introduces a generally applicable method using Fenton chemistry for epitope mapping in a standard mass spectrometry laboratory. It emphasizes the importance of controls, particularly the inclusion of a negative antibody control, not widely utilized in HRPF epitope mapping. Quantification by TMT labelling is introduced to reduce false positives, enabling direct comparison between sample conditions and biological triplicates. Additionally, six technical replicates were incorporated to enhance the depth of analysis. Observations on the receptor-binding domain (RBD) of SARS-CoV-2 Spike Protein, Alpha and Delta variants, revealed both binding and opening regions. Significantly changed peptides upon mixing with a negative control antibody suggested structural alterations or nonspecific binding induced by the antibody alone. Integration of negative control antibody experiments and high overlap between biological triplicates led to the exclusion of 40% of significantly changed regions. The final identified binding region correlated with existing literature on neutralizing antibodies against RBD. The presented method offers a straightforward implementation for HRPF analysis in a generic mass spectrometry-based laboratory. Enhanced data reliability was achieved through increased technical and biological replicates alongside negative antibody controls.

Peptide libraries: from epitope mapping to in-depth high-throughput analysis

Peptide arrays are a valuable instrument in the characterization of protein-protein interactions (PPIs) and immunogenic regions. New methods were developed to exploit the high-throughput potential of peptide arrays to obtain more in-depth information, replacing traditional resource-intensive experiments. Here, we discuss the recent advances in peptide-array-based technologies and the remaining challenges.

AllergenAI: a deep learning model predicting allergenicity based on protein sequence

Innovations in protein engineering can help redesign allergenic proteins to reduce adverse reactions in sensitive individuals. To accomplish this aim, a better knowledge of the molecular properties of allergenic proteins and the molecular features that make a protein allergenic is needed. We present a novel AI-based tool, AllergenAI, to quantify the allergenic potential of a given protein. Our approach is solely based on protein sequences, differentiating it from previous tools that use some knowledge of the allergens' physicochemical and other properties in addition to sequence homology. We used the collected data on protein sequences of allergenic proteins as archived in the three well-established databases, SDAP 2.0, COMPARE, and AlgPred 2, to train a convolutional neural network and assessed its prediction performance by cross-validation. We then used Allergen AI to find novel potential proteins of the cupin family in date palm, spinach, maize, and red clover plants with a high allergenicity score that might have an adverse allergenic effect on sensitive individuals. By analyzing the feature importance scores (FIS) of vicilins, we identified a proline-alanine-rich (P-A) motif in the top 50% of FIS regions that overlapped with known IgE epitope regions of vicilin allergens. Furthermore, using∼ 1600 allergen structures in our SDAP database, we showed the potential to incorporate 3D information in a CNN model. Future, incorporating 3D information in training data should enhance the accuracy. AllergenAI is a novel foundation for identifying the critical features that distinguish allergenic proteins.

Natural Antibodies Produced in Vaccinated Patients and COVID-19 Convalescents Recognize and Hydrolyze Oligopeptides Corresponding to the S-Protein of SARS-CoV-2

The S-protein is the major antigen of the SARS-CoV-2 virus, against which protective antibodies are generated. The S-protein gene was used in adenoviral vectors and mRNA vaccines against COVID-19. While the primary function of antibodies is to bind to antigens, catalytic antibodies can hydrolyze various substrates, including nucleic acids, proteins, oligopeptides, polysaccharides, and some other molecules. In this study, antibody fractions with affinity for RBD and S-protein (RBD-IgG and S-IgG) were isolated from the blood of COVID-19 patients vaccinated with Sputnik V. The fractions were analyzed for their potential to hydrolyze 18-mer oligopeptides corresponding to linear fragments of the SARS-CoV-2 S-protein. Here, we show that the IgG antibodies hydrolyze six out of nine oligopeptides efficiently, with the antibodies of COVID-19-exposed donors demonstrating the most significant activity. The IgGs of control donors not exposed to SARS-CoV-2 were found to be inactive in oligopeptide hydrolysis. The antibodies of convalescents and vaccinated patients were found to hydrolyze oligopeptides in a wide pH range, with the optimal pH range between 6.5 and 7.5. The hydrolysis of most oligopeptides by RBD-IgG antibodies is inhibited by thiol protease inhibitors, whereas S-IgG active centers generally combine several types of proteolytic activities. Ca2+ ions increase the catalytic activity of IgG preparations containing metalloprotease-like active centers. Thus, the proteolytic activity of natural antibodies against the SARS-CoV-2 protein is believed to be due to the similarity of catalytic antibodies' active centers to canonical proteases. This work raises the question of the possible physiological role of proteolytic natural RBD-IgG and S-IgG resulting from vaccination and exposure to COVID-19.

Hydrogen-Deuterium Exchange Epitope Mapping of Glycosylated Epitopes Enabled by Online Immobilized Glycosidase

Hydrogen-deuterium exchange coupled with mass spectrometry (HDX-MS) is widely used for monoclonal antibody (mAb) epitope mapping, which aids in the development of therapeutic mAbs and vaccines, as well as enables the understanding of viral immune evasion. Numerous mAbs are known to recognize N-glycosylated epitopes and to bind in close proximity to an N-glycan site; however, glycosylated protein sites are typically obscured from HDX detection as a result of the inherent heterogeneity of glycans. To overcome this limitation, we covalently immobilized the glycosidase PNGase Dj on a solid resin and incorporated it into an online HDX-MS workflow for post-HDX deglycosylation. The resin-immobilized PNGase Dj exhibited robust tolerance to various buffer conditions and was employed in a column format that can be readily adapted into a typical HDX-MS platform. Using this system, we were able to obtain full sequence coverage of the SARS-CoV-2 receptor-binding domain (RBD) and map the glycosylated epitope of the glycan-binding mAb S309 to the RBD.

Understanding and Modulating Antibody Fine Specificity: Lessons from Combinatorial Biology

Combinatorial biology methods such as phage and yeast display, suitable for the generation and screening of huge numbers of protein fragments and mutated variants, have been useful when dissecting the molecular details of the interactions between antibodies and their target antigens (mainly those of protein nature). The relevance of these studies goes far beyond the mere description of binding interfaces, as the information obtained has implications for the understanding of the chemistry of antibody–antigen binding reactions and the biological effects of antibodies. Further modification of the interactions through combinatorial methods to manipulate the key properties of antibodies (affinity and fine specificity) can result in the emergence of novel research tools and optimized therapeutics.