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

Francino-Urdániz, Irene M.; Whitehead, Timothy A.

doi:10.1039/d1cb00169h

Cited by 8 publications

(5 citation statements)

References 78 publications

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“…The region identified here as the binding epitope matches that found by both Yuan et al [47] and Wu et al [48]. The binding site mapped to the complete trimer of the SARS-CoV-2 spike protein, shown in Figure 4F, showed that the binding site is located on the tip of the receptor-binding domain and is accessible in both closed and open structure [49].…”

Section: Discussionsupporting

confidence: 83%

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

Larsen,

Kaczmarek,

Palarasah

et al. 2023

Preprint

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Epitope mapping methods using hydroxyl radical protein footprinting (HRPF) are currently hampered by high initial cost and complex experimental setup. Here we set out to present a generally applicable method using Fenton chemistry for mapping of epitopes and paratopes in a standard laboratory setting. Furthermore, the described method illustrates the importance of controls on several levels when performing mass spectrometry-based epitope mapping. In particular, the inclusion of negative antibody controls has not previously been widely utilized in epitope mapping by HRPF analysis. In order to limit the false positives, we further introduced quantification by TMT labelling, thereby allowing for direct comparison between sample conditions and biological triplicates. Lastly, up to six technical replicates were incorporated in the experimental setup in order to achieve increased depth of the final analysis.Both binding and openings of regions on receptor binding domain (RBD) from SARS-CoV-2 Spike Protein, Alpha and Delta variants, were observed. The negative control antibody experiment, combined with the high overlap between biological triplicates, resulted in the exclusion of 40% of the significantly changed regions, including both binding and opening regions. The identified binding region agrees with the literature for neutralizing antibodies towards SARS-CoV-2 Spike Protein.The presented method is an easily implementable technique for the analysis of HRPF in a generic MS-based laboratory. The high reliability of the data was achieved by increasing the number of technical and biological replicates combined with negative antibody controls.

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Section: Discussionsupporting

confidence: 83%

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

Larsen,

Kaczmarek,

Palarasah

et al. 2023

Preprint

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“…Hydrogen–deuterium exchange coupled with mass spectrometry (HDX–MS) is a method often used for antibody epitope mapping. − Recent examples include the use of HDX–MS to augment electron microscopy data for mapping the binding sites for a panel of monoclonal antibodies (mAbs) in the work that led to the discovery of LY-CoV555, and for the screening of receptor-binding domain (RBD) epitopes in the development of the REGEN-COV mAb cocktail . In a typical HDX–MS experiment, the protein of interest is incubated in a D 2 O buffer, allowing the hydrogens to exchange with deuterium for set time periods.…”

mentioning

confidence: 99%

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

O’Leary¹,

Balasubramaniam

Hughes

et al. 2023

Anal. Chem.

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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 glycanbinding mAb S309 to the RBD.

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“…Most of the current methods for diagnosing allergenic proteins predominantly rely on the accurate mapping of IgE epitopes, which requires detailed peptide analysis [30], or high-resolution techniques [31], such as the 3D structure of an Antigen-Antibody complex solved by X-ray crystallography. Existing allergen prediction methods are limited by their reliance on relatively small datasets that contain, at most, a few hundred proteins, with a high degree of redundancy [17].…”

Section: Discussionmentioning

confidence: 99%

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

Yang,

Negi,

Schein

et al. 2024

Preprint

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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.

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

Abstract: This mini-review presents a critical survey of techniques used for epitope mapping on the SARS-CoV-2 Spike protein. The sequence and structures for common neutralizing and non-neutralizing epitopes on the Spike...

Cited by 8 publications

References 78 publications

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

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

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

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

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