Combination of Experimental and Bioinformatic Approaches for Identification of Immunologically Relevant Protein–Peptide Interactions

Protein–peptide interactions are an essential player in cellular processes and, thus, of great interest as potential therapeutic agents. However, identifying the protein’s interacting surface has been shown to be a challenging task. Here, we present a methodology for protein–peptide interaction identification, implementing phage panning, next-generation sequencing and bioinformatic analysis. One of the uses of this methodology is identification of allergen epitopes, especially suitable for globular inhaled and venom allergens, where their binding capability is determined by the allergen’s conformation, meaning their interaction cannot be properly studied when denatured. A Ph.D. commercial system based on the M13 phage vector was used for the panning process. Utilization of various bioinformatic tools, such as PuLSE, SAROTUP, MEME, Hammock and Pepitope, allowed us to evaluate a large amount of obtained data. Using the described methodology, we identified three peptide clusters representing potential epitopes on the major wasp venom allergen Ves v 5

Combination of Experimental and Bioinformatic Approaches for Identification of Immunologically Relevant Protein–Peptide Interactions

Protein–peptide interactions are an essential player in cellular processes and, thus, of great interest as potential therapeutic agents. However, identifying the protein’s interacting surface has been shown to be a challenging task. Here, we present a methodology for protein–peptide interaction identification, implementing phage panning, next-generation sequencing and bioinformatic analysis. One of the uses of this methodology is identification of allergen epitopes, especially suitable for globular inhaled and venom allergens, where their binding capability is determined by the allergen’s conformation, meaning their interaction cannot be properly studied when denatured. A Ph.D. commercial system based on the M13 phage vector was used for the panning process. Utilization of various bioinformatic tools, such as PuLSE, SAROTUP, MEME, Hammock and Pepitope, allowed us to evaluate a large amount of obtained data. Using the described methodology, we identified three peptide clusters representing potential epitopes on the major wasp venom allergen Ves v 5

Solid cancer patients achieve adequate immunogenicity and low rate of severe adverse events after SARS-CoV-2 vaccination

Background: SARS-CoV-2 vaccination in cancer patients is crucial to prevent severe COVID-19 disease course. Methods: This study assessed immunogenicity of cancer patients on active treatment receiving mRNA-based SARS-CoV-2 vaccine by detection of anti-SARS-CoV-2 S1 IgG antibodies in serum, before, after the first and second doses and 3 months after a complete primary course of vaccination. Results were compared with healthy controls. Results: Of 112 patients, the seroconversion rate was 96%. A significant reduction in antibody levels was observed 3 months after vaccination in patients receiving immune checkpoint inhibitors versus control participants (p < 0.001). Adverse events were mostly mild. Conclusion: Immunogenicity after mRNA-based vaccine in cancer patients is adequate but influenced by the type of anticancer therapy. Antibody levels decline after 3 months, and thus a third vaccination is warranted

Ara h 2-specific IgE epitope-like peptides inhibit the binding of IgE to Ara h 2 and suppress lgE-dependent effector cell activation

Background: Clinical and experimental analyses indicate a pathognomonic role for allergen IgE crosslinking through epitope–paratope interactions as a major initial step in the cascade leading to effector cell activation and clinical manifestations of lgE-mediated food allergies. We aimed to undertake the initial development and assessment of Ara h 2-specific IgE epitope-like peptides that can bind to allergenspecific IgE paratopes and suppress effector cell activation. Methods: We performed biopanning, screening, IgE binding, selection and mapping of peptides. We generated synthetic peptides for use in all functional experiments. ImmunoCAP inhibition, basophil and mast cell activation tests, with LAD2 cells, a human mast cell line were performed. Twenty-six children or young adults who had peanut allergy were studied. Results: We identified and selected three linear peptides (DHPRFNRDNDVA, DHPRYGP and DHPRFST), and immunoblot analyses revealed binding to lgE from peanut-allergic individuals. The peptide sequences were aligned to the disordered region corresponding to the loop between helices 2 and 3 of Ara h 2, and conformational mapping showed that the peptides match the surface of Ara h 2 and h 6 but not other peanut allergens. In ImmunoCAP inhibition experiments, the peptides significantly inhibit the binding of IgE to Ara h 2 (p < .001). In basophil and mast cell activation tests, the peptides significantly suppressed Ara h 2-induced effector cell activation (p < .05) and increased the half-maximal Ara h 2 effective concentration (p < .05). Binding of the peptides to specific IgEs did not induce activation of basophils or mast cells. Conclusions: These studies show that the indicated peptides reduce the allergenic activity of Ara h 2 and suppress lgE-dependent basophil and mast cell activation. These observations may suggest a novel therapeutic strategy for food allergy based on epitope–paratop blocking

Immunophenotypes of anti-SARS-CoV-2 responses associated with fatal COVID-19

Background The relationship between anti-SARS-CoV-2 humoral immune response, pathogenic inflammation, lymphocytes and fatal COVID-19 is poorly understood. Methods A longitudinal prospective cohort of hospitalised patients with COVID-19 (n=254) was followed up to 35 days after admission (median, 8 days). We measured early anti-SARS-CoV-2 S1 antibody IgG levels and dynamic (698 samples) of quantitative circulating T-, B- and natural killer lymphocyte subsets and serum interleukin-6 (IL-6) response. We used machine learning to identify patterns of the immune response and related these patterns to the primary outcome of 28-day mortality in analyses adjusted for clinical severity factors. Results Overall, 45 (18%) patients died within 28 days after hospitalisation. We identified six clusters representing discrete anti-SARS-CoV-2 immunophenotypes. Clusters differed considerably in COVID-19 survival. Two clusters, the anti-S1-IgGlowestTlowestBlowestNKmodIL-6mod, and the anti-S1-IgGhighTlowBmodNKmodIL-6highest had a high risk of fatal COVID-19 (HR 3.36–21.69; 95% CI 1.51–163.61 and HR 8.39–10.79; 95% CI 1.20–82.67; p≤0.03, respectively). The anti-S1-IgGhighestTlowestBmodNKmodIL-6mod and anti-S1-IgGlowThighestBhighestNKhighestIL-6low cluster were associated with moderate risk of mortality. In contrast, two clusters the anti-S1-IgGhighThighBmodNKmodIL-6low and anti-S1-IgGhighestThighestBhighNKhighIL-6lowest clusters were characterised by a very low risk of mortality. Conclusions By employing unsupervised machine learning we identified multiple anti-SARS-CoV-2 immune response clusters and observed major differences in COVID-19 mortality between these clusters. Two discrete immune pathways may lead to fatal COVID-19. One is driven by impaired or delayed antiviral humoral immunity, independently of hyper-inflammation, and the other may arise through excessive IL-6-mediated host inflammation response, independently of the protective humoral response. Those observations could be explored further for application in clinical practice