Advancement of Neutralizing Nanobodies and Proteins for Peanut Allergy Tolerance and Detection

Developing treatment strategies for peanut allergies are crucial due to their significant prevalence and severe impact. In the United States, approximately 8% of children and 5% of adults suffer from food allergies, with peanut allergy being a leading cause. Peanut allergies are responsible for the majority of fatal food-induced anaphylaxis cases, underscoring the critical need for effective treatments. The economic burden is substantial, with families facing costs related to emergency treatments, allergist visits, and medications, compounded by the broader impacts of missed school and workdays. The current primary method to prevent allergic reactions is strict avoidance of peanuts which is impractical and difficult to maintain, leading to accidental exposures. These allergies negatively impact the quality of life, causing anxiety, social limitations, and dietary restrictions. With the incidence of peanut allergies increasing over recent decades, research and innovation in this area are vital. The goal of this proposal is to enhance the capabilities of food science (safety) and biotechnology programs at DSU and AAMU through collaborative research, teaching, and extension efforts focused on new approaches to prevention strategies for food allergies. By using advanced artificial intelligence machine learning (AI/ML) approaches to detect and prevent food allergens, and validating these findings with advanced experimental tools, the project aims to innovate in the field.Below I am providing here with specific goals for this grant proposal:Deep learning evolution of Ara h 2 binding nanobody-like proteins for detection and elimination of peanut allergens: Our goal is to develop nanobody-like proteins (Nb-Prot) to prevent and detect peanut allergens. Nb-Prots can bind with specific allergens of peanuts to prevent their binding with allergen-specific Immunoglobulin E (IgE) antibodies that trigger the allergy. High-affinity Nb-Prots (< 122 amino acids) that bind to Ara h 2 will be generated using RFdiffusion, which is an advanced AI generative deep learning framework to design structures of high-affinity protein binders. Techniques such as surface plasmon resonance (SPR), isothermal titration calorimetry (ITC), in vitro binding assays, and Cryo-EM have been used to confirm the binding affinity and specificity of these designed protein binders. The Nb-Prots will be used as therapeutics for peanut allergy.Modifying the Ara h 2 allergen using CRISPR-Cas9: Another strategy will utilize clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated protein 9 (CRISPR-Cas9) to edit the Ara h 2 allergen gene to reduce its antigenicity. Among the major allergens in peanuts are Ara h 1, Ara h 2, and Ara h 3. We will then use our Nb-Prots to detect if the Ara h 2 is reduced in the transgenic peanuts. These studies will lay the groundwork for a gene modification approach to reduce protein allergenicity that can be applied broadly in peanuts and other foods.Profiling modified peanut allergen: Given the importance of peanuts in health and disease, several strategies have been developed to mitigate their high allergic potential. The efficacy of the strategies may be investigated using several analytical techniques to assess the qualities of the modified Ara h 2 proteins. We will identify and quantify changes in the modified peanut allergen. UV spectra analysis will be used to monitor the modification of the chemical structures, while FTIR spectroscopy will be used to observe alteration in the secondary and tertiary structure post-modification. Changes in protein solubility will be assessed by measuring the changes in protein content. The stability of the peanut allergens following gastric and intestinal digestion as well as the effect of the alteration on the efficiency of digestion enzymes will be evaluated. Proteomic analysis will combine the use of electrophoretic and mass spectrometric approaches to detect modifications.Train graduate and undergraduate students: The project team will work collaboratively to train graduate and undergraduate students in biochemistry, food science and biotechnology, computational chemistry, and data science through laboratory experiments and classroom discussions. Graduate students will mentor undergraduates, and senior undergraduates will mentor younger students, fostering their research and leadership skills. Providing graduate and undergraduate students with training in both computational methodologies and experimental techniques will prepare them for diverse career paths in academia, industry, and government sectors where such expertise is increasingly valued.The proposed research aims to develop innovative, simple, rapid, and reliable tools for detecting and eliminating peanut allergen Ara h 2 and serve as the basis for potential therapeutics. These tools can be employed by commercial food companies, state resource managers, and educational institutions. The project's objectives and expected outcomes will enhance our understanding of food safety, monitoring strategies, agriculture, and human health. Additionally, the findings will assist state, federal, and international organizations in developing risk assessment tools for controlling peanut allergens.Student research training, particularly in areas such as food allergens, antigens, nanobodies, computational chemistry and data science, molecular biology, biochemistry, and outreach, will contribute to the USDA's mission by recruiting and retaining a diverse new workforce. Resources and facilities obtained through this program will strengthen the capabilities of DSU and AAMU, enabling participating faculty to seek external funding to support ongoing research in food safety, detection strategies, and human health. Student involvement in computational and experimental research will enhance skills, competencies, and job prospects.