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Protein Deimmunization
Protein Deimmunization
There exists a vast reservoir of potential therapeutic proteins that have not been utilized due to immunogenicity issues. Our research aims to introduce mutations at immunogenic epitopes to reduce immunogenicity. By employing computational protein design techniques, we strive to both maintain protein stability and minimize immunogenicity.
TCR-pMHC interactions
TCR-pMHC interactions
The TCR-pMHC complex is critical for antigen presentation, a fundamental process in the adaptive immune response. To protect against a nearly infinite number of pathogens, these molecules have evolved with immense combinatorial diversity. MHC molecules are most polymorphic genes, capable of binding many peptides, while T Cell Receptor (TCR) Complementarity-Determining Regions (CDRs) undergo VDJ recombination, resulting in approximately 10^15 possible combinations. Given this complexity, computational methods are essential for understanding and predicting TCR-pMHC interactions.
Our research focuses on modeling these interactions to gain insights and develop practical applications. These applications include designing effective vaccines, enhancing cancer immunotherapy, understanding autoimmune disease mechanisms, improving infectious disease treatments, and predicting transplant compatibility. By leveraging AI-driven computational methods, we aim to advance immunology and therapeutic interventions.
Antibody Humanization
Antibody Humanization
Monoclonal antibodies are currently driving the drug market. While antibodies offer many advantages, reducing immunogenicity by increasing their "humanness" has been known to be crucial for drug safety. Although humanization techniques have been practiced in the industry for a long time, increasing humanness does not always equate to low immunogenicity. Our research aims to investigate the human/nonhuman dichotomy and its relationship with immunogenicity. By combining protein deimmunization techniques, we aim to directly target and decrease immunogenic regions, thereby reducing the potential immunogenicity risk of therapeutic antibodies.
Allergenicity Prediction
Allergenicity Prediction
Allergies are hypersensitive immune responses to typically harmless substances, leading to side effects ranging from mild itching to severe respiratory issues and anaphylaxis. Allergies affect a significant portion of the global population, making them a widespread health concern.
The immune system’s reaction to allergens starts with Immunoglobulin E (IgE) antibodies. Upon exposure to an allergen, IgE antibodies bind to mast cells and basophils. Subsequent exposures cause these cells to release chemicals like histamines, resulting in allergic symptoms. Tolerization, or reducing the immune system's responsiveness to an allergen, is a key research focus to mitigate reactions over time.
To detect and remove allergens, predicting allergenic epitopes is essential. Due to the complexity of immune responses, computational approaches may be applied. However, current computational tools are often unreliable. To address this, we aim to develop reliable computational prediction methods using AI techniques. By improving the accuracy of allergen epitope predictions, we can better identify potential allergens and enhance treatments and preventive measures, ultimately improving public health outcomes.
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