Date of Completion
2024
Document Type
Thesis
Degree Name
Bachelor of Science in Biochemistry
Keywords
Clostridium perfringens, peptide epitope, vaccine, antigenic, non-allergenic, immunity
Abstract
The enterotoxin-producing, Gram-positive Clostridium perfringens is one of the common causes of foodborne illnesses causing human gastrointestinal infections worldwide. While individuals with severe enteric C. perfringens infections use antibiotics for treatment, rising and alarming clinical cases of antibiotic resistance has led researchers to explore pharmacologic and prophylactic alternatives, such as peptide epitope-based vaccines. This study explored C. perfringens NanH, NanI, and NanJ proteins as sources of peptide epitopes for future vaccine applications using artificial intelligence (AI)-based tools and vaccine-related simulations. AI-based programs were used to elucidate the 3D structures of NanH, NanI, and NanJ, revealing that these proteins are potentially and physicochemically potent for peptide epitope prediction based on stability, hydropathicity, and identified disordered regions. A total of 34 B-cell, 312 Class I T-cell, and 284 Class II T-cell epitopes for NanH, 65 B-cell, 543 Class I T-cell, and 499 Class II T-cell epitopes for Nanl, and 116 B-cell, 963 Class I T-cell, and 893 Class Il T-cell epitopes for NanJ were generated using comprehensive bioinformatic identification. The identified top C. perfringens antigenic and non-allergenic epitopes were successfully docked with HLA-A and HLA-DRB1 molecules showing H-bond distances between 2.0 to 3.5 Å and binding affinity values ranging from -6.1 to -13.3 kcal/mol. C-ImmSim trials showed that NanH B-cell, Class I T-cell, and the linked top epitopes from the C. perfringens proteins were able to elicit promising B-cell, T cell, and cytokine responses. Overall, the results suggest that the NanH, NanI, and NanJ proteins hold enormous potential in anti-C. perfringens vaccine development, potentially leading to (1) antibody-mediated host immunity and (2) the gradual launch of representative demonstration and implementation of peptide-based “bench-to-bedside” applications to combat foodborne bacterial pathogens.
First Advisor
Nedrick T. Distor
APA Citation
Cuevas, E. O., Gaffud, L. D., & Leccio, R. D. (2024). Artificial intelligence (AI)-based protein structure prediction and linear peptide epitope identification in NanH, NanI, and NanJ for Clostridium perfringens vaccine applications. [Bachelor's thesis, De La Salle Medical and Health Sciences Institute]. GreenPrints. https://greenprints.dlshsi.edu.ph/bch/133