Applications of Artificial Intelligence and the Immunoinformatic Prediction Tools for Some Potential B and T Cell Epitopes within the Matrix and the Small Envelope Proteins of the Avian Infectious Bronchitis Virus
Faculty Mentor
Maged Gomaa Hemida
Area of Research
Biomedical Science
Major
Veterinary Medicine
Description
The avian Infectious bronchitis virus (AIBV) is a highly contagious viral infection that poses a serious threat to the poultry industry. It causes several clinical syndromes in the affected chickens, including respiratory diseases, neuropathogenic disorders, reproductive tract malfunctions, and serious production losses. The currently used AIBV vaccines do not provide complete protection against the currently circulating strains of the virus. The major problems of the AIBV live attenuated vaccines are the possibility of reverting to virulence and the possibility of recombination with the field isolates. These scenarios complicated the AIBV infection in chickens. One of the new trends in vaccine design and development is the application of artificial intelligence and immunoinformatic tools to identify the most potential protective epitopes across the structural proteins of the AIBV. The main goal of the current study is to use the most recent AI and bioinformatics tools to map the major B cell epitopes across the AIBV matrix (M) and small envelope (E) proteins. To achieve these goals, we retrieved and aligned the full-length genomes of AIBV representing all the viral genotypes (GI-1 to GIX). We used the most conserved consciences representing the MSA of these AIBV genotypes for the downstream epitope prediction. We then used various bioinformatics tools such as ABCPred, IEDB, Bepipred-2.0, BCPred, AAP, and FBCPred for prediction of B cell epitopes, while T cell epitopes were identified using IEDB’s MHC-I and MHC-II prediction tools. Our results identified the most conserved epitopes based on their antigenicity, non-allergenicity, and non-toxicity. For the E protein, these regions are: (53LFWYTWVVVPGAKGTA68, 24AVYIFVGFVALYLLGR39 and 102PANFQDVQRKLYSSD117). While in the case of the M protein, the top-ranked mapped epitopes are (119NAVGSILLTNGQQCNF134, 87TVFACLSFVGYWIQSI102, and 57WCFWPLNIAVGVISCI72). These identified epitopes across the E and M proteins showed high scores of immunogenicity, non-allergenicity, and non-toxic. These novel epitopes represent a strong basis for the development of some multiepitope-based AIBV vaccines. However, further studies are required to confirm the immunogenicity and efficacy of this potential multiepitope-based vaccine in chickens.
Applications of Artificial Intelligence and the Immunoinformatic Prediction Tools for Some Potential B and T Cell Epitopes within the Matrix and the Small Envelope Proteins of the Avian Infectious Bronchitis Virus
The avian Infectious bronchitis virus (AIBV) is a highly contagious viral infection that poses a serious threat to the poultry industry. It causes several clinical syndromes in the affected chickens, including respiratory diseases, neuropathogenic disorders, reproductive tract malfunctions, and serious production losses. The currently used AIBV vaccines do not provide complete protection against the currently circulating strains of the virus. The major problems of the AIBV live attenuated vaccines are the possibility of reverting to virulence and the possibility of recombination with the field isolates. These scenarios complicated the AIBV infection in chickens. One of the new trends in vaccine design and development is the application of artificial intelligence and immunoinformatic tools to identify the most potential protective epitopes across the structural proteins of the AIBV. The main goal of the current study is to use the most recent AI and bioinformatics tools to map the major B cell epitopes across the AIBV matrix (M) and small envelope (E) proteins. To achieve these goals, we retrieved and aligned the full-length genomes of AIBV representing all the viral genotypes (GI-1 to GIX). We used the most conserved consciences representing the MSA of these AIBV genotypes for the downstream epitope prediction. We then used various bioinformatics tools such as ABCPred, IEDB, Bepipred-2.0, BCPred, AAP, and FBCPred for prediction of B cell epitopes, while T cell epitopes were identified using IEDB’s MHC-I and MHC-II prediction tools. Our results identified the most conserved epitopes based on their antigenicity, non-allergenicity, and non-toxicity. For the E protein, these regions are: (53LFWYTWVVVPGAKGTA68, 24AVYIFVGFVALYLLGR39 and 102PANFQDVQRKLYSSD117). While in the case of the M protein, the top-ranked mapped epitopes are (119NAVGSILLTNGQQCNF134, 87TVFACLSFVGYWIQSI102, and 57WCFWPLNIAVGVISCI72). These identified epitopes across the E and M proteins showed high scores of immunogenicity, non-allergenicity, and non-toxic. These novel epitopes represent a strong basis for the development of some multiepitope-based AIBV vaccines. However, further studies are required to confirm the immunogenicity and efficacy of this potential multiepitope-based vaccine in chickens.