Design and Evaluation of Synthetic Peptide Vaccines for Soybean Mosaic Virus Using Computer-Aided Multi-Parameter Antigen Modeling
DOI:
https://doi.org/10.31305/rrijm2025.v05.n01.024Keywords:
antigenic peptides, vaccine design, immune response, protein sequence analysisAbstract
The coat protein (CP) of potyviruses is essential for aphid transmission, viral movement, and particle assembly, interacting with viral RNA and host components. Peptide fragments from CP aid in identifying immunogenic nonamers for vaccine development and immune response studies. Computational tools like support vector machines (SVMs) and artificial neural networks (ANNs) improve MHC binding affinity predictions, achieving ~80% accuracy across 42 alleles. High-affinity peptides for MHC class II molecules were identified, including YKTAKDLLT (880), PILAPDGTI (2577), and KVTKVDGRT (1438) for MHCII-IAb; GSFIITNGH (2079) and FIHLYGVEP (1911) for MHCII-IAd; SDAAEAYIE (2962) and WYNAVKDEY (2891) for MHCII-IAg7; and KTATQLQLE (1114) and STAENASLQ (413) for MHCII-RT1.B. One peptide exhibited a high binding affinity score (1.915). These findings underscore their potential in peptide-based vaccine design. Additionally, computational multiparametric antigen design enabled the development of synthetic peptide vaccines against soybean mosaic virus, demonstrating the efficacy of in silico approaches in advancing immunotherapeutics and antiviral strategies.
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