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Using a novel structure/function approach to select diverse swine major histocompatibility complex 1 alleles to predict epitopes for vaccine development.
Bioinformatics 2023 September 23
MOTIVATION: Swine leukocyte antigens (SLAs; i.e. swine major histocompatibility complex proteins (MHC)) conduct a fundamental role in swine immunity. To generate a protective vaccine across an outbred species, such as pigs, it is critical that epitopes that bind to diverse SLA alleles are used in the vaccine development process. We introduced a new strategy for epitope prediction.
RESULTS: We employed molecular dynamic simulation (MDS) to identify key amino acids for interactions (CAAI) with epitopes. We developed an algorithm wherein each SLA-1 is compared to a crystalized reference allele with unique weighting for non-conserved amino acids based on R group and position. We then performed homology modelling and electrostatic contact mapping to visualize how relatively small changes in sequences impacted the charge distribution in the binding site. We selected eight diverse SLA-1 alleles and performed homology modelling followed, by protein-peptide docking and binding affinity analyses, to identify porcine reproductive and respiratory syndrome virus (PRRSV) matrix protein (M) epitopes that bind with high affinity to these alleles. We also performed docking analysis on the epitopes identified as strong binders using NetMHCpan 4.1. Epitopes predicted to bind to our eight SLA-1 alleles had equivalent or higher energetic interactions than those predicted to bind to the NetMHCpan 4.1 allele repertoire. This approach of selecting diverse SLA-1 alleles, followed by homology modelling, and docking simulations, can be used as a novel strategy for epitope prediction that complements other available tools and is especially useful when available tools do not offer a prediction for SLAs/MHC.
AVAILABILITY: The data underlying this article are available in the online supplementary material.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
RESULTS: We employed molecular dynamic simulation (MDS) to identify key amino acids for interactions (CAAI) with epitopes. We developed an algorithm wherein each SLA-1 is compared to a crystalized reference allele with unique weighting for non-conserved amino acids based on R group and position. We then performed homology modelling and electrostatic contact mapping to visualize how relatively small changes in sequences impacted the charge distribution in the binding site. We selected eight diverse SLA-1 alleles and performed homology modelling followed, by protein-peptide docking and binding affinity analyses, to identify porcine reproductive and respiratory syndrome virus (PRRSV) matrix protein (M) epitopes that bind with high affinity to these alleles. We also performed docking analysis on the epitopes identified as strong binders using NetMHCpan 4.1. Epitopes predicted to bind to our eight SLA-1 alleles had equivalent or higher energetic interactions than those predicted to bind to the NetMHCpan 4.1 allele repertoire. This approach of selecting diverse SLA-1 alleles, followed by homology modelling, and docking simulations, can be used as a novel strategy for epitope prediction that complements other available tools and is especially useful when available tools do not offer a prediction for SLAs/MHC.
AVAILABILITY: The data underlying this article are available in the online supplementary material.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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