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Viral epitopes have a distinct codon usage that reflects their dual role in infection and immunity. On the one hand, epitopes are part of proteins important to viral function; on the other hand, they are targets of the immune response. Studies of selection are most commonly based on changes of amino acid and seen through the accumulation of non-synonymous mutations. An independent measure of selection is the codon usage and underlying changeability of the nucleotide sequences. We here use multiple tools and a large-scale analysis of viral genomes to demonstrate that viral epitopes have a distinct codon usage and that this codon usage reflects distinct short- and long-term types of selection during viral evolution. We show that CD8 T cell epitopes are encoded by codons more distant from stop codons and more changeable than codons outside epitopes. This biased codon usage reflects the viral population toggling back and forth from a wild-type sequence to an escape mode, which enable them to avoid immune detection when needed, and go back to the functionally favorable form when the threat is removed (i.e., in a new host). [ABSTRACT FROM AUTHOR]

The binding of peptides to classical major histocompatibility complex (MHC) class I proteins is the single most selective step in antigen presentation. However, the peptide-binding specificity of cattle MHC (bovine leucocyte antigen, BoLA) class I (BoLA-I) molecules remains poorly characterized. Here, we demonstrate how a combination of high-throughput assays using positional scanning combinatorial peptide libraries, peptide dissociation, and peptide-binding affinity binding measurements can be combined with bioinformatics to effectively characterize the functionality of BoLA-I molecules. Using this strategy, we characterized eight BoLA-I molecules, and found the peptide specificity to resemble that of human MHC-I molecules with primary anchors most often at P2 and P9, and occasional auxiliary P1/P3/P5/P6 anchors. We analyzed nine reported CTL epitopes from Theileria parva, and in eight cases, stable and high affinity binding was confirmed. A set of peptides were tested for binding affinity to the eight BoLA proteins and used to refine the predictors of peptide-MHC binding NetMHC and NetMHCpan. The inclusion of BoLA-specific peptide-binding data led to a significant improvement in prediction accuracy for reported T. parva CTL epitopes. For reported CTL epitopes with weak or no predicted binding, these refined prediction methods suggested presence of nested minimal epitopes with high-predicted binding affinity. The enhanced affinity of the alternative peptides was in all cases confirmed experimentally. This study demonstrates how biochemical high-throughput assays combined with immunoinformatics can be used to characterize the peptide-binding motifs of BoLA-I molecules, boosting performance of MHC peptide-binding prediction methods, and empowering rational epitope discovery in cattle. [ABSTRACT FROM AUTHOR]

Binding of peptides to major histocompatibility complex (MHC) molecules is the single most selective step in the recognition of pathogens by the cellular immune system. The human MHC genomic region (called HLA) is extremely polymorphic comprising several thousand alleles, each encoding a distinct MHC molecule. The potentially unique specificity of the majority of HLA alleles that have been identified to date remains uncharacterized. Likewise, only a limited number of chimpanzee and rhesus macaque MHC class I molecules have been characterized experimentally. Here, we present NetMHCpan-2.0, a method that generates quantitative predictions of the affinity of any peptide–MHC class I interaction. NetMHCpan-2.0 has been trained on the hitherto largest set of quantitative MHC binding data available, covering HLA-A and HLA-B, as well as chimpanzee, rhesus macaque, gorilla, and mouse MHC class I molecules. We show that the NetMHCpan-2.0 method can accurately predict binding to uncharacterized HLA molecules, including HLA-C and HLA-G. Moreover, NetMHCpan-2.0 is demonstrated to accurately predict peptide binding to chimpanzee and macaque MHC class I molecules. The power of NetMHCpan-2.0 to guide immunologists in interpreting cellular immune responses in large out-bred populations is demonstrated. Further, we used NetMHCpan-2.0 to predict potential binding peptides for the pig MHC class I molecule SLA-1*0401. Ninety-three percent of the predicted peptides were demonstrated to bind stronger than 500 nM. The high performance of NetMHCpan-2.0 for non-human primates documents the method’s ability to provide broad allelic coverage also beyond human MHC molecules. The method is available at . [ABSTRACT FROM AUTHOR]

Focuses on the importance of simian immunodeficiency virus (SIV) infection of rhesus macaques as a model for the study of HIV infection in humans. Importance in controlling viral replication in infected hosts; Expansion on the utility of the rhesus macaque as a model for vaccine and pathogenesis study; Understanding on the importance of cytotoxic T lymphocytes responses during SIV infection.