Computational Prediction of Potential Vaccine Candidates From tRNA Encoded peptides (tREP) Using a Bioinformatic Workflow and Molecular Dynamics Validations.

ABSTRACT

Transfer RNAs (tRNA) are non-coding RNAs. Encouraged by biological applications discovered for peptides derived from other non-coding genomic regions, we explore the possibility of deriving epitope-based vaccines from tRNA encoded peptides (tREP) in this study. Epitope-based vaccines have been identified as an effective strategy to mitigate safety and specificity concerns observed in vaccine development. In this study, we explore the potential of tREP as a source for epitope-based vaccines for virus pathogens. We present a computational workflow that uses verified data sources and community-validated predictive tools to produce a ranked list of plausible epitope-based vaccines starting from tRNA sequences. The top epitope, bound to the predicted HLA molecule, for the virus pathogen is computationally validated through 200 ns molecular dynamics (MD) simulations followed by binding free energy calculations. The simulation results indicate that two tRNA encoded epitope-based vaccines, RRHIDIVV and IMVRFSAE for Mamastrovirus 3 and Norovirus GII, respectively, are likely candidates. Peptides originating from tRNAs provide unexplored opportunities for vaccine design. Encouraged by our previous experimental study, which established the inhibitory properties of tREPs against infectious parasites, we have proposed a computationally validated set of peptides derived from tREPs as vaccines for viral pathogens.