Techniques for Theoretical Prediction of Immunogenic Peptides

Abstract

Small peptides are an important component of the vertebrate immune system. They are important molecules for distinguishing proteins that originate in the host from proteins derived from a pathogenic organism, such as a virus or bacterium. Consequently, these peptides are central for the vertebrate host response to intracellular and extracellular pathogens. Computational models for prediction of these peptides have been based on a narrow sample of data with an emphasis on the position and chemical properties of the amino acids. In past literature, this approach has resulted in higher predictability than models that rely on the geometrical arrangement of atoms. However, protein structure data from experiment and theory are a source for building models at scale, and, therefore, knowledge on the role of small peptides and their immunogenicity in the vertebrate immune system. The following sections introduce procedures that contribute to theoretical prediction of peptides and their role in immunogenicity. Lastly, deep learning is discussed as it applies to immunogenetics and the acceleration of knowledge by a capability for modeling the complexity of natural phenomena.

A Proof-of-Concept Study to Develop a Peptide-Based Vaccine against Salmon Lice Infestation in Atlantic Salmon (Salmo salar L.)

“…Individual proteins were thoroughly studied based on their biochemical properties and role in antiparasitic activity, as reported in the literature. Only, a few proteins of high relevance were shortlisted [8][9][10][11]. Protein sequences (with >70% identity match to L. salmonis) from the Uniprot database were compared to similar protein sequences from Atlantic salmon and rainbow trout using multiple sequence aligning tools (https://www.ebi.ac.uk/Tools/ msa/muscle/, accessed on 7 March 2021), and several short sequences of 10-13 amino acids unique to salmon lice were identified.…”