Friedman, R. Techniques for Theoretical Prediction of Immunogenic Peptides. Encyclopedia 2024, 4, 600–621, doi:10.3390/encyclopedia4010038.
Friedman, R. Techniques for Theoretical Prediction of Immunogenic Peptides. Encyclopedia 2024, 4, 600–621, doi:10.3390/encyclopedia4010038.
Friedman, R. Techniques for Theoretical Prediction of Immunogenic Peptides. Encyclopedia 2024, 4, 600–621, doi:10.3390/encyclopedia4010038.
Friedman, R. Techniques for Theoretical Prediction of Immunogenic Peptides. Encyclopedia 2024, 4, 600–621, doi:10.3390/encyclopedia4010038.
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 of 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 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.
Keywords
immunogenetics; immunogenic peptides; pathogenic organism; vertebrate host; computational models; protein structure; T-cell receptor; adaptive immunity; deep learning.
Subject
Biology and Life Sciences, Immunology and Microbiology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
