Abstract
T cells play a crucial role in controlling and driving the immune response with their ability to discriminate peptides derived from healthy as well as pathogenic proteins. In this review, we focus on the currently available computational tools for epitope prediction, with a particular focus on tools aimed at identifying neoepitopes, i.e. cancer-specific peptides and their potential for use in immunotherapy for cancer treatment. This review will cover how these tools work, what kind of data they use, as well as pros and cons in their respective applications.
Highlights
T cells recognize and survey peptides presented by major histocompatibility complex (MHC) molecules on the surface of nucleated cells
We mainly focus on the second part of the interaction: predicting T cell recognition of peptide-MHC complexes (pMHC) complexes
According to Yewdell et al around 1 in 200 peptides bind to MHC class I with an affinity strong enough (500 nM or lower) to induce a immune response [78]
Summary
T cells recognize and survey peptides (epitopes) presented by major histocompatibility complex (MHC) molecules on the surface of nucleated cells. The main requirement of T cell activation is the molecular recognition between the T cell receptor (TCR) expressed on the T cell surface and peptide-MHC complexes (pMHC) presented on the surface of other cells This precise recognition process is of paramount importance for a well-functioning immune system, and is shaped by a mechanism named central tolerance. In order to ensure that T cells do not react against ubiquitous peptides found in an individual, T cells undergo the process of negative selection In their development, T cells are presented with a plethora of self-peptides, where any T cell that recognizes self-peptides is eliminated, leaving only T cells with little or no specificity for self. This review will give an overview of both general and cancer specific epitope prediction tools and discuss the pros and cons of the different tools and future perspectives in the field
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