T-Cell Epitope Prediction Methods: An Overview

Abstract

The scientific community is overwhelmed by the voluminous increase in the quantum of data on biological systems, including but not limited to the immune system. Consequently, immunoinformatics databases are continually being developed to accommodate this ever increasing data and analytical tools are continually being developed to analyze the same. Therefore, researchers are now equipped with numerous databases, analytical and prediction tools, in anticipation of better means of prevention of and therapeutic intervention in diseases of humans and other animals. Epitope is a part of an antigen, recognized either by B- or T-cells and/or molecules of the host immune system. Since only a few amino acid residues that comprise an epitope (instead of the whole protein) are sufficient to elicit an immune response, attempts are being made to identify or predict this critical stretch or patch of amino acid residues, i.e., T-cell epitopes and B-cell epitopes to be included in multiple-subunit vaccines. T-cell epitope prediction is a challenge owing to the high degree of MHC polymorphism and disparity in the volume of data on various steps encountered in the generation and presentation of T-cell epitopes in the living systems. Many algorithms/methods developed to predict T-cell epitopes and Web servers incorporating the same are available. These are based on approaches like considering amphipathicity profiles of proteins, sequence motifs, quantitative matrices (QM), artificial neural networks (ANN), support vector machines (SVM), quantitative structure activity relationship (QSAR) and molecular docking simulations, etc. This chapter aims to introduce the reader to the principle(s) underlying some of these methods/algorithms as well as procedural and practical aspects of using the same.

Isolation of neoantigen-specific T cells from tumor and peripheral lymphocytes

“…The strategy presented herein relies on several factors such as the precise determination of tumor genomic data, the fidelity of epitope prediction algorithms, and the availability of T cells. Robust algorithms capable of accurately predicting peptide immunogenicity (21) and proteasomal processing (22) are needed to determine the optimal threshold for the inclusion of epitopes in the initial screening phase. Identification of the optimal TEAD1 epitope, which was not present in the initial screening assays carried out with TILs from patient 3466, illustrates one of the problems encountered using current peptide prediction algorithms.…”

The Use of Planar Electromagnetic Fields in Effective Vaccine Design

“…The coupling of HLA-II molecules with peptide antigens is therefore critical for vaccine design [3,4] because it is necessary to induce immune memory.…”

“…The high polymorphism of these molecules represents one of the greatest difficulties in vaccine development [2][3][4], as HLA-II/peptide coupling is restricted by this polymorphism.…”

Databases and Algorithms in Allergen Informatics

“…Machine learning-based approaches are very commonly employed for this purpose and are found to be very efficient [137]. The details of epitope prediction methods/tools for B cells and T cells have been reviewed elsewhere [138,139]. Some of the important tools/servers that perform the prediction of T-cell epitopes are listed in Table 5.…”