Prediction of MHC-Peptide Binding: A Systematic and Comprehensive Overview

Abstract

T cell immune responses are driven by the recognition of peptide antigens (T cell epitopes) that are bound to major histocompatibility complex (MHC) molecules. T cell epitope immunogenicity is thus contingent on several events, including appropriate and effective processing of the peptide from its protein source, stable peptide binding to the MHC molecule, and recognition of the MHC-bound peptide by the T cell receptor. Of these three hallmarks, MHC-peptide binding is the most selective event that determines T cell epitopes. Therefore, prediction of MHC-peptide binding constitutes the principal basis for anticipating potential T cell epitopes. The tremendous relevance of epitope identification in vaccine design and in the monitoring of T cell responses has spurred the development of many computational methods for predicting MHC-peptide binding that improve the efficiency and economics of T cell epitope identification. In this report, we will systematically examine the available methods for predicting MHC-peptide binding and discuss their most relevant advantages and drawbacks.

Defining CD8+ T Cell Determinants during Human Viral Infection in Populations of Asian Ethnicity

“…Algorithms have been designed to facilitate this process that can predict potential CD8 + T cell epitopes on the basis of the individual's HLA class I profile. Some methods combine MHC binding scores with predictors of proteasomal processing and TAP transport to give an overall score for a peptide's intrinsic potential of being a T cell epitope (5,6). The use of these algorithms allows us to considerably reduce the number of peptides that need to be synthesized and tested (7).…”

“…In contrast with inbred mice (that are often homozygous at all MHC-I loci), humans display high levels of genetic polymorphisms at the HLA loci (both in the individual and on a population level), which give rise to a large variety of different peptide-HLA binding specificities. Predictions of MHC-peptide binding affinities are based on experimentally derived peptidebinding data and/or on the sequence and structure of the MHC molecule (6). Because most studies to date have focused on Caucasian populations, this information is largely available for the most frequently expressed Caucasian molecules (e.g., HLA-A*0201), although it is scarce or not present at all for many HLA class I alleles present in individuals of different ethnicities.…”

Mapping T and B cell epitopes in sperm protein 17 to support the development of an ovarian cancer vaccine

“…The design of peptide-based vaccines takes advantage of an emergent computational paradigm that couples immunoinformatic prediction, facilitating the identification of epitopes within protein antigens [30,31]. T cell and B cell epitope prediction relies primarily on the anticipation of peptide-binding to MHC molecules [32] or B cell receptors or antibodies in their native structure [33,34], and many methods drawn from bioinformatics and/or chemoinformatics are known to produce satisfactory results [35][36][37]. The development of effective peptide-based vaccines also requires a full spectrum of validation on vaccine delivery routes, adjuvant, and desired responses required.…”

An overview of bioinformatics tools for epitope prediction: Implications on vaccine development

“…Launching a Critical Assesment of Techniques for Epitope Prediction will indeed benefit the field. It has been proposed that computational methods will be used to performed blinded de novo epitope prediction from query proteins previously screened experimentally [72,73]. Comparison of different methods is yet a complex task due to many aspects including the following: (i) inadequate documentation of datasets and prediction methods, (ii) unavailability of the benchmark dataset used to evaluate the methods, (iii) unavailability of the code that implements the method, (iv) the lack of a unified output format, which complicates the process of combining the results of several servers in order to obtain consensus predictions [74,75].…”