PREDIVAC: CD4+ T-cell epitope prediction for vaccine design that covers 95% of HLA class II DR protein diversity

Abstract

BackgroundCD4+ T-cell epitopes play a crucial role in eliciting vigorous protective immune responses during peptide (epitope)-based vaccination. The prediction of these epitopes focuses on the peptide binding process by MHC class II proteins. The ability to account for MHC class II polymorphism is critical for epitope-based vaccine design tools, as different allelic variants can have different peptide repertoires. In addition, the specificity of CD4+ T-cells is often directed to a very limited set of immunodominant peptides in pathogen proteins. The ability to predict what epitopes are most likely to dominate an immune response remains a challenge.ResultsWe developed the computational tool Predivac to predict CD4+ T-cell epitopes. Predivac can make predictions for 95% of all MHC class II protein variants (allotypes), a substantial advance over other available methods. Predivac bases its prediction on the concept of specificity-determining residues. The performance of the method was assessed both for high-affinity HLA class II peptide binding and CD4+ T-cell epitope prediction. In terms of epitope prediction, Predivac outperformed three available pan-specific approaches (delivering the highest specificity). A central finding was the high accuracy delivered by the method in the identification of immunodominant and promiscuous CD4+ T-cell epitopes, which play an essential role in epitope-based vaccine design.ConclusionsThe comprehensive HLA class II allele coverage along with the high specificity in identifying immunodominant CD4+ T-cell epitopes makes Predivac a valuable tool to aid epitope-based vaccine design in the context of a genetically heterogeneous human population.The tool is available at: http://predivac.biosci.uq.edu.au/.

Current methods of epitope identification for cancer vaccine design

“…When tested in vivo with DA-3TM tumor bearing mice, there was an increased survival in the vaccinated mouse cohort compared to the cohort treated with GMCSF adjuvant alone and significantly increased when compared to another MUC1 peptide vaccine plus adjuvant (P < 0.01) [41]. Newer peptide predictive algorithms are an amalgamation of multiple sequence alignments based on prediction with structural or physical data (electrochemical and thermodynamic data) known as specific-determining residue (SDR) based, or Quantitative Structure Activity Relationship (QSAR) regression, algorithms [27,42]. These hybrid algorithms of sequence based calculations and structure based epitope calculations, together known as pan-specific algorithms, make generalized binding forecasts over multiple MHC alleles with little to no pre-existing binding data for the epitope in question [43].…”

“…In silico prediction of peptide binding affinity as an indicator of cancer vaccine efficacy Immunoinformatics allows for identification of peptides with the highest affinity to MHC complexes (pMHC), an event potentially necessary to induce T-cell activation [22][23][24]. Likewise, immunoinformatics may identify promiscuous epitopes by searching for commonalities in high binding sequences between polymorphic MHC alleles which allows epitope spreading [25][26][27][28]. These tools used in peptide based vaccine development include two types of in silico predictive methods to predict pMHC binding; sequence-based and structure based-methods.…”

“…The prediction algorithms that are most popular utilize computer models that are built on data-driven comparisons that rely on the differences of the peptide-binding segments (motifs) among sequences. Altogether, these algorithms are known as sequence based algorithms [23,27], but can be further divided into subclasses based on the approach of the algorithm or the test data that is used. These subclasses include motif alignment/positioning of specific domains [23,29], quantitated matrix-based approaches [27], and machine learning-based algorithms [27].…”

T-Cell Epitope Prediction Methods: An Overview

“…It is a pan-specifi c method for CD4+ T-cell epitope prediction based on the specifi city-determining residues (SDR) [ 51 ]. These are amino acid residues that are responsible for specifi c interactions between a given pair of interacting proteins, or between a protein and a peptide.…”

In Silico Modeling and Immunoinformatics Probing Disclose the Epitope Based PeptideVaccine Against Zika Virus Envelope Glycoprotein

“…For this prediction we chose seven abundant HLA class II alleles DRB1*01:01, DRB1*04:01, DRB1*07:01, DRB1*11:01 and DRB1*15:01 from the selection panel [52]. The predicted T-cell epitopes having IC50 value less than 50 were considered as potential T-cell epitopes and their corresponding scores for respective alleles were determined by PREDIVAC (http://predivac.biosci.uq.edu.au/cgi-bin/binding.py) [53].…”