The Utility and Limitations of Current Web-Available Algorithms To Predict Peptides Recognized by CD4 T Cells in Response to Pathogen Infection

Abstract

The ability to track CD4 T cells elicited in response to pathogen infection or vaccination is critical because of the role these cells play in protective immunity. Coupled with advances in genome sequencing of pathogenic organisms, there is considerable appeal for implementation of computer-based algorithms to predict peptides that bind to the class II molecules, forming the complex recognized by CD4 T cells. Despite recent progress in this area, there is a paucity of data regarding the success of these algorithms in identifying actual pathogen-derived epitopes. In this study, we sought to rigorously evaluate the performance of multiple Web-available algorithms by comparing their predictions with our results—obtained by purely empirical methods for epitope discovery in influenza that used overlapping peptides and cytokine ELISPOTs—for three independent class II molecules. We analyzed the data in different ways, trying to anticipate how an investigator might use these computational tools for epitope discovery. We come to the conclusion that currently available algorithms can indeed facilitate epitope discovery, but all shared a high degree of false-positive and false-negative predictions. Therefore, efficiencies were low. We also found dramatic disparities among algorithms and between predicted IC50 values and true dissociation rates of peptide–MHC class II complexes. We suggest that improved success of predictive algorithms will depend less on changes in computational methods or increased data sets and more on changes in parameters used to “train” the algorithms that factor in elements of T cell repertoire and peptide acquisition by class II molecules.

CD4 T Cell Help Is Limiting and Selective during the Primary B Cell Response to Influenza Virus Infection

“…The murine CD4 T cell response to a recently circulating strain of influenza virus, A/New Caledonia/20/99, has been well characterized by our laboratory (27,(30)(31)(32)(33), with many CD4 T cell epitopes recognized in the primary response to infection in the context of many different class II haplotypes having been identified. Nucleoprotein (NP) was chosen for the current study because, as noted in the aforementioned studies, CD4 T cells with NP specificity are elicited in multiple MHC haplotypic backgrounds.…”

Identification of Conserved Peptides Comprising Multiple T Cell Epitopes of Matrix 1 Protein in H1N1 Influenza Virus

“…SMM align and NetMHC II v2.2 employs a position-specific weight matrix and ANN, respectively, for binding affinity to different HLA-DR, and their binding affinity is given as IC 50 values. These T cell epitope prediction tools have been extensively used and their efficacy has been verified experimentally by various studies (9,11,20,41). In addition to the multiimmunogenic factor and different prediction algorithms, the consensus approach also offers advantages with regard to predicting large numbers of different HLA alleles.…”

T‐cell recognition is shaped by epitope sequence conservation in the host proteome and microbiome

“…25 Given the discrepancy between our ability to predict MHC class II binding and MHC class II T-cell immunogenicity, 14 any such additional factors are highly desirable. However, at the current stage, the magnitude of the detected differences in similarity to either the human proteome or the microbiome are very small.…”

“…15 Although this is practically very useful, it also demonstrates that mechanisms beyond MHC binding affinity shape immune recognition patterns. 14 One effect that is expected to influence the immunogenicity of a given peptide is the suppression of immune responses that could be harmful to the host. T cells reacting to peptides conserved in the human proteome are expected to be deleted by negative selection during T-cell maturation.…”

“…This is particularly evident for MHC class II restricted epitopes, where binding predictions correlate well with measured binding affinities, 5 but when attempting to predict immunogenic peptides the performance is far from perfect. 14 We have recently demonstrated that a combination of HLA class II binding predictions selecting a set of the top 20% candidate peptides will cover 50% of the immune response. 15 Although this is practically very useful, it also demonstrates that mechanisms beyond MHC binding affinity shape immune recognition patterns.…”