Positional bias of MHC class I restricted T-cell epitopes in viral antigens is likely due to a bias in conservation

Abstract

The immune system rapidly responds to intracellular infections by detecting MHC class I restricted T-cell epitopes presented on infected cells. It was originally thought that viral peptides are liberated during constitutive protein turnover, but this conflicts with the observation that viral epitopes are detected within minutes of their synthesis even when their source proteins exhibit half-lives of days. The DRiPs hypothesis proposes that epitopes derive from Defective Ribosomal Products (DRiPs), rather than degradation of mature protein products. One potential source of DRiPs is premature translation termination. If this is a major source of DRiPs, this should be reflected in positional bias towards the N-terminus. By contrast, if downstream initiation is a major source of DRiPs, there should be positional bias towards the C-terminus. Here, we systematically assessed positional bias of epitopes in viral antigens, exploiting the large set of data available in the Immune Epitope Database and Analysis Resource. We show a statistically significant degree of positional skewing among epitopes; epitopes from both ends of antigens tend to be under-represented. Centric-skewing correlates with a bias towards class I binding peptides being over-represented in the middle, in parallel with a higher degree of evolutionary conservation.

Figure 1. Distributions of normalized positions of MHC-I restricted T-cell epitopes for viruses.

(A and B) For each category of peptides indicated (i.e. ‘Positive’ or ‘Negative’), peptides were mapped onto all proteins of the corresponding genome with peptide similarity cutoff of 100%. For each peptide:antigen mapping, a normalized position was calculated. The set of normalized positions was plotted as a histogram. (C) To show positional bias of epitopes, the box plot shows results of 1000 bootstrap sampling of Positive and Negative data sets of normalized positions and plotting ratios of their probabilities for each bin. Boxes cover the range from 25^th to 75^th percentiles. Whiskers extend out from boxes 1.0 times the interquartile range.

Figure 2. Positional biases of predicted binders for 12 HLA supertypes.

For each supertype, 9-mer peptide binding predictions were carried out and ratios of probability masses of predicted ‘binders’ and ‘non-binders’ were calculated. Peptide binding predictions were made for alleles belonging to each supertype, using SMM^PMBEC method. All possible 9-mer peptides were generated from a set of viral proteins that contain at least one tested peptide from Table 1. Relationships between HLA molecules and supertypes are provided in .

Figure 3. Positional bias of conservation for viral proteins.

For each viral protein which also contained at least one tested peptide from Table 1, conservation scores at the residue-level were calculated using Rate4Site . Residue positions were converted into normalized positions and corresponding conservation scores were binned (5 bins of equal size). Higher conservation score indicates higher degree of conservation. Conservation scores were normalized for each protein (i.e. mean = 0; sd = 1). One thousand bootstrapping over proteins was used to estimate confidence intervals for the means of conservation scores. Each box covers 25^th and 75^th percentiles. Whiskers extend out from each box 1.0 times the interquartile range.

Figure 4. Estimating positional bias of epitopes from conservation data.

(A) Bootstrap sampling of conservation scores for positive and negative peptides are shown as two boxplots placed next to each other. The bins used are of variable lengths to ensure a sufficient count in each bin. Each bin contains ∼20% of data points. Middle positions of bins are indicated on the x-axis. The difference between the means of the two conservation score distributions is statistically significant (Welch's t-test; one-sided; p-value = 5.9×10⁻⁶). (B) Estimating probability ratios from the conservation score distributions. This is simply taking a ratio of positive and negative peptide probabilities as a function of a conservation score. Confidence intervals are derived from bootstrap sampling. (C) Estimated probability ratios as a function of normalized position, using the mapping shown in the second panel. As input, distributions of means of conservation scores shown in Fig. 3 were used. (D) Positional bias curves derived from observed normalized positions (black) and conservation scores (gray).

Figure 5. Distributions of conservation scores of peptides with positive and negative T-cell assay results for viruses in the context of immunization with peptides.

The difference between the two distributions is not statistically significant (Welch's t-test; one-sided; p-value = 0.62).