Characterizing T cell epitope loss potential through peptidome surveillance across evolving SARS-CoV-2 lineages

Supplementary information and interactive content to accompany published analysis of HLA binding of the SARS-CoV-2 peptidome across viral variants, and the impact on potential loss of CD8+ and CD4+ T-cell epitopes.

Our Frontiers in Immunology paper: https://www.frontiersin.org/articles/10.3389/fimmu.2022.918928/full

Binder count fraction at pan-HLA hotspots in most frequent S,N of VOC lineages

Interactive plots of binder count fraction relative to reference (NCBI Reference Sequence: NC_045512) at pan-HLA hotspots.

• B.1.1.7 (Alpha)
  • S - most frequent (Fig. 10A)
  • S - 2nd most frequent
  • N - most frequent (Fig. 10G)
  • N - 2nd most frequent
• B.1.351 (Beta)
  • S - most frequent (Fig. 10B)
  • N - most frequent (Fig. 10H)
• B.1.617.2 (Delta)
  • S - most frequent (Fig. 10C)
  • S - 2nd most frequent
  • N - most frequent (Fig. 10I)
  • N - 2nd most frequent
• P.1 (Gamma)
  • S - most frequent (Fig. 10D)
  • N - most frequent (Fig. 10J)
• B.1.1.529 (Omicron)
  • S - most frequent (Fig. 10E)
  • S - 2nd most frequent (Fig. 10F)
  • N - most frequent (Fig. 10K)
  • N - 2nd most frequent (Fig. 10J)

Distribution of binder count fraction at pan-HLA hotspots for VOC and VOI lineages

• S: HLA-I binder count fraction at pan-HLA hotspots (Fig. 9A)
• N: HLA-I binder count fraction at pan-HLA hotspots (Fig. 9B)
• S: HLA-II binder count fraction at pan-HLA hotspots (Fig. 9C)
• N: HLA-II binder count fraction at pan-HLA hotspots (Fig. 9D)

N candidate epitope conservation in SARS-CoV-2 lineages with representative S protein ranking in the top percent for HLA binder loss

restriction	worst case (most loss) protein	most frequent protein
HLA-I	Fig. 6A	Fig. 6B
HLA-II	Fig. 6C	Fig. 6D
HLA-I & HLA-II	Fig. 6E	Fig. 6F

Binder count fraction for all HLAs across all unique versions of S and N

Note that, for each protein considered, binder count fraction (relative to the reference SARS-CoV-2 genome) is computed after summation of binder count across all pan-HLA hotspots. See Methods in paper for details.

date	S, HLA-I	N, HLA-I	S, HLA-II	N, HLA-II
11/26/2021	plot	plot	plot	plot
09/25/2021	plot	plot	plot	plot
07/27/2021	plot	plot	plot	plot
05/11/2021	plot	plot	plot	plot
03/19/2021	Fig. 5A	Fig. 5B	Fig. 5C	Fig. 5D

HLA cluster assignment and representative set selection

The .csv files below include our cluster label assignment for all processed HLA-I and HLA-II. Each file contains a column explicitly indicating which HLAs were included in our analysis set (selected in HLA-I, and selected_ab in HLA-II).

For quick browsing, interactive plots are also included below.

• Interavtive plots
  • HLA-I PHATE (38 clusters)
  • HLA-II alpha + beta PHATE (93 clusters)
  • HLA-II alpha chain PHATE (18 clusters)
  • HLA-II beta chain PHATE (16 clusters)
• CSV files
  • HLA-I cluster labels and representative set selection csv file
  • HLA-II cluster labels and representative set selection csv file

Evaluation of Predictions

Empirically observed T-cell response frequencies (RF) align with aggregated HLA-binder peaks

Each of the sub-figures can be accessed as a separate interactive file below. Click on the legend to toggle which data to display.

• S: CD8+ T cell epitope RF vs. HLA-I & pan-HLA predictions (Fig. 2A)
• N: CD8+ T cell epitope RF vs. HLA-I & pan-HLA predictions (Fig. 2B)
• S: CD4+ T cell epitope RF vs. HLA-II & pan-HLA predictions (Fig. 2C)
• N: CD4+ T cell epitope RF vs. HLA-II & pan-HLA predictions (Fig. 2D)

HLA binding prediction benchmark details

To supplement the published overall mean ROC AUC and PPV of our RNN and CNN across HLAs, we include per-HLA results below:

• RNN_vs_CNN_vs_NetMHC41_perHLA_results.csv

For each HLA, the table summarizes the sample count (n_pos, n_neg), how many samples were classified as ambiguous by each system (RNN_n_ambig, CNN_n_ambig), and the fraction of the total sample count classified as ambiguous (RNN_n_ambig_fract, CNN_n_ambig_fract). On average, across all HLAs, only 2.2% of samples were excluded as ambiguous by both RNN and CNN systems, with HLAs showing the lowest levels of ambiguous sample counts being at 0.7%, and highest ambiguous sample counts at 6%.

As referenced in our paper, performance metrics of other systems reported for individual HLAs were obtained from Reynisson et al. 2020, Supplementary Table 8.