Antigenic Variation and Immune Recognition Inference From the COVID‐19 Pandemic: An In Silico Comparative Analysis of Spike T Cell Epitopes From the SARS‐CoV‐2 Variants of Concern

Katherine L Li; Paul Sandstrom; Hezhao Ji

doi:10.1111/irv.70286

. 2026 Jul 20;20(7):e70286. doi: 10.1111/irv.70286

Antigenic Variation and Immune Recognition Inference From the COVID‐19 Pandemic: An In Silico Comparative Analysis of Spike T Cell Epitopes From the SARS‐CoV‐2 Variants of Concern

Katherine L Li ^1,², Paul Sandstrom ^1,², Hezhao Ji ^1,^2,^✉

PMCID: PMC13385213 PMID: 42477893

ABSTRACT

Background

With increased transmission but reduced disease severity, the Omicron SARS‐CoV‐2 variants have contributed to the multifaceted transition of COVID‐19 from a global pandemic to an endemic disease. Nonetheless, the persistence of hypermutable viral variants and their ability to infect both vaccinated and previously infected individuals raise concerns about continued viral evolution and immune evasion.

Methods

This study employs comprehensive in silico analyses to compare the five former variants of concern (VOCs) to elucidate their T cell antigenic variations in relation to human leukocyte antigen (HLA) recognition and binding.

Results

Our major histocompatibility complex (MHC) class I and II epitope predictions suggest that the Omicron BA.1 variant harbors more putative epitopes than other VOCs (2.0–11.0 times more). Moreover, the distribution of predicted MHC‐II epitopes across HLA alleles differs substantially, with Omicron displaying significant differences from at least three VOCs in all analyses. Investigation of HLA‐epitope binding affinities indicates that Omicron epitopes often exhibit enhanced HLA binding compared with the Wuhan Reference (57.8%, 81.8%, and 60.0% of MHC‐I, MHC‐II regular, and MHC‐II promiscuous pairs, respectively), which could influence immune responses.

Conclusions

These findings reveal marked differences in the putative epitope profiles of Omicron BA.1 and the Wuhan Reference, as well as other pre‐Omicron VOCs, highlighting its altered HLA recognition status. Given the continued transmission and emergence of new SARS‐CoV‐2 lineages, this study highlights the importance of ongoing research to understand the evolutionary dynamics of high‐risk SARS‐CoV‐2 variants, their host immune system interactions, and the downstream implications in vaccine and therapeutic development.

Keywords: antigenic variation, computational biology, immune recognition, SARS‐CoV‐2, T cell epitope, variants of concern

1. Introduction

Throughout the COVID‐19 pandemic, five high‐risk SARS‐CoV‐2 lineages were designated as variants of concern (VOCs) by the World Health Organization (WHO) due to characteristics like increased transmissibility, altered disease presentation, or immune escape, which posed a serious threat to global public health [1]. The first VOC, Alpha (B.1.1.7), arose in the United Kingdom in September 2020 with a clear fitness advantage over the original strain [2, 3, 4]. By December 2020, Beta (B.1.351) was detected in South Africa, followed by Gamma (P.1) in travelers returning from Brazil in January 2021 [2]. In May 2021, the B.1.617 lineage was designated as Delta following a transmission surge in India, with emergence suspected earlier in September 2020 [2, 5]. Each of these lineages carry 8–10 characteristic spike (S) mutations, reflecting stepwise antigenic diversification during successive waves of circulation [2, 4, 6]. The Omicron BA.1 lineage (B.1.1.529.1; henceforth referred to as “Omicron”) was the fifth VOC designation, following its detection in South Africa in November 2021 [1, 7]. Omicron immediately dominated global transmission and garnered attention as a hypermutable lineage, with 34 mutations in the S protein alone [7, 8, 9, 10]. Notably, all lineages have since been deescalated by the WHO and no VOC classifications currently remain active [9].

The accumulation of VOC mutations has reduced the neutralization capacity of the initial SARS‐CoV‐2 vaccines against newer variants, thus warranting frequent vaccine updates [11]. Remarkably, Omicron was reported to be more transmissible than earlier lineages even among vaccinated individuals [7, 10, 12, 13, 14, 15], yet was associated with milder clinical symptoms [8, 13, 16, 17, 18], and a lowered risk of hospitalization [19, 20] and mortality [21]. Despite the conclusion of COVID‐19 as a global public health emergency, new Omicron descendants continue to emerge and actively circulate [9, 17, 22, 23, 24]. This persistence reinforces the importance of understanding the genetic and antigenic evolution of SARS‐CoV‐2 and the intricate host‐virus interactions of high‐risk VOC lineages [25, 26].

Despite evidence that VOC mutations influence humoral and cell‐mediated immunity, their specific effects on the CD4⁺ and CD8⁺ T cell responses remain understudied, particularly regarding HLA‐specific vulnerabilities and clinical correlates of VOC T cell escape, whole viral genome immune responses, and epitope cross‐recognition among lineages [7, 25, 27, 28, 29, 30, 31]. The rapid progression of SARS‐CoV‐2 evolution has produced numerous high‐risk variants, requiring prompt research efforts to facilitate the adaptation of diagnostics, surveillance, and countermeasure development. The continued diversification of Omicron sublineages illustrates this challenge as it drives a persistent developmental race to keep up with the latest variant before the next supersedes it [14, 28]. Such adaptability is increasingly achievable with computational techniques like in silico epitope prediction, which presents a time‐ and cost‐effective advantage that is often unachievable for experimental strategies due to laboratory‐based dependencies [32, 33, 34].

Here, we aimed to investigate how mutated spike T cell epitopes from the Alpha, Beta, Gamma, Delta, and Omicron BA.1 VOCs influence predicted HLA‐mediated antigen presentation and epitope recognition compared with the ancestral Wuhan Reference strain. Using the Immune Epitope Database and Analysis Resource (IEDB‐AR) and Comparative Analysis of Variant Epitope Sequences (CAVES), we conducted computational analyses of CD4⁺ and CD8⁺ T cell epitopes for these six lineages. By comparing predicted epitope sequences and quantities, human leukocyte antigen (HLA) recognition, and HLA‐epitope binding affinities, we identified putative differences in aspects of spike‐associated immune recognition among the VOC lineages that dominated the COVID‐19 pandemic [32, 35].

2. Methods

The comparative analysis workflow is illustrated in Figure 1. The procedures described below for the Omicron BA.1 lineage were replicated with Alpha, Beta, Gamma, and Delta, each individually juxtaposed against the ancestral Wuhan Reference.

Comparative analysis workflow. Representative genomes from the Alpha, Beta, Gamma, Delta, and Omicron BA.1 lineages were individually compared with the Wuhan Reference strain using computational methodologies.

2.1. Sequence Processing

A representative Omicron BA.1 genome was selected from the GISAID database as a complete, high‐quality human‐derived sequence, collected near the time and location of VOC emergence, and containing all lineage‐defining mutations [36]. The genome was aligned to the Wuhan Reference sequence using the MAFFT v7 option for SARS‐CoV‐2 with the default parameters [37, 38], then trimmed to each open reading frame and translated into amino acids in MEGA X [39]. All further analyses were conducted on the S gene.

2.2. Regular and Promiscuous Epitope Prediction

The S amino acid sequences from the Wuhan Reference and Omicron BA.1 were uploaded to the IEDB‐AR TepiTool for T cell MHC class I (MHC‐I) and II (MHC‐II) epitope prediction [40, 41]. TepiTool MHC‐II parameters included: Host species: human; Allele class: class II; Alleles: pre‐selected panel of 26 most frequent HLA‐DR,‐DP, and‐DQ alleles in the global population (> 99% global population coverage); Peptides: default settings for low number of peptides (15mers); Prediction method: IEDB recommended; Selection of predicted peptides: based on predicted consensus percentile rank, cutoff = 10. Parameters for MHC‐I differed by: Allele class: class I; Alleles: pre‐selected panel of 27 most frequent HLA‐A and‐B alleles in the global population (> 97% global population coverage); Peptides: default settings for low number of peptides (9mers); and were otherwise the same. Output included epitope sequences, loci, HLA binders, and percentile rank scores [42]. The HLA panels are described in the Supporting Information.

Putative epitopes classified as promiscuous binders can bind multiple HLA alleles despite variable binding grooves through either a common anchor or overlapping binding cores [43]. This facilitates broad‐population coverage, which is beneficial for immunity‐focused research and vaccine design [44, 45]. Promiscuous epitope prediction used the same sequences and was available for MHC‐II only [40]. Contrasting the regular MHC‐II epitopes described above, promiscuous epitopes used a consensus percentile rank threshold of 20 and were required to bind at least 50% of the HLA panel (following IEDB‐AR criteria), thus providing a different analysis context from the same sequences [46].

2.3. CAVES Epitope Pairs

The putative epitopes from the Omicron BA.1 VOC were compared with those from the ancestral Wuhan Reference lineage, with MHC‐I and MHC‐II regular and promiscuous epitopes considered separately but treated the same for all analyses. CAVES was used to compare the Wuhan Reference and Omicron BA.1 lineages with the default parameters [32]. To generate the multiple sequence alignment for CAVES input, NCBI accession P0DTC2 was aligned to the Wuhan Reference and Omicron BA.1 S sequences in MAFFT v7 option for SARS‐CoV‐2 with the default parameters [37].

Epitopes considered for further investigation were those listed in the CAVES novel epitope (L1N) list, featuring pairs of highly similar Wuhan Reference and Omicron‐derived epitopes that were flagged due to mutations in the Omicron sequence. This list could also contain lineage‐specific epitopes lacking a match in the opposing lineage, suggesting they were only predicted for one of the two lineages compared. From the CAVES L1N list, epitope pairs were selected for further study if they were identical epitopes (except for the mutated residues) or offset by only one amino acid (sequences started one position adjacent to each other and were otherwise identical besides the mutated residue) to minimize data loss in the absence of perfectly aligned epitopes (Figure S1). Epitope pairs offset by two or more amino acids were excluded.

2.4. HLA Binding Analyses

The selected Wuhan Reference–VOC epitope pairs were compared by their predicted HLA allele binders and the corresponding percentile rank scores indicating binding affinity. Each comparison assessed the number of HLA alleles predicted to bind each epitope and whether any alleles were predicted to bind only the Wuhan Reference epitope or only the Omicron epitope, indicating the loss or gain of binders in the descendent lineage. Additionally, comparisons considered variations in the percentile rank score for HLA alleles shared between Wuhan Reference and Omicron epitopes. An increased or decreased score for the same allele signified improved or diminishing binding affinity in the descendent lineage.

2.5. Statistical Analysis and Data Visualization

Statistical analysis, using R Statistical Software v4.2.1 with the core stats package, was performed using the Fisher's exact test with Benjamini–Hochberg corrections (to assess HLA alleles that were gained, lost, or shared among epitope pairs, and the number of epitopes predicted per HLA allele) and the Wilcoxon signed rank test with continuity and Benjamini–Hochberg corrections (to assess differences in percentile rank scores) [47]. Effect sizes were calculated as odds ratios for the HLA alleles gained, lost, or shared among epitope pairs, Cramér's V (V) from chi‐Squared for the number of epitopes predicted per HLA allele, and Rank Biserial Correlations (r _rb) for the difference in percentile rank scores. All epitopes and HLA alleles were considered as independent binding events and all p values reported are adjusted, with p < 0.05 considered statistically significant. Figures 1, S1, and S2 were created with BioRender. All other figures were created using R Statistical Software v4.2.1 with ggplot2, dplyr, stringr, gtools, ggforce, readr, and tidyr.

3. Results

3.1. Sequence Processing and Epitope Prediction

A single genome was chosen to represent the original lineage for each former VOC, excluding subsequent sublineages. The Pango lineages and GISAID accessions included: Wuhan Reference strain (B, EPI_ISL_402124); Alpha (B.1.1.7, EPI_ISL_601443); Beta (B.1.351, EPI_ISL_700452); Gamma (P.1, EPI_ISL_2614541); Delta (B.1.617.2, EPI_ISL_1841382); Omicron BA.1 (B.1.1.529.1, EPI_ISL_7545782). Further sequence and GISAID information are provided in the Supporting Information file. The S mutations identified in each genome are depicted in Figure S2.

Among all lineages, Omicron BA.1 displayed the largest number of putative epitopes across all analyses (Table 1). For MHC‐II regular and promiscuous epitopes, Omicron contained three and four more epitopes than the Wuhan Reference strain (respectively), with three to five and two to five more epitopes than any other VOC (respectively). Similarly, for MHC‐I, Omicron displayed 12 more epitopes than the Wuhan Reference and 6–15 more epitopes than any other VOC.

TABLE 1.

Quantitative analysis of putative MHC class I and II spike epitopes from the examined SARS‐CoV‐2 lineages.

	Lineage	Wuhan Reference	Alpha VOC	Beta VOC	Gamma VOC	Delta VOC	Omicron VOC
MHC‐I epitopes	Total ^a	896	897	897	902	893	908
	Mutated epitopes in VOCs ^b		55	49	81	52	160
	Wuhan Reference–VOC epitopes pairs ^c		49	43	73	47	132
	Independent sequence regions containing epitope pairs ^d		9	8	11	8	5
	Unique epitopes in VOCs ^e		0	0	0	0	0
MHC‐II regular epitopes	Total ^a	108	108	106	108	107	111
	Mutated epitopes in VOCs ^b		13	7	14	9	30
	Wuhan Reference–VOC epitopes pairs ^c		10	5	10	9	12
	Independent sequence regions containing epitope pairs ^d		7	5	10	7	10
	Unique epitopes in VOCs ^e		0	0	0	0	0
MHC‐II promiscuous epitopes	Total ^a	28	27	28	30	27	32
	Mutated epitopes in VOCs ^b		2	2	1	1	11
	Wuhan Reference–VOC epitopes pairs ^c		2	1	1	0	5
	Independent sequence regions containing epitope pairs ^d		2	1	1	0	5
	Unique epitopes in VOCs ^e		0	0	0	0	5

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^{^a}

Quantity of distinct epitopes predicted, excluding duplicates with different HLA allele predictions.

^{^b}

VOC epitopes with high similarity to a Wuhan Reference epitope but containing mutated amino acids, or completely unique epitopes with no epitope remotely similar in the Wuhan Reference data.

^{^c}

Paired epitopes that covered the same gene loci and started on the same residue or were offset by only one amino acid. Paired epitopes must differ by at least one mutation within the VOC epitope.

^{^d}

Quantity of spike gene regions containing epitopes from the filtered list of epitope pairs, where multiple overlapping epitopes could be predicted within a single immunogenic hotspot.

^{^e}

Epitopes found exclusively in the VOC lineage with no epitope remotely similar in the Wuhan Reference data.

3.2. CAVES Epitope Pairs

Epitopes from each VOC were compared against Wuhan Reference epitopes with CAVES to identify mutated and unique VOC epitopes relative to the Wuhan Reference lineage. Notably, Omicron exhibited the highest number of mutated epitopes in all analyses, with 2.0–3.3 times more MHC‐I epitopes, 2.1–4.3 times more MHC‐II regular epitopes, and 5.5–11.0 times more MHC‐II promiscuous epitopes than other lineages (Table 1). Omicron was the only VOC to acquire new epitopes, including five MHC‐II promiscuous epitopes absent from the Wuhan Reference. The new epitopes all covered non‐synonymous S mutations, including T95I, Q493R, G496S, D796Y, and N969K.

Further analyses used Wuhan Reference–VOC epitope pairs containing mutation(s) within the VOC epitope and starting at the same locus or offset by one amino acid (Figure S1). Still, Omicron retained the most epitope pairs meeting these criteria, with 1.8–3.1 times more MHC‐I pairs, 1.2–2.4 times more MHC‐II regular pairs, and 2.5–5.0 times more MHC‐II promiscuous pairs than any other VOC (Table 1). Epitope pairs spanned across the S gene, including regions like the receptor binding domain (RBD), furin cleavage site, and fusion peptide. In the MHC‐II regular analysis, Omicron and Delta contained the most epitopes with RBD mutations (33.3% each), and in the MHC‐I analysis, Omicron and Beta contained the largest proportions within the RBD at 40.2% and 39.5%, respectively. A complete list of epitope sequences, mutations, and loci is available as supplementary data.

3.3. HLA Alleles as Epitope Binders

Within each Wuhan Reference–VOC epitope pair, the predicted HLA binders were compared. HLA alleles exclusively binding the Wuhan Reference epitope were considered to have lost their ability to recognize the VOC lineage. Conversely, alleles exclusively binding the VOC epitope were considered to have gained the ability to recognize the VOC lineage. Alleles binding the epitopes of both lineages were considered as conserved binders. A detailed description of these results and the corresponding Figures S3–S9 and Table S1 is provided as supplementary analyses in the Supporting Information.

3.4. HLA‐Epitope Binding Affinity

Conserved HLA alleles predicted to bind both epitopes within a pair were used to investigate changes in binding affinity from the ancestral Wuhan Reference to the mutated VOC lineages. Binding affinity was assessed using percentile rank scores (IEDB recommended metric) for each HLA‐epitope combination [46, 48]. These scores are generated by comparing a putative epitope's half maximal inhibitory concentration (IC50) against the IC50 values from a randomly drawn set of existing peptides in the UniProt/Swiss‐Prot database and determining the fraction that would bind as well as, or better than the putative epitope in question [48]. As such, a lower percentile rank score indicates higher binding affinity [46, 48]. In all analyses, percentile rank scores were averaged across predicted HLA binders for each epitope (Figures 2, 3, 4). The raw percentile rank scores for each allele in each epitope pair are depicted in Figures S10–S23.

Average percentile rank scores of shared HLA alleles for each MHC class II regular epitope pair in each VOC analysis. Lower percentile rank scores indicate higher HLA‐epitope binding affinities. The quantity of Wuhan Reference–VOC epitope pairs used in the Alpha, Beta, Gamma, Delta, and Omicron MHC‐II regular analyses were 10, 5, 10, 9, and 12 (respectively). The observed differences between lineages were not significant at p < 0.05.

Average percentile rank scores of shared HLA alleles for each MHC class II promiscuous epitope pair in each VOC analysis. Lower percentile rank scores indicate higher HLA‐epitope binding affinities. The quantity of Wuhan Reference–VOC epitope pairs used in the Alpha, Beta, Gamma, and Omicron MHC‐II promiscuous analyses were 2, 1, 1, and 5 (respectively). The observed differences between lineages were not significant at p < 0.05.

Average percentile rank scores of shared HLA alleles for each MHC class I epitope pair in each VOC analysis. Lower percentile rank scores indicate higher HLA‐epitope binding affinities. The quantity of Wuhan Reference–VOC epitope pairs used in the Alpha, Beta, Gamma, Delta, and Omicron MHC‐I analyses were 49, 43, 73, 47, and 132 (respectively). The observed differences between lineages were not significant at p < 0.05.

3.4.1. MHC Class II Regular Epitope Binding Affinities

In the MHC‐II regular analyses, Omicron showed the greatest difference from the Wuhan Reference out of all VOCs. Among shared HLA alleles, 9/11 pairs showed improved average binding affinities (lower average percentile rank scores) in the Omicron epitope versus its Wuhan Reference counterpart, while only two pairs reported worse average binding affinities in Omicron. As illustrated in Figure 2e, Omicron scores cluster lower than the Wuhan Reference scores, despite encompassing a broader range overall.

The Alpha and Beta VOCs also reported most pairs showing stronger average binding affinities compared with the Wuhan Reference, with 7/10 pairs and 3/5 pairs, respectively. Gamma exhibited a split pattern, with equal pairs (5/10) showing improved and worsened average binding affinities in the Gamma epitopes. Conversely, Delta was the only VOC to possess more pairs with worse average binding affinities in the VOC epitopes versus their Wuhan Reference counterparts (5/9 pairs).

3.4.2. MHC Class II Promiscuous Epitope Binding Affinities

As discussed previously, the MHC‐II promiscuous analysis contained fewer epitope pairs than the MHC‐II regular analysis. Both Alpha VOC epitope pairs reported lower average percentile rank scores in the Alpha epitopes than the Wuhan Reference epitopes, indicating stronger HLA binding (Figure 3a). The raw scores per HLA allele within Pairs 1 and 2 were significantly different at p < 0.01 (r _rb = −1) and p < 0.05 (r _rb = −0.91), respectively (Figure S15).

Conversely, the only pair in both the Beta and Gamma analyses showed higher average percentile rank scores in the VOC epitope, although the difference within the Gamma pair was minimal. Consequently, the raw scores per HLA allele for the Beta pair were significantly different at p < 0.05 (r _rb = 0.82) (Figure S16). In the Omicron analysis, 3/5 epitope pairs showed improved binding overall through lower average percentile rank scores. Like the regular epitope analysis, Figure 3d depicts the Omicron promiscuous epitope scores clustering slightly lower than their Wuhan Reference counterparts, despite covering a broader range of scores overall. The raw scores per HLA allele were significantly different for each of these pairs (Pairs 1, 2, and 4) at p < 0.05 (r _rb = −0.97, −0.79, and −0.90, respectively) (Figure S18).

3.4.3. MHC Class I Epitope Binding Affinities

Binding affinity trends were less pronounced in the MHC‐I analyses than in MHC‐II, with greater variability among epitope pairs. Beta showed the highest percentage of pairs with improved average binding affinities (61.9%) in the Beta epitopes compared with their Wuhan Reference counterparts, with Beta's scores clustering at a slightly lower range in Figure 4b. Beta was followed closely by Omicron (57.8% of pairs) and Gamma (56.9% of pairs). Delta had the lowest percentage of pairs with improved average binding affinities (44.4%) and the highest percentage with worse average binding affinities in the Delta epitopes compared with their Wuhan Reference counterparts (55.6%), which was also seen in the MHC‐II regular analysis.

Significant differences in the raw scores per HLA allele occurred within individual pairs, but the directionality among pairs varied. Specifically, Alpha Pairs 10, 30, and 39 were significant at p < 0.05 (r _rb = 0.99, 1, and 1, respectively) (Figure S19) and Pair 46 was significant at p < 0.01 (r _rb = −0.95). Beta Pair 6 and 40 were significant at p < 0.01 (r _rb = 0.93) and p < 0.05 (r _rb = −1), respectively (Figure S20). Gamma Pair 30 and Delta Pair 43 were significant at p < 0.05 (r _rb = 0.96 and −1, respectively) (Figures S21 and S22). Omicron had the most significant pairs (Figure S23), with Pairs 11, 25, 46, 52, 60, 106, 120, and 121 significant at p < 0.05 (r _rb = −0.98, 0.91, −1, −1, −0.89, −1, −0.85, and −0.71, respectively), and Pairs 19, 31, 51, 64, 119, and 129 significant at p < 0.01 (r _rb = 0.93, 1, −1, −1, 0.93, and −0.98, respectively).

3.5. HLA‐Epitope Distribution

The IEDB panel of the 26 most frequent HLA‐DP,‐DQ, and‐DR alleles was utilized for MHC‐II analyses, which quantified the number of epitopes predicted to bind each allele. Alleles were categorized as binding more, less, or an equal number of VOC epitopes compared with the Wuhan Reference data. The MHC‐I analysis used the same comparison with the IEDB panel of the 27 most frequent HLA‐A and‐B alleles. These data pertain to the total set of TepiTool epitope predictions (Table 1), as opposed to the consolidated dataset of epitope pairs discussed from CAVES epitope pairs to MHC class I epitope binding affinities above.

3.5.1. MHC Class II Regular Epitope Distribution

In the MHC‐II regular analyses, the Alpha, Beta, Gamma, and Delta VOCs had most HLA alleles report the same number of epitopes in both the VOC and Wuhan Reference lineages (61.5%, 57.7%, 57.7%, and 69.2%, respectively) (Figure 5a). Conversely, the Omicron VOC reported only 11.5% of HLA alleles with the same number of epitopes in both Omicron and the Wuhan Reference, while most alleles (53.8%) reported an increased number of epitopes predicted per allele in Omicron compared with the Wuhan Reference. Omicron also had the largest proportion of HLA alleles with fewer epitopes per allele than the Wuhan Reference (34.6%), although other VOCs had comparable quantities (19.2%–30.8%). Consequently, Omicron was significantly different from all other lineages, with p < 0.01 when compared with Alpha, Beta, and Gamma (V = 0.54, 0.54, and 0.49, respectively), and p < 0.001 when compared with Delta (V = 0.65).

Percentage of HLA alleles with more, less, or the same number of MHC‐II regular (A), MHC‐II promiscuous (B), or MHC‐I (C) epitope binders in the VOCs versus the Wuhan Reference strain. Epitopes were predicted with the IEDB panels of the 26 most frequent HLA‐DP,‐DQ, and‐DR alleles (MHC‐II) or 27 most frequent HLA‐A and‐B alleles (MHC‐I). The number of epitopes predicted to bind to each allele was compared with the Wuhan Reference dataset independently for each VOC. *p < 0.05, **p < 0.01, ***p < 0.001.

3.5.2. MHC Class II Promiscuous Epitope Distribution

Notably, the Delta VOC is included here as this comparison did not require epitope pairing like the Promiscuous epitope binders and Promiscuous epitope binding affinities sections, above. Like the MHC‐II regular analyses, Omicron had the most HLA alleles with an increased number of MHC‐II promiscuous epitopes predicted per allele in Omicron compared with the Wuhan Reference (96.2%, Figure 5b). Interestingly, Gamma was the only other VOC where the majority of the HLA panel reported more epitopes in the VOC than the Wuhan Reference (76.9%), mimicking the results of Omicron. As such, Omicron and Gamma were significantly different from all other lineages except each other, both with p < 0.001 when compared with Alpha, Beta, and Delta (Omicron V = 0.96, 0.75, and 0.96, Gamma V = 0.81, 0.55, and 0.37, respectively).

The Alpha analysis was split with 50.0% of the panel reporting less epitopes per allele in Alpha than the Wuhan Reference, and 50.0% reporting the same number of epitopes per allele. Likewise, the Delta analysis displayed an offset split, with 46.2% of the panel reporting less epitopes than the Wuhan Reference and 53.8% reporting the same number of epitopes. Both the Alpha and Delta analyses had no alleles with more epitopes than what was predicted for the Wuhan Reference.

3.5.3. MHC Class I Epitope Distribution

Like the MHC‐II analyses, Gamma and Omicron had the most HLA alleles with an increased number of epitopes predicted per allele in the MHC‐I analysis (92.6% and 70.4%, respectively; Figure 5c). Delta was the only VOC to report a larger proportion of the HLA panel with less epitopes predicted per allele than the Wuhan Reference (66.7%) rather than more epitopes per allele (29.6%). Alpha and Beta reported the largest proportions of HLA alleles with the same number of epitopes predicted between the VOC and Wuhan Reference lineages (29.6% and 22.2%, respectively), whereas the other VOCs stated smaller percentages (2.7% for Delta and Omicron, and 0.0% for Gamma). Consequently, Gamma was significantly different from Alpha at p < 0.01 (V = 0.48) and from Beta and Delta at p < 0.001 (V = 0.56 and 0.65, respectively), while Delta was also significantly different from Alpha and Omicron at p < 0.01 (V = 0.52 and 0.42, respectively) (Figure 5c).

4. Discussion

Comparative analysis of epitopes from closely related viral lineages offers insights into how antigenic changes influence host‐virus interactions through HLA recognition [32]. Here, T cell epitope pairs were selected to juxtapose ancestral Wuhan Reference epitopes with highly similar counterparts from former VOCs, differing by lineage‐specific mutations. This study addresses two pivotal questions: (1) How do HLA binders differ between the Wuhan Reference and VOC lineages; and (2) How do binding affinities of shared HLA alleles differ between Wuhan Reference and VOC epitopes.

Notably, the MHC‐II regular epitope analysis revealed distinct trends in the Omicron BA.1 lineage compared with other VOCs. Omicron exhibited the highest proportion (50.0%) of MHC‐II regular epitopes gaining new HLA alleles as predicted binders compared with the Wuhan Reference strain (supplementary analyses Figure S3) and demonstrated the highest percentage (81.8%) of pairs reporting improved average binding affinities in the Omicron MHC‐II regular epitopes for shared HLA alleles (Figure 2). Furthermore, most HLA alleles bound more putative Omicron epitopes than Wuhan Reference epitopes across all three MHC analyses (Figure 5), which differed significantly from all other VOCs in the MHC‐II regular analysis and all but Gamma in the MHC‐II promiscuous analysis. These findings suggest that MHC‐II epitopes from the Omicron S protein may be recognized by a broader range of HLA genotypes and may exhibit stronger affinity binding within this computational prediction‐based framework, warranting further hypothesis‐driven investigation.

The acquisition of new HLA alleles as putative Omicron epitope binders underscores the potential involvement of HLA phenotypes in COVID‐19 clinical presentation [49]. HLA class I and II molecules present viral peptides to CD8⁺ and CD4⁺ T lymphocytes (respectively), initiating antigen‐specific immune responses [50, 51]. Given the diversity of HLA alleles, the quantity and variety predicted to bind viral epitopes are pivotal determinants of S‐specific immune recognition [49, 51, 52, 53, 54]. Broader HLA‐epitope recognition may enhance immune responses across a larger proportion of the human population than previous VOCs; however, its potential contribution to Omicron's milder disease manifestation should be considered alongside other determinants like natural immunity from prior infection, global rates of vaccination during the period of Omicron BA.1 dominance, biological nuances of host‐specific immunity, and the functional consequences of numerous viral mutations [8, 13, 16, 17, 18, 19, 20, 21].

Interestingly, the Gamma VOC demonstrated similar trends to Omicron in the HLA‐epitope distribution of the MHC‐I and MHC‐II promiscuous analyses, with both lineages reporting more VOC epitopes binding per allele than the Wuhan Reference across most HLA alleles. While both lineages exhibited increased transmission, Gamma was associated with substantial hospitalization and mortality burdens, contrasting Omicron's reported reduction in hospitalizations and clinical severity [2, 55]. Nonetheless, Gamma remains less studied than Omicron in the context of T cell‐mediated immunity, and the implications of broader putative HLA‐epitope recognition likely depend on numerous viral and host factors beyond epitope breadth alone.

The enhanced binding affinity of Omicron epitopes compared with Wuhan Reference epitopes further highlights the impact of viral mutations on HLA class II epitope recognition. Previous studies have demonstrated that even a single mutation within SARS‐CoV‐2 epitopes can influence HLA binding affinity and the elicitation of T cell responses [56, 57, 58]. Spike mutations such as G446S in the Omicron VOC are reported to affect antigen processing and presentation, consequently augmenting antiviral activity through enhanced T cell recognition [59]. Alternatively, L452R (in Delta, and Omicron BA.4 and BA.5) is reported to cause a loss of the A*24:02‐restricted CD8⁺ T cell response [25].

Multiple mutations may exist within a single VOC epitope, further complicating HLA‐epitope interactions. In the MHC‐II regular analysis, Omicron Pair 5 (containing G446S in the Omicron epitope) had identical putative HLA allele binders as the corresponding Wuhan Reference epitope and overall unchanged trends in binding affinity. Conversely, Omicron Pair 4 (containing N440K and G446S) had lost one HLA allele as a putative binder compared with its paired Wuhan Reference epitope and had slightly worse binding affinity for its only shared allele, thus demonstrating how the cumulative effect of multiple mutations within an epitope may differ from a single isolated mutation. Accordingly, these results can direct future research efforts by identifying putative VOC epitopes that may have functionally important alterations impacting cellular immunity, thus inviting further experimental investigation.

In the MHC‐I analysis, Omicron produced the highest proportion of epitope pairs gaining new HLA alleles as predicted binders (65.2%) and the second highest percentage of pairs with improved average binding affinities in the Omicron epitopes for shared alleles (57.8%), supporting the trends observed in the MHC‐II analyses. Nonetheless, the distinctions between VOCs were less pronounced in the MHC‐I analysis, perhaps due to differences in the antigen processing and immune response dynamics of CD4⁺ and CD8⁺ T cells. While MHC‐II epitopes are commonly 12–16 amino acids, MHC‐I epitopes are shorter (8–10 residues) and more vulnerable to binding disruptions from even a single mutation [60]. The open conformation of MHC‐II facilitates a higher tolerance of sequence variation in both the core and flanking residues, allowing greater binding flexibility [61]. Conversely, the MHC‐I binding groove is closed and has strict anchor requirements, making it prone to interferences in antigen processing and T cell receptor binding from mutations [61, 62, 63]. Accordingly, differences among the analyses of these structurally and functionally distinct cell types are not unexpected.

Omicron is widely described as a heavily mutated lineage and this was reflected in our data [7, 10]. The Omicron VOC produced more epitope pairs than any other VOC in all analyses and was the only VOC to gain new epitopes, although epitope quantity is not necessarily a direct correlate of immunogenicity. Omicron emerged in the context of widespread vaccine‐ and infection‐induced T cell immunity, which has been shown to remain broadly cross‐reactive against many variants despite extensive spike mutations [27, 58, 64, 65]. These studies often evaluate aggregate immune responses across full‐length proteins, where many immunodominant epitopes fall within unmutated loci and are conserved among lineages. In contrast, this study exclusively uses mutated epitopes and thus the putative differences identified here should be considered as localized alterations that may occur in an HLA‐dependent manner. This interpretation is consistent with previous reports demonstrating that despite the general maintenance of T cell immunity against VOCs, certain mutations can trigger changes in HLA recognition and reactivity [25, 56, 58, 59]. Nonetheless, these predicted changes may present differently in the context of pre‐existing immunity but are hypothesized to influence HLA recognition in a way that maintains the dominant HLA‐epitope interactions or expands the binding repertoire [58].

Notably, previous VOCs emerged from separate lineages rather than the current dominant strain, whereas Omicron variants have given rise to multiple sublineages with a common ancestor that have since dominated global circulation [9, 17, 18]. Combined with factors such as viral immune evasion and waning host immunity, the high transmissibility and mild pathogenicity of Omicron are perhaps the ideal evolutionary outcome as they facilitate a stable persistence within human populations without causing the excessive mortality of potential hosts [66]. Indeed, Omicron descendants continue to circulate, emphasizing the persistent need to understand the impacts of their mutations [9].

As this study uses a representative genome for each VOC, antigenic variation beyond what is reported here is possible within each viral lineage. Given the diversity and mutational complexity of Omicron descendants, the findings reported for the BA.1 variant should not be generally applied unless a given epitope is conserved among sublineages. Furthermore, this study focuses on the SARS‐CoV‐2 spike gene, which produces a highly immunogenic surface protein used as the main viral target in vaccine development [67, 68, 69]. Accordingly, these findings should be interpreted within the context of spike‐associated T cell immunity, as other genes may have unique host immune interactions [25, 68]. Future exploration of additional genes would provide valuable insight toward the broader whole genome antigenic variations of the VOCs.

Sliding window prediction permits the generation of overlapping epitopes within the same immunogenic region, thus improving biological accuracy by incorporating all possible epitopes produced by natural antigen processing and all combinations of anchor residues and binding cores [40]. As such, sequence overlap does not necessarily indicate biological redundancy, which was reflected in our data as overlapping epitopes often produced vastly different HLA profiles and binding scores. Consequently, all epitopes were treated as distinct HLA‐binding events. Although computational predictions are an invaluable tool for epitope identification, experimental validation is essential to confirm viability and functional relevance. As computational epitope prediction is inherently influenced by the prediction model and parameters used, we adhered to rigorous standards for epitope selection and pairing to enhance the robustness of our findings. Nonetheless, peptide presentation by an HLA molecule does not guarantee immunogenicity or the ability to induce T cell activation [70].

Epitope immunogenicity can depend on multiple factors, such as the relative abundance of peptide‐HLA complexes on the cell surface, the timing of protein expression from which the peptide is derived, binding competition from other peptides with varying immunodominance hierarchies, and the degree of sequence homology with host‐derived peptides [70]. Accordingly, HLA‐matched peripheral blood mononuclear cell stimulations coupled with functional assays such as cytokine secretion ELISPOTs, intracellular cytokine staining, or proliferation, activation induced marker, and multimer‐based assays may validate putative epitopes sequences and assess T cell receptor cross reactivity [71, 72]. Further work is currently underway to validate several putative epitopes of interest from this study.

Overall, our study provides valuable insights into the dynamic interplay between viral evolution and HLA recognition across the five former VOC lineages that dominated the pandemic era. Continued research in this area is essential to guide public health responses and inform the development of effective countermeasures against the ever‐evolving SARS‐CoV‐2 threat.

Author Contributions

Hezhao Ji: funding acquisition, supervision, writing – review and editing, conceptualization, methodology, validation. Katherine L. Li: conceptualization, investigation, funding acquisition, writing – original draft, methodology, formal analysis, writing – review and editing, visualization, validation. Paul Sandstrom: supervision, writing – review and editing, funding acquisition.

Funding

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Data S1: Supporting Information.

IRV-20-e70286-s001.xlsx^{(59.2KB, xlsx)}

Figure S1: irv_70286‐sup‐0002‐Li2026_SupplementaryMaterials_IORV_Clean_R1.d. Wuhan Reference‐VOC epitope pair identification. Epitope pairs were identified for each VOC analysis by comparing putative MHC class I and II epitopes (independently) from the Alpha, Beta, Gamma, Delta, and Omicron BA.1 lineages against putative epitopes from the ancestral Wuhan Reference lineage. Pairs must contain at least one mutation within the VOC epitope and start at the same S protein locus or be offset by no more than one amino acid.

Figure S2: Spike protein mutations identified in the five specific Variant of Concern genomes used in this study. Mutations depicted here include non‐synonymous substitutions, insertions, and deletions.

Figure S3: Analysis of MHC class II regular (A‐C), MHC class II promiscuous (D‐F), and MHC class I (G‐I) putative epitope pairs in the spike proteins of all examined VOCs. The proportion of VOC epitopes reporting a gain (A, D, G) or loss (B, E, H) of predicted HLA allele binders compared to their paired Wuhan Reference epitope, or where the paired VOC and Wuhan Reference epitopes reported at least one HLA binder in common (C, F, I) are demonstrated. The Delta VOC was omitted in the MHC class II promiscuous analysis as no epitope pairs qualified. Proportions for each lineage were calculated using the total number of Wuhan Reference‐VOC epitope pairs, as described in Table 1. The observed differences between lineages were not significant at p < 0.05.

Figure S4: Analysis of MHC class II regular putative epitope pairs in the spike protein of all examined VOCs. The quantity of unique HLA alleles gained as new putative binders for the mutated VOC epitope, that were not previously predicted as binders for the corresponding paired epitope from the ancestral Wuhan Reference lineage are shown.

Figure S5: Analysis of MHC class II regular putative epitope pairs in the spike protein of all examined VOCs. The quantity of unique HLA alleles lost as putative binders for the mutated VOC epitope, that were previously predicted as binders for the corresponding paired epitope from the ancestral Wuhan Reference lineage are shown.

Figure S6: Analysis of MHC class II regular putative epitope pairs in the spike protein of all examined VOCs. The quantity of unique HLA alleles shared as putative binders as they were predicted to bind to both the mutated VOC epitope and the corresponding paired epitope from the ancestral Wuhan Reference lineage are shown.

Table S1: Analysis of MHC class II promiscuous putative epitope pairs in the spike protein of all examined VOCs. The quantity of HLA alleles gained, lost, and shared as binders in the VOC epitope of designated Wuhan Reference‐VOC promiscuous epitope pairs.

Figure S7: Analysis of MHC class I putative epitope pairs in the spike protein of all examined VOCs. The quantity of unique HLA alleles gained as new putative binders for the mutated VOC epitope, that were not previously predicted as binders for the corresponding paired epitope from the ancestral Wuhan Reference lineage are shown.

Figure S8: Analysis of MHC class I putative epitope pairs in the spike protein of all examined VOCs. The quantity of unique HLA alleles lost as putative binders for the mutated VOC epitope, that were previously predicted as binders for the corresponding paired epitope from the ancestral Wuhan Reference lineage are shown.

Figure S9: Analysis of MHC class I putative epitope pairs in the spike protein of all examined VOCs. The quantity of unique HLA alleles shared as putative binders as they were predicted to bind to both the mutated VOC epitope and the corresponding paired epitope from the ancestral Wuhan Reference lineage are shown.

Figure S10: Percentile rank scores for the shared HLA alleles of each MHC class II regular epitope pair in the Alpha VOC analysis. Lower percentile rank scores indicate a higher binding affinity between the HLA allele and the putative epitope peptide. The observed differences between lineages were not significant at p < 0.05.

Figure S11: Percentile rank scores for the shared HLA alleles of each MHC class II regular epitope pair in the Beta VOC analysis. Lower percentile rank scores indicate a higher binding affinity between the HLA allele and the putative epitope peptide. The observed differences between lineages were not significant at p < 0.05.

Figure S12: Percentile rank scores for the shared HLA alleles of each MHC class II regular epitope pair in the Gamma VOC analysis. Lower percentile rank scores indicate a higher binding affinity between the HLA allele and the putative epitope peptide. The observed differences between lineages were not significant at p < 0.05.

Figure S13: Percentile rank scores for the shared HLA alleles of each MHC class II regular epitope pair in the Delta VOC analysis. Lower percentile rank scores indicate a higher binding affinity between the HLA allele and the putative epitope peptide. The observed differences between lineages were not significant at p < 0.05.

Figure S14: Percentile rank scores for the shared HLA alleles of each MHC class II regular epitope pair in the Omicron VOC analysis. Lower percentile rank scores indicate a higher binding affinity between the HLA allele and the putative epitope peptide. Pair 1 omitted as the HLA allele profiles were completely different and had no shared alleles that could be used for this comparison. The observed differences between lineages were not significant at p < 0.05.

Figure S15: Percentile rank scores for the shared HLA alleles of each MHC class II promiscuous epitope pair in the Alpha VOC analysis. Lower percentile rank scores indicate a higher binding affinity between the HLA allele and the putative epitope peptide. * p < 0.05, ** p < 0.01.

Figure S16: Percentile rank scores for the shared HLA alleles of the MHC class II promiscuous epitope pair in the Beta VOC analysis. Lower percentile rank scores indicate a higher binding affinity between the HLA allele and the putative epitope peptide. * p < 0.05.

Figure S17: Percentile rank scores for the shared HLA alleles of the MHC class II promiscuous epitope pair in the Gamma VOC analysis. Lower percentile rank scores indicate a higher binding affinity between the HLA allele and the putative epitope peptide. The observed differences between lineages were not significant at p < 0.05.

Figure S18: Percentile rank scores for the shared HLA alleles of each MHC class II promiscuous epitope pair in the Omicron VOC analysis. Lower percentile rank scores indicate a higher binding affinity between the HLA allele and the putative epitope peptide. * p < 0.05.

Figure S19: Percentile rank scores for the shared HLA alleles of each MHC class I epitope pair in the Alpha VOC analysis. Lower percentile rank scores indicate a higher binding affinity between the HLA allele and the putative epitope peptide. Pair 1 omitted as the HLA allele profiles were completely different and had no shared alleles that could be used for this comparison. * p < 0.05, ** p < 0.01.

Figure S20: Percentile rank scores for the shared HLA alleles of each MHC class I epitope pair in the Beta VOC analysis. Lower percentile rank scores indicate a higher binding affinity between the HLA allele and the putative epitope peptide. * p < 0.05, ** p < 0.01.

Figure S21: Percentile rank scores for the shared HLA alleles of each MHC class I epitope pair in the Gamma VOC analysis. Lower percentile rank scores indicate a higher binding affinity between the HLA allele and the putative epitope peptide. Pair 7 omitted as the HLA allele profiles were completely different and had no shared alleles that could be used for this comparison. * p < 0.05.

Figure S22: Percentile rank scores for the shared HLA alleles of each MHC class I epitope pair in the Delta VOC analysis. Lower percentile rank scores indicate a higher binding affinity between the HLA allele and the putative epitope peptide. Pairs 3 and 15 omitted as the HLA allele profiles were completely different and had no shared alleles that could be used for this comparison. * p < 0.05.

Figure S23: Percentile rank scores for the shared HLA alleles of each MHC class I epitope pair in the Omicron VOC analysis. Lower percentile rank scores indicate a higher binding affinity between the HLA allele and the putative epitope peptide. Pairs 3, 6, 39, and 69 omitted as the HLA allele profiles were completely different and had no shared alleles that could be used for this comparison. * p < 0.05, ** p < 0.01.

IRV-20-e70286-s002.docx^{(16.4MB, docx)}

Acknowledgments

This work was supported by the National Microbiology Laboratory Branch of the Public Health Agency of Canada. K.L.L. is a participant of the Government of Canada Research Affiliate Program, and K.L.L. was a participant of the Visual and Automated Disease Analytics program co‐hosted by University of Manitoba and University of Victoria, Canada. The authors gratefully acknowledge all data contributors, including the authors and their originating laboratories for providing the specimens, as well as their submitting laboratories for generating the genetic sequence and metadata and sharing via the GISAID Initiative, on which this study is based. The authors would like to thank Connor Lowey for contributing to the automation of data organization. Figures 1, S1, and S2 were created with BioRender.

Data Availability Statement

The SARS‐CoV‐2 genomes used in this study are cataloged under GISAID EPI_SET ID: EPI_SET_240607gs. Further details regarding the GISAID information are located at the end of the Supporting Information file. All epitope datasets supporting the conclusions of this article are included within the article and its Supporting Information files. All computational tools used were freely available online and were run as web‐based platforms or standalone executables on a standard Windows 10 machine. The R code used for the generation of statistical data and figures is available upon request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data S1: Supporting Information.

IRV-20-e70286-s001.xlsx^{(59.2KB, xlsx)}

IRV-20-e70286-s002.docx^{(16.4MB, docx)}

Data Availability Statement

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PERMALINK

Antigenic Variation and Immune Recognition Inference From the COVID‐19 Pandemic: An In Silico Comparative Analysis of Spike T Cell Epitopes From the SARS‐CoV‐2 Variants of Concern

Katherine L Li

Paul Sandstrom

Hezhao Ji

ABSTRACT

Background

Methods

Results

Conclusions

1. Introduction

2. Methods

FIGURE 1.

2.1. Sequence Processing

2.2. Regular and Promiscuous Epitope Prediction

2.3. CAVES Epitope Pairs

2.4. HLA Binding Analyses

2.5. Statistical Analysis and Data Visualization

3. Results

3.1. Sequence Processing and Epitope Prediction

TABLE 1.

3.2. CAVES Epitope Pairs

3.3. HLA Alleles as Epitope Binders

3.4. HLA‐Epitope Binding Affinity

FIGURE 2.

FIGURE 3.

FIGURE 4.

3.4.1. MHC Class II Regular Epitope Binding Affinities

3.4.2. MHC Class II Promiscuous Epitope Binding Affinities

3.4.3. MHC Class I Epitope Binding Affinities

3.5. HLA‐Epitope Distribution

3.5.1. MHC Class II Regular Epitope Distribution

FIGURE 5.

3.5.2. MHC Class II Promiscuous Epitope Distribution

3.5.3. MHC Class I Epitope Distribution

4. Discussion

Author Contributions

Funding

Conflicts of Interest

Supporting information

Acknowledgments

Data Availability Statement

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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