In silico analysis suggests less effective MHC-II presentation of SARS-CoV-2 RBM peptides: Implication for neutralizing antibody responses

Andrea Castro; Kivilcim Ozturk; Maurizio Zanetti; Hannah Carter

doi:10.1371/journal.pone.0246731

. 2021 Feb 11;16(2):e0246731. doi: 10.1371/journal.pone.0246731

In silico analysis suggests less effective MHC-II presentation of SARS-CoV-2 RBM peptides: Implication for neutralizing antibody responses

Andrea Castro ^1,², Kivilcim Ozturk ², Maurizio Zanetti ^3,^4,^*, Hannah Carter ^2,^4,^*

Editor: Jayanta Bhattacharya⁵

PMCID: PMC7877779 PMID: 33571241

Abstract

SARS-CoV-2 antibodies develop within two weeks of infection, but wane relatively rapidly post-infection, raising concerns about whether antibody responses will provide protection upon re-exposure. Here we revisit T-B cooperation as a prerequisite for effective and durable neutralizing antibody responses centered on a mutationally constrained RBM B cell epitope. T-B cooperation requires co-processing of B and T cell epitopes by the same B cell and is subject to MHC-II restriction. We evaluated MHC-II constraints relevant to the neutralizing antibody response to a mutationally-constrained B cell epitope in the receptor binding motif (RBM) of the spike protein. Examining common MHC-II alleles, we found that peptides surrounding this key B cell epitope are predicted to bind poorly, suggesting a lack MHC-II support in T-B cooperation, impacting generation of high-potency neutralizing antibodies in the general population. Additionally, we found that multiple microbial peptides had potential for RBM cross-reactivity, supporting previous exposures as a possible source of T cell memory.

Introduction

Upon infection with SARS-CoV-2 the individual undergoes seroconversion. In mildly symptomatic patients, seroconversion occurs between day 7 and 14, includes IgM and IgG, and outlasts virus detection with generally higher IgG levels in symptomatic than asymptomatic groups in the early convalescent phase [1]. Alarmingly, the IgG levels in both asymptomatic and symptomatic patients decline during the early convalescent phase, with a median decrease of ~75% within 2–3 months after infection [2]. This suggests that the systemic antibody response which follows natural infection with SARS-CoV-2 is short-lived, with the possibility of no residual immunity after 6–12 months [3] affecting primarily neutralizing antibodies in plasma [4]. Early activated B cells produce antibodies in quasi-germline configuration and are likely ‘innate-like B cells’ [5–8] that have not undergone somatic hypermutation and maturation. Consistent with the above argument, a lack of germinal center formation but robust activation of non-germinal type B cells has been reported in cases of severe COVID-19 infection, impairing production of long-lived memory or high affinity B cells [9].

The generation of an antibody response requires cooperation between a B cell producing specific antibody molecules and a CD4 T cell (helper cell) activated by an epitope on the same antigen as that recognized by the B cell (T-B cooperation) [10]. This reaction occurs in the germinal center [11, 12]. Excluded from this rule are responses against carbohydrates and antigens with repeating motifs that alone cross-link the B cell antigen receptor leading to B cell activation [13]. Discovered over 50 years ago [14–16], it also became apparent that T-B cooperation is restricted by Major Histocompatibility Complex class II (MHC-II) molecules [17–19]. T-B cooperation plays a key role in the facilitation and strength of the antibody response [15, 20] and the size of the antibody response is proportional to the number of Th cells activated by the B cell during T-B cooperation [18, 19, 21]. The importance of T cell help during the activation of antigen specific B cells to protein antigens driving B cell selection is emphasized by recent experiments where the injection of a conjugate of antigen (OVA) linked with an anti-DEC205 antibody induced a greater proliferation of DEC205+ relative to DEC205- B cells consistent with a T helper effect on B cell activation [22].

T-B cooperation requires that the epitopes recognized by the B and T cell be on the same portion of the antigen [16, 23, 24] leading to a model requiring the contextual internalization and co-processing of T and B cell epitopes [10] which is consistent with the principle of linked (aka associative) recognition of antigen [25]. Studies in vitro using human T and B lymphocytes showed that an antigen specific B cell can present antigen to CD4 T cells even if antigen is present at very low concentration (10⁻¹¹–10⁻¹² M) [26]. Presentation of antigen by the B cell also facilitates the cooperation between CD4 T cells of different specificities resulting in enhanced generation of memory CD4 T cells [27]. However, T-B cooperation is not the only form of cooperative interaction among lymphocytes as cooperation exists between CD4 T and CD8 T cells [28] and between two CD4 T cells responding to distinct epitopes on the same antigen [29].

A model based on coprocessing of T and B epitopes also led to the suggestion that preferential T-B pairing could be based on topological proximity [30–34] so that during BCR-mediated internalization the T cell epitope is protected by the paratope of the BCR. Indeed, a more recent study showed that not only is CD4 T cell help a limiting factor in the development of antibodies to smallpox (vaccinia virus), but that there also exists a deterministic epitope linkage of specificities in T-B cooperation against this viral pathogen [35]. Collectively, it appears that T-B pairing and MHC-II restriction are key events in the selection of the antibody response to pathogens and that operationally T-B cooperation and MHC-II restriction are key events in the generation of an adaptive antibody response, suggesting that lack of or defective T-B preferential pairing could result in an antibody response that is suboptimal, short-lived, or both.

The relevance of T-B cooperation in protective antiviral responses has been documented in numerous systems. In the influenza A virus (PR8) system it was shown that while Th1 CD4 T cell responses on their own are ineffective at promoting recovery from infection, antibodies generated through T-B cooperation were indispensable in the protective response against the virus [36]. In a different influenza A strain, it was shown that T-B cooperation and CD4 T cells represent a limiting factor in the kinetics and early magnitude of the primary B cell response to virus challenge and provide help in a preferential way (i.e. intra-molecular but nor inter-molecular) [37]. Additionally, CD40-CD40L (costimulatory molecules found on B cells and CD4 T cells, respectively) interaction is required for the generation of antibody responses, isotype switching and memory responses in non-viral model systems [38]. In LCMV (lymphocytic choriomeningitis virus) and VSV (vesicular stomatitis virus) abrogation of CD40-CD40L interaction prevented T-B cooperation and thus inhibited antiviral protection [39]. Interestingly, this study also showed that the activation of CD4 T cells (e.g., inflammatory CD4 T cells) not associated with the activation of B cells was not compromised [39]. These data demonstrate the relevance of T-B cooperation in the antibody response in protection against viral infection.

In SARS-CoV-2, neutralizing antibodies (NAbs) are a key defense mechanism against infection and transmission. NAbs generated by single memory B cell VH/VL cloning from convalescent COVID-19 patients have been extremely useful in defining the fine epitope specificity of the antibody response in COVID-19 individuals. At present, SARS-CoV-2 NAbs can be distinguished into three large categories. 1) Repurposed antibodies, that is, NAbs discovered and characterized in the context of SARS-CoV and subsequently found to neutralize SARS-CoV-2 via cross-reactivity. These antibodies map away from the receptor binding domain (RBD) of the spike protein [40–42]. 2) Non-RBD neutralizing antibodies discovered in SARS-CoV-2 patients whose paratope is specific for sites outside the RBD [43]. 3) RBD antibodies, including NAbs, derived from SARS-CoV-2 patients that map to a restricted site in the RBD [7, 44–49]. Cryo-EM of this third antibody category shows that they bind to residues in or around the four amino acids Phe-Asp-Cys-Tyr (FNCY) in the receptor binding motif (RBM) (residues 437–508) which is inside the larger RBD (residues 319–541) at the virus:ACE2 interface [45]. Although the RBD has been shown to be an immunodominant target of serum antibodies in COVID-19 patients [50], high potency NAbs are directed against a conserved portion of the RBM on or around the FNCY patch, a sequence only found in the RBD of SARS-CoV-2 and not in other coronaviruses. NAbs that make contact with the FNCY patch outperform other NAbs that do not in competition binding assays, highlighting the importance of the region in neutralizing ACE2 binding [43]. Indeed while the RBD is mutationally tolerant, the RBM is constrained to the wild-type amino acids [51], implying that the B cell epitope included in this region of the virus:ACE2 interface is resistant to antigenic drift. Thus, we may refer to this site as a key RBM B cell epitope in the generation of potent NAbs.

Antibody responses against SARS-CoV-2 depend on CD4 T cell help. Spike-specific CD4 T cell responses have been found to correlate with the magnitude of the anti-RBD IgG response whereas non-spike CD4 T cell responses do not [52]. However, in unexposed patients, spike-specific CD4 T cells reactive with MHC-II peptides proximal to the central B cell epitope represent a minority (~10%) of the total CD4 T cell responses, which are dominated by responses against either the distal portion of the spike protein or other structural antigens [53]. Surprisingly, these CD4 T cell responses are largely cross-reactive and originate from previous coronavirus infections [54].

As mounting evidence suggests that the NAb response in COVID-19 patients is relatively short-lived, we decided to test the hypothesis that associative recognition of a key RBM B cell epitope (in and around the FNCY patch) and proximal MHC-II-restricted epitopes may be defective with detrimental effects on preferential T-B pairing. Specifically, we hypothesize that the inability to present SARS-CoV-2 peptide sequences near putative B cell epitopes may impair memory cell generation and consequently reduce the strength and longevity of overall and neutralizing antibody responses. To quantify the potential effects of T-B cooperation in vivo, we analyzed all 15mer putative MHC-II epitopes (+/- 50 amino acid residues) relative to the key RBM B cell epitope for coverage by all known 5,620 human MHC-II alleles and predicted binding affinity. The analysis shows that there exists in general less availability of effective T cell epitopes in close proximity to the key RBM B cell epitope in the human population.

Results

Topology of a key RBM B cell epitope

Within the 222 amino acid long RBD of the spike protein (residues 319–541), the RBM (residues 437–508) is the portion of the spike protein that establishes contact with the ACE2 receptor (Fig 1A). The contact residues span a relatively large surface involving approximately 17 residues [45], among them residues F486, N487, Y489 form a loop, which we term the FNCY patch, which is surface exposed and protrudes up towards the ACE2 receptor from the bulge of the RBD (Fig 1B and 1C). F486 forms hydrophobic interactions with three ACE2 residues (L79, M82, W83). N487 forms hydrogen bonds with Q24 and W83, and Y489 is linked with K31 via a hydrophobic interaction. This makes the amino acid residues in or around the FNCY patch a logical B cell epitope target for antibodies blocking the virus:receptor interaction. In addition, these core residues are mutationally constrained by the ACE2 contact surface [51]. Not surprisingly, a set of recently reported potently neutralizing antibodies generated by single B cell VH/VL cloning from convalescent COVID-19 patients all bear paratopes that include the FNCY patch in their recognition site [43, 47–49, 55] (Fig 1D). While other residues (Q493, N501, and Y505) are also shared between ACE2 and the paratope of these antibodies, they are not as protruding and are on a β-sheet unlike the FNCY patch which is organized in a short loop as a result of the C480:C488 disulfide bond. Thus, blockade of the RBM:ACE2 interaction (neutralization) depends at least in part on a B cell epitope in the RBM that is structurally and functionally critical to the interaction, virus internalization, and cell infectivity.

Fig 1 — (A) 3D structure of the SARS-CoV-2 spike protein RBD (white) binding the ACE2 receptor (green) (PDB: 6M0J) with contact residues highlighted in blue and the FNCY patch highlighted in red. (B-C) Spike protein RBD with ACE2 contact residues and FNCY patch residues labeled in two orientations (front and back). (D) Heatmap of neutralizing antibody contact residues (purple) on the spike protein RBM region (positions 437–508). Black dots indicate ACE2 contact residues and the FNCY patch is highlighted in red. Source data available in S1 Table.

Prediction of MHC-II affinity for 15mer peptides proximal to the RBM B cell epitope

In the T-B cooperation model, B cell activation and production of NAbs is dependent on CD4 T cell responses to MHC-II restricted peptides. To test the hypothesis that the generation of NAbs against a mutationally constrained B cell epitope in the RBM reflects the efficiency of processing and presentation of MHC-II peptides proximal to the FNCY patch, we evaluated the landscape of MHC-II peptide restriction across the entire SARS-CoV-2 spike protein with respect to common MHC-II alleles in the human population. To assess the potential for effective restriction by MHC-II molecules in a reasonable proportion of the population, we devised a position-based score that assigns each amino acid residue the median affinity of the best overlapping peptide, where median affinity is calculated across the 1911 most common MHC-II alleles (Fig 2A), which was highly correlated with scores across all 5620 MHC-II alleles (Fig 2B; Pearson rho = 0.99, p<2.2e-308). While a number of sites along the spike protein are predicted to generate high affinity peptides for most common MHC-II alleles, the region around the FNCY patch was depleted for generally effective binders (Fig 2C, Fisher’s exact OR = 0.21, p = 0.015, Methods, S1 Fig). Interestingly, the RBM region containing the FNCY patch was free of glycans that could potentially mask the epitope (Fig 2D). We further evaluated the distributions of binding affinities for the 20 best-ranked peptides across all sites in the spike protein (Fig 2E), and in comparison, the distributions for the best 20 peptides overlapping positions within +/- 50 residues of the FNCY patch (Fig 2F). In the best case, less than half of the considered MHC-II alleles bound a shared peptide close to the FNCY patch, whereas at other sites there were multiple peptides that could be bound by nearly all of the MHC-II alleles (Fig 2E). This suggested overall less availability of effective T cell epitopes in close proximity to the FNCY B cell epitope, which could limit the availability of T cell help during an epitope-specific T-B cooperative interaction in the germinal center.

Fig 2 — (A) Overview of the position affinity score. (B) Scatterplot showing position affinity scores estimated using only common (>10% frequency, from [56]) MHC-II alleles (x-axis) versus across all MHC-II alleles (y-axis). (C) Lineplot showing the position affinity scores across common MHC-II alleles (Methods). Annotated domains from UniProt are highlighted. (D) Heatmap showing amino acid positions that are glycosylated [57]. (E) Barplots (top) and boxplots (bottom) describing the fraction of binding MHC-II alleles and corresponding affinity percentile rank distributions respectively for the top 20 peptides with the highest fraction of common binding alleles. The binding threshold of 10 is shown as a dotted line, with values less than 10 indicating binding. Colors correspond to the regions listed in C. (F) Barplots (top) and boxplots (bottom) describing the fraction of binding MHC-II alleles and corresponding affinity percentile rank distributions respectively for the top 20 peptides within +/-50 amino acids of the FNCY B cell epitope. Colors correspond to the regions listed in C.

To further assess whether population variation in MHC-II MHC alleles might contribute to heterogeneity in potential to generate neutralizing antibodies, we also evaluated the potential of MHC-II supertypes to restrict peptides from neighboring the FNCY patch. Greenbaum et al. previously defined 7 supertypes that group MHC-II alleles based on shared binding repertoire. These 7 supertypes account for between 46%-77% of haplotypes and cover over 98% of individuals when all four loci are considered together [58]. We revisited our analysis of peptide restriction proximal to the FNCY patch treating each supertype separately. There was considerable variability in potential to effectively present FNCY patch proximal sequences across supertypes (Fig 3A and 3B, Χ² = 175, p = 3.75e-35, S2 Fig). Only 3 supertypes (DP2, main DP and DR4) commonly presented peptides overlapping the FNCY patch (Fig 3B). We were able to obtain population allele frequencies for four populations from the Be The Match registry [59] and Du et al. [60]. These data show that DR4 is relatively infrequent across the populations evaluated, whereas main DR, main DP, and DP2 are more common (Fig 3C), and thus could be more important for MHC-II restriction supportive of neutralizing antibodies. While there were some large population-specific differences in main DP and DP2 supertype frequencies, these frequency estimates are based on a limited population sample and may provide only a rough approximation. In general, DP and DR haplotypes were able to restrict more FNCY patch proximal sequences (Fig 3D).

Fig 3 — (A) Barplot showing the aggregated supertype position affinity scores for each position +/- 50 amino acids from the FNCY patch (grey zone). (B) Scatterplot showing the specific supertype position scores for each position +/- 50 amino acids from the FNCY patch (grey zone). The binding threshold of 10 is shown as a dashed blue line, with points below the threshold indicating binding. (C) Barplot showing United States population frequencies, summed across the available alleles in each supertype. (D) Fraction of positions falling below the binding threshold within the region of interest for each supertype.

Cross-reactivity to a non-coronavirus MHC-II binding peptide as a potential driver of T cell responses helping antibody response to the RBM B cell epitope

Interestingly, Mateus et al. reported pre-existing CD4 T cell responses to peptides derived from the spike protein using T cells from unexposed individuals, suggesting previous exposures to other human coronaviruses could potentially generate protective immunity toward SARS-CoV-2. Indeed, regions of higher coronavirus homology were associated with more T cell responses in their data [54]. This represents the most comprehensive interrogation of the spike protein with response to CD4 T cell responses to date. They screened all 15mers of the spike protein in pooled format and further evaluated 66 predicted MHC-II peptides that generated CD4 T cell responses. Visualizing the landscape of the CD4 T cell responses described in their work by percent positive response (Fig 4A) or spot forming cells (Fig 4B), we noted relatively few responses proximal to the FNCY patch in the RBM. Accordingly, few other coronaviruses had limited homology to the FNCY region, and none fully included the FNCY patch (Fig 5A).

Fig 4 — (A) Barplot showing the percentage of positive responses toward SARS-CoV-2 peptides from unexposed individuals. (B) Barplot showing the number of spot-forming cells (SFC) for tested SARS-CoV-2 peptides against PBMCs from unexposed individuals. Data from S1 Table from [54].

Fig 5 — (A) Multiple sequence alignment between SARS-CoV-2, SARS1, MERS, and other human coronaviruses, focusing on the region surrounding the FNCY B cell epitope. (B) SeqLogo plot obtained by clustering IEDB peptides reported to bind to DRB1*01:01. (C) Top results after blasting the FNCYFPLQSYGFQPT peptide against all reference proteins. (D) Barplot describing best peptide affinities across MHC-II alleles of the top 35 unique organisms with one or more peptides matching a peptide with high similarity to 15mers +/-30aa from the FNCY binding epitope based on BLAST analysis. The closer to 0, the greater the binding potential.

A notable exception in Mateus’ results is peptide 486FNCYFPLQSYGFQPT500, which was reported to induce a CD4 T cell response in an unexposed individual. In this case, the peptide was restricted by HLA-DRB1*0101 or HLA-DQA1*0101/DQB1*0501. We found that the peptide sequence had greater in silico predicted affinity to HLA-DRB1*0101. To explain the conundrum, we blasted this peptide against the “refseq_protein” database excluding SARS-CoV-2 (Methods). Surprisingly, the sequences with the best homology for this query were not from coronaviruses but rather from common pathogens, first among them parasites of the Cryptosporidium genus of apicomplexan parasitic alveolates. These sequences included conserved anchor positions for the HLA-DRB*0101 allele making it plausible that a prior exposure could account for the formation of a memory CD4 T cell response (Fig 5B and 5C). To further assess the potential for other prior exposures in generating immune memory for sequences proximal to the FNCY patch we blasted all 15mers within +/-30 amino acids of the FNCY patch and filtered the resulting sequences based on restriction by consensus MHC-II supertypes [58] (S2 Table). We found peptides associated with multiple microbial organisms that may meet the criteria to potentially generate CD4 T cell memory relevant to the RBM of SARS-CoV-2 (Fig 5D).

Discussion

SARS-CoV-2 uses the RBD of the spike protein to bind to the ACE2 receptor on target cells. The actual contact with ACE2 is mediated by a discrete number of amino acids that have been visualized by cryo-EM (Lan et al., 2020; Shang et al., 2020). Although several SARS-related coronaviruses share 75% homology and interact with ACE2 on target cells (Ge et al., 2013; Ren et al., 2008; Yang et al., 2015) the RBM in SARS-CoV-2 is unique to this virus. In vitro binding measurements show that SARS-CoV-2 RBD binds to ACE2 with an affinity in the low nanomolar range (Walls et al., 2020). Mutations in this motif could be detrimental to the virus’s ability to infect ACE2 positive human cells. Since the RBD is an immunodominant site in the antibody response in humans [50] it is not surprising that the paratope of some antibodies isolated from convalescent individuals via single B cell VH/VL cloning, and selected on the basis of high neutralization potency, all seem to bind a surface encompassing the FNCY patch in the RBM [7, 8, 44, 46–49]. Arguably, this motif corresponds to a relevant B cell epitope in the spike protein of SARS-CoV-2 and is a logical target of potent neutralizing antibodies.

Although antibodies directed to this site have been isolated by different groups, little is known about their contribution to the pool of antibodies in serum of SARS-CoV-2 infected individuals, but evidence suggests they are likely to be rare. In one study they were found to represent a subdominant fraction of the anti-RBD response [49] while the estimated frequency of antigen-specific B cells ranges from 0.07 to 0.005% of all the total B cells in COVID-19 convalescent individuals [61]. In a second study, the identification of two ultra-potent NAbs having a paratope involving the FNCY patch required screening of 800 clones from twelve individuals [8]. This suggests that a potent NAb response to a mutationally constrained RBM epitope is a rare component of the total anti-virus response consistent, with the observation that there is no correlation between RBM site-specific neutralizing antibodies and serum half-maximal neutralization titer (NT50) [61]. Here we show that the core RBM B cell epitope is apparently uncoupled from preferential T-B pairing, a prerequisite for a coordinated activation of B cells against the pathogen. We analyzed MHC-II binding of 15mer peptides in the spike protein upstream (-50 aa) or downstream (+50 aa) of the central RBM B cell epitope and found both low coverage by 1911 common MHC-II alleles and a depletion of binding 15mers proximal to the FNCY patch versus other exposed areas on the spike protein. This could be due to the fact that a sizeable proportion (40%) of CD4 T cells responding to the spike protein are memory responses found in SARS-CoV-2 unexposed individuals [52, 62] or other structural protein of SARS-CoV-2 such as the N protein [53]. Thus, it is possible that these conserved responses are used as a decoy mechanism to polarize the response away from the RBM. However, this does not rule out the contribution of a bias in frequency of specific B cells in the available repertoire.

Corroboration to our hypothesis also comes from Mateus et al. [54] who tested sixty-six 15mer peptides of the spike protein in SARS-CoV-2 unexposed individuals and found that CD4 T cell responses against this narrow RBM site account for only 2/110 (1.8%) of the total CD4 T cell response to 15mer peptides of the spike protein. Surprisingly, a CD4 T cell response against peptide FNCYFPLQSYGFQPT was by CD4 T cells of an unexposed individual. Since this peptide has low homology with previous human coronaviruses, we reasoned that this could either represent a case of TCR cross-reactivity since a single TCR can engage large numbers of unique MHC/peptide combinations without requiring degeneracy in their recognition [63, 64]. Remarkably, however, a BLAST analysis revealed a 10 amino acid sequence match with proteins from pathogens including those from the Cryptosporidium genus, with identity in binding motif and anchor residues (agretope) for the restricting MHC-II allele strongly suggesting peptide cross-reactivity. Cryptosporidium hominis is a parasite that causes watery diarrhea that can last up to 3 weeks in immunocompetent patients [65]. Additional possibilities for cross-reactivity to the RBM, albeit of a lesser stringency, involve antigens from Micromonospora, Pseudomonas, Blastococcus, Lactobacillus, and Bacteroides (Fig 5D). Thus, it appears as if memory CD4 T cells reactive with peptides in the RBM may reflect the immunological history of the individual that, as evidenced by this case, can be unrelated to infection by other coronaviruses. Interestingly, the great majority (64–88%) of COVID-19 positive individuals in homeless shelters in Los Angeles and Boston were found to be asymptomatic [66]. This suggests that the status of the immune system, which itself reflects past antigenic exposure, may be a determining factor in the generation of a protective immune response after SARS-CoV-2 infection.

The findings reported herein have considerable implications for natural immunity to SARS-CoV-2. The fact that there seems to be an overall suboptimal T-B preferential pairing suggests that B cells that respond to the RBM B cell epitope may receive inadequate T cell help. This is consistent with the observation that in general potent neutralizing antibodies to the RBM undergo very limited somatic mutation [8, 46] and are by and large in quasi-germline configuration [67]. Since T cell help is also necessary to initiate somatic hypermutation in B cell through CD40 or CD38 signaling in the germinal center [68], it follows that one important implication of our study is that defective T-B pairing may negatively influence the normal process of germinal center maturation of the B cell response in response to SARS-CoV-2 infection in a critical way.

Which antigens can generate T cell responses depends on the binding specificities of MHC-II molecules, which are highly polymorphic in the human population. We noted a general trend for MHC-II alleles to less effectively present peptides from the RBM region, but also observed some variability across MHC-II supertypes. The main DP and DP2 haplotypes were both common and had the highest potential to present peptides, suggesting that most individuals should carry at least one allele capable of presenting peptides in this region. Which of the two DP haplotypes was more common varied by ancestral population, thus it is possible that differences in the haplotypes could translate to differences in T-B cooperativity levels within groups, though binding affinities for epitopes near the FNCY patch were similar for both. DQ and DR supertypes were less able to present peptides near FNCY, with the exception of DR4, which is among the less common supertypes. Importantly, our analysis was limited to predicted affinity of peptides to MHC-II, and other characteristics such as expression levels, stability or differences in interactions with molecular chaperones likely also contribute to whether FNCY proximal peptides are available to support T-B cooperation [69].

The present study assesses the probability of SARS-CoV-2 peptides of the Spike protein to bind and be presented by MHC-II molecules. Our study is limited by the following: results are an estimate based on an algorithm that encompasses many biophysical variables for MHC-II presentation but certainly not all. In addition, while we believe the epitope containing the FNCY patch is promising for inducing a protective neutralizing response, it is not the sole determinant of a protective antibody response to SARS-CoV-2; as neutralizing antibodies against other portions of the spike and other non-structural proteins have been reported [41, 42, 70–73].

In light of our findings, it can be predicted that, in general, a specific RBM antibody response may be short-lived and that residual immunity from a primary infection may not be sufficient to prevent reinfection after 6–9 months. Sporadic cases of re-infection have been reported by the media in Hong Kong and Nevada [74]. A third case has been reported in a care-home resident who after the second infection produced only low levels of antibodies [75]. Finally, silent re-infections in young workers in a COVID-19 ward who tested positive for the new coronavirus and became reinfected several months later with no symptoms in either instance have been reported [76]. It is tempting to speculate that waning antibody levels or a poorly developed specific NAb antibody response to SARS-CoV-2 can potentially put people at risk of reinfection. Other factors to consider are a bias in the available B cell repertoire in the population and the extent to which a defective T-B cooperation influences the longevity of terminally differentiated plasma cells in the bone marrow [77].

In summary, we provide evidence that MHC-II constrains the CD4 T cell response for epitopes that are best positioned to facilitate T-B pairing in generating and sustaining a potent neutralizing antibody response against a mutationally constrained RBM B cell epitope. Furthermore, we show that the immunological history of the individual, not necessarily related to infection by other coronaviruses, may confer immunologic advantage. Finally, these findings may have implications for the quality and persistence of a protective, neutralizing antibody response to RBM induced by current SARS-CoV-2 vaccines.

Materials and methods

Data and code are available at https://github.com/cartercompbio/SARS_CoV_2_T-B_co-op.

Affinity analysis

NetMHCIIpan version 4.0 was used to predict peptide-MHC-II affinity [78] for generated 15mers along the SARS-CoV-2 spike protein.

Spike protein analyses

SARS-CoV-2 spike protein sequence and protein regions were obtained from https://www.uniprot.org/uniprot/P0DTC2. Glycan data were obtained from [57] and true-positive sites were aggregated across 3 replicates. To assess depletion of effective binders near the FNCY patch, we performed a Fisher’s exact test for binding (median affinity across common alleles <10) versus proximity (+/- 50 amino acids) to FNCY for positions free of glycans. We excluded positions within 10 amino acids of a glycan using the data obtained from Watanabe et al. and added a pseudocount of 1.

The SARS1, MERS1, HCoV-229E, HCoV-NL63, HCoV-OC43, and HCoV-HKU1 spike protein sequences were also downloaded from UniProt (P59594, K9N5Q8, P15423, Q6Q1S2, P36334, Q0ZME7, respectively). Multiple sequence alignment was performed on the EMBL-EBI Clustal Omega web server using default parameters [79].

Structure analysis

The 6M0J 3D X-ray structure for the protein complex containing the SARS-CoV-2 spike protein RBD (P0DTC2) interaction with ACE2 (Q9BYF1) from [45]. The structure figures were prepared using VMD [80].

Supertype analysis

Supertypes were obtained from [58]. All alpha/beta combinations spanning any of these types were included, resulting in 279 alleles. US supertype frequencies for alleles in DRB1 and DQB1 were obtained from the Be the Match registry [59], US frequencies for alleles in DPB1 were obtained from [60] as DPB1 was not available from the Be the Match registry. Available allele frequencies within each supertype were summed for Fig 3C.

Motif analysis

All 13-20mer peptides adhering to the following parameters were downloaded from the IEDB [81]: MHC-II assay, positive only, DRB1*01:01 allele, linear peptides; and any peptides with post-translational modifications or noncanonical amino acids were removed. The remaining 10,117 peptides were input into Gibbs cluster v2.0 [82] using the default MHC-II ligand parameters.

BLAST analysis

15mers were generated along a sliding window +/-30 amino acids from the FNCY patch start and end (455–518, 0-index) and input into NCBI BLAST [83] using the ‘refseq_protein’ database and excluding SARS-CoV-2 (taxid:2697049). Identified peptides (S2 Table) were then evaluated for binding affinity and any peptide binding to at least one allele was retained for Fig 5D.

Supporting information

S1 Table. SARS-CoV-2 neutralizing antibody residues and references used to generate Fig 1D.

(XLSX)

Click here for additional data file.^{(13.2KB, xlsx)}

S2 Table. BLAST-identified peptides with affinity, and binding fraction.

(XLSX)

Click here for additional data file.^{(57.9KB, xlsx)}

S1 Fig. Distribution of position scores along the spike protein using the 25th percentile affinity instead of the median affinity.

(PDF)

Click here for additional data file.^{(112.1KB, pdf)}

S2 Fig. Overview of subject peptides that bind at least one retrieved from BLAST search.

(A) Pileup of corresponding query peptides’ start positions of BLAST-identified peptides that bind to at least one common MHC-II allele. The below barplot shows Fig 3A for reference: the aggregated position scores across supertypes for positions proximal to FNCY. (B) Scatterplot showing the median supertype affinities of BLAST-identified peptides that may bind (median affinity <20) along the corresponding start positions of queried peptides along the spike protein. The FNCY motif region is highlighted in grey. (C) Clustermap showing the median supertype affinities of BLAST-identified peptides that may bind (median affinity <20) to at least one supertype. Median affinities greater than 20 have been adjusted to 20 for better visualization of binding peptides.

(PDF)

Click here for additional data file.^{(213.6KB, pdf)}

S1 Graphical abstract

(TIF)

Click here for additional data file.^{(430.9KB, tif)}

Acknowledgments

The graphical abstract was created using BioRender and used the PDB [84] structure 6VXX from [85].

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

This work was supported by an NIH National Library of Medicine Training Grant T15LM011271 to A.C., an Emerging Leader Award #18-022-ELA from The Mark Foundation for Cancer Research (https://themarkfoundation.org), a Canadian Institute For Advanced Research (CIFAR) (https://www.cifar.ca) fellowship #FL-000655 to H.C. and NIH NCI RO1 CA220009 to M.Z. and H.C.

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PLoS One. doi: 10.1371/journal.pone.0246731.r001

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Jayanta Bhattacharya

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PONE-D-20-38239

MHC-II constrains the natural neutralizing antibody response to the SARS-CoV-2 spike RBM in humans

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Reviewer #1: Andrea Castro et al, have performed a prediction analysis of the binding efficiency of peptides (proximal to the RBM), identified to be potential B cell epitopes targeted by neutralizing antibodies, to MHC-II alleles and their key observation was a poor binding interaction and a limited availability of effective T cell epitopes in close proximity to the RBM B cell epitopes. Based on their findings, the authors suggest that this lack of MHC-II binding may impact the B cell mediated antigen specific and MHC restricted T cell activation, and limit the CD4 T cell help that is required for affinity maturation of the B cells in the germinal centres, plausibly leading to less potent neutralizing antibodies and limited memory responses.

An extensive ex vivo functional analysis by Jose Mateus, also referred to herein, suggested that previous exposures to other human coronaviruses could potentially generate protective immunity toward SARS-CoV-2 as they found relatively few responses of pre-existing CD4 T cell responses to peptides proximal to the FNCY patch in the RBM, derived from the spike protein in the T cells from unexposed individuals. The authors in this study further identified peptides associated with multiple microbial organisms that plausibly meet the criteria to potentially generate CD4 T cell memory relevant to the RBM of SARS-CoV-2. Based on these observations, the authors conclude that MHC II constrains the CD4 responses towards neutralizing determinants that are in close proximity to facilitate interaction between antigen specific T and B cell, required for the generation of potent neutralizing antibodies and memory cells. Further, the above factors may be responsible for the short lived RBM directed neutralizing antibody responses and that memory response of an individual, not necessarily related to infection by other coronaviruses, may confer immunologic advantage from a primary infection.

The findings of this study are interesting and add to the growing body of information on factors that plausibly influence the pattern of humoral immune responses and limited memory observed during natural infection. However, the information generated in this study is based on prediction analysis and not backed by ex vivo experiments to demonstrate the limited ability of the peptides in the RBM motif to activate T cells due to poor binding to MHCII. This should be discussed as a limitation of the study and the title should be modified accordingly.

Reviewer #2: The manuscript presents an interesting hypothesis and accompanying exclusive in silico predictions in support. These, in my opinion, are not enough to warrant publication without the addition of at least some validation data (retrospective meta analysis of COVID-19 data and other viral infections; immunogenicity experiments) as detailed in the comments below.

The introduction does not clearly delineate the influence of T-B cooperation on 1) memory cell generation and persistence and 2) potent neutralizing responses as two possibly mutually exclusive events. Both are dependent on T-B cooperation but not necessary linked. This confuses the rationale of the study. Authors should consider clearly state what aspect their study attempts to shed light on. Are they trying to say that proximal epitope/neutralization paratope combinations are elicitors of protective responses restricted by MHCII?

Lines 98-103: This is not necessarily supportive of the link between memory cell generation/persistence (‘high magnitude’) and neutralizing responses. Also, does the occurrence of the ‘minority’ proximal MHCII peptide specific CD4+T cell population correlate with the aforementioned aspects of humoral immunity in infected/convalescent individuals? An analysis of this sort could strengthen the weight of the conclusions in this manuscript.

Why do the authors assume a single neturalizing epitope, the one they focus on, is the sole determinant of a protective response? While their exclusively in silico results generally (but not overwhelmingly) support the hypothesis, this study should have included some in vivo data (even basic immunization studies in small animals) to validate the predicted defect in presentation of proximal (to the neutralization paratope) epitopes. Also, comparative (retrospective??) analysis of other viral infections where much more is known about T-B cooperation would strengthen the claims of the manuscript. These are too generalized in implication not to have supporting in vivo data considering the large gaps and mainly empirical knowledge that exists regarding the generation of protective anti-viral responses.

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PLoS One. 2021 Feb 11;16(2):e0246731. doi: 10.1371/journal.pone.0246731.r002

Author response to Decision Letter 0

23 Jan 2021

We thank the reviewer for this feedback and have sought to emphasize the computational nature of the analysis in the discussion as well as the title. The following has been added to the discussion:

● “The present study assesses the probability of SARS-CoV-2 peptides of the Spike protein to bind and be presented by MHC-II molecules. Our study is limited by the following: results are an estimate based on an algorithm that encompasses many biophysical variables for MHC-II presentation but certainly not all. In addition, while we believe the epitope containing the FNCY patch is promising for inducing a protective neutralizing response, it is not the sole determinant of a protective antibody response to SARS-CoV-2; as neutralizing antibodies against other portions of the spike and other non-structural proteins have been reported (Yuan et al. 2020; Pinto et al. 2020; Wang et al. 2020; McAndrews et al. 2020; Okba et al. 2020; Fenwick et al. 2021).”

We have also revised the title to:

● In silico analysis suggests less effective MHC-II presentation of SARS-CoV-2 RBM peptides: Implication for neutralizing antibody responses

We thank the reviewer for this comment. We have revised the introduction to clarify that memory cell generation does not necessarily imply a potent neutralizing response, but that an optimal neutralizing response should entail memory cell generation and persistence. We have clarified our hypothesis: because memory B cell maturation heavily depends on interaction with a CD4 T cell, peptides proximal to or including the B cell epitope need to be effectively presented via MHC-II. Furthermore, this process needs to happen for B cells that produce neutralizing antibodies targeting the RBM; and their differentiation into memory B cells. Memory responses for non-neutralizing antibodies may occur but are not pivotal to protection. On the other hand, it is still poorly understood whether early neutralizing antibodies in COVID-19 patients lead to memory B cells producing antibodies of the same specificity. Therefore one could argue that the inability to present sequences near putative B cell epitopes bound by neutralizing antibodies may inhibit memory cell generation and affect the strength of neutralizing antibody response.

The rationale is clarified in the introduction as follows:

● “Specifically, we hypothesize that the inability to present SARS-CoV-2 peptide sequences near putative B cell epitopes may impair memory cell generation and consequently reduce the strength and longevity of overall and neutralizing antibody responses.”

Initial COVID-19 responses appear to rely largely on early activated B cells that produce antibodies in quasi-germline configuration and use a restricted VH rearrangement (IGHV3-23 and IGHV3-7), suggesting that these cells are ‘innate-like B cells’ (Wen et al. 2020; Ju et al. 2020; Liu et al. 2020; Tortorici et al. 2020) and have not undergone somatic hypermutation and maturation. Little is known if these early activated B cells expand in patients or if they fully mature, maintaining antibody specificity. We have added this to the introduction:

● “Early activated B cells produce antibodies in quasi-germline configuration and are likely ‘innate-like B cells’ (Wen et al. 2020; Ju et al. 2020; Liu et al. 2020; Tortorici et al. 2020) that have not undergone somatic hypermutation and maturation.”

Germline/innate B cells might provide protection initially but will decay with time, thus memory B cells are required for long term protection, a process that depends on T-B cooperation at germinal centers. A recent study has shown there is a lack of germinal center type B cells in COVID-19 patients, which will affect long-lived memory and high-affinity B cells (Kaneko et al. 2020). We have added this new reference to the introduction:

● “Consistent with the above argument, a lack of germinal center formation but robust activation of non-germinal type B cells has been reported in cases of severe COVID-19 infection, impairing production of long-lived memory or high affinity B cells (Kaneko et al. 2020).”

We have reviewed lines 98-103 and have updated the text for clarity. We meant for this section to emphasize the finding in (Mateus et al. 2020) that CD4 T cell responses by T cells obtained from unexposed individuals largely occur outside of the RBD region (Figure 4). Separately, an earlier study by Ni et al. has observed that numbers of IFN-γ-secreting RBD-specific T cells were much lower than those of nucleocapsid protein (NP) specific T cells (Ni et al. 2020).

Updated text:

● “Antibody responses against SARS-CoV-2 depend on CD4 T cell help. Spike-specific CD4 T cell responses have been found to correlate with the magnitude of the anti-RBD IgG response whereas non-spike CD4 T cell responses do not (44). However, in unexposed patients, spike-specific CD4 T cells reactive with MHC-II peptides proximal to the central B cell epitope represent a minority (~10%) of the total CD4 T cell responses, which are dominated by responses against either the distal portion of the spike protein or other structural antigens (45). Surprisingly, these CD4 T cell responses are largely cross-reactive and originate from previous coronavirus infections (46).”

We thank the reviewer for this question. We have revised the language in the introduction and added to the discussion (below, respectively) to clarify that this epitope is promising for a protective response, but is not the sole determinant of one.

● “...we decided to test the hypothesis that associative recognition of a key RBM B cell epitope (in and around the FNCY patch) and proximal MHC-II-restricted epitopes may be defective with detrimental effects on preferential T-B pairing.”

● “In addition, while we believe the epitope containing the FNCY patch is promising for inducing a protective neutralizing response, it is not the sole determinant of a protective antibody response to SARS-CoV-2; as neutralizing antibodies against other portions of the spike and other non-structural proteins have been reported (Yuan et al. 2020; Pinto et al. 2020; Wang et al. 2020; McAndrews et al. 2020; Okba et al. 2020; Fenwick et al. 2021).”

We focus on the FNCY patch for a few reasons: (1) in a competition binding assay with other neutralizing antibodies (NAbs), NAbs that came in contact with this region outperformed NAbs that did not across samples from hospitalized, symptomatic, and asymptomatic patients (Figure R1 taken from (Piccoli et al. 2020)). (2) Monoclonal antibodies that have been generated by COVID-19 patients tend to neutralize by binding to the region involving the FNCY patch as shown in Figure 1D in the manuscript. Furthermore, it was recently shown that residue E484, which is 2aa upstream of the FNCY patch, accounts for most of the neutralization via polyclonal serum antibodies targeting the RBD (Greaney et al. 2021), highlighting the importance of this region. This does not exclude the possibility that antibodies against other portions of the spike including those generated against previous coronaviruses may also neutralize (Yuan et al. 2020).

We have added additional text in the introduction to clarify our focus on the FNCY patch:

● “NAbs that make contact with the FNCY patch outperform other NAbs that do not in competition binding assays, highlighting the importance of the region in neutralizing ACE2 binding (Piccoli et al. 2020)”

Figure R1. (Figure 7H from (Piccoli et al. 2020)). S2H14 and S2H13 antibodies span the FNCY patch (highlighted in the red box) while the remaining antibodies do not. This figure describes the results of a competition binding assay to ACE2 for hospitalized, symptomatic, and asymptomatic patients’ sera.

We agree with the reviewer that validating our prediction in vivo is ideal. While we do not have access to appropriate humanized transgenic mice, we found a recent study where Prakash et al. immunized HLA-A02/HLA-DRB1 double transgenic mice and found that the B cell epitope S471-501 (spanning the FNCY patch) induced a low number of Antibody Secreting Cells (ASC) compared to other spike or non-spike peptides (Figure R2). This epitope was also variably recognized by antibodies in sera from HLA-A02 positive SARS-CoV-2 infected individuals, though comprehensive MHC-I and MHC-II data was not available for the human samples and likely affected their results (Figure R2). These results, albeit limited to HLA-DRB1*0101 in mice and unknown MHC-II types in the human samples, are consistent with our prediction that it is harder to get memory B cell responses to this epitope. Importantly, we believe that the conclusions made in the manuscript based on a global in silico analysis of the MHC-II alleles would not be possible to validate in the mouse because there are very few HLA Class II transgenic mice available that lack endogenous HLA, and any result would be by definition of very limited value compared to the breadth of our study.

Figure R2. (Figure 9 from (Prakash et al. 2020)). (Top left) barplot of antibody secreting cell (ASC) levels for various B cell epitopes along the spike protein in HLA-A02/DRB1 mice. (Top right) barplot of IgG response levels for various B cell epitopes along the spike protein in HLA-A02/DRB1 mice. (Bottom) barplot of IgG response levels for COVID-19 patient sera against various B cell epitopes along the spike protein. Red arrows indicate the S471-501 epitope that spans the FNCY patch.

Also, comparative (retrospective??) analysis of other viral infections where much more is known about T-B cooperation would strengthen the claims of the manuscript. These are too generalized in implication not to have supporting in vivo data considering the large gaps and mainly empirical knowledge that exists regarding the generation of protective anti-viral responses.

We thank the reviewer for this comment. We now include more references to studies on a variety of viruses in which T-B cooperation has been studied in the mouse:

● In the influenza A virus (PR8) system it was shown that while Th1 CD4 T cell responses on their own are ineffective at promoting recovery from infection, antibodies generated through T-B cooperation were indispensable in the protective response against the virus (Mozdzanowska et al. 1997). Subsequent studies using a different influenza A strain confirmed the relevance of T-B cooperation and that CD4 T cells represent a limiting factor in the kinetics and early magnitude of the primary B cell response to virus challenge and provide help in a preferential way (i.e. intra-molecular but nor inter-molecular) (Alam et al. 2014).

● Additional information comes from studies examining the role CD40-CD40L interaction in T-B cooperation. The CD40-CD40L interaction is required for the generation of antibody responses, isotype switching and generation of memory responses, to T-dependent antigens in non-viral model systems (Parker 1993). In a two virus model, LCMV (lymphocytic choriomeningitis virus) and VSV (vesicular stomatitis virus) T-B cooperation was shown to be necessary for antiviral protection and require CD40-CD40L interactions (Oxenius et al. 1996). Interestingly, this study also showed that the activation of CD4 T cells not associated with the activation of B cells (e.g., inflammatory reaction) and antibody production was not compromised (Oxenius et al. 1996).

We have added the following text to the introduction:

● “The relevance of T-B cooperation in protective antiviral responses has been documented in numerous systems. In the influenza A virus (PR8) system it was shown that while Th1 CD4 T cell responses on their own are ineffective at promoting recovery from infection, antibodies generated through T-B cooperation were indispensable in the protective response against the virus (Mozdzanowska et al. 1997). In a different influenza A strain, it was shown that T-B cooperation and CD4 T cells represent a limiting factor in the kinetics and early magnitude of the primary B cell response to virus challenge and provide help in a preferential way (i.e. intra-molecular but nor inter-molecular) (Alam et al. 2014). Additionally, CD40-CD40L (costimulatory molecules found on B cells and CD4 T cells, respectively) interaction is required for the generation of antibody responses, isotype switching and memory responses in non-viral model systems (Parker 1993). In LCMV (lymphocytic choriomeningitis virus) and VSV (vesicular stomatitis virus) abrogation of CD40-CD40L interaction prevented T-B cooperation and thus inhibited antiviral protection (Oxenius et al. 1996). Interestingly, this study also showed that the activation of CD4 T cells (e.g., inflammatory CD4 T cells) not associated with the activation of B cells was not compromised (Oxenius et al. 1996). These data demonstrate the relevance of T-B cooperation in the antibody response in protection against viral infection.”

In addition, we performed retrospective analysis on 100 COVID-19 bulk RNA-seq samples from (Overmyer et al. 2020). We found that patients who received mechanical ventilation or who were treated in the ICU had lower levels of various co-stimulatory molecules or cytokines indicative of T-B cooperation (Figure R3). This suggests impairment of CD40-mediated class switching (ICOS), T-cell dependent B cell proliferation (OX40) and T cell activation and proliferation (CD28) as well as lack of T cell activation (IL4) contribute to poorer outcomes.

Figure R3. Gene expression of co-stimulatory molecules CD40, OX40, CD28, ICOS and cytokine IL4 for COVID-19 patients (top panel) treated in or out of the ICU or (bottom panel) who received mechanical ventilation.

Alam, Shabnam, Zackery A. G. Knowlden, Mark Y. Sangster, and Andrea J. Sant. 2014. “CD4 T Cell Help Is Limiting and Selective during the Primary B Cell Response to Influenza Virus Infection.” Journal of Virology 88 (1): 314–24.

Greaney, Allison J., Andrea N. Loes, Katharine H. D. Crawford, Tyler N. Starr, Keara D. Malone, Helen Y. Chu, and Jesse D. Bloom. 2021. “Comprehensive Mapping of Mutations to the SARS-CoV-2 Receptor-Binding Domain That Affect Recognition by Polyclonal Human Serum Antibodies.” Cold Spring Harbor Laboratory. https://doi.org/10.1101/2020.12.31.425021.

Ju, Bin, Qi Zhang, Jiwan Ge, Ruoke Wang, Jing Sun, Xiangyang Ge, Jiazhen Yu, et al. 2020. “Human Neutralizing Antibodies Elicited by SARS-CoV-2 Infection.” Nature 584 (7819): 115–19.

Kaneko, Naoki, Hsiao-Hsuan Kuo, Julie Boucau, Jocelyn R. Farmer, Hugues Allard-Chamard, Vinay S. Mahajan, Alicja Piechocka-Trocha, et al. 2020. “Loss of Bcl-6-Expressing T Follicular Helper Cells and Germinal Centers in COVID-19.” Cell 183 (1): 143–57.e13.

Liu, Lihong, Pengfei Wang, Manoj S. Nair, Jian Yu, Micah Rapp, Qian Wang, Yang Luo, et al. 2020. “Potent Neutralizing Antibodies against Multiple Epitopes on SARS-CoV-2 Spike.” Nature 584 (7821): 450–56.

Mateus, Jose, Alba Grifoni, Alison Tarke, John Sidney, Sydney I. Ramirez, Jennifer M. Dan, Zoe C. Burger, et al. 2020. “Selective and Cross-Reactive SARS-CoV-2 T Cell Epitopes in Unexposed Humans.” Science, August. https://doi.org/10.1126/science.abd3871.

Mozdzanowska, K., M. Furchner, K. Maiese, and W. Gerhard. 1997. “CD4+ T Cells Are Ineffective in Clearing a Pulmonary Infection with Influenza Type A Virus in the Absence of B Cells.” Virology 239 (1): 217–25.

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Overmyer, Katherine A., Evgenia Shishkova, Ian J. Miller, Joseph Balnis, Matthew N. Bernstein, Trenton M. Peters-Clarke, Jesse G. Meyer, et al. 2020. “Large-Scale Multi-Omic Analysis of COVID-19 Severity.” Cell Systems, October. https://doi.org/10.1016/j.cels.2020.10.003.

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PLoS One. doi: 10.1371/journal.pone.0246731.r003

Decision Letter 1

Jayanta Bhattacharya

26 Jan 2021

In silico analysis suggests less effective MHC-II presentation of SARS-CoV-2 RBM peptides: implication for neutralizing antibody responses

PONE-D-20-38239R1

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PLoS One. doi: 10.1371/journal.pone.0246731.r004

Acceptance letter

Jayanta Bhattacharya

2 Feb 2021

PONE-D-20-38239R1

In silico analysis suggests less effective MHC-II presentation of SARS-CoV-2 RBM peptides: implication for neutralizing antibody responses

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Associated Data

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Supplementary Materials

S1 Table. SARS-CoV-2 neutralizing antibody residues and references used to generate Fig 1D.

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S2 Table. BLAST-identified peptides with affinity, and binding fraction.

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S1 Fig. Distribution of position scores along the spike protein using the 25th percentile affinity instead of the median affinity.

(PDF)

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S2 Fig. Overview of subject peptides that bind at least one retrieved from BLAST search.

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S1 Graphical abstract

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Data Availability Statement

All relevant data are within the manuscript and its Supporting Information files.

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PERMALINK

In silico analysis suggests less effective MHC-II presentation of SARS-CoV-2 RBM peptides: Implication for neutralizing antibody responses

Andrea Castro

Kivilcim Ozturk

Maurizio Zanetti

Hannah Carter

Roles

Abstract

Introduction

Results

Topology of a key RBM B cell epitope

Fig 1. Visualization of the FNCY core of the RBM B cell epitope on the SARS-CoV-2 spike protein RBD.

Prediction of MHC-II affinity for 15mer peptides proximal to the RBM B cell epitope

Fig 2. Landscape of MHC-II binding affinity across spike protein 2D sequence.

Fig 3. Population variation affecting availability of FNCY proximal T cell epitopes.

Cross-reactivity to a non-coronavirus MHC-II binding peptide as a potential driver of T cell responses helping antibody response to the RBM B cell epitope

Fig 4. Immunological history of relevance to SARS-CoV-2.

Fig 5. Learned immunity to other targets that could support T cell responses to SARS-CoV-2.

Discussion

Materials and methods

Affinity analysis

Spike protein analyses

Structure analysis

Supertype analysis

Motif analysis

BLAST analysis

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Jayanta Bhattacharya

Roles

Author response to Decision Letter 0

Decision Letter 1

Jayanta Bhattacharya

Roles

Acceptance letter

Jayanta Bhattacharya

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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