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. 2020 Aug 13;81(10):588–595. doi: 10.1016/j.humimm.2020.08.001

Conserved HLA binding peptides from five non-structural proteins of SARS-CoV-2—An in silico glance

Jose Marchan 1,⁎
PMCID: PMC7425717  PMID: 32888767

Abstract

Coronavirus Disease 2019 (COVID-19) is a dangerous global threat that has no clinically approved treatment yet. Bioinformatics represent an outstanding approach to reveal key immunogenic regions in viral proteins. Here, five severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) non-structural proteins (NSPs) (NSP7, NSP8, NSP9, NSP12, and NSP13) were screened to identify potential human leukocyte antigen (HLA) binding peptides. These peptides showed robust viral antigenicity, immunogenicity, and a marked interaction with HLA alleles. Interestingly, several peptides showed affinity by HLA class I (HLA-I) alleles that commonly activates to natural killer (NK) cells. Notably, HLA biding peptides are conserved among SARS-CoV-2, severe acute respiratory syndrome coronavirus (SARS-CoV), and Middle Eastern respiratory syndrome coronavirus (MERS-CoV). Interestingly, HLA-I and HLA class II (HLA-II) binding peptides induced humoral and cell-mediated responses after in silico vaccination. These results may open further in vitro and in vivo investigations to develop novel therapeutic strategies against coronaviral infections.

Abbreviations: COVID-19, coronavirus disease 2019; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; SARS-CoV, severe acute respiratory syndrome coronavirus; MERS-CoV, Middle Eastern respiratory syndrome coronavirus; S, spike glycoprotein; E, envelope protein; M, membrane protein; N, nucleocapsid protein; ORF1ab, open reading frame 1ab; ORF3a, open reading frame 3a; ORF6, open reading frame 6; ORF7a, open reading frame 7; ORF8, open reading frame 8; ORF10, open reading frame 10; NSP, non-structural proteins; HLA, human leukocyte antigen; HLA-I, human leukocyte antigen class I; HLA-II, human leukocyte antigen class II; NSP7, non-structural protein 7; NSP8, non-structural protein 8; NSP9, non-structural protein 9; NSP12, non-structural protein 12; NSP13, non-structural protein 13; IFN-g, interferon-gamma; IEDB-AR, immune epitope database and analysis resource; 3D, three-dimensional; PDB, protein data bank; P, peptide; Ig, immunoglobulin; CTC, cytotoxic T cell; THC, T helper cell; IL-2, interleukin 2; SARS, severe acute respiratory syndrome; MERS, Middle Eastern respiratory syndrome; aKIR, activating killer immunoglobulin-like receptors; NK, natural killer cell; CTRL+, control positive; NCBI, National Center for Biotechnology Information

Keywords: Non-structural proteins, COVID-19, SARS, MERS, Epitopes, In silico

1. Introduction

SARS-CoV-2 is the causative agent of Coronavirus Disease 2019 (COVID-19) [1] and is classified in the family Coronaviridae and the genus Betacoronavirus. This genus includes other pathogenic coronaviruses for human that share a marked sequence identity to SARS-CoV-2 such as severe acute respiratory syndrome coronavirus (SARS-CoV) and the Middle Eastern respiratory syndrome coronavirus (MERS-CoV), whose explosive outbreak in recent years has also led to global public health crisis—severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), respectively [2], [3].

The SARS-CoV-2 single-stranded positive sense RNA genome encodes for both structural and non-structural proteins [2]. The former comprises the spike glycoprotein (S), envelope protein (E), membrane protein (M), and nucleocapsid protein (N) [3]. The latter, on the other hand, includes the open reading frame 1ab (ORF1ab), ORF3a, ORF6, ORF7a, ORF8, and ORF10. In turn, the ORF1ab expresses a polyprotein whose proteolytical cleaved forms 16 non-structural proteins (NSPs, numbered 1 to 16) [3]. The expression and activity of several NSPs constitute a fundamental aspect for SARS-CoV-2 pathogenesis [2]. For instance, the protein complex formed by NSP7, NSP8, and NSP12 give rise to the RNA polymerase, which is necessary for genome replication [4], whereas NSP13 represents the virus helicase, which unwinds duplex RNA [4]. On the other hand, it is thought that NSP9 binds to RNA, thereby improving viral replication [5].

Several works have shown that NSPs from other RNA viruses are fundamental factors to elicit effective immune responses [6], [7]. For instance, the presentation of conserved helicase peptides from several flaviviruses in the context of human leukocyte antigen class I (HLA-I) is sufficient to inhibit viral replication [6]. Therefore, peptides derived from critical SARS-CoV-2 NSPs such as those involved in viral replication and pathogenesis (e.g., NSP7, NSP8, NSP9, NSP12, and NSP13) represent optimal targets to develop future prophylactic and therapeutics approaches against COVID-19. Recent studies have reported promising viral peptides from other SARS-CoV-2 NSPs, including ORF3a [8], ORF6, ORF7, and ORF8 [9]. Likewise, SARS-CoV-2 structural proteins (S, E, M, and N) haven been extensively explored for potential immunogenic peptides [8], [9], [10], [11], [12], [13]. In fact, increasing data suggest that peptides from these structural proteins can be efficiently used in multi-epitope vaccine models [12], [13]. Regardless of these remarkable contributions, our knowledge on the existence of similar peptides in SARS-CoV-2 NSP7, NSP8, NSP9, NSP12, and NSP13 is still poor. Therefore, this immunoinformatics work aimed to identify HLA-I and HLA class II (HLA-II) binding peptides from these key SARS-CoV-2 NSPs, thereby providing more immunogenic targets with strong potential to fight COVID-19.

2. Materials and methods

2.1. Data collection and availability

Amino acid sequences from NSPs of SARS-CoV-2, SARS-CoV, and MERS-CoV were downloaded from the National Center for Biotechnology Information (NCBI) using the following accession numbers: YP_009725303.1 (NSP7), YP_009725304.1 (NSP8), YP_009725305.1 (NSP9), YP_009725307.1 (NSP12), and YP_009725308.1 (NSP13) for SARS-CoV-2; NP_828865.1 (NSP7), NP_828866.1 (NSP8), NP_828867.1 (NSP9), NP_828869.1 (NSP12), and NP_828870.1 (NSP13) for SARS-CoV; YP_009047235.1 (NSP7), YP_009047236.1 (NSP8), YP_009047237.1 (NSP9), YP_009047223.1 (NSP12), YP_009047224.1 (NSP13) for MERS-CoV.

2.2. Prediction of HLA binding peptides from SARS-CoV-2 NSPs

A recent bioinformatics SARS-CoV-2 research [8] with minor modifications was used to identify HLA binding peptides. Briefly, HLA-I binding peptides (9 amino acids of length) were predicted using the NetMHCpan EL 4.0 algorithm [14]. The HLA-I alleles used for this analysis represent some of the most common alleles in human population: HLA-A*01:01, HLA-A*02:01, HLA-A*02:05, HLA-A*03:01, HLA-A*03:02, HLA-A*11:01, HLA-A*24:02, HLA-B*07:02, HLA-B*07:05, HLA-B*08:01, HLA-B*13:02, HLA-B*14:02, HLA-B*35:01, HLA-B*40:01, HLA-B*44:02, HLA-B*44:03, HLA-C*01:02, HLA-C*02:02, HLA-C*03:03, HLA-C*03:04, HLA-C*04:01, HLA-C*06:02, and HLA-C*12:02. HLA-II epitopes (15 amino acids of length) were predicted using the Consensus method [15]. The HLA-II alleles considered in the study were HLA-DRB1*01:01, HLA-DRB1*01:02, HLA-DRB1*03:01, HLA-DRB1*03:06, HLA-DRB1*03:07, HLA-DRB1*04:01, HLA-DRB1*04:02, HLA-DRB1*04:04, and HLA-DRB1*04:05, HLA-DRB1*07:01. In general, HLA-I and HLA-II binding peptides were selected with a percentile rank of prediction cut-off < 20. Peptides with lower percentile values have a higher affinity by HLA molecules [16].

2.3. General assessment of predicted HLA binding peptides

Viral antigenicity (cut-off ≥ 0.5), allergenicity and toxicity predictions were conducted on Vaxijen (www.ddg-pharmfac.net/vaxijen/) [17], AlergenFP (http://ddg-pharmfac.net/AllergenFP/index.html) [18], and ToxinPred (http://crdd.osdd.net/raghava/toxinpred/) [19], respectively. Moreover, HLA-II binding peptides were also selected by their potential capability to induce interferon-gamma (IFN-g), which was evaluated on IFNepitope web server (http://crdd.osdd.net/raghava/ifnepitope/) [20].

2.4. Conservancy analysis

Conservation of HLA binding peptides within NSPs (NSP7, NSP8, NSP9, NSP12, and NSP13) across SARS-CoV-2, SARS-CoV and MERS-CoV was studied by an epitope conservancy analysis on the Immune Epitope Database and Analysis Resource (IEDB-AR) (http://tools.immuneepitope.org/main/) [21]. Conservancy is described as the fraction of protein sequences that have the peptide whereas the identity is referred to the degree of correspondence (similarity) between several sequences [21]. Results were represented as a barplot using Rstudio software (Version 3.5.3).

2.5. Interaction between HLA alleles and viral peptides: molecular docking

HLA-I and HLA-II binding peptides were docked with representative HLA-I and HLA-II alleles. The molecular docking simulation study was performed as follows. First, the three-dimensional (3D) structures of 9-mer and 15-mer peptides were obtained from PEPFOLD server (https://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD3/) [22]. Second, the receptor‐ligand docking simulations were conducted on ClusPro (version 2.0) server (https://cluspro.bu.edu/) [23]. For this purpose, the Protein Data Bank (PDB) accession numbers of HLA-A*02:01 (1DUZ) [24] and DRB1*01:01 (1AQD) [25] along with the PDB format file of each peptide were used as input on ClusPro server. Third, the receptor-ligand docking complexes were visualized on VMD software (Version 1.9.3) [26]. The best allele-peptide complexes were selected based upon visual inspection and the ClusPro criteria, as well as they were compared with the co-crystal ligands of the HLA allele, which are considered as control peptides in this study. Finally, the Gibbs free energy (ΔG) of the each HLA-viral peptide complex was predicted on the PRODIGY server (https://bianca.science.uu.nl//prodigy/) [27]. Free energy values were represented as barplots using Rstudio software (Version 3.5.3).

2.6. Immune response simulations

To evaluate the potential use of HLA binding peptides on future vaccine trials, a vaccine amino acid sequence was develop with the predicted HLA-I and HLA-II binding peptides, which were linked using AAY and GPGPG linkers, respectively, as previously reported [28]. This vaccine construct was subjected to immune response simulations on the C-ImmSim server (http://150.146.2.1/C-IMMSIM/index.php) [29]. Three injections were applied four weeks apart as described by Nain et al. 2020 [28]. For immune interpretation purpose, the Simpson index D was used.

3. Results

3.1. Prediction of HLA binding peptides

31 HLA-I binding peptides were identified as the best (numbered, P1 to P31) (Table 1 ). These viral peptides showed robust viral antigenicity (≥0.5) and absence of either allergenic or toxic residues (Table 1). The higher number of peptides was observed in NSP12 and NSP13 (10 and 11 peptides, respectively). Interestingly, overlapping residues were observed among P7, P8, and P9, and among P22, P23, P24, P25, and P26 (Table 1). Regarding the HLA-I interacting alleles, most of the viral peptides showed promiscuity by several HLA-I molecules including A*02:01, B*08:01, C*02:02, and C*12:02 (Table 1).

Table 1.

Potential HLA-I binding peptides identified in five non-structural proteins of SARS-CoV-2.

Protein Peptide ID Peptide sequence Position (star-end) Percentile Rank Viral antigenicity Toxicity and Allergenicity HLA-I interacting allele
NSP7 P1 LRVESSSKL 20–28 1.60 0.60 Negative C*06:02
P2 LSMQGAVDI 60–68 1.50 0.78 Negative C*01:02, C*02:02, C*04:01, C*12:02
P3 EMLDNRATL 74–82 0.17 0.63 Negative B*08:01, C*01:02, C*02:02, C*06:02, C*12:02,



NSP8 P4 AAFATAQEA 13–21 1.10 0.53 Negative C*12:02, C*02:02,
P5 CVPLNIIPL 114–122 0.87 1.37 Negative C*01:02
P6 LAWPLIVTA 180–188 0.38 1.02 Negative A*02:01, C*02:02, C*04:01, C*12:02, B*35:01



NSP9 P7 GLNNLNRGM 93–102 1.80 1.01 Negative A*02:01, C*02:02, C*02:09, C*12:02
P8 NNLNRGMVL 95–103 0.09 0.52 Negative B*08:01, C*06:02
P9 GMVLGSLAA 100–108 1.20 0.60 Negative A*02:01
P10 VLGSLAATV 102–100 0.20 0.66 Negative A*02:01, A*02:05, C*02:09, C*02:02



NSP12 P11 WEPEFYEAM 116–124 0.21 0.53 Negative A*02:01
P12 MRNAGIVGV 196–204 0.02 1.63 Negative C*06:02
P13 LVYAADPAM 372–380 0.02 0.52 Negative C*03:02, C*12:02, B*35:01, C*02:09, C*02:02, C*08:01
P14 TVKPGNFNK 409–417 0.02 1.38 Negative A*11:01
P15 NAAISDYDY 447–455 0.07 1.29 Negative B*35:01, A*01:01, C*03:03
P16 FAYTKRNVI 538–546 0.06 1.03 Negative B*08:01, C*03:02, C*12:02, C*08:01, C*02:02, C*02:09, C*01:02, C*06:02
P17 IAATRGATV 579–587 0.16 0.89 Negative B*08:01, C*01:02, C*02:02, C*03:02, C*02:09, C*08:01, C*12:02,
P18 ATVVIGTSK 585–593 0.02 0.76 Negative A*11:01
P19 RLANECAQV 654–662 0.16 0.93 Negative A*02:01
P20 MLDMYSVML 899–907 0.08 0.56 Negative A*02:01, A*01:01, C*01:02, C*08:01,



NSP13 P21 HVISTSHKL 33–41 0.10 0.69 Negative C*01:02, C*02:02, C*03:02, C*12:02, B*35:01,
P22 TEETFKLSY 141–149 0.65 0.65 Negative A*01:01
P23 YGIATVREV 149–157 0.37 1.43 Negative C*02:02, C*03:02, C*12:02,
P24 PTLVPQEHY 238–246 0.43 0.78 Negative A*01:01
P25 TLVPQEHYV 239–247 0.04 0.54 Negative A*02:01, C*02:02
P26 EHYVRITGL 244–252 0.33 0.57 Negative B*08:01
P27 SHFAIGLAL 289–297 0.35 1.39 Negative C*02:02, C*03:02, C*12:02, B*40:01,
P28 VVFDEISMA 371–379 0.11 0.74 Negative A*02:01, A*02:05, C*02:02, C*12:02
P29 VVNARLRAK 386–394 0.08 1.93 Negative A*03:01, A*11:01
P30 EIVDTVSAL 447–455 0.26 0.66 Negative C*02:02, C*03:02, C*04:01, C*12:02, B*35:01
P31 ITRAKVGIL 565–573 0.25 0.91 Negative B*08:01
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46 HLA-II binding peptides were predicted (numbered, P32 to P76). In this regard, 1 peptide was identified for NSP7, 13 peptides for NSP8, 9 peptides for NSP9, 17 peptides for NSP12, and 6 peptides for NSP13 (Table 2 ). Similar to HLA-I binding peptides, strong viral antigenicity and lack of allergenicity and toxicity were observed in these 15-mer viral peptides. Of note, each HLA-II binding peptides was classified as a potential inductor of IFN-g (Table 2). Likewise, viral peptides from NSP8, NSP9, NSP12, and NSP13 showed the presence of overlapping residues. On the other hand, DRB1*01:01, DRB1*03:01, and DRB1*04:01were identified as the most common HLA-II interacting alleles (Table 2).

Table 2.

Potential HLA-II binding peptides identified in five non-structural proteins of SARS-CoV-2.

Protein Peptide ID Peptide sequence Position (star-end) Percentile Rank Viral antigenicity Toxicity and Allergenicity IFN-g induction HLA-I interacting allele
NSP7 P32 VVLLSVLQQLRVESS 11–25 0.60 0.50 Negative Positive DRB1*01:01, DRB1*01:02, DRB1*03:01, DRB1*03:07, DRB1*04:01, DRB1*04:02, DRB1*04:04,



NSP8 P33 IASEFSSLPSYAAFA 2–16 0.13 0.54 Negative Positive DRB1*01:01, DRB1*04:01, DRB1*04:04
P34 SEFSSLPSYAAFATA 4–18 0.15 0.54 Negative Positive DRB1*01:01, DRB1*04:01, DRB1*04:04
P35 FSSLPSYAAFATAQE 6–20 7.20 0.51 Negative Positive DRB1*01:01, DRB1*04:01, DRB1*04:04
P36 GCVPLNIIPLTTAAK 113–127 3.00 1.12 Negative Positive DRB1*03:06, DRB1*03:07, DRB1*04:04, DRB1*04:01
P37 PLNIIPLTTAAKLMV 116–130 6.10 0.82 Negative Positive DRB1*03:06, DRB1*03:07, DRB1*04:04, DRB1*04:01
P38 WEIQQVVDADSKIVQ 154–168 7.60 0.53 Negative Positive DRB1*03:01, DRB1*03:06, DRB1*03:07
P39 IQQVVDADSKIVQLS 156–170 7.20 0.59 Negative Positive DRB1*03:01, DRB1*03:06, DRB1*03:07
P40 PNLAWPLIVTALRAN 178–192 8.50 0.92 Negative Positive DRB1*01:01
P41 NLAWPLIVTALRANS 179–193 3.50 0.99 Negative Positive DRB1*03:06, DRB1*03:07, DRB1*04:04
P42 LAWPLIVTALRANSA 180–194 3.10 0.80 Negative Positive DRB1*01:01, DRB1*03:06, DRB1*03:07, DRB1*04:04
P43 AWPLIVTALRANSAV 181–195 3.30 0.72 Negative Positive DRB1*01:01, DRB1*03:06, DRB1*03:07, DRB1*04:04
P44 WPLIVTALRANSAVK 182–196 2.60 0.74 Negative Positive DRB1*01:01, DRB1*03:06, DRB1*03:07, DRB1*04:04
P45 PLIVTALRANSAVKL 183–197 0.45 0.66 Negative Positive DRB1*01:01, DRB1*01:02, DRB1*03:01, DRB1*03:06, DRB1*03:07, DRB1*04:01, DRB1*04:04



NSP9 P46 GPKVKYLYFIKGLNN 82–96 7.20 0.70 Negative Positive DRB1*04:04
P47 PKVKYLYFIKGLNNL 83–97 6.10 0.62 Negative Positive DRB1*01:01, DRB1*04:04
P48 KVKYLYFIKGLNNLN 84–98 4.40 0.91 Negative Positive DRB1*04:01, DRB1*01:01, DRB1*04:04
P49 KYLYFIKGLNNLNRG 86–100 0.38 0.64 Negative Positive DRB1*01:01, DRB1*04:01, DRB1*04:04
P50 YLYFIKGLNNLNRGM 87–101 0.31 0.52 Negative Positive DRB1*01:01, DRB1*04:01, DRB1*04:04
P51 LYFIKGLNNLNRGMV 88–102 0.31 0.52 Negative Positive DRB1*01:01, DRB1*04:01, DRB1*04:04
P52 NNLNRGMVLGSLAAT 95–109 1.20 0.82 Negative Positive DRB1*03:06, DRB1*03:07, DRB1*04:02
P53 NLNRGMVLGSLAATV 96–110 1.20 0.83 Negative Positive DRB1*03:06, DRB1*03:07, DRB1*04:02
P54 LNRGMVLGSLAATVR 97–111 1.20 0.92 Negative Positive DRB1*04:04



NSP12 P55 NAGIVGVLTLDNQDL 198–212 1.70 1.30 Negative Positive DRB1*04:05, DRB1*04:03
P56 RLSFKELLVYAADPA 365–379 3.50 0.59 Negative Positive DRB1*04:01, DRB1*04:03
DRB1*01:01
P57 LSFKELLVYAADPAM 366–380 3.00 0.65 Negative Positive DRB1*04:01
DRB1*04:03, DRB1*01:01
DRB1*04:05
P58 AASGNLLLDKRTTCF 382–396 2.40 0.89 Negative Positive DRB1*03:01
P59 ASGNLLLDKRTTCFS 383–397 1.70 0.94 Negative Positive DRB1*03:01
P60 SGNLLLDKRTTCFSV 384–398 1.40 1.04 Negative Positive DRB1*03:01
P61 GNLLLDKRTTCFSVA 385–399 1.90 1.20 Negative Positive DRB1*03:01
P62 NLLLDKRTTCFSVAA 386–400 3.80 1.20 Negative Positive DRB1*03:01
P63 KHFFFAQDGNAAISD 438–452 1.10 0.54 Negative Positive DRB1*04:01, DRB1*01:03
DRB1*04:03, DRB1*01:01
P64 KRNVIPTITQMNLKY 532–546 0.96 1.25 Negative Positive DRB1*04:03
P65 ITQMNLKYAISAKNR 539–553 1.20 1.51 Negative Positive DRB1*04:03, DRB1*01:03
DRB1*04:01
P66 QMNLKYAISAKNRAR 541–555 0.32 1.50 Negative Positive DRB1*04:03, DRB1*01:03, DRB1*04:01, DRB1*04:05
P67 MNLKYAISAKNRART 542–556 0.44 1.44 Negative Positive DRB1*04:03, DRB1*04:01
DRB1*01:03
P68 NLKYAISAKNRARTV 543–557 0.96 1.34 Negative Positive DRB1*04:03, DRB1*01:03
DRB1*04:01
P69 MLRIMASLVLARKHT 629–643 0.43 0.53 Negative Positive DRB1*04:03, DRB1*03:01
DRB1*01:01, DRB1*01:03
DRB1*04:01, DRB1*04:05
P70 LRIMASLVLARKHTT 630–644 0.53 0.67 Negative Positive DRB1*03:01, DRB1*04:03
DRB1*01:01, DRB1*01:03,
DRB1*04:01
P71 ASQGLVASIKNFKSV 771–785 0.85 0.58 Negative Positive DRB1*04:03, DRB1*04:01
DRB1*03:01



NSP13 P72 EVLSDRELHLSWEVG 156–170 2.90 1.54 Negative Positive DRB1*03:01
P73 TTYKLNVGDYFVLTS 215–229 2.50 0.60 Negative Positive DRB1*03:02
P74 LNVGDYFVLTSHTVM 219–233 3.00 0.61 Negative Positive DRB1*01:01, DRB1*01:03
P75 DYFVLTSHTVMPLSA 223–237 0.96 0.56 Negative Positive DRB1*01:01, DRB1*01:03
P76 RFNVAITRAKVGILC 560–574 3.60 1.32 Negative Positive DRB1*04:02, DRB1*01:03
P77 KVGILCIMSDRDLYD 569–583 0.98 1.51 Negative Positive DRB1*03:01, DRB1*03:02
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Notably, HLA-I and HLA-II binding peptides are 100% conserved between SARS-CoV and SARS-CoV-2 with exception of P4, P12, P31, P33, P34, P35, P55, P69, P70, P71, and P76, that showed 50% of conservancy (Fig. 1 ). In contrast, most of the SARS-CoV-2 peptides common to MERS-CoV reached 50% of conservancy, with exception of P9 and P76 (0% of conservancy each) and P19, P66, P67, and P68 (100% of conservancy each) (Fig. 1).

Fig. 1.

Fig. 1

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Conservancy analysis of HLA binding peptides.

3.2. HLA allele-viral peptide interaction

Molecular docking simulations were conducted to evaluate the presentation of HLA binding peptides in the context of HLA molecules. To achieve this aim, A*02:01 and DRB1*01:01 were selected as representative HLA-I and HLA-II alleles, respectively. The analysis revealed that both 9-mer (Fig. 2 ) and 15-mer (Fig. 3 ) peptides rightly interact with the groove of their respective HLA molecule in a similar way to control peptides, as well as they showed different HLA binding patterns (Figs. 2 and 3).

Fig. 2.

Fig. 2

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Top view of HLA-A*02:01 presenting 9-mer viral peptides (numbered, P1 to P31) from selected SARS-CoV-2 NSPs. HLA alleles, viral peptides, and control peptides are shown in grey, cyan, and green, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 3.

Fig. 3

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Top view of HLA-DRB1*01:01 presenting 15-mer viral peptides (numbered, P32 to P77) from selected SARS-CoV-2 NSPs. HLA alleles, viral peptides, and control peptides are shown in grey, cyan, and green, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Remarkably, these HLA-viral peptide complexes showed strong potential interactions (free energy values <−6 kcal/mol−1) comparable to control peptides (Fig. 4 A,B). In this regard, P1, P5, P10, P11, P12, P21, P25, P28, P29, P38, P39, P41, P42, P47, P52, P54, P55, P65, P68, P70, and P76 showed the lowest affinity values (Fig. 4A,B). Interestingly, P52 obtained the lower free energy value (-13.3 kcal/mol−1) compared to other HLA-II binding peptides (Fig. 4B).

Fig. 4.

Fig. 4

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Free energy for each HLA-viral peptide interaction. A: free energy values for the binding between HLA-A*02:01 and 9-mer viral peptides (numbered, P1 to P31). B: free energy values for the binding between HLA-DRB1*01:01 and 15-mer viral peptides (numbered, P32 to P377). Note that the lowest values are shown in cyan bars while values of control peptides (CTRL+) are shown in green bars. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

3.3. Immune responses simulations

The immune responses simulations with the vaccine construct showed high levels of immunoglobulin M (IgM) as well as isotypes IgG1 and IgG2 along with reduced immunogen levels during the secondary humoral response. Moreover, active B cell population increased after each dose (Fig. 5 ). Likewise, a marked increase of cytotoxic T cell (CTC) and T helper cell (THC) populations were observed along with effector cell generation, which decreased after immunogen clearance (Fig. 5). Finally, higher levels of key cytokines such as IFN-g and interleukin 2 (IL-2) were documented after each dose (Fig. 5). Taken together, these results suggest that HLA-I and HLA-II binding peptides could elicit humoral and cellular-mediated immune responses to SARS-CoV-2.

Fig. 5.

Fig. 5

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Immune response simulations after in silico active immunization (3 doses) with the vaccine construct.

4. Discussion

The present study using an integrated in silico approach showed that five SARS-CoV-2 NSPs (NSP7, NSP8, NSP9, NSP12, and NSP13) harbour conserved viral peptides, whose properties—high viral antigenicity, absence of allergenic and toxic residues, potential IFN-g induction, interaction with HLA alleles, and suitable immune responses after in silico vaccination—make them potential targets for future prophylactic and/or therapeutics against the overwhelming COVID-19 pandemic. In this regard, HLA binding peptides from SARS-CoV-2 NSPs showed a robust viral antigenicity and immunogenicity similar to initial works based on peptide prediction from SARS-CoV-2 S, E, M, and N proteins [8], [10], [11]. Therefore, these peptides could successfully improve in vitro and in vivo immune responses against SARS-CoV-2. In fact, immune responses were observed after active immunization simulations, whose results (e.g., production of antibodies, T cell generation, and cytokines profile) are comparable to other vaccine candidates against infectious diseases [28], [30]. On the other hand, conservancy analysis on IEDB-AR revealed that most of the peptides are strongly conserved across highly pathogenic betacoronaviruses—SARS-CoV-2, SARS-CoV, and MERS-CoV. This result is in agreement, at least partially, with a former report that has demonstrated a robust amino acid sequence identity between SARS-CoV-2 NSPs and SARS-CoV NSPs [4]. Hence, HLA biding peptides here predicted could evoke—perhaps—simultaneous immune responses against other betacoronaviruses such as SARS-CoV, and MERS-CoV.

The clear and proper interaction between conserved viral peptides and HLA alleles suggest that they could elicit not only adaptive immune responses by CD8+ CTC and CD4+ THC but also an innate immune response by Natural Killer (NK) cells—one of the first lines of defence against viruses []. In fact, it has been shown a decisive role of activating killer immunoglobulin-like receptors (aKIR) in viral recognition by NK cells [31]. Moreover, it has been reported that the interaction between aKIRs and HLA-C molecules is conditioned by C1 and C2 epitopes, for instance, KIR2DS2 may recognize HLA-C allotypes carrying the C1 epitope (N80), including HLA-C*01:02, HLA-C*14:02, and HLA-C*16:01, whereas KIR2DS4 binds to HLA-C allotypes with C1 (N80) or C2 (K80) epitopes, including HLA-C*02:01, HLA-C*04:01, and HLA-C*05:01 [31]. Interestingly, Nayer and colleagues reported that KIR2DS2 directly recognizes conserved viral peptides in the context of HLA-C*01:02, thereby leading to the inhibition of the hepatitis C virus and dengue virus replication [6]. In the present study, several 9-mer viral peptides showed affinity by HLA-C*01:02 as well as by HLA-C*02:02, which activates to KIRD2DS4 [31]. However, further research is required to reveal peptide-specific recognition of SARS-CoV-2 and other betacoronaviruses by certain subsets within the NK cell compartment.

This study was limited by 1) all data only reflect immunoinformatics analyses. Future in vitro and in vivo investigations are required to ensure safety and efficacy of these peptides prior to their use in human trials; 2) Future studies should address other SARS-CoV-2 NSPs derived from ORF1ab to determine their probable immunogenicity. However, NSP7, NSP8, NSP9, NSP12, and NSP13 used in this research represent optimal targets for anti-SARS-CoV-2 immune responses as exposed earlier; and 3) although the vaccine construct showed robust humoral and cell-mediated responses, further investigations are necessary to assess its physicochemical properties, 3D structure, and refinement.

In summary, these initial results describing the immunogenic potential of conserved peptides from SARS-CoV-2 NSPs are promising, and they could be strong candidates to evaluate functional NK cells, humoral, and T cell responses in experimental animal models. These results open a powerful window for future fundamental and translational studies focus on coronaviral diseases.

Declarations of interest

None.

Founding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Acknowledgments

I wish to thank the support of the Venezuelan Institute for Scientific Research (IVIC) – Venezuela, and Laboratory of Cellular and Molecular Pathology-IVIC.

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