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Generation of SARS-CoV-2 S1 Spike Glycoprotein Putative Antigenic Epitopes in Vitro by Intracellular Aminopeptidases
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Generation of SARS-CoV-2 S1 Spike Glycoprotein Putative Antigenic Epitopes in Vitro by Intracellular Aminopeptidases
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Journal of Proteome Research

Cite this: J. Proteome Res. 2020, 19, 11, 4398–4406
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https://doi.org/10.1021/acs.jproteome.0c00457
Published September 15, 2020
Copyright © 2020 American Chemical Society

Abstract

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Presentation of antigenic peptides by MHCI is central to cellular immune responses against viral pathogens. While adaptive immune responses versus SARS-CoV-2 can be of critical importance to both recovery and vaccine efficacy, how protein antigens from this pathogen are processed to generate antigenic peptides is largely unknown. Here, we analyzed the proteolytic processing of overlapping precursor peptides spanning the entire sequence of the S1 spike glycoprotein of SARS-CoV-2, by three key enzymes that generate antigenic peptides, aminopeptidases ERAP1, ERAP2, and IRAP. All enzymes generated shorter peptides with sequences suitable for binding onto HLA alleles, but with distinct specificity fingerprints. ERAP1 was the most efficient in generating peptides 8–11 residues long, the optimal length for HLA binding, while IRAP was the least efficient. The combination of ERAP1 with ERAP2 greatly limited the variability of peptide sequences produced. Less than 7% of computationally predicted epitopes were found to be produced experimentally, suggesting that aminopeptidase processing may constitute a significant filter to epitope presentation. These experimentally generated putative epitopes could be prioritized for SARS-CoV-2 immunogenicity studies and vaccine design. We furthermore propose that this in vitro trimming approach could constitute a general filtering method to enhance the prediction robustness for viral antigenic epitopes.

Copyright © 2020 American Chemical Society

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This article is part of the Proteomics in Pandemic Disease special issue.

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This article is made available via the ACS COVID-19 subset for unrestricted RESEARCH re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

Introduction

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the pathogen responsible for coronavirus disease 19 (COVID-19) that is behind a major ongoing pandemic. (1−3) Virus entry into host cells is dependent on the S1 spike glycoprotein that forms homotrimers on the surface of the virion and interacts with the ACE2 receptor in susceptible cells. (4−6)
Many studies on clinical characteristics and mortality resulting from SARS-CoV-2 infection have highlighted the need for a detailed understanding of immune responses against this pathogen. (7) While appropriate innate and adaptive immune responses are necessary for recovery from infection, aberrant immune responses can be a major contributing factor to mortality. (8,9) In parallel, understanding immune recognition of SARS-CoV-2 is crucial to the ongoing massive global effort into developing an effecting vaccine against this pathogen. (10) Although early analyses have focused on the development of neutralizing antibodies, cellular immune responses are emerging of vital importance (11) both for understanding normal immune response against this pathogen and for designing and optimizing vaccines. (12) In particular, T-cell mediated immunity appears to be important for both viral clearance and for long-term immunity. (11) Thus, analysis of antigenic epitopes from SARS-CoV-2 should be a priority for the design of vaccines that induce effective and long-lasting cellular immune responses. (13)
Cytotoxic T-cell responses against virus-infected cells hinge on the presentation of small peptidic fragments of viral proteins, called antigenic peptides, by specialized proteins on the cell surface that belong to the Major Histocompatibility Class I complex (MHCI, also called Human Leukocyte Antigens, HLA, in humans). Antigenic peptides are derived from viral proteins that are proteolytically degraded by complex proteolytic cascades. (14) Intracellular aminopeptidases, ER aminopeptidase 1 (ERAP1), ER aminopeptidase 2 (ERAP2), and insulin-regulated aminopeptidase (IRAP) play important roles in producing antigenic peptides, by down-sizing longer peptides to the correct length for binding onto MHCI. (15) Appropriate processing of pathogen antigens by these enzymes can determine the generation of cytotoxic immune responses, and aberrant processing can lead to immune evasion. (16) Thus, it is important to understand how these enzymes process SARS-CoV-2 antigens, so as to gain insight into the efficacy of antiviral cytotoxic responses and reveal possible avenues to enhance them.
In this study, we utilized a novel approach to analyze antigen trimming by intracellular aminopeptidases ERAP1, ERAP2, and IRAP, focusing on the largest antigen of SARS-CoV-2, namely, S1 spike glycoprotein. By using tandem LC-MS/MS analysis, we were able to follow trimming in parallel of a large ensemble of peptides derived from the full length of S1 protein. This approach was inspired by two established observations: (i) that these enzymes are expected to normally encounter a very large number of potential substrates concurrently in the cell and (ii) accommodation of peptides inside a large cavity of each enzyme can lead to complex interactions between substrates that have to compete for the same space in the cavity. (17−19) Our analysis provides novel insight into the differences in substrate specificity between the three enzymes and provides a potential filter of traditional bioinformatic approaches that aim to predict antigenic epitopes. Finally, we propose a limited list of peptides that are potential ligands for common HLA alleles and could be prioritized for further immunological analyses and vaccine design efforts.

Experimental Procedures

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Enzyme Expression and Purification

Recombinant ERAP1, ERAP2, and IRAP were expressed and purified as described previously. Briefly, ERAP1 and ERAP2 were expressed by Hi5 insect cells in culture after infection with baculovirus carrying the appropriate gene and purified by affinity chromatography using a C-terminal his tag. (20,21) The enzymatic extracellular domain of IRAP was expressed by stably transfected HEK 293S GnTI(−) cells and purified by affinity chromatography using a C-terminal Rhodopsin 1D4 tag. (22) Enzymes were stored with 10% glycerol in aliquots at −80 °C until needed.

Peptides

The PepMix SARS-CoV-2 peptide mixture was purchased by JBT Peptide Technologies GmbH. Peptide pools were dissolved in DMSO. Prior to reactions the two peptide collections (158 and 157 peptides respectively) were mixed at equimolar concentrations and diluted in buffer containing 10 mM Hepes pH 7, 100 mM NaCl to a final concentration of 48 μM.

Enzymatic Reactions

Enzymatic reactions were performed in triplicate in a total volume of 50 μL in 10 mM Hepes pH 7, 100 mM NaCl. Freshly thawed enzyme stocks were added to each reaction to a final concentration of 50 nM. Reactions were incubated at 37 °C for 2 h, stopped by the addition of 7.5 μL of a 10% TFA solution, flash frozen in liquid nitrogen, and stored at −80 °C until analysis.

LC-MS/MS Analysis

The sample was preconcentrated on a pepmap LC trapping column (0.3 × 5 mm) at a rate of 30 μL of Buffer A (0.1% Formic acid in water) in 5 min. The LC gradient used was 5% Buffer B (0.1% Formic acid in Acetonitrile) to 25% in 36 min followed by an increase to 36% in 5 min and a second increase to 80% in 0.5 min and then was kept constant for 2 min. The column was equilibrated for 15 min prior to the next injection. A full MS was acquired with a Q Exactive HF-X Hybrid Quadropole-Orbitrap mass spectrometer, in the scan range of 350–1400 m/z using 60K resolving power with an AGC of 3 × 106 and max IT of 45 ms, followed by MS/MS scans of the 12 most abundant ions, using 15K resolving power with an AGC of 1 × 105 and max IT of 22 ms and an NCE of 28.

Database Search

We employed the MaxQuant computational proteomics platform version 1.6.14.0 to search the peak lists against the Spike glycoprotein SARS2 FASTA file (SwissProt accession number P0DTC2) and a file containing 247 frequently observed contaminants. N-terminal acetylation (42.010565 Da) and methionine oxidation (15.994915 Da) were set as variable modifications. The second peptide identification option in Andromeda was enabled. The false discovery rate (FDR) was set to 0.01 for both peptides. The enzyme specificity was set as unspecific. The minimum peptide length was set to 6 amino acids. The initial allowed mass deviation of the precursor ion was set to 4.5 ppm and the maximum fragment mass deviation was set to 20 ppm.

Results and Discussion

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To investigate the trimming of antigenic epitope precursors by intracellular aminopeptidases that generate antigenic peptides, we used a mixture of 315 synthetic peptides derived from the sequence of the SARS-CoV-2 S1 spike glycoprotein. All peptides were 15 residues long and spanned the entire sequence of the protein with an 11 residue overlap between adjacent peptides. This mixture allows the systematic sampling of the entire sequence of the protein. The peptide mixture was incubated with either ERAP1, ERAP2, or IRAP at a substrate to enzyme ratio of 1000:1 and the digestion products analyzed by LC-MS/MS using a custom search database generated by in silico digestions of the full S1 protein sequence (UniProt ID: P0DTC2). We also tested an equimolar mixture of ERAP1 and ERAP2 since previous studies have suggested that these two proteins can form functional equimolar heterodimers in cells. (23,24) All analyses were performed on three biological replicates for each reaction, as well as a control reaction that was performed in the absence of enzymes. An additional technical replicate for the control sample was also analyzed. For statistical robustness, we performed a t test between the control sample and each reaction and selected for further analysis only the peptides for which the quantification value changed by a statistically significant degree (p-value < 0.05).
The relative abundance of each peptide before and after the reaction was compared by label-free quantification. Analysis identified 263 unique 15mers in the samples out of the total 315 in the mixture. This represents an 83% coverage of included peptides which may be due to poor ionization and detection for some peptide sequences. As a result, further analysis was limited to the peptides detectable by our experimental setup. On average, incubation with the enzyme reduced the relative abundance of the 15mer peptides indicating successful digestion (Figure 1A,B). This reduction was much more evident for ERAP1 and ERAP2 (and their mixture) than for IRAP. Each enzyme featured a unique digestion fingerprint, suggesting different selectivity, as suggested in previous studies. (25) The full list of generated peptides is shown in Supplemental Table S1. Since the majority of peptides presented by HLA are 8–11 residues long, we analyzed the comparative abundance of 8–11mers generated from each reaction (Figure 1C,D). Of the three enzymes, ERAP1 was the most efficient in generating peptides within this length range, consistent with previous reports on the mechanism of action of this enzyme. (17,26) ERAP2 and the ERAP1/ERAP2 mixture followed, while IRAP was the least efficient. Similar to the trimming of 15mers, the generation of 8–11mers followed a unique fingerprint for each enzyme. This is consistent with the previous hypotheses that each of these enzymes accommodate peptides in a large internal cavity and selectivity is driven by interactions with the whole sequence of the peptide. (17,21,22)

Figure 1

Figure 1. (A) Heatmap showing trimming of 15mers by aminopeptidases for each biological replicate. (B) Average label-free quantification (LFQ) signal of 15mers in the sample before and after incubation with the indicated enzyme. (C) Heatmap showing LFQ signal for 8–11mers produced after digestion. (D) Average signal of 8–11mers.

Indeed, comparing the peptide sequences generated by each enzyme, out of 1184 peptides identified, 142 were common between all three enzymes, 244 between ERAP1 and ERAP2 and 220 between ERAP1 and IRAP (Figure 2A). Furthermore, 169 peptides were unique for ERAP1, 234 for ERAP2, and 303 for IRAP. A similar situation was evident for 8–11mer peptides (Figure 2B). Strikingly, the mixture of ERAP1 with ERAP2 generated the fewest number of distinct sequences of 8–11mers (Figure 2C). This was in contrast to the finding that the ERAP1/ERAP2 mixture generated about the same average signal intensity as ERAP2 (Figure 1D). This was due to ERAP1/ERAP2 mixture generating fewer, in terms of sequence, distinct peptides, which were however relatively abundant. This finding is consistent with the proposed synergism of ERAP1 and ERAP2 (27) and suggests that the combination of these two enzymes is more efficient in trimming variable sequences and can thus overtrim peptides to lengths below 7 residues that are not detectable in our experimental setup and should not be able to stably bind onto MHCI (Figure 2C). As a result, incubation with ERAP1/ERAP2 mixture accumulates only peptides that are resistant to degradation by both enzymes.

Figure 2

Figure 2. Venn diagrams indicating overlap between peptide sequences produced by each enzyme. Numerals indicate number of peptides in each segment. Analysis was performed using BioVenn. (28) (A) Comparison of all peptides produced by each enzyme. (B) Comparison of 8–11mers produced by each enzyme. (C) Comparison of 8–11mers produced by ERAP1, ERAP2, as well as their mixture (ERAP1/2).

Since epitope length is a key parameter for binding onto MHCI (the majority of presented peptides are 9mers) we analyzed the distribution of lengths of peptides generated by each enzymatic reaction (Figure 3). ERAP1 was very efficient in trimming the 15mer substrates and generated primarily 9mer peptides, consistent with its proposed property as a “molecular ruler”. (26) Neither ERAP2 nor IRAP were able to accumulate 9mers preferably, but still generated significant numbers. The mixture of ERAP1 and ERAP2 showed a similar fractional distribution of peptide lengths (Figure 3B), but produced a much lower number of distinct peptide sequences (Figure 3A), presumably due to overtrimming to smaller lengths or even single amino acids.

Figure 3

Figure 3. Length distribution of peptides detected by LC-MS/MS after enzymatic digestion. (A) Number of peptides detected of each length. (B) Relative fraction of each length compared to the total number of peptides detected.

The main determinant in antigen presentation is stable binding of antigen-generated peptides onto MHCI. To evaluate the potential of the generated peptides to bind onto MHCI we used the HLAthena prediction server to rank the peptides for binding onto a collection of common HLA alleles (29) specifically HLA-A01:01, HLA-A02:01, HLA-A03:01, HLA-A24:02, HLA-A26:01, HLA-B07:02, HLA-B08:01, HLA-B27:05, HLA-B39:01, HLA-B40:01, HLA-B58:01, and HLA-B15:01 (Supplemental Table S2). For each peptide we selected the best scoring HLA-allele and plotted the calculated percentile rank of the predicted score for each enzymatic reaction (Figure 4A). The geometric mean of the predicted affinity was lowest for ERAP1 (indicating that the ERAP1 generated peptides had the highest average affinity for HLA), followed by IRAP and then ERAP2. Only a subset of generated peptides was predicted to bind with sufficient affinity onto at least one HLA: 23% for ERAP1, 22% for ERAP2, 6% for ERAP1/ERAP2 mixture, and 21% for IRAP (peptide sequences are listed in Table 1). These peptides spanned the whole sequence of the S1 spike glycoprotein, although each enzyme presented a unique signature onto this sequence (Figure 4B). Although currently we cannot know if any of these peptides can be antigenic, we compared them to known antigenic peptides for SARS-CoV-1 found in the immune epitope database (http://www.iedb.org/). (30) ERAP1 generated 5 antigenic peptides reported to be antigenic for SARS-CoV-1, ERAP2 generated 4, and IRAP 1. This finding indicates that some cross-reactivity between SARS-CoV-1 and SARS-CoV-2 may be mediated through the generation of appropriate epitopes by intracellular aminopeptidases.
Table 1. Peptides Generated by Each Aminopeptidase and Are Predicted to Bind onto at Least One of the Common HLA-Allelesa
ERAP1ERAP2IRAP
peptidescoreallelepeptidescoreallelepeptidescoreallele
KFLPFQQF0.01A2402EVFNATRF0.00A2601EVFNATRF0.00A2601
VYYPDKVF0.01A2402VLNDILSR0.01A0301VLNDILSR0.01A0301
GYLQPRTF0.05A2402GRLQSLQTY0.06B2705HADQLTPTW0.02B5801
VRFPNITNL0.05B2705YRFNGIGV0.08B2705GYLQPRTF0.05A2402
GRLQSLQTY0.06B2705NQKLIANQF0.08B1501LADAGFIKQY0.09A0101
YRFNGIGV0.08B2705DAGFIKQY0.10A2601DAGFIKQY0.10A2601
NQKLIANQF0.08B1501SLGAENSVAY0.12B1501NYNYLYRL0.18A2402
NLREFVFK0.09A0301VFKNIDGYFKI0.14A2402STEKSNIIRGW0.21B5801
DAGFIKQY0.10A2601STEKSNIIRGW0.21B5801TLADAGFIK0.25A0301
DSFKEELDKY0.10A2601ILSRLDKV0.24A0201GVYYPDKVF0.32B1501
SVASQSIIAY0.11B1501FKEELDKY0.24A0101TLADAGFIKQY0.39A2601
TYVPAQEKNF0.20A2402TRFASVYAWNR0.25B2705ALNTLVKQL0.48A0201
KRFDNPVLPFN0.21B2705QRNFYEPQI0.35B2705KVTLADAGFIK0.48A0301
TLADAGFIK0.25A0301IEDLLFNK0.38A0101YADSFVIR0.49A0101
GVYYPDKVF0.32B1501LPIGINITRF0.39B0702PFGEVFNATRF0.52A2402
ATRFASVY0.34A0101LGAENSVAY0.41B1501GAGAALQI0.57B5801
GVYYPDKV0.39A0201LPQGFSAL0.47B0702APHGVVFL0.61B0702
SVLNDILSR0.42A0301TRFQTLLA0.49B2705NIDGYFKI0.65A0101
LPQGFSAL0.47B0702YADSFVIR0.49A0101ITGRLQSLQTY0.68A0101
KVTLADAGFIK0.48A0301TPINLVRDL0.50B0702TRGVYYPDKVF0.78B2705
YADSFVIR0.49A0101APHGVVFL0.61B0702QLTPTWRV0.79A0201
TPINLVRDL0.50B0702DPLSETKCTL0.66B0702YPDKVFRSSV0.83B0702
NYKLPDDF0.56A2402ALGKLQDV0.66A0201VLYENQKLI0.84A0201
NFYEPQII0.57A2402ITGRLQSLQTY0.68A0101NTLVKQLSSNF0.89A2601
NIDGYFKI0.65A0101TRGVYYPDKVF0.78B2705TTDNTFVS0.96A0101
DPLSETKCTL0.66B0702QLTPTWRV0.79A0201LPPAYTNSF1.10B0702
ALGKLQDV0.66A0201GINITRFQTL0.79B0801QRNFYEPQII1.15B2705
NESLIDLQEL0.67B4001QPYRVVVLSF0.82B0702QPRTFLLKY1.16A0101
FVIRGDEV0.71A0201LYENQKLI0.83A2402NVYADSFVIR1.19A0301
QLTPTWRV0.79A0201YPDKVFRSSV0.83B0702GEVFNATRF1.21B4001
QPYRVVVLSF0.82B0702VLYENQKLI0.84A0201EELDKYFKNH1.24A2601
LYENQKLI0.83A2402GVLTESNKKF0.87A2601YYPDKVFRSSV1.24A2402
YPDKVFRSSV0.83B0702VRDLPQGFSAL0.93B2705AEVQIDRLITG1.29B4001
KNIDGYFKI0.84A2402DPLQPELDSF1.09B0702EPLVDLPI1.30B0702
VLYENQKLI0.84A0201LPPAYTNSF1.10B0702TGRLQSLQTY1.31B1501
VYADSFVIR0.85A2402DILSRLDKV1.11B0801SVLHSTQDLFL1.33A0201
TVYDPLQP0.88A0301NGIGVTQNVLY1.12A2601YGVSPTKL1.37A2601
HFPREGVF0.89A2402QRNFYEPQII1.15B2705VTLADAGFIK1.39A0301
QDVVNQNAQAL0.93B4001QPRTFLLKY1.16A0101LPFQQFGRDIA1.44B0801
FEYVSQPF0.96B4001EELDKYFKNH1.24A2601QKFNGLTVLPP1.45B2705
SIIAYTMSL1.04B0801LPFQQFGRDI1.29B0702DGYFKIYSKH1.55A2601
KKFLPFQQFGR1.11B2705EPLVDLPI1.30B0702YENQKLIANQF1.55B1501
DILSRLDKV1.11B0801TGRLQSLQTY1.31B1501FPREGVFVSN1.56B0702
QPRTFLLKY1.16A0101QKLIANQF1.31B1501GNYNYLYRL1.63B2705
NVYADSFVIR1.19A0301TTEILPVSM1.32A0101NATRFASVY1.82A2601
GEVFNATRF1.21B4001SPDVDLGDISG1.33B0702YRLFRKSNLKP1.83B2705
EELDKYFKNH1.24A2601DVVIGIVNN1.42A2601HADQLTPTWRV1.98A0101
QKLIANQF1.31B1501LPFQQFGRDIA1.44B0801YVTQQLIRA2.00A0201
TTEILPVSM1.32A0101QKFNGLTVLPP1.45B2705   
SLLIVNNA1.37A0201DIADTTDAVRD1.52A2601   
YGVSPTKL1.37A2601DGYFKIYSKH1.55A2601   
VTLADAGFIK1.39A0301GNYNYLYRL1.63B2705   
VGYQPYRV1.42A0201SRLDKVEAEV1.64B2705   
DIADTTDAVRD1.52A2601NATRFASVY1.82A2601   
DGYFKIYSKH1.55A2601YRLFRKSNLKP1.83B2705   
RDLPQGFSAL1.55B4001HADQLTPTWRV1.98A0101   
LEPLVDLPI1.64B4001YVTQQLIRA2.00A0201   
SRLDKVEAEV1.64B2705      
NATRFASVY1.82A2601      
YVTQQLIRA2.00A0201      
a

HLA-A01:01, HLA-A02:01, HLA-A03:01, HLA-A24:02, HLA-A26:01, HLA-B07:02, HLA-B08:01, HLA-B27:05, HLA-B39:01, HLA-B40:01, HLA-B58:01, and HLA-B15:01. Scores indicated are percentile ranks corresponding to the predicted affinity score for each allele (range 0–100, 0 is best, ranks below 2 are considered binders). In bold are peptides reported to be antigenic for SARS-CoV-1 found in the immune epitope database (http://www.iedb.org/).

Figure 4

Figure 4. (A) Scatter plot showing the predicted affinity of produced peptides for common HLA alleles as calculated by HLAthena. Color region encompasses peptides that are predicted to bind to at least one of the common HLA alleles used in the analysis. (B) Schematic representation of relative locations in the S1 protein sequence where the generated peptides are found. (C) Venn diagrams depicting overlap between peptides of S1 protein predicted to bind to common HLA alleles and peptides produced experimentally by ERAP1, ERAP2, IRAP, or ERAP1/ERAP2 mixture. Numerals indicate number of peptides in each separate segment.

In a recent publication, the authors proposed that different HLA alleles can have significant variability in their ability to present SARS-CoV-2 epitopes, with HLA-B46:01 having the capability to present the fewest and HLA-B15:03 being able to present the most. (31) We thus used the NetMHCpan 4.1 server to predict potential epitopes form the S1 spike glycoprotein sequence that could be presented by HLA-B46:01 and HLA-B15:03 (Supplemental Table S3) and compared them to the experimentally produced peptides. ERAP1 was found to produce 15 potential ligands for HLA-B15:03 but only 6 for HLA-B46:01, consistent with the proposed trend. In contrast, ERAP2 produced 8 potential ligands for both alleles and IRAP produced 6 for HLA-B15:03 and 4 for HLA-B46:01. Strikingly, the mixture of ERAP1 with ERAP2 produced 3 peptides that could bind onto HLA-B15:03, but no peptides predicted to bind onto HLA-B46:01 (Table 2). Thus, our findings appear to validate the hypothesis that HLA-B15:03 is likely to present more SARS-CoV-2 epitopes than HLA-B46:01, but only for ERAP1, which however is considered the dominant aminopeptidase activity in the cell for generating antigenic peptides.
Table 2. Peptides Generated by Each Aminopeptidase That Are Predicted to Bind to HLA-B15:03 and HLA-B46:01a
ERAP1ERAP2IRAPERAP1/ERAP2
sequencescoresequencescoresequencescoresequencescore
HLA-B15:03HLA-B15:03HLA-B15:03HLA-B15:03
ATRFASVY0.37VGYLQPRTF0.53VGYLQPRTF0.53VGYLQPRTF0.54
VGYLQPRTF0.53NQKLIANQF0.58LKYNENGTITD1.40NQKLIANQF0.58
NQKLIANQF0.58GRLQSLQTY0.65GYLQPRTF1.59LKYNENGTITD1.41
GRLQSLQTY0.65LGAENSVAY0.84GEVFNATRF1.63  
FEYVSQPF0.83QKLIANQF0.91GVYYPDKVF1.71  
QKLIANQF0.91LKYNENGTITD1.40AGAALQIPF1.91  
KAHFPREGVF1.34SLGAENSVAY1.48    
LKYNENGTITD1.40NGIGVTQNVLY1.89    
AQYTSALLA1.42      
KRFDNPVLPFN1.58      
GYLQPRTF1.59      
SVASQSIIAY1.59      
GEVFNATRF1.63      
GVYYPDKVF1.71      
VYYPDKVF1.80      
HLA-B46:01HLA-B46:01HLA-B46:01HLA-B46:01
FEYVSQPF0.62LGAENSVAY0.037TLADAGFIKQY0.08 
SIIAYTMSL0.64LPIGINITRF0.71YVGYLQPRTF1.66  
ATRFASVY0.65SLGAENSVAY0.72GVYYPDKVF1.75  
SVASQSIIAY0.91GVLTESNKKF1.34TGRLQSLQTY1.98  
IANQFNSAI1.25YVGYLQPRTF1.65    
GVYYPDKVF1.75NGIGVTQNVLY1.84    
  FKEELDKY1.90    
  TGRLQSLQTY1.98    
a

Scores indicated are percentile ranks corresponding to the predicted score for each allele (range 0–100, 0 is best, ranks below 2 are considered binders).

Bioinformatic epitope predictions based on antigen sequence are often used as a tool to study the potential antigenicity of a particular epitope or pathogen. The power of those predictions is constantly evolving and primarily relies on predictions of binding affinity on HLA. To compare such predictions to our experimentally generated peptides, we used the full sequence of the S1 spike glycoprotein and the NetMHCpan 4.1 server (32) to predict potential epitopes that could be presented by the common HLA alleles indicated in the previous paragraph. The server predicted 929 potential epitopes with lengths of 8–12 residues (Supplemental Table S4). Of those potential epitopes, however, less than 7% were found to be produced experimentally by one of the enzymes tested and more specifically 58 by ERAP1, 52 by ERAP2, 4 for the ERAP1/2 mixture, and 53 by IRAP (Figure 4C). This finding suggests that intracellular antigen processing by aminopeptidases can significantly limit which peptides can be presented by MHCI. Indeed, it has been recently proposed that the main function of ERAP1 is to limit the peptide pool available for MHCI. (33) In this context, this experimental approach could be useful in optimizing bioinformatic predictions of potential MHCI epitopes.
Our findings provide new information on both the general biological functions of intracellular aminopeptidases that generate antigenic peptides as well as on specific processing of a key antigen from SARS-CoV-2. Specifically, our results highlight that each enzyme bears a unique trimming fingerprint to antigen processing. Although this has been suggested before on the basis of differences in specificity toward specific peptide substrates, (23,27,34,35) it has not been observed in the context of peptide ensembles. This is potentially important since competition of different peptides for the cavity in these enzymes could result in complex substrate interactions. At first glance, major differences in trimming fingerprints between each or the three enzymes, may appear to impose an unnecessary complication to antigenic peptide generation. It is conceivable, however, that this trimming variability is desirable for the immune system, since it can expand the breadth of possible antigenic peptides detected in different immunological settings and cell types. Our results also highlight a previously proposed property of ERAP1: the specialization in trimming large peptides and producing peptides that have the ideal length for MHCI binding—most of the ERAP1 products fall well within that range. (26) In contrast, both ERAP2 and IRAP appear to be less optimized for length selection. However, they are still able to produce many peptides that are potential cargo for MHCI, casting some doubt on whether the unique trimming properties of ERAP1 are absolutely necessary for this basic function. Furthermore, the combination of ERAP1 with ERAP2 appears to provide significant synergism in trimming, to the point of overtrimming peptides and limiting available sequences. Synergism between ERAP1 and ERAP2 has been demonstrated before in trimming isolated peptides and these two enzymes have been proposed to also form functional dimers. (23,24) According to our observations, their combination is especially efficient in trimming. While the biological repercussions of this are not fully clear yet, it is conceivable that the strong associations between ERAP2 activity and predisposition to autoimmunity may be related to this effect. (36)
Despite the current importance in understanding immune reactions in COVID-19, very little is known about the cellular adaptive immune responses against SARS-CoV-2. Cellular immune responses are emerging as a central player in clearing the infection and as targets for vaccine efforts. (37,38) Furthermore, HLA polymorphic variation has been suggested to underlie the large variability in virus clearance that has been observed among individuals. (39) Our analysis of the largest antigen of SARS-CoV-2, S1 spike glycoprotein, suggests that aminopeptidase trimming can be a significant filter that helps shape which peptides will be presented by HLA. Thus, we propose a short list of candidate peptides that could be prioritized in downstream antigenic analysis as well as in vaccine design and efficacy studies (Tables 1 and 2).
While the functions of ERAP1, ERAP2, and IRAP have been studied in both in vitro and in vivo contexts during the past decade, their relative functional differences have only been compared in processing specific substrates at a time. However, all these enzymes have a broad substrate specificity and can normally encounter thousands of different peptides in the ER or endosomal compartments. On the other hand, studies focusing on the presented immunopeptidome have revealed effects attributed to ERAP1 and ERAP2 trimming, but direct comparisons have been difficult because of the dominant effect of MHCI affinity on presentation. (40) Our approach stands in-between these two types of studies. It mimics the multiple-substrate situation that is likely normal in vivo but focuses on antigenic peptide precursor trimming. In this context, our approach may have broader application for the quick prediction of potential antigenic epitopes as an additional filter on bioinformatic predictions. Indeed, bioinformatic predictions result in many candidate peptides, very few of which will provoke an immune response; adding more filters can increase the usefulness of these rapid approaches. However, our approach also has limitations that need to be taken into account when interpreting results. Due to differences in ionization and detection by the LC-MS/MS some peptides may not be detected or may be under-represented compared to other sequences, making comparisons between different peptides less reliable. Furthermore, it is an in vitro approach that is limited to the peptide pool used and cannot take into account the dynamics of MHCI binding that can protect peptides from further aminopeptidase degradation (41) or peptide proofreading by chaperone components or the peptide loading complex. (42) Due to those limitations, we restricted our analysis to statistical comparisons and avoided drawing conclusions regarding particular peptide sequences. Recent studies have also suggested that ERAP1 may trim peptides while they are bound onto MHCI. (43) Further systematic analysis of this phenomenon however, suggested that it is either very rare or misinterpreted in the case of rapid-dissociating peptides. (44) We therefore did not attempt to characterize trimming of prebound peptides in this study.
In summary, we analyzed the trimming of a peptide ensemble spanning the sequence of the S1 spike glycoprotein of SARS-CoV-2, the pathogen responsible for the recent COVID-19 pandemic. Our analysis provided novel insight into the function of antigen trimming enzymes and suggested that aminopeptidase trimming may be a significant filter in determining which peptides can be presented by MHCI. Furthermore, we have identified a limited set of peptides that were experimentally produced by elongated precursors which could be prioritized in future studies aiming to investigate the antigenicity of SARS-CoV-2 infected cells and assist in the design of highly effective vaccines that aim to produce adaptive cytotoxic responses. We propose that our experimental approach may also be useful as a general tool for enhancing bioinformatic predictions of antigenic epitopes.

Supporting Information

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00457.

  • Table S1: List of generated peptides after digestion by each enzyme (XLSX)

  • Table S2: List of generated 8–11mers from each enzyme with best predicted binding score based on the HLAthena server (XLSX)

  • Table S3: Predicted binding score of peptides from the S1 spike glycoprotein of SARS-CoV-2 for HLA-B15:03 and HLA-B46:01, based on the HLAthena prediction server; in red, the peptides that are predicted to be binders (XLSX)

  • Table S4: List of potential epitopes from the sequence of the S1 spike glycoprotein of SARS-CoV-2 predicted using the NetMHCpan 4.1 server (XLSX)

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Author Information

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  • Corresponding Author
  • Authors
    • George Stamatakis - Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Attica, Greece
    • Martina Samiotaki - Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Attica, Greece
    • Anastasia Mpakali - National Centre for Scientific Research “Demokritos”, 15310 Agia Paraskevi, Attica, GreeceOrcidhttp://orcid.org/0000-0003-0869-9680
    • George Panayotou - Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Attica, Greece
  • Funding

    This research was supported by NCSR Demokritos and by “InfrafrontierGR/Phenotypos” (MIS 5002135) and by “The Greek Research Infrastructure for Personalised Medicine (pMED-GR)” (MIS 5002802), both funded by the Operational Programme “Competitiveness, Entrepreneurship and Innovation” (NSRF 2014–2020) and cofinanced by Greece and the EU (ERDF).

  • Notes
    The authors declare no competing financial interest.

    The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (45) partner repository with the data set identifier PXD019901 (http://www.ebi.ac.uk/pride/archive/).

References

Click to copy section linkSection link copied!

This article references 45 other publications.

  1. 1
    Coronaviridae Study Group of the International Committee on Taxonomy of Viruses The species Severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nat. Microbiol. 2020, 5 (4), 536544,  DOI: 10.1038/s41564-020-0695-z
  2. 2
    Wu, F.; Zhao, S.; Yu, B.; Chen, Y. M.; Wang, W.; Song, Z. G.; Hu, Y.; Tao, Z. W.; Tian, J. H.; Pei, Y. Y.; Yuan, M. L.; Zhang, Y. L.; Dai, F. H.; Liu, Y.; Wang, Q. M.; Zheng, J. J.; Xu, L.; Holmes, E. C.; Zhang, Y. Z. A new coronavirus associated with human respiratory disease in China. Nature 2020, 579 (7798), 265269,  DOI: 10.1038/s41586-020-2008-3
  3. 3
    Zhou, P.; Yang, X. L.; Wang, X. G.; Hu, B.; Zhang, L.; Zhang, W.; Si, H. R.; Zhu, Y.; Li, B.; Huang, C. L.; Chen, H. D.; Chen, J.; Luo, Y.; Guo, H.; Jiang, R. D.; Liu, M. Q.; Chen, Y.; Shen, X. R.; Wang, X.; Zheng, X. S.; Zhao, K.; Chen, Q. J.; Deng, F.; Liu, L. L.; Yan, B.; Zhan, F. X.; Wang, Y. Y.; Xiao, G. F.; Shi, Z. L. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 2020, 579 (7798), 270273,  DOI: 10.1038/s41586-020-2012-7
  4. 4
    Tortorici, M. A.; Veesler, D. Structural insights into coronavirus entry. Adv. Virus Res. 2019, 105, 93116,  DOI: 10.1016/bs.aivir.2019.08.002
  5. 5
    Walls, A. C.; Park, Y. J.; Tortorici, M. A.; Wall, A.; McGuire, A. T.; Veesler, D. Structure, Function, and Antigenicity of the SARS-CoV-2 Spike Glycoprotein. Cell 2020, 181 (2), 281292,  DOI: 10.1016/j.cell.2020.02.058
  6. 6
    Shang, J.; Ye, G.; Shi, K.; Wan, Y.; Luo, C.; Aihara, H.; Geng, Q.; Auerbach, A.; Li, F. Structural basis of receptor recognition by SARS-CoV-2. Nature 2020, 581 (7807), 221224,  DOI: 10.1038/s41586-020-2179-y
  7. 7
    St John, A. L.; Rathore, A. P. S. Early Insights into Immune Responses during COVID-19. J. Immunol. 2020, 205 (3), 555564,  DOI: 10.4049/jimmunol.2000526
  8. 8
    Song, P.; Li, W.; Xie, J.; Hou, Y.; You, C. Cytokine storm induced by SARS-CoV-2. Clin. Chim. Acta 2020, 509, 280287,  DOI: 10.1016/j.cca.2020.06.017
  9. 9
    Li, K.; Hao, Z.; Zhao, X.; Du, J.; Zhou, Y. SARS-CoV-2 infection-induced immune responses: Friends or foes?. Scand. J. Immunol. 2020, 92 (2), e12895  DOI: 10.1111/sji.12895
  10. 10
    Peeples, L. News Feature: Avoiding pitfalls in the pursuit of a COVID-19 vaccine. Proc. Natl. Acad. Sci. U. S. A. 2020, 117 (15), 82188221,  DOI: 10.1073/pnas.2005456117
  11. 11
    Leslie, M. T cells found in coronavirus patients ‘bode well’ for long-term immunity. Science 2020, 368 (6493), 809810,  DOI: 10.1126/science.368.6493.809
  12. 12
    Manners, C.; Larios Bautista, E.; Sidoti, H.; Lopez, O. J. Protective Adaptive Immunity Against Severe Acute Respiratory Syndrome Coronaviruses 2 (SARS-CoV-2) and Implications for Vaccines. Cureus 2020, 12 (6), e8399  DOI: 10.7759/cureus.8399
  13. 13
    Mukherjee, S.; Tworowski, D.; Detroja, R.; Mukherjee, S. B.; Frenkel-Morgenstern, M. Immunoinformatics and Structural Analysis for Identification of Immunodominant Epitopes in SARS-CoV-2 as Potential Vaccine Targets. Vaccines (Basel, Switz.) 2020, 8 (2), 290,  DOI: 10.3390/vaccines8020290
  14. 14
    Rock, K. L.; Goldberg, A. L. Degradation of cell proteins and the generation of MHC class I-presented peptides. Annu. Rev. Immunol. 1999, 17, 73979,  DOI: 10.1146/annurev.immunol.17.1.739
  15. 15
    Weimershaus, M.; Evnouchidou, I.; Saveanu, L.; van Endert, P. Peptidases trimming MHC class I ligands. Curr. Opin. Immunol. 2013, 25 (1), 906,  DOI: 10.1016/j.coi.2012.10.001
  16. 16
    Hammer, G. E.; Kanaseki, T.; Shastri, N. The final touches make perfect the peptide-MHC class I repertoire. Immunity 2007, 26 (4), 397406,  DOI: 10.1016/j.immuni.2007.04.003
  17. 17
    Giastas, P.; Mpakali, A.; Papakyriakou, A.; Lelis, A.; Kokkala, P.; Neu, M.; Rowland, P.; Liddle, J.; Georgiadis, D.; Stratikos, E. Mechanism for antigenic peptide selection by endoplasmic reticulum aminopeptidase 1. Proc. Natl. Acad. Sci. U. S. A. 2019, 116 (52), 2670926716,  DOI: 10.1073/pnas.1912070116
  18. 18
    Stratikos, E.; Stern, L. J. Antigenic peptide trimming by ER aminopeptidases--insights from structural studies. Mol. Immunol. 2013, 55 (3–4), 2129,  DOI: 10.1016/j.molimm.2013.03.002
  19. 19
    Evnouchidou, I.; Kamal, R. P.; Seregin, S. S.; Goto, Y.; Tsujimoto, M.; Hattori, A.; Voulgari, P. V.; Drosos, A. A.; Amalfitano, A.; York, I. A.; Stratikos, E. Coding single nucleotide polymorphisms of endoplasmic reticulum aminopeptidase 1 can affect antigenic peptide generation in vitro by influencing basic enzymatic properties of the enzyme. J. Immunol. 2011, 186 (4), 190913,  DOI: 10.4049/jimmunol.1003337
  20. 20
    Stamogiannos, A.; Maben, Z.; Papakyriakou, A.; Mpakali, A.; Kokkala, P.; Georgiadis, D.; Stern, L. J.; Stratikos, E. Critical Role of Interdomain Interactions in the Conformational Change and Catalytic Mechanism of Endoplasmic Reticulum Aminopeptidase 1. Biochemistry 2017, 56 (10), 15461558,  DOI: 10.1021/acs.biochem.6b01170
  21. 21
    Mpakali, A.; Giastas, P.; Mathioudakis, N.; Mavridis, I. M.; Saridakis, E.; Stratikos, E. Structural Basis for Antigenic Peptide Recognition and Processing by Endoplasmic Reticulum (ER) Aminopeptidase 2. J. Biol. Chem. 2015, 290 (43), 2602132,  DOI: 10.1074/jbc.M115.685909
  22. 22
    Mpakali, A.; Saridakis, E.; Harlos, K.; Zhao, Y.; Papakyriakou, A.; Kokkala, P.; Georgiadis, D.; Stratikos, E. Crystal Structure of Insulin-Regulated Aminopeptidase with Bound Substrate Analogue Provides Insight on Antigenic Epitope Precursor Recognition and Processing. J. Immunol. 2015, 195 (6), 28422851,  DOI: 10.4049/jimmunol.1501103
  23. 23
    Saveanu, L.; Carroll, O.; Lindo, V.; Del Val, M.; Lopez, D.; Lepelletier, Y.; Greer, F.; Schomburg, L.; Fruci, D.; Niedermann, G.; van Endert, P. M. Concerted peptide trimming by human ERAP1 and ERAP2 aminopeptidase complexes in the endoplasmic reticulum. Nat. Immunol. 2005, 6 (7), 68997,  DOI: 10.1038/ni1208
  24. 24
    Evnouchidou, I.; Weimershaus, M.; Saveanu, L.; van Endert, P. ERAP1-ERAP2 dimerization increases peptide-trimming efficiency. J. Immunol. 2014, 193 (2), 9018,  DOI: 10.4049/jimmunol.1302855
  25. 25
    Zervoudi, E.; Papakyriakou, A.; Georgiadou, D.; Evnouchidou, I.; Gajda, A.; Poreba, M.; Salvesen, G. S.; Drag, M.; Hattori, A.; Swevers, L.; Vourloumis, D.; Stratikos, E. Probing the S1 specificity pocket of the aminopeptidases that generate antigenic peptides. Biochem. J. 2011, 435 (2), 41120,  DOI: 10.1042/BJ20102049
  26. 26
    Chang, S. C.; Momburg, F.; Bhutani, N.; Goldberg, A. L. The ER aminopeptidase, ERAP1, trims precursors to lengths of MHC class I peptides by a “molecular ruler” mechanism. Proc. Natl. Acad. Sci. U. S. A. 2005, 102 (47), 1710712,  DOI: 10.1073/pnas.0500721102
  27. 27
    Lorente, E.; Barriga, A.; Johnstone, C.; Mir, C.; Jimenez, M.; Lopez, D. Concerted in vitro trimming of viral HLA-B27-restricted ligands by human ERAP1 and ERAP2 aminopeptidases. PLoS One 2013, 8 (11), e79596  DOI: 10.1371/journal.pone.0079596
  28. 28
    Hulsen, T.; de Vlieg, J.; Alkema, W. BioVenn - a web application for the comparison and visualization of biological lists using area-proportional Venn diagrams. BMC Genomics 2008, 9, 488,  DOI: 10.1186/1471-2164-9-488
  29. 29
    Sarkizova, S.; Klaeger, S.; Le, P. M.; Li, L. W.; Oliveira, G.; Keshishian, H.; Hartigan, C. R.; Zhang, W.; Braun, D. A.; Ligon, K. L.; Bachireddy, P.; Zervantonakis, I. K.; Rosenbluth, J. M.; Ouspenskaia, T.; Law, T.; Justesen, S.; Stevens, J.; Lane, W. J.; Eisenhaure, T.; Lan Zhang, G.; Clauser, K. R.; Hacohen, N.; Carr, S. A.; Wu, C. J.; Keskin, D. B. A large peptidome dataset improves HLA class I epitope prediction across most of the human population. Nat. Biotechnol. 2020, 38 (2), 199209,  DOI: 10.1038/s41587-019-0322-9
  30. 30
    Vita, R.; Zarebski, L.; Greenbaum, J. A.; Emami, H.; Hoof, I.; Salimi, N.; Damle, R.; Sette, A.; Peters, B. The immune epitope database 2.0. Nucleic Acids Res. 2010, 38 (Database issue), D854D862,  DOI: 10.1093/nar/gkp1004
  31. 31
    Nguyen, A.; David, J. K.; Maden, S. K.; Wood, M. A.; Weeder, B. R.; Nellore, A.; Thompson, R. F. Human leukocyte antigen susceptibility map for SARS-CoV-2. J. Virol. 2020,  DOI: 10.1128/JVI.00510-20
  32. 32
    Reynisson, B.; Alvarez, B.; Paul, S.; Peters, B.; Nielsen, M. NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res. 2020, 48 (W1), W449W454,  DOI: 10.1093/nar/gkaa379
  33. 33
    Komov, L.; Kadosh, D. M.; Barnea, E.; Milner, E.; Hendler, A.; Admon, A. Cell Surface MHC Class I Expression Is Limited by the Availability of Peptide-Receptive “Empty” Molecules Rather than by the Supply of Peptide Ligands. Proteomics 2018, 18 (12), e1700248  DOI: 10.1002/pmic.201700248
  34. 34
    Hearn, A.; York, I. A.; Rock, K. L. The specificity of trimming of MHC class I-presented peptides in the endoplasmic reticulum. J. Immunol. 2009, 183 (9), 552636,  DOI: 10.4049/jimmunol.0803663
  35. 35
    Georgiadou, D.; Hearn, A.; Evnouchidou, I.; Chroni, A.; Leondiadis, L.; York, I. A.; Rock, K. L.; Stratikos, E. Placental leucine aminopeptidase efficiently generates mature antigenic peptides in vitro but in patterns distinct from endoplasmic reticulum aminopeptidase 1. J. Immunol. 2010, 185 (3), 158492,  DOI: 10.4049/jimmunol.0902502
  36. 36
    Kuiper, J. J.; Van Setten, J.; Ripke, S.; Van, T. S. R.; Mulder, F.; Missotten, T.; Baarsma, G. S.; Francioli, L. C.; Pulit, S. L.; De Kovel, C. G.; Ten Dam-Van Loon, N.; Den Hollander, A. I.; Huis in het Veld, P.; Hoyng, C. B.; Cordero-Coma, M.; Martin, J.; Llorenc, V.; Arya, B.; Thomas, D.; Bakker, S. C.; Ophoff, R. A.; Rothova, A.; De Bakker, P. I.; Mutis, T.; Koeleman, B. P. A genome-wide association study identifies a functional ERAP2 haplotype associated with birdshot chorioretinopathy. Hum. Mol. Genet. 2014, 23 (22), 60816087,  DOI: 10.1093/hmg/ddu307
  37. 37
    Grifoni, A.; Weiskopf, D.; Ramirez, S. I.; Mateus, J.; Dan, J. M.; Moderbacher, C. R.; Rawlings, S. A.; Sutherland, A.; Premkumar, L.; Jadi, R. S.; Marrama, D.; de Silva, A. M.; Frazier, A.; Carlin, A. F.; Greenbaum, J. A.; Peters, B.; Krammer, F.; Smith, D. M.; Crotty, S.; Sette, A. Targets of T Cell Responses to SARS-CoV-2 Coronavirus in Humans with COVID-19 Disease and Unexposed Individuals. Cell 2020, 181 (7), 14891501,  DOI: 10.1016/j.cell.2020.05.015
  38. 38
    Wilk, A. J.; Rustagi, A.; Zhao, N. Q.; Roque, J.; Martinez-Colon, G. J.; McKechnie, J. L.; Ivison, G. T.; Ranganath, T.; Vergara, R.; Hollis, T.; Simpson, L. J.; Grant, P.; Subramanian, A.; Rogers, A. J.; Blish, C. A. A single-cell atlas of the peripheral immune response in patients with severe COVID-19. Nat. Med. 2020, 26 (7), 10701076,  DOI: 10.1038/s41591-020-0944-y
  39. 39
    Wang, W.; Zhang, W.; Zhang, J.; He, J.; Zhu, F. Distribution of HLA allele frequencies in 82 Chinese individuals with coronavirus disease-2019 (COVID-19). HLA 2020, 96 (2), 194196,  DOI: 10.1111/tan.13941
  40. 40
    de Castro, J. A. L. How ERAP1 and ERAP2 Shape the Peptidomes of Disease-Associated MHC-I Proteins. Front. Immunol. 2018, 9, 2463,  DOI: 10.3389/fimmu.2018.02463
  41. 41
    Infantes, S.; Samino, Y.; Lorente, E.; Jimenez, M.; Garcia, R.; Del Val, M.; Lopez, D. Cutting Edge: H-2L(d) Class I Molecule Protects an HIV N-Extended Epitope from In Vitro Trimming by Endoplasmic Reticulum Aminopeptidase Associated with Antigen Processing. J. Immunol. 2010, 184 (7), 33513355,  DOI: 10.4049/jimmunol.0901560
  42. 42
    Thomas, C.; Tampe, R. MHC I chaperone complexes shaping immunity. Curr. Opin. Immunol. 2019, 58, 915,  DOI: 10.1016/j.coi.2019.01.001
  43. 43
    Chen, H.; Li, L.; Weimershaus, M.; Evnouchidou, I.; van Endert, P.; Bouvier, M. ERAP1-ERAP2 dimers trim MHC I-bound precursor peptides; implications for understanding peptide editing. Sci. Rep. 2016, 6, 28902,  DOI: 10.1038/srep28902
  44. 44
    Mavridis, G.; Arya, R.; Domnick, A.; Zoidakis, J.; Makridakis, M.; Vlahou, A.; Mpakali, A.; Lelis, A.; Georgiadis, D.; Tampe, R.; Papakyriakou, A.; Stern, L. J.; Stratikos, E. A systematic re-examination of processing of MHCI-bound antigenic peptide precursors by endoplasmic reticulum aminopeptidase 1. J. Biol. Chem. 2020, 295 (21), 71937210,  DOI: 10.1074/jbc.RA120.012976
  45. 45
    Perez-Riverol, Y.; Csordas, A.; Bai, J.; Bernal-Llinares, M.; Hewapathirana, S.; Kundu, D. J.; Inuganti, A.; Griss, J.; Mayer, G.; Eisenacher, M.; Perez, E.; Uszkoreit, J.; Pfeuffer, J.; Sachsenberg, T.; Yilmaz, S.; Tiwary, S.; Cox, J.; Audain, E.; Walzer, M.; Jarnuczak, A. F.; Ternent, T.; Brazma, A.; Vizcaino, J. A. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 2019, 47 (D1), D442D450,  DOI: 10.1093/nar/gky1106

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  11. Irma Saulle, Chiara Vicentini, Mario Clerici, Mara Biasin. Antigen presentation in SARS-CoV-2 infection: the role of class I HLA and ERAP polymorphisms. Human Immunology 2021, 82 (8) , 551-560. https://doi.org/10.1016/j.humimm.2021.05.003
  12. Silvia D’Amico, Patrizia Tempora, Valeria Lucarini, Ombretta Melaiu, Stefania Gaspari, Mattia Algeri, Doriana Fruci. ERAP1 and ERAP2 Enzymes: A Protective Shield for RAS against COVID-19?. International Journal of Molecular Sciences 2021, 22 (4) , 1705. https://doi.org/10.3390/ijms22041705
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Journal of Proteome Research

Cite this: J. Proteome Res. 2020, 19, 11, 4398–4406
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https://doi.org/10.1021/acs.jproteome.0c00457
Published September 15, 2020
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  • Abstract

    Figure 1

    Figure 1. (A) Heatmap showing trimming of 15mers by aminopeptidases for each biological replicate. (B) Average label-free quantification (LFQ) signal of 15mers in the sample before and after incubation with the indicated enzyme. (C) Heatmap showing LFQ signal for 8–11mers produced after digestion. (D) Average signal of 8–11mers.

    Figure 2

    Figure 2. Venn diagrams indicating overlap between peptide sequences produced by each enzyme. Numerals indicate number of peptides in each segment. Analysis was performed using BioVenn. (28) (A) Comparison of all peptides produced by each enzyme. (B) Comparison of 8–11mers produced by each enzyme. (C) Comparison of 8–11mers produced by ERAP1, ERAP2, as well as their mixture (ERAP1/2).

    Figure 3

    Figure 3. Length distribution of peptides detected by LC-MS/MS after enzymatic digestion. (A) Number of peptides detected of each length. (B) Relative fraction of each length compared to the total number of peptides detected.

    Figure 4

    Figure 4. (A) Scatter plot showing the predicted affinity of produced peptides for common HLA alleles as calculated by HLAthena. Color region encompasses peptides that are predicted to bind to at least one of the common HLA alleles used in the analysis. (B) Schematic representation of relative locations in the S1 protein sequence where the generated peptides are found. (C) Venn diagrams depicting overlap between peptides of S1 protein predicted to bind to common HLA alleles and peptides produced experimentally by ERAP1, ERAP2, IRAP, or ERAP1/ERAP2 mixture. Numerals indicate number of peptides in each separate segment.

  • References


    This article references 45 other publications.

    1. 1
      Coronaviridae Study Group of the International Committee on Taxonomy of Viruses The species Severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nat. Microbiol. 2020, 5 (4), 536544,  DOI: 10.1038/s41564-020-0695-z
    2. 2
      Wu, F.; Zhao, S.; Yu, B.; Chen, Y. M.; Wang, W.; Song, Z. G.; Hu, Y.; Tao, Z. W.; Tian, J. H.; Pei, Y. Y.; Yuan, M. L.; Zhang, Y. L.; Dai, F. H.; Liu, Y.; Wang, Q. M.; Zheng, J. J.; Xu, L.; Holmes, E. C.; Zhang, Y. Z. A new coronavirus associated with human respiratory disease in China. Nature 2020, 579 (7798), 265269,  DOI: 10.1038/s41586-020-2008-3
    3. 3
      Zhou, P.; Yang, X. L.; Wang, X. G.; Hu, B.; Zhang, L.; Zhang, W.; Si, H. R.; Zhu, Y.; Li, B.; Huang, C. L.; Chen, H. D.; Chen, J.; Luo, Y.; Guo, H.; Jiang, R. D.; Liu, M. Q.; Chen, Y.; Shen, X. R.; Wang, X.; Zheng, X. S.; Zhao, K.; Chen, Q. J.; Deng, F.; Liu, L. L.; Yan, B.; Zhan, F. X.; Wang, Y. Y.; Xiao, G. F.; Shi, Z. L. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 2020, 579 (7798), 270273,  DOI: 10.1038/s41586-020-2012-7
    4. 4
      Tortorici, M. A.; Veesler, D. Structural insights into coronavirus entry. Adv. Virus Res. 2019, 105, 93116,  DOI: 10.1016/bs.aivir.2019.08.002
    5. 5
      Walls, A. C.; Park, Y. J.; Tortorici, M. A.; Wall, A.; McGuire, A. T.; Veesler, D. Structure, Function, and Antigenicity of the SARS-CoV-2 Spike Glycoprotein. Cell 2020, 181 (2), 281292,  DOI: 10.1016/j.cell.2020.02.058
    6. 6
      Shang, J.; Ye, G.; Shi, K.; Wan, Y.; Luo, C.; Aihara, H.; Geng, Q.; Auerbach, A.; Li, F. Structural basis of receptor recognition by SARS-CoV-2. Nature 2020, 581 (7807), 221224,  DOI: 10.1038/s41586-020-2179-y
    7. 7
      St John, A. L.; Rathore, A. P. S. Early Insights into Immune Responses during COVID-19. J. Immunol. 2020, 205 (3), 555564,  DOI: 10.4049/jimmunol.2000526
    8. 8
      Song, P.; Li, W.; Xie, J.; Hou, Y.; You, C. Cytokine storm induced by SARS-CoV-2. Clin. Chim. Acta 2020, 509, 280287,  DOI: 10.1016/j.cca.2020.06.017
    9. 9
      Li, K.; Hao, Z.; Zhao, X.; Du, J.; Zhou, Y. SARS-CoV-2 infection-induced immune responses: Friends or foes?. Scand. J. Immunol. 2020, 92 (2), e12895  DOI: 10.1111/sji.12895
    10. 10
      Peeples, L. News Feature: Avoiding pitfalls in the pursuit of a COVID-19 vaccine. Proc. Natl. Acad. Sci. U. S. A. 2020, 117 (15), 82188221,  DOI: 10.1073/pnas.2005456117
    11. 11
      Leslie, M. T cells found in coronavirus patients ‘bode well’ for long-term immunity. Science 2020, 368 (6493), 809810,  DOI: 10.1126/science.368.6493.809
    12. 12
      Manners, C.; Larios Bautista, E.; Sidoti, H.; Lopez, O. J. Protective Adaptive Immunity Against Severe Acute Respiratory Syndrome Coronaviruses 2 (SARS-CoV-2) and Implications for Vaccines. Cureus 2020, 12 (6), e8399  DOI: 10.7759/cureus.8399
    13. 13
      Mukherjee, S.; Tworowski, D.; Detroja, R.; Mukherjee, S. B.; Frenkel-Morgenstern, M. Immunoinformatics and Structural Analysis for Identification of Immunodominant Epitopes in SARS-CoV-2 as Potential Vaccine Targets. Vaccines (Basel, Switz.) 2020, 8 (2), 290,  DOI: 10.3390/vaccines8020290
    14. 14
      Rock, K. L.; Goldberg, A. L. Degradation of cell proteins and the generation of MHC class I-presented peptides. Annu. Rev. Immunol. 1999, 17, 73979,  DOI: 10.1146/annurev.immunol.17.1.739
    15. 15
      Weimershaus, M.; Evnouchidou, I.; Saveanu, L.; van Endert, P. Peptidases trimming MHC class I ligands. Curr. Opin. Immunol. 2013, 25 (1), 906,  DOI: 10.1016/j.coi.2012.10.001
    16. 16
      Hammer, G. E.; Kanaseki, T.; Shastri, N. The final touches make perfect the peptide-MHC class I repertoire. Immunity 2007, 26 (4), 397406,  DOI: 10.1016/j.immuni.2007.04.003
    17. 17
      Giastas, P.; Mpakali, A.; Papakyriakou, A.; Lelis, A.; Kokkala, P.; Neu, M.; Rowland, P.; Liddle, J.; Georgiadis, D.; Stratikos, E. Mechanism for antigenic peptide selection by endoplasmic reticulum aminopeptidase 1. Proc. Natl. Acad. Sci. U. S. A. 2019, 116 (52), 2670926716,  DOI: 10.1073/pnas.1912070116
    18. 18
      Stratikos, E.; Stern, L. J. Antigenic peptide trimming by ER aminopeptidases--insights from structural studies. Mol. Immunol. 2013, 55 (3–4), 2129,  DOI: 10.1016/j.molimm.2013.03.002
    19. 19
      Evnouchidou, I.; Kamal, R. P.; Seregin, S. S.; Goto, Y.; Tsujimoto, M.; Hattori, A.; Voulgari, P. V.; Drosos, A. A.; Amalfitano, A.; York, I. A.; Stratikos, E. Coding single nucleotide polymorphisms of endoplasmic reticulum aminopeptidase 1 can affect antigenic peptide generation in vitro by influencing basic enzymatic properties of the enzyme. J. Immunol. 2011, 186 (4), 190913,  DOI: 10.4049/jimmunol.1003337
    20. 20
      Stamogiannos, A.; Maben, Z.; Papakyriakou, A.; Mpakali, A.; Kokkala, P.; Georgiadis, D.; Stern, L. J.; Stratikos, E. Critical Role of Interdomain Interactions in the Conformational Change and Catalytic Mechanism of Endoplasmic Reticulum Aminopeptidase 1. Biochemistry 2017, 56 (10), 15461558,  DOI: 10.1021/acs.biochem.6b01170
    21. 21
      Mpakali, A.; Giastas, P.; Mathioudakis, N.; Mavridis, I. M.; Saridakis, E.; Stratikos, E. Structural Basis for Antigenic Peptide Recognition and Processing by Endoplasmic Reticulum (ER) Aminopeptidase 2. J. Biol. Chem. 2015, 290 (43), 2602132,  DOI: 10.1074/jbc.M115.685909
    22. 22
      Mpakali, A.; Saridakis, E.; Harlos, K.; Zhao, Y.; Papakyriakou, A.; Kokkala, P.; Georgiadis, D.; Stratikos, E. Crystal Structure of Insulin-Regulated Aminopeptidase with Bound Substrate Analogue Provides Insight on Antigenic Epitope Precursor Recognition and Processing. J. Immunol. 2015, 195 (6), 28422851,  DOI: 10.4049/jimmunol.1501103
    23. 23
      Saveanu, L.; Carroll, O.; Lindo, V.; Del Val, M.; Lopez, D.; Lepelletier, Y.; Greer, F.; Schomburg, L.; Fruci, D.; Niedermann, G.; van Endert, P. M. Concerted peptide trimming by human ERAP1 and ERAP2 aminopeptidase complexes in the endoplasmic reticulum. Nat. Immunol. 2005, 6 (7), 68997,  DOI: 10.1038/ni1208
    24. 24
      Evnouchidou, I.; Weimershaus, M.; Saveanu, L.; van Endert, P. ERAP1-ERAP2 dimerization increases peptide-trimming efficiency. J. Immunol. 2014, 193 (2), 9018,  DOI: 10.4049/jimmunol.1302855
    25. 25
      Zervoudi, E.; Papakyriakou, A.; Georgiadou, D.; Evnouchidou, I.; Gajda, A.; Poreba, M.; Salvesen, G. S.; Drag, M.; Hattori, A.; Swevers, L.; Vourloumis, D.; Stratikos, E. Probing the S1 specificity pocket of the aminopeptidases that generate antigenic peptides. Biochem. J. 2011, 435 (2), 41120,  DOI: 10.1042/BJ20102049
    26. 26
      Chang, S. C.; Momburg, F.; Bhutani, N.; Goldberg, A. L. The ER aminopeptidase, ERAP1, trims precursors to lengths of MHC class I peptides by a “molecular ruler” mechanism. Proc. Natl. Acad. Sci. U. S. A. 2005, 102 (47), 1710712,  DOI: 10.1073/pnas.0500721102
    27. 27
      Lorente, E.; Barriga, A.; Johnstone, C.; Mir, C.; Jimenez, M.; Lopez, D. Concerted in vitro trimming of viral HLA-B27-restricted ligands by human ERAP1 and ERAP2 aminopeptidases. PLoS One 2013, 8 (11), e79596  DOI: 10.1371/journal.pone.0079596
    28. 28
      Hulsen, T.; de Vlieg, J.; Alkema, W. BioVenn - a web application for the comparison and visualization of biological lists using area-proportional Venn diagrams. BMC Genomics 2008, 9, 488,  DOI: 10.1186/1471-2164-9-488
    29. 29
      Sarkizova, S.; Klaeger, S.; Le, P. M.; Li, L. W.; Oliveira, G.; Keshishian, H.; Hartigan, C. R.; Zhang, W.; Braun, D. A.; Ligon, K. L.; Bachireddy, P.; Zervantonakis, I. K.; Rosenbluth, J. M.; Ouspenskaia, T.; Law, T.; Justesen, S.; Stevens, J.; Lane, W. J.; Eisenhaure, T.; Lan Zhang, G.; Clauser, K. R.; Hacohen, N.; Carr, S. A.; Wu, C. J.; Keskin, D. B. A large peptidome dataset improves HLA class I epitope prediction across most of the human population. Nat. Biotechnol. 2020, 38 (2), 199209,  DOI: 10.1038/s41587-019-0322-9
    30. 30
      Vita, R.; Zarebski, L.; Greenbaum, J. A.; Emami, H.; Hoof, I.; Salimi, N.; Damle, R.; Sette, A.; Peters, B. The immune epitope database 2.0. Nucleic Acids Res. 2010, 38 (Database issue), D854D862,  DOI: 10.1093/nar/gkp1004
    31. 31
      Nguyen, A.; David, J. K.; Maden, S. K.; Wood, M. A.; Weeder, B. R.; Nellore, A.; Thompson, R. F. Human leukocyte antigen susceptibility map for SARS-CoV-2. J. Virol. 2020,  DOI: 10.1128/JVI.00510-20
    32. 32
      Reynisson, B.; Alvarez, B.; Paul, S.; Peters, B.; Nielsen, M. NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res. 2020, 48 (W1), W449W454,  DOI: 10.1093/nar/gkaa379
    33. 33
      Komov, L.; Kadosh, D. M.; Barnea, E.; Milner, E.; Hendler, A.; Admon, A. Cell Surface MHC Class I Expression Is Limited by the Availability of Peptide-Receptive “Empty” Molecules Rather than by the Supply of Peptide Ligands. Proteomics 2018, 18 (12), e1700248  DOI: 10.1002/pmic.201700248
    34. 34
      Hearn, A.; York, I. A.; Rock, K. L. The specificity of trimming of MHC class I-presented peptides in the endoplasmic reticulum. J. Immunol. 2009, 183 (9), 552636,  DOI: 10.4049/jimmunol.0803663
    35. 35
      Georgiadou, D.; Hearn, A.; Evnouchidou, I.; Chroni, A.; Leondiadis, L.; York, I. A.; Rock, K. L.; Stratikos, E. Placental leucine aminopeptidase efficiently generates mature antigenic peptides in vitro but in patterns distinct from endoplasmic reticulum aminopeptidase 1. J. Immunol. 2010, 185 (3), 158492,  DOI: 10.4049/jimmunol.0902502
    36. 36
      Kuiper, J. J.; Van Setten, J.; Ripke, S.; Van, T. S. R.; Mulder, F.; Missotten, T.; Baarsma, G. S.; Francioli, L. C.; Pulit, S. L.; De Kovel, C. G.; Ten Dam-Van Loon, N.; Den Hollander, A. I.; Huis in het Veld, P.; Hoyng, C. B.; Cordero-Coma, M.; Martin, J.; Llorenc, V.; Arya, B.; Thomas, D.; Bakker, S. C.; Ophoff, R. A.; Rothova, A.; De Bakker, P. I.; Mutis, T.; Koeleman, B. P. A genome-wide association study identifies a functional ERAP2 haplotype associated with birdshot chorioretinopathy. Hum. Mol. Genet. 2014, 23 (22), 60816087,  DOI: 10.1093/hmg/ddu307
    37. 37
      Grifoni, A.; Weiskopf, D.; Ramirez, S. I.; Mateus, J.; Dan, J. M.; Moderbacher, C. R.; Rawlings, S. A.; Sutherland, A.; Premkumar, L.; Jadi, R. S.; Marrama, D.; de Silva, A. M.; Frazier, A.; Carlin, A. F.; Greenbaum, J. A.; Peters, B.; Krammer, F.; Smith, D. M.; Crotty, S.; Sette, A. Targets of T Cell Responses to SARS-CoV-2 Coronavirus in Humans with COVID-19 Disease and Unexposed Individuals. Cell 2020, 181 (7), 14891501,  DOI: 10.1016/j.cell.2020.05.015
    38. 38
      Wilk, A. J.; Rustagi, A.; Zhao, N. Q.; Roque, J.; Martinez-Colon, G. J.; McKechnie, J. L.; Ivison, G. T.; Ranganath, T.; Vergara, R.; Hollis, T.; Simpson, L. J.; Grant, P.; Subramanian, A.; Rogers, A. J.; Blish, C. A. A single-cell atlas of the peripheral immune response in patients with severe COVID-19. Nat. Med. 2020, 26 (7), 10701076,  DOI: 10.1038/s41591-020-0944-y
    39. 39
      Wang, W.; Zhang, W.; Zhang, J.; He, J.; Zhu, F. Distribution of HLA allele frequencies in 82 Chinese individuals with coronavirus disease-2019 (COVID-19). HLA 2020, 96 (2), 194196,  DOI: 10.1111/tan.13941
    40. 40
      de Castro, J. A. L. How ERAP1 and ERAP2 Shape the Peptidomes of Disease-Associated MHC-I Proteins. Front. Immunol. 2018, 9, 2463,  DOI: 10.3389/fimmu.2018.02463
    41. 41
      Infantes, S.; Samino, Y.; Lorente, E.; Jimenez, M.; Garcia, R.; Del Val, M.; Lopez, D. Cutting Edge: H-2L(d) Class I Molecule Protects an HIV N-Extended Epitope from In Vitro Trimming by Endoplasmic Reticulum Aminopeptidase Associated with Antigen Processing. J. Immunol. 2010, 184 (7), 33513355,  DOI: 10.4049/jimmunol.0901560
    42. 42
      Thomas, C.; Tampe, R. MHC I chaperone complexes shaping immunity. Curr. Opin. Immunol. 2019, 58, 915,  DOI: 10.1016/j.coi.2019.01.001
    43. 43
      Chen, H.; Li, L.; Weimershaus, M.; Evnouchidou, I.; van Endert, P.; Bouvier, M. ERAP1-ERAP2 dimers trim MHC I-bound precursor peptides; implications for understanding peptide editing. Sci. Rep. 2016, 6, 28902,  DOI: 10.1038/srep28902
    44. 44
      Mavridis, G.; Arya, R.; Domnick, A.; Zoidakis, J.; Makridakis, M.; Vlahou, A.; Mpakali, A.; Lelis, A.; Georgiadis, D.; Tampe, R.; Papakyriakou, A.; Stern, L. J.; Stratikos, E. A systematic re-examination of processing of MHCI-bound antigenic peptide precursors by endoplasmic reticulum aminopeptidase 1. J. Biol. Chem. 2020, 295 (21), 71937210,  DOI: 10.1074/jbc.RA120.012976
    45. 45
      Perez-Riverol, Y.; Csordas, A.; Bai, J.; Bernal-Llinares, M.; Hewapathirana, S.; Kundu, D. J.; Inuganti, A.; Griss, J.; Mayer, G.; Eisenacher, M.; Perez, E.; Uszkoreit, J.; Pfeuffer, J.; Sachsenberg, T.; Yilmaz, S.; Tiwary, S.; Cox, J.; Audain, E.; Walzer, M.; Jarnuczak, A. F.; Ternent, T.; Brazma, A.; Vizcaino, J. A. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 2019, 47 (D1), D442D450,  DOI: 10.1093/nar/gky1106
  • Supporting Information

    Supporting Information


    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00457.

    • Table S1: List of generated peptides after digestion by each enzyme (XLSX)

    • Table S2: List of generated 8–11mers from each enzyme with best predicted binding score based on the HLAthena server (XLSX)

    • Table S3: Predicted binding score of peptides from the S1 spike glycoprotein of SARS-CoV-2 for HLA-B15:03 and HLA-B46:01, based on the HLAthena prediction server; in red, the peptides that are predicted to be binders (XLSX)

    • Table S4: List of potential epitopes from the sequence of the S1 spike glycoprotein of SARS-CoV-2 predicted using the NetMHCpan 4.1 server (XLSX)


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