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. Author manuscript; available in PMC: 2023 Oct 7.
Published in final edited form as: J Proteome Res. 2022 Sep 15;21(10):2443–2452. doi: 10.1021/acs.jproteome.2c00325

Evaluation of Variant-Specific Peptides for Detection of SARS-CoV-2 Variants of Concern

Kantaphon Suddhapas 1, M Hannah Choi 1, Michael R Shortreed 3, Aaron Timperman 1,2
PMCID: PMC10318299  NIHMSID: NIHMS1906831  PMID: 36108102

Abstract

The SARS-CoV-2 omicron variant presented significant challenges to the global effort to counter the pandemic. SARS-CoV-2 is predicted to remain prevalent for the foreseeable future, making the ability to identify SARS-CoV-2 variants imperative in understanding and controlling the pandemic. The predominant variant discovery method, genome sequencing, is time-consuming, insensitive, and expensive. Ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) offers an exciting alternative detection modality provided that variant-containing peptide markers are sufficiently detectable from their tandem mass spectra (MS/MS). We have synthesized model tryptic peptides of SARS-CoV-2 variants alpha, beta, gamma, delta, and omicron and evaluated their signal intensity, HCD spectra, reverse phase retention time. Detection limits of 781, 781, 65, and 65 amol are obtained for the molecular ions of the proteotypic peptides: beta (QIAPGQTGNIADYNYK), gamma (TQLPSAYTNSFTR), delta (VGGNYNYR), and omicron (TLVKQLSSK), from neat solutions. These detection limits are on par with the detection limits of a previously reported proteotypic peptide from the SARS-CoV-2 spike protein, HTPINLVR. This study demonstrates the potential to differentiate SARS-CoV-2 variants through their proteotypic peptides with an approach that is broadly applicable across a wide range of pathogens.

Keywords: SARS-CoV-2, Covid-19, Mass Spectrometry, Proteomics, Viral proteins, Peptides, Variants of Concern, SARS-CoV-2 variants

INTRODUCTION

Since the emergence of SARS-CoV-2 in 2019, an extraordinary effort has been devoted to controlling the pandemic, yet the virus is predicted to remain for the foreseeable future.1 Unfortunately, one of the attributes of RNA viruses such as SARS-CoV-2 is a higher mutation rate than DNA viruses.2, 3 Moreover, variants are naturally selected for increased infectivity.4 Alpha, beta, gamma, and delta transmissibility have increased up to 100%, 113%, 140%, and 113% compared to the original Wuhan strain.5 The infectivity of the omicron variant is significantly higher than the previous variants with an increase in transmissibility of 3–6 times over the delta variant.6 The variants can also have a significant impact on the antigenic sites and can decrease the vaccine efficacy.7, 8 Currently, general testing for SARS-CoV-2 infection is performed by reverse transcription-polymerase chain reaction (RT-PCR) while variant surveillance is performed separately by whole genome sequencing (WGS)9, 10 and multiplexed RT-PCR.11, 12 WGS provides improved coverage and flexibility compared to multiplexed RT-PCR, while the limitations of WGS are mainly increased cost, time, and resources. Genome sequencing has greater flexibility than multiplexed RT-PCR for variant surveillance and currently costs a few hundred dollars per sample.13.

The gold standard of SAR-CoV-2 detection, RT-PCR, is fast and inexpensive ($1.21 – 4.39).14 However, mutated SARS-CoV-2 may cause a false negative when the mutations interfere with primer binding.15–17 Tahan et al found that the 26372G>U mutation of the E gene reduces primer binding and PCR assay sensitivity.16 Commercially available PCR assays frequently exhibit spike gene target failure (SGTF), resulting in the inability to detect non-targeted mutated virus.11 Standard RT-PCR testing is also unable to distinguish the variants, but it has been multiplexed by Vogels et al. who developed a set of dedicated primers for variants sharing common mutations to detect the SARS-CoV-2 virus.11 Whole genome sequencing can be used to identify the variants,8, 18–22 but the technique is time-consuming and expensive. Additionally, the mutations of SARS-CoV-2 variants can have a negative impact on rapid antigen based testing by interfering with antibody-antigen binding.23 Thus, a cost-effective method for simultaneous detection of COVID-19 infection and SARS-CoV-2 variant surveillance that is complementary to the current RT-PCR and antigen-based would be highly beneficial.

Since the beginning of the pandemic, research groups have investigated the viability of MS-based proteomics approaches for the detection of SARS-CoV-2 infection. This pioneering research has demonstrated that MS-based proteomics approaches can detect clinically relevant levels of proteins and excellent selectivity can be achieved. As proteomics lacks an amplification strategy similar to PCR, efficient recovery of viral proteins is critical. Sample preparation methods reported include precipitation,24, 25 and solid-phase extraction.26, 27 Both selective, e.g. antibody based extraction of N-protein,28 and non-selective, e.g. hydrophilic interaction chromatography (HILIC) extraction targeting total protein have been reported.29–31

Data dependent acquisition has been used to determine the tryptic peptides that yield the highest intensity to provide the best performance, although the best proteotypic peptides have varied among reported studies.32–34 A peptide is considered proteotypic if the peptide is an unique indicator of a particular protein and sufficiently detectable by their MS/MS spectra.35, 36 The wide variation of the best proteotypic peptides reported for SARS-CoV-2 detection indicates that the protein extraction and separation methods also play a key role in determining the ion intensity in the MS.

To improve detection limits and quantitative reproducibility of the viral peptides within the complex sample matrix, data independent analyses (DIA) have been utilized in several SRM and MRM, and PRM approaches. These approaches use preset channels each with a specified retention time and m/z signals that are searched. To keep the MS analysis time short, typically four peptides are detected.37–39 DIA has been performed in nasal pharyngeal swabs, saliva, and gargle solutions to provide fast and sensitive analyses using an inclusion list of tryptic peptides from SAR-CoV-2 protein.37, 38, 40, 41 To compare the limits of detection (LOD)s, we converted the unit to ng (0.6 ng viral protein material = 1 pfu)33 and found reported varied from 2 to 460 ng. The Cov-MS consortium reported detection limits of 2 pg (0.00333 pfu), using an 8-minute MRM schedule consisting of 30 transitions for 10 selected peptides with dedicated collision energy and cone voltage.41 A highly automated platform for high throughput clinical analyses using SP3 sample prep, turbulent flow extraction, an analytical C18 column, and parallel reaction monitoring (PRM) MS analysis was developed by Cardozo et al.39 Additionally, large community wide efforts, such as the Cov-MS consortium, are improving interlaboratory reproducibility and optimization with the aim of translating MS-based proteomics detection of SARS-CoV-2 to the clinic.41

At least two other groups have begun to evaluate the efficacy of MS-based proteomics approaches for the detection of SARS-CoV-2 variants of concern. In their pioneering work, Mann et al pioneered MALDI-FT-ICR MS a peptide mass fingerprinting method to detect the alpha, beta, delta, and gamma variants. As a peptide mass fingerprinting approach, MS/MS spectra were not acquired, and the method requires isolation of the S-protein. They successfully measured the masses of mutated peptides and constructed a phylogenetic tree to track variant evolution.42 Maus et al noted the presence of n-protein mutations that can interfere with general SARS-CoV-2 detection, and have modified their LC-MS-PRM method to include the new tryptic peptides.43 They also highlighted the importance of including variants of concern in assay design and detected peptides from ORF1ab. This work preceded the Omicron variant, which presents a great challenge to such mass fingerprinting approaches, because some proteotypic peptides have two and even three amino acid mutations.

Although many gains have been made and convincing rationale has been articulated, the barriers to utilizing MS for SARS-CoV-2 detection remain high. Much of the compelling rationale for MS-based proteomics methods for SARS-CoV-2 detection is derived from high selectivity, reduced reagent consumption, and the absence of specialized reagents, such as PCR primers whose performance may be adversely affected by mutations. The performance of MS-based proteomics approaches will only continue to improve as MS performance improves. However, it remains unlikely that clinical utilization of MS-based methods will increase without providing a capability that is not currently performed with nucleic acid-based approaches.

We believe that the greatest benefit of MS approaches for SARS-CoV-2 detection lies in the great flexibility or structural characterization capability of MS, and that this flexibility could be more fully utilized for SARS-CoV-2 detection. As the COVID-19 pandemic continues, new variants will continue to arise that complicate detection. Exciting new research is investigating the mutational space of SARS-CoV-2, and assays have been performed to measure the effect of spike protein mutations on binding to the ACE-2 receptor.44 Sequences from this research can be added to the database, enabling their detection if they appear within the sampled population. Thus, universal variant detection by MS requires an ever-increasing number of proteotypic peptides with sufficient sensitivity to detect clinically relevant viral loads. The caveat for variant detection by bottom-up proteomics, is that the detection of a variant is dependent on the detection of a limited number or even a single proteotypic peptide. While some peptide variants may contain multiple amino acid substitutions compared to the canonical sequence, some variants, such as omicron, have tryptic peptides with single amino acid mutations. Additionally, the spike protein is heavily glycosylated, and glycosylation of residues within a variant-specific peptide will complicate detection. Given this current and probable future situation of the ever-increasing repertoire of SARS-CoV-2 protein mutations, a method for universal and selective detection of SARS-CoV-2 variants would be of great value. A first step to achieving this capability is establishing that a proteolytic enzyme, such as trypsin, can produce proteotypic peptides for each variant. Thus, the first question that must be answered for bottom-up proteomics approaches is: can MS-detectable proteotypic peptides be found for the variants of concern?

In this report, we investigate the potential for selective detection of the alpha, beta, gamma, delta, and omicron variants of SARS-CoV-2 using tryptic peptides and bottom-up proteomic methods. We begin with in silico digestion of known variant sequences and synthesis of the variant specific candidate proteotypic peptides. The detection limits of these variant specific candidate peptides are investigated through measurement of the signal intensity using LC-MS analysis for fifteen peptides of which four have been previously reported as proteotypic peptides for SARS-CoV-2 detection in the literature.32, 39 The six peptides that provide the greatest signal intensity are combined and HCD spectra are acquired over a range of peptide concentrations and a calibration curve is created using UPLC-MS/MS. Additionally, the known sites of modification, such as glycosylation that make detection more challenging, are shown in Table 1 and appear in only a few of the potential proteotypic peptides. These data indicate that adequate signal intensity is generated with proteotypic peptides specific to the four variants considered while the HCD spectra are sufficient for identification and the reference spectra generated are shared publicly.

Table 1.

Model peptides with residue location, variation type, mutation, grand average of hydropathicity (GRAVY) index, adjusted retention time, Metamorhpeus score, precursor m/z, PTMs, and b- and y- ions observed.

Peptide Residues Variant type Mutation GRAVY Index Adjusted RT (min) Score Precursor m/z PTM b and y-ions
VGGNYNYR 445 – 452 Delta B.1.617,
Epsilon B.1.427/B.1.429,
Kappa B.1.617.1,
Lambda C.37, 19B/501Y		 L452R −1.65 11 10.7 471.7241 no 2–4/1–7
CASYQTQTNSR 671 – 681 Delta B.1.617,
Kappa B.1.617.1		 P681R −1.36 10.4 9.31 629.7766 disulfide (662–671),
Glycosylation (676 and 678)		 2–5/1–9
LQSLQTYVTQQLIR 1001 – 1014 N/A N/A −0.14 13.3 19.5 845.976 no 2–8,10/1–12
HTPINLVR 207 – 214 N/A N/A −0.13 11.3 8.16 475.2819 no 2–3/1–7
TQLPSAYTNSFTR 22 – 34 Gamma B.1.1.28.1 P26S −0.75 12.3 16.4 743.37 no 2–7/1–9
QIAPGQTGNIADYNYK 409 – 424 Beta B.1.351,
Gamma P.1,
Omicron B.1.1.529		 K417N −0.91 11.9 17.3 876.9302 no 2,3,5,6/2–12
QLSSNFGAISSVLNDILAR 965 – 983 Alpha B.1.1.7 S982A 0.468 15.4 25.4 1003.5379 no 2–10/1–17
YNLAPFFTFK 369 – 378 Omicron B.1.1.529 S371L 0.3 13 12.4 624.3264 no 2–4,8/1–9
S373P
S375F
T478K
AGFNCYFPLR 484 – 493 Omicron B.1.1.529 E484A 0.24 12.2 9.26 594.2839 Disulfide bond (480–488) 2–4/1–5
Q493R
YGVGHQPYR 501 – 509 Omicron B.1.1.529 N501Y −1.02 9.4 15.3 538.7644 no 2–8/1–8
Y505H
TLVKQLSSK 961 – 969 Omicron B.1.1.529 N969K −0.2 9.7 15.2 502.3088 no 2–8/1–8
YQTQTKSHR 674 – 682 Omicron B.1.1.529 N679K −2.46 8.2 12.2 574.791 Glycosylation (676 and 678) 2–4,7,8/1–8
P681H
DGIIWVATEGALNTPK 128 – 143 N/A N/A 0.094 12.4 21.3 842.9431 No 2–7/2–13
IGMEVTPSGTWLTYTGAIK 320 – 338 N/A N/A 0.247 12.5 18.4 1013.5156 No 2–7,9–11/2–14
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EXPERIMENTAL METHODS

Materials

Heptafluorobutyric Acid (HFBA), acetic acid, formic acid, and acetonitrile (LC-MS grade) were purchased from Thermo Fisher Scientific. In silico tryptic digests cleaved the sequence after R and K except when preceded by P and assumed zero missed cleavages. The model peptides: VGGNYNYR, CASYQTQTNSR, LQSLQTYVTQQLIR, HTPINLVR, TQLPSAYTNSFTR, QIAPGQTGNIADYNYK, QLSSNFGAISSVLNDILAR, YNLAPFFTFK, EIYG AGNK, AGFNCYFPLR, YGVGHQPYR, TLVKQLSSK, YQTQTKSHR, DGIIWVATEGALNTPK, and IGMEVTPSGTWLTYTGAIK were synthesized by GenScript. To confirm that the proteotypic peptides are specific for SARS-CoV-2, the putative proteotypic peptide sequences were searched against the NR using BLASTP on August 11, 2022.

UPLC-MS/MS

Data-dependent analysis was performed with Exploris 240 mass spectrometer coupled with Ultimate 3000 RSLC nano liquid chromatography (Thermo Fischer Scientific).

An initial range finding analysis of the individual peptides (data shown in Table 1) was reconstituted in 4% acetonitrile, 0.2% acetic acid, and 0.05% HFBA and separated by Acclaim PepMap 100 C18 HPLC Column 75 μm × 150 mm (Thermo Fisher Scientific) using 0.1% formic acid in water and 0.1% formic acid in acetonitrile as A and B mobile phases. Sample (1 μL) was loaded containing 0.2, 0.1, 0.05, 0.025, 0.0125, and 0.00625 pmol with one replicate. The gradient was as follows: 1% B from 0–2 min, 1–12% B from 2–4, 12–60% B from 4–19 min, 60–80% B from 19–20 min, and 80% from 20–23 min at the flow rate of 0.3 μL/min. The stainless-steel emitter was induced with 2.2 kV and the ion transfer tube temperature was 280°C. The MS1 full scan range was m/z 380–2010 with a resolution of 120,000–240,000. The MS/MS was acquired with a resolution of 240,000.

After the initial analyses the peptides providing higher signal intensity were selected and analyzed as a mixture with a reduced HFBA concentration (data shown in Table 2, 3 and Figure 1). The peptides were reconstituted in 4% acetonitrile, 0.2% acetic acid, and 0.005% HFBA and separated by ACQUITY UPLC M-Class Peptide BEH C18 Column, 130 Å, 1.7 μm, 75 μm X 100 mm using 0.1% formic acid in water and 0.1% formic acid, 80% acetonitrile in water as A and B mobile phases. Injections were 1 μL and contained 0.2, 0.1, 0.05, 0.025, 0.0125, and 0.00625 pmol. The gradient was as follows: 1% B from 0–2 min, 1–35% B from 2–5 min, 35–70% B from 5–15 min, 70–80% B from 15–15.5 min, and 80–95% B from 15.5–22 min, 95% B from 22–31 min at the flow rate of 0.3 μL/min. An Acclaim PepMap 100 C18 HPLC Column 75 μm × 150 mm (Thermo Fisher Scientific) using 0.1% formic acid in water and 0.1% formic acid in acetonitrile as A and B mobile phases. Sample (1 μL) was loaded containing 6250, 3125, 1562, 781, 390, 195, 65, and 21 amol. The gradient was as follows: 1% B from 0–2 min, 1–8% B from 2–4 min, 4–12% B from 8–17.5 min, 17.5–50% B from 12–13 min, and 50–80% from 13–19 min, 80% from 19–25 min at the flow rate of 0.3 μL/min. The experiments were performed with 5 replicates for each loading amount. An electrospray voltage of 2.2–2.8 kV was applied to the fused silica emitter and the ion transfer tube temperature was set to 325°C. The MS1 full scan range was m/z 400–1600 with automatic gain control (AGC) target set at 300% and a resolution of 120,000. The top five scans were selected with MIPs (Monoisotopic Precursor Selection), intensity threshold of 5,000, potential charge state of 2–6, and dynamic exclusion of 30s. The MS/MS spectra were acquired with an AGC target set at 50% and a resolution of 15,000. The isolation window was 2 m/z.

Table 2.

LODs of selected peptides with signal to noise ratio (S/N) and signal intensity at the LOD.

Peptide variant LOD (amol) S/N Signal Intensity
VGGNYNYR Delta B.1.617, EpsilonB.1.427/B.1.429, Kappa B.1.617.1, Lambda C.37, 19B/501Y 65 78 8.E+04
HTPINLVR N/A 65 64.64 8.E+04
TQLPSAYTNSFTR Gamma B.1.1.28.1 781 17.48 4.E+04
QIAPGQTGNIADYNYK Beta B.1.351, Gamma P.1, Omicron B.1.1.529 781 22.23 3.E+04
YNLAPFFTFK Omicron B.1.1.529 195 30.66 2.E+04
TLVKQLSSK Omicron B.1.1.529 65 57.66 6.E+04
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Table 3.

Fragment ion coverage, b-/y-ions (top) and score generated by MetaMorpheus (bottom) as a function of loading amount for the proteotypic variant tryptic peptides. The LODs for peptide identification, as determined by a MetaMorpheus score of > 6.5, are underlined.

b- /y- ion series
TLVKQLSSK (omicron) VGGNYNYR (Delta) HTPINLVR (Standard Armengaud) YNLAPFFTFK (Omicron) TQLPSAYTNSFTR (Gamma) QIAPGQTGNIADYNYK (Beta)
65 amol 2/6,7 2/6,7 2/1,6 2,3/6 N/A N/A
195 amol 2/6,7 2/1–7 2/6,7 2/6,7 N/A N/A
390 amol 2,6/1–7 2/2–4,6,7 2/1,4,6,7 2/3,6 N/A N/A
781 amol 2,3,5/1–8 2,3/1,2–7 2/1,4,6,7 2,3/3,4,6,7 2/5,6,7 3/3
1562 amol 2/1–8 2–4/1–7 1,2/1,4–7 2,3/4,6–8 2/7,10,11 2,3/6,13
3750 amol 2,3,5–7/1–8 2–4/1–7 1,2/1,2,4–7 2,3/2–4,6–8 2,3/7,9–11 2,3/4,6,10,13
6250 amol 2–6/1–8 2–4/1–7 1,2/1–7 2–4/1–9 2–4,6/1–12 1–3/1–14
MetaMorpheus score
65 amol 3.122 3.410 3.291 3.252 N/A N/A
195 amol 3.181 8.638 3.432 3.389 N/A N/A
390 amol 9.246 6.558 5.248 3.360 N/A N/A
781 amol 11.302 8.633 5.387 6.444 4.232 N/A
1562 amol 9.310 10.636 7.261 6.345 4.645 4.362
3750 amol 13.331 10.650 8.236 8.397 6.571 7.410
6250 amol 13.345 10.616 9.356 12.414 16.503 17.345
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Figure 1.

Figure 1.

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Calibration curves of SARS-CoV-2 variants (QIAPGQTGNIADYNYK (beta), TQLPSAYTNSFTR (gamma), VGGNYNYR (delta), TLVKQLSSK (omicron), YNLAPFFTFK (omicron), HTPINLVR (standard), and with 0.2, 0.1, 0.05, 0.025, 0.0125, 0.00625 pmol loading amount constituted in 4% acetonitrile, 0.2% acetic acid and 0.005% HFBA.

The raw data was processed using MetaMorpheus45 0.0.320 (https://github.com/smith-chem-wisc/MetaMorpheus) for peptide identification with precursor mass tolerance of 5 ppm and product mass tolerance of 20 ppm with intensity threshold of 1%. The peak areas and calibration curve were plotted with Xcalibur 4.4 (Thermo Fisher Scientific). The HCD spectra are illustrated by Interactive Peptide Spectral Annotator (Coon laboratories, University of Wisconsin-Madison).

The MS1 LODs were estimated as the loading amount that provided the last detectable signal as the signal rapidly decreases near the LOD, presumably from surface adsorption. MS2 LODs were determined based on MetaMorpheus score of 6.5 or greater obtained with MetaMorpheus 0.0.320. Known PTMs, including glycosylation and disulfide bonds, were annotated using data from UniProt accessed on March 30th, 2022.

RESULTS AND DISCUSSION

In silico digestion of the sequences of the SARS-Cov-2 variants: alpha, beta, delta, gamma, and omicron reveal the potential proteotypic peptides that can be used for selective variant detection. As in any bottom-up proteomics experiment with highly homologous proteins, detection of a specific variant hinges on detection of the mutated sequence, which may be contained in only a single peptide. When comparing the sequences of the key variants of concern of SARS-CoV-2, alpha, beta, gamma, and delta, and omicron key variants of concern typically have had at least six unique proteotypic peptides for tryptic digestion with a minimum peptide length of seven residues as shown in Table S1.

Thus, there is a relatively small number of unique proteotypic peptides to distinguish each variant and variant detection is heavily dependent on the key properties of its proteotypic peptides, such as their ionization efficiency, fragmentation characteristics, and hydrophobicity. If a key variant specific proteotypic peptide is not detected, detection of the specific variant may be missed. Consequently, to gauge the potential effectiveness of bottom-up proteomics methods for differentiation of SARS-CoV-2 variants, eleven potential proteotypic peptides and four peptides from previous literature reports32, 43 were synthesized to investigate their electrospray signal intensities and HCD spectral quality.

The fifteen-model variant specific peptides produced a wide range of signal intensities when analyzed by RP-LC-MS that had LODs ranging from 65 amol to 0.2 pmol as shown in Table 2 and S2. Among the peptide standards previously reported in the literature, LQSLQTYVTQQLIR, HTPINLVR, DGIIWVATEGALNTPK, and IGMEVTPSGTWLTYTGAIK; HTPINLVR produces the greatest signal intensity in our system (Table S2), so HTPINLVR is chosen as a reference for comparison with the variant model peptides. Even though DGIIWVATEGALNTPK and IGMEVTPSGTWLTYTGAIK provided low intensity in our system and by Karina et al39, DGIIWVATEGALNTPK was used as a standard for nucleocapsid protein detection in many reports.39, 41, 46–49 Of the eleven variant model peptides: VGGNYNYR (delta), TQLPSAYTNSFTR (gamma), QIAPGQTGNIADYNYK (beta), YNLAPFFTFK (omicron), TLVKQLSSK (omicron) produce high intensities that are comparable to the intensity of HTPNLVR, which was previously used as a SARS-CoV-2 proteotypic peptide in the literature.32, 39 These six peptides are selected for more thorough analysis and their signal intensity is recorded for different loaded amounts, producing the calibration curve shown in Figure 1. High linearity is achieved with r-squared values of 0.9966, 0.9924, 0.9738, 0.9962, 0.9963, 0.9778 for VGGNYNYR, TLVKQLSSK, HTPINLVR, QIAPGQTGNIADYNYK, TQLPSAYTNSFTR, and YNLAPFFTFK, respectively. The LODs are 65, 65, 781, 781, 195, and 65 amol for VGGNYNYR, TLVKQLSSK, HTPINLVR, QIAPGQTGNIADYNYK, TQLPSAYTNSFTR, and YNLAPFFTFK. However, the cutoff MetaMorpheus score of 6.5 should be employed to detect peptide with high confidence. Applying the cutoff, The LODs are 390, 390, 1562, 3750, 3750, and 3750 amol for VGGNYNYR, TLVKQLSSK, HTPINLVR, QIAPGQTGNIADYNYK, TQLPSAYTNSFTR, and YNLAPFFTFK, illustrating on Table 3. The one alpha variant peptide tested, QLSSNFGAISSVLNDILAR, was excluded because LOD was larger than the nominal cutoff used, and its greater hydrophobicity hampered dissolution and decreased measured intensities with increasing autosampler dwell time.

Previous reports show that typical viral loads are largely detectable as viral loads in symptomatic patients have a range 104 − 108 SARS-CoV-2 virions/mL.50 We have focused on the spike protein for variant detection as the spike protein has the highest mutation rate. Armengaud’s group first reported using HTPINLVR as a highly sensitive proteotypic peptides for the spike protein with an LOD of 460 ng.33 Subsequently, Feldberg’s group detected HTPINLVR in nasopharyngeal swabs with RT-PCR validated SARS-CoV-2 infections using 20 min LC-MS/MS.24 The spike protein amount in clinical setting can be estimated as follows: individual virions contain ~228 spike protein per virion, which is the average of the estimated from each virion containing 25–127 spike protein trimers.51 Multiplying 230 by moderate virion/mL of 107 yields 2.3×108 spike protein molecules/mL or 3,800 amol/mL. All peptides reported in Table 2, including the HTPINLVR reference peptide, have LODs lower than estimated spike protein LOD of 3,800 amol/mL. Thus, TLVKQLSSK, which has a LOD by protein identification of ~390 amol, can be detected at a low viral load of 2 × 105 virions/mL for a 5 mL sample. Because ESI is concentration sensitive, the LODs could be improved by decreasing the column size and band length. Therefore, a key finding is variant specific proteotypic peptides for beta, delta, gamma, and omicron variants, have MS/MS protein identification LOD, that are similar to the previously reported HTPINLVR standard. Additionally, the LODs increased as a function of dwell time in the autosampler. Presumably, this decrease in detectability is caused by surface adsorption of the peptides. Such loss has been observed by others and indicates that the samples should be processed immediately.46

High fragment ion coverage in MS2 spectra is critical in obtaining reliable peptide identification. Ideally, all b- and y-ions are observed, but observing all the b- and y-ions is the exception rather than the norm. As a gauge of the quality of the fragment ion spectra, both the MetaMorpheus score and the number of b- and y- ions detected are reported in Table 3. The MetaMorpheus score is a composite of the number of fragment ions followed by the fraction of spectrum intensity apportioned to the peptide in question. Proteotypic peptide fragmentation spectra are shown in Figures 2 and S1-S11. TLVKQLSSK displays complete coverage of every theoretical fragment, while AGFNCYFPLR yields b2 – b4 and y1 – y5 ions. TLVKQLSSK yields b2 – b6 and y1 – y8 at 6250 amol, on the other hand the peptide yields b2 and y2, y6, y7 at LOD of 65 amol. Fragment ion coverage decreases along with loading amount as shown in Table 3. The decrease of fragment ion coverage with reduced sample loading is consistent with previous observations in single-cell proteomics.52

Figure 2.

Figure 2

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MS/MS spectra of QIAPGQTGNIADYNYK (beta) A, TQLPSAYTNSFTR (gamma) B, VGGNYNYR (delta) C, and TLVKQLSSK (omicron) D variants.

Post-translational modifications (PTMs) increase the complexity of the real clinical samples. The key PTM of the spike glycoprotein, as the name suggests, is glycosylation. Any PTM will alter the spectra of the model peptide compared to the same sequence from a biological source. Due to this caveat, the known modifications are also reported in Table 1, and it is found that of the model tryptic peptides used in this study, only two: CASYQTQTNSR and YQTQTKSHR are both glycosylated at residues 676 and 678. The most efficient strategy is to avoid peptides with PTMs if possible because structural determination of glycosylations can be difficult. Although deglycosylation can be used, if other proteotypic peptides are present they should be used rather than increasing the assay expense and complexity with deglycosylation. If the modified peptide cannot be avoided, a decrease in the signal intensity may be expected. By comparing the signal intensity of synthetic peptides and their glycopeptide analogs, the glycopeptide intensity is about 50–61% relative to the unglycosylated peptides in nanoLC-MS.53 Thus glycopeptide LODs increase approximately by a factor of two because the LOD is linearly correlated to signal intensity.

Real clinical samples, such as saliva and nasal pharyngeal swabs, present a highly complex background matrix that increases the background and worsens the LODs. Many factors contribute to the observed minimum detectable viral load from the initial sample processing through the final data analysis. The signal intensity directly corresponds to ionized peptide molecule, however, the signal intensity varies in each peptide. This intensity variation is caused by the difference in ionization efficiencies of each peptide and ion suppression, which comes from the competition for available charge and the interaction among target peptides and background, such as other peptides, polymer residues, ion pairing reagents, detergent, etc.54 The highly complex background matrix of clinical increases the number of ions that co-elute with the SARS-CoV-2 proteotypic peptides. If many ions coelute, the key proteotypic peptides can be missed as the SAR is limited and the ions are analyzed according to their intensities. The data acquisition mode of the MS is critical as DIA and DDA each have their advantages and disadvantages. Typically, the LODs are slightly higher for DDA, than DIA and consequently, the initial focus has been on using DIA methods. However, DDA is more flexible and allows the detection of greater numbers of peptides. The higher detection limits for DDA are caused by the limited SAR and the inability to fragment all the co-eluting ions. If the number of ions eluting in a given time period exceeds the SAR of the MS, MS/MS spectra of some of the lower intensity ions will be missed. Additionally, in DDA many of the fragment ions are often not collected at the apex of the chromatographic peak, which decreases the spectral quality. PRM is constrained by narrow m/z windows that makes it nearly impossible to monitor co-eluting peptides with a significant difference in m/z. For example, Hober et al. developed a SARS-CoV-2 detection protocol that detected four proteotypic peptides in a 4.5 min gradient, each with a dedicated retention time window.46 The retention times of the fifteen peptides are reported in Table 1. The efficiency and selectivity of the sample preparation protocols can also have a large impact on the minimal detectable viral load. Lastly, given that viral peptides available in the clinical setting are significantly higher than LOD, the LOD improvement achieved with DIA is not essential.

The application of this approach to the analysis of real samples does not require the synthesis and evaluation of the signal intensity of model peptides. While the model peptides provide a baseline for the achievable LODs and can enhance quantitation, they are not required for DDA based methods. We envision entering all the proteotypic peptides for new variants of concern into the database for searching. This strategy is very simple and comes at only the small cost of increasing database size. De novo sequencing, which does not need a database for sequence determination, provides a more universal approach but is typically less sensitive.55Thus, de novo sequencing could also be paired with data base searching to look for new mutations.

CONCLUSIONS

In summary, the four SARS-CoV-2 variants evaluated in this work have unique peptides that produce sufficient signal intensity to be detected at clinically relevant viral loads using RP-LC-MS/MS. While variant detection requires the detection of at least one of the few peptides containing the variant mutation, peptides unique to the beta, delta, gamma, and omicron variants produce sufficiently high signal intensities when compared with reported proteotypic peptides for SARS-CoV-2 detection. Only a single peptide was investigated for the alpha variant and the peptide was not selected for the mixture because the LOD was higher than the LOD of non-variant peptide HTPINLVR of 0.0125 pmol in the initial run. The HCD spectra of the model tryptic proteotypic peptides are easily identified by database searching. The reference spectra are available via ProteomeXchange with identifier PXD033777 and Universal Spectrum Identifiers for the peptides in Table S3. Future work will evaluate the detection of variant specific peptides within the background of clinical saliva and nasopharyngeal swab samples.

Supplementary Material

Supplementary material

Table S. A list of unique tryptic peptides for each variant.

Table S2. Limits of detection and peak areas of the Table 1 peptides.

Table S3. Universal Spectrum Identifiers (USIs) of the peptides.

Figure S1. MS/MS spectrum of the peptide YNLAPFFTKF.

Figure S2. MS/MS spectrum of the peptide AGFNCYFPLR.

Figure S3. MS/MS spectrum of the peptide YGVGHQPYR.

Figure S4. MS/MS spectrum of the peptide YQTQTKSHR.

Figure S5. MS/MS spectrum of the peptide IGMEVTPSGTWLTYTGAIK.

Figure S6. MS/MS spectrum of the peptide DGIIWVATEGALNTPK.

Figure S7. MS/MS spectrum of the peptide LQSLQTYVTQQLIR.

Figure S8. MS/MS spectrum of the peptide HTPINLVR.

Figure S9. MS/MS spectrum of the peptide QLSSNFGAISSVLNDILAR.

Figure S10. MS/MS spectrum of the peptide CASYQTQTNSR.

Acknowledgment

The authors acknowledge funding from the NIH-NIBIB grant R01EB025268-04S1. We would like to thank Peter Timperman for help with the cover art.

Footnotes

Conflict of Interest

There are no conflicts of interest.

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Associated Data

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

Supplementary Materials

Supplementary material

Table S. A list of unique tryptic peptides for each variant.

Table S2. Limits of detection and peak areas of the Table 1 peptides.

Table S3. Universal Spectrum Identifiers (USIs) of the peptides.

Figure S1. MS/MS spectrum of the peptide YNLAPFFTKF.

Figure S2. MS/MS spectrum of the peptide AGFNCYFPLR.

Figure S3. MS/MS spectrum of the peptide YGVGHQPYR.

Figure S4. MS/MS spectrum of the peptide YQTQTKSHR.

Figure S5. MS/MS spectrum of the peptide IGMEVTPSGTWLTYTGAIK.

Figure S6. MS/MS spectrum of the peptide DGIIWVATEGALNTPK.

Figure S7. MS/MS spectrum of the peptide LQSLQTYVTQQLIR.

Figure S8. MS/MS spectrum of the peptide HTPINLVR.

Figure S9. MS/MS spectrum of the peptide QLSSNFGAISSVLNDILAR.

Figure S10. MS/MS spectrum of the peptide CASYQTQTNSR.

NIHMS1906831-supplement-Supplementary_material.pdf (609KB, pdf)

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