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Intelligent system based comparative analysis study of SARS-CoV-2 spike protein and antigenic proteins in different types of vaccines

Beni-Suef University Journal of Basic and Applied Sciences volume 11, Article number: 34 (2022) Cite this article

Abstract

Background

Coronaviruses, members of the Coronavirinae subfamily in the Coronaviridae family, are enveloped and positive-stranded RNA viruses that infect animals and humans, causing intestinal and respiratory infections. Coronavirus disease 2019 (COVID-19) is caused by a novel coronavirus, named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). This disease appeared, for the first time (December 2019), in China and has spread quickly worldwide causing a large number of deaths. Considering the global threat, the World Health Organization (WHO) has declared, in March 2020, COVID-19 as a pandemic. Many studies suggest the great effect of the existing vaccines to protect against symptomatic cases of death by the COVID-19 virus. This paper, proposes to compare the main antigenic proteins sequences of the existing vaccines with Spike (S) protein of the SARS-CoV-2 genome. Our choice of S protein is justified by the major role that plays it in the receptor recognition and membrane fusion process based on an intelligent system. Herein, we focus on finding a correlation between S protein and compulsory vaccines in the countries that have a less death number by COVID-19 virus. In this work, we have used a combination of coding methods, signal processing, and bioinformatic techniques with the goal to localize the similar patterns between the S gene of the SARS-Cov-2 genome and 14 investigated vaccines.

Results

A total of 8 similar sequences which have a size more than 6 amino acids were identified. Further, these comparisons propose that these segments can be implicated in the immune response against COVID-19, which may explain the wide variation by country in the severity of this viral threat.

Conclusions

Our in silico study suggests a possible protective effect of Poliovirus, HIB, Hepatitis B, PCV10, Measles, Mumps, and Rubella (MMR) vaccines against COVID-19.

Background

Since December 2019, COVID-19, caused by the novel coronavirus SARS-CoV-2, has spread to more than 223 countries to date causing huge health and economic crisis [1,2,3]. The genome of SARS-CoV-2, like other families of Coronaviruses (CoVs), is an enveloped positive-sense single-stranded RNA virus. It belongs to the Coronaviridae family, the Orthocoronavirinae subfamily and the Betacoronavirus genus [1,2,3]. Coronaviruses (CoVs) can mainly infect mammals by Alpha (α) or Beta (β) coronaviruses, and predominantly can infect birds by Gamma (γ) or Delta (δ) Coronaviruses [4, 5]. The SARS-CoV-2 viral genome of 29,903 nucleotides, approximately, contains 5’and 3’ untranslated regions and 11 Open Reading Frames (ORFs) encoding 11 proteins including the S protein [6]. Probably, the modes of SARS-Cov-2 transmission among humans are via three primary pathways: inhaling respiratory droplets directly from infected persons, or contact with infected environmental surfaces know as “fomites” and touching your mucous membranes with soiled hands, or inhaling infected airborne particles. Recent researches indicate a high correlation between the SARS-COV-2 genome and the two genomes of the bat-CoV RaTG13 and the pangolin-CoV MP789 followed by two other genomes: CoVZC45 and CoVZXC21 [7, 8].

The outbreak of SARS-CoV-2 caused 2,797,435 deaths with a total of 127,847,262 confirmed cases (December 20, 2019 to March 29, 2021) that have variable damages from one country to another. In the majority of cases, huge damages were reported, such as in the USA, Brazil, and India causing respectively 562,526, 312,299 and 161,881, deaths as well as 30,962,803, 12,534,688, and 12,039,644 confirmed cases up to 29 March 2020 [9] Nevertheless, in other regions, such as, Laos, Vietnam, and Finland damages seem to be limited and the number of deaths, respectively, did not exceed 0, 35, and 822 [9]. However, for other regions, like, Nicaragua, Sierra Leone, Vietnam, and Madagascar, cases and damages seem to be limited and the number of deaths, respectively, did not exceed three hundred cases. These temporal variances in a number of case fatality rates can be caused by different factors: political and economic strategies, cultural behavior, age, and also health infrastructure, [10, 11]. Furthermore, the population’s immunological background is probably due to the vaccination strategies used in these countries [11,12,13,14,15]. From another point of view, different vaccines such as BCG (Bacillus Calmette-Guérin), OPV (Oral Poliovirus Vaccine), and MMR (Measles, Mumps, and Rubella vaccines) demonstrated an immune response to fight various pathogens [12, 14, 16, 17].

On the other hand, these different variations may be attributed to the adoption of a universal and long-standing BCG as again found to be very significantly protective for whom vaccination records were available [11, 18, 19]. In addition, the MMR vaccine protective potential was investigated based on S protein bioinformatic analysis [20]. Based on this computational biology analysis, the MMR vaccine was investigated as being potentially protective for adults and provides advantageous protection for children against COVID-19 as well. However, experimental analysis is required. Furthermore, pneumococcal vaccination PCV13 was again found to be very efficient in a study of 137,037 individuals who received SARS-CoV-2 PCR tests [21]. A recent study proves great similarities between the SARS-CoV-2 genome and pneumococcal vaccines PspA and PspC [22]. Indeed, other researchers found that polio, Hemophilus influenzae type-B (HIB), varicella, geriatric flu, MMR, PCV13, and hepatitis A / B (HepA-HepB) vaccines administered in the past 1, 2, and 5 years are associated with decreased SARS-CoV-2 infection rates [22, 23].

In this work, we propose in silico study to investigate the potential protective effect of 14 investigated vaccines (Bordetella Pertussis, Tetanus, Haemophilus influenzae type B (Hib), Corynebacterium Diphtheriae, Streptococcus pneumoniae, Hepatitis A, and Hepatitis B) against COVID-19. We aim to localize similar amino acid (aa) regions in the S protein of the SARS-CoV-2 genome and the main antigenic proteins in other vaccines which may lead to the production of cross-reactive antibodies against the target viruses as well as SARS-CoV-2. To achieve this goal, we used a combination of bioinformatics, and signal processing tools to identify the common amino-acid (aa) sequences of the main antigenic protein of SARS-CoV-2 and investigated vaccines.

Methods

Recent research [7] has been suggested the SARS-Cov-2 genome shares 96% genetic similarity with a RATG13 coronavirus genome and 86% genetic similarity with the pangolin coronavirus genome. Subfigure (a) of Fig. 1 presents the distribution of nucleotide modifications by percentage along the SARS-COV-2 region compared to the RATG13 and the MP789 coronavirus genomes. Spike protein presents high recombination between RATG13 and the MP789 coronavirus genomes as shown in subfigure (a) of Fig. 1. Specifically in the position 1251 to 1600 base pairs, the total nucleotide mutation is equal to 14.28% (50 nucleotides) between the two coronavirus genomes (the pangolin and the SARS-CoV-2). These mutations are less than the mutation located between bat coronavirus and SARS -CoV-2 S gene (35% and 125 modified nucleotides). This research [7] suggests that the SARS-CoV-2 genome is the result of recombination events between two coronavirus genomes: The bat-CoV RATG13 and the pangolin-CoV MP789.

Fig. 1
figure 1

Percentage of nucleotide modifications of Sars-Cov-2 genome comparing to RATG13 and MP789 genomes [7]: a Percentage of nucleotide modifications of SARS-CoV-2 genome comparing to RATG13 and Pangolin genomes; b percentage of nucleotide modifications of S gene of SARS-CoV-2 genome comparing to the S genes of RATG13 and Pangolin genomes [7]

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Figure 2 presents the Flowchart diagram of our adopted localization methodology to find similar amino acid sequences between SARS-CoV-2 genome and our investigated sequences. Here we have used the recombination between bioinformatics techniques and signal processing tools.

Fig. 2
figure 2

Flowchart diagram of methodology to localize the similarities between two amino acid sequences

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Vaccines and Sequences investigated (databak accession numbers)

Our study is focused on the vaccines included in one of the countries presenting a very low number of confirmed COVID-19 cases. Our investigated vaccines include main old and more recent vaccines (a number equal to 14) with 34 protein sequences: BCG, Poliovirus, Measles, Mumps, Corynebacterium diphtheria, Tetanus, and Bordetella pertussis vaccines and more recent vaccines against hepatitis B and A viruses, Rubella virus, Hemophilus influenzae type B (Hib) and Streptococcus pneumoniae (PCV10, pspC, protein PspC, Protein A) [24]. The amino-acid sequences of these antigenic proteins (n = 34) constituting those vaccines as well as the amino acid sequence of Spike protein of the Wuhan Sars-Cov-2 genome strain were obtained from the database NCBI Genbank (https://www.ncbi.nlm.nih.gov). Accession numbers are presented in Table 1.

Table 1 Investigated vaccines and their corresponding antigenic proteins obtained from NCBI Genbank (https://www.ncbi.nlm.nih.gov)
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Amino acid sequence alignment and hot spot analysis

We aim to detect similar amino-acid (aa) sequences between protein sequences. The presented results were obtained using Blastp program. This program can detect identical amino-acids and, or similar amino-acids of two genomic sequences. These results were presented using BioEdit software (version 7.2.5) (http://www.mybiosoftware.com/bioedit-7-0-9-biological-sequence-alignment-editor.html). Figure 3 presents an example of our obtained results after comparing the S protein of the Sars-Cov-2 genome and two sequences of the tetanus vaccine. These obtained results present different similar patterns with their position in each genome.

Fig. 3
figure 3

Example of Bioedit ((http://www.mybiosoftware.com/bioedit-7-0-9-biological-sequence-alignment-editor.html) version 7.2.5) results to identify similar pattern located between S protein of SARS-CoV-2 genome and two protein sequences of vaccines corresponding of two viruses (Poliovirus and Hemophilus influenzae serotype B (Hib))

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Numerical mapping of amino acid sequences

The conversion of the genomic sequences into numerical ones using signal processing tools is an important step to characterize the unknown region [25,26,27,28]. It allows rapid image observation of similar patterns before assessing more precise analysis. By applying the Electron–Ion Interaction Potential (EIIP) coding technique, we can obtain for each protein sequence a signal. This type of coding technique (EIIP) has been used to transform the amino acid (aa) sequence into numerical representation [25]. For that, we converted our amino-acid sequences into numerical ones using the following Table 2, where each amino acid was represented by its corresponding value of the EIIP coding technique.

Table 2 EIIP coding technique for transformation of the amino acid sequence into a signal
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Figure 4 shows the 1-D signal representation of protein sequence corresponding to 150 amino acids of S-protein of Sars-Cov-2 genome. Each signal value corresponds to an amino acid position in the protein sequence.

Fig. 4
figure 4

EIIP Numerical representation (generated signal) of 150 amino acids of Sars-Cov-2 Spike protein

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After transforming the amino-acid sequence into a signal, we aimed to see it in a 2-D representation to focus more on the information that can contain. The Continuous Wavelet Transform (CWT) (along 64 scales with w0 ~ 5.5) was applied to protein signal to obtain a protein image (2-D scalogram) [27, 28].

$$w_{0} = 2*\pi *f_{0}$$

\(w_{0}\) corresponds to the oscillations number of wavelet transform, and the parameter f0 is the central frequency of the basic wavelet. These images (wavelet scalograms) are the best method to detect the homologous regions between two amino-acid sequences [27, 28].

Results

Numerical mapping

To transform a protein sequence into numerical ones, we need to apply a coding technique to have a signal and an analysis technique to have an image.

In this work, and after applying different methods, we have chosen to use the EIIP as a coding technique and the wavelet analysis as an analysis technique. After applying the EIIP coding technique and CWT of an amino acid sequence, we obtained a scalogram image (modulo of the matrix contains CWT coefficients). The amino acid scalograms (2D) are the best way to see the similarities between two sequences if it exists. As a result, we obtained two similar patterns (scalograms) that confirm these similarities. Figure 5 presents images corresponding to similar patterns between the region of S protein of Wuhan SARS-CoV-2 genome and some regions of our investigated amino-acid sequences.

Fig. 5
figure 5

Wavelet representation (Scalograms) of similar regions (patterns) of amino acid region of SARS-COV-2 S gene (QJT73034.1) compared with the acid amino vaccines; the first column shows the S gene of the SARS-cov-2 genome and the second shows the vaccine sequence

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Alignment of amino acid sequences

As a result, similar segments (4–8 aa) between the S protein of the SARS-Cov-2 genome and our investigated vaccine antigenic proteins were identified (Fig. 6 and Table 3). Only antigenic protein included in Hepatitis B, Hib, Poliovirus, PCV10 vaccines showed 6 to 8 similar consecutive amino-acids. The corresponding motifs are indicated in Table 3. PCV10 antigenic proteins shared 2 similar motifs ( ) of 8 and 6 amino acids.

Fig. 6
figure 6

Highly similar sequences were identified in our investigated vaccines and SARS-CoV-2 proteins using BioEdit software (http://www.mybiosoftware.com/bioedit-7-0-9-biological-sequence-alignment-editor.html)

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Table 3 Results of total similar patterns number were identified in our investigated vaccines and SARS-CoV-2 proteins
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Figure 6 contains 23 similar patterns corresponding to more of 4 similar consecutive amino-acids of the S protein and our investigated vaccines. A total of 11 similar patterns have a size equal or more than 6 amino acids were identified.

Table 3 presents the total repetition number of each similar pattern that was identified between vaccine and S protein of Sars-Cov-2 genome.

To more see if these patterns are present in the other genes of the SARS-Cov-2 genome, we were also able to map and compare all other SARS-CoV-2 genes with our investigated vaccines. We confirm that these presented similar patterns ( , , , and ) between the Sars-Cov-2 S gene and vaccines are not presented in the other genes of the Sars-Cov-2 genome. Moreover, we detect a new similar pattern ‘ ’ between the BCG vaccine and Sars-Cov-2N gene (Fig. 7).

Fig. 7
figure 7

Scalograms (amino acids Wavelet images) of different amino acids region of different genes of SARS-COV-2 genome compared with the acid amino vaccines

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Discussion

Different researchers prove that the Sars-Cov-2 genome is the result of the recombination between two beta-coronavirus genomes: Pangolin and Bat coronavirus [7, 8, 29, 30]. Various in silico studies search for the effect of the vaccination, especially with pneumococcal vaccines, to protect against symptomatic cases of SARS-CoV-2 infection and death [31,32,33,34,35,36]. Our main objective was to find similar amino-acid sequences in the S protein of the Sars-Cov-2 genome and the investigated proteins sequences mentioned above using the recombination between bioinformatics and signal processing tools. This study using an intelligent system suggests the possible influence of Hepatitis B, Hib, Poliovirus, Tetanus, and PCV10 vaccines in protecting against COVID-19. In addition, this work could explain the variation in the number of deaths among countries and the possibility that these vaccines contain antigens that might be cross-reactive with SARS-CoV-2. For this, we consider that some of these investigated vaccines as real supportive protective measures in the fight against the COVID-19 pandemic. A recent review [37] presents a comparative table that contains multiple vaccine platforms and has been explored to develop the COVID-19 vaccine. Each vaccine platform has unique advantages and disadvantages. Most of these vaccines use either the Spike protein or its receptor-binding domain (RBD) as the antigen.

For this, we studied the effect of our investigated vaccines and see if it can be implicated to boost our immune system against COVID-19. We investigate the possibility of the protection against COVID-19 through inducing a cross-reactive antibody by different existing vaccines. To achieve this goal, we used a combination of bioinformatic and signal processing tools to compare the amino-acid sequences of the main S protein of SARS-CoV-2 genome and our investigated vaccines. A numerical mapping showed two similar patterns corresponding to an exposed site have a size of 7 and 8 amino-acids: ‘ ’ detected in HBsAg-of Hepatitis B vaccine and ‘ ’ detected in PCV10 vaccine. Two other patterns (‘ ’ and ‘ ’) in two investigated vaccines (Polio, PCV10,) are identified in the S gene of the SARS-CoV-2 genome. Given the novel aspects of the importance of Hepatitis B, Streptococcus pneumoniae, and Polio immunities as potential protective factors in the COVID-19 pandemic, measures to organize mass immunization against these viruses should be strengthened. Vaccination with attenuated viruses may explain partially the low symptomatic infection rate among children as there are other factors yet to be considered.

Conclusions

The main of this work is tracking the effect of existing vaccines against COVID-19 threats using suitable computational techniques. As a conclusion, our obtained results indicate that some antigenic proteins in these investigated vaccines may protect against severe COVID-19. We suggest that these types of vaccines can reduce the risk of COVID-19 mortality, but we note that this silico study must be confirmed using in vitro analysis.

Availability of data and materials

All data generated or analyzed as part of this study are included in this published article.

Abbreviations

CoV:

Coronaviruse

CoVs:

Coronaviruses

COVID-19:

Coronavirus disease 2019

SARS-CoV-2:

Severe acute respiratory syndrome coronavirus 2

S:

Spike

ORFs:

Open reading frames

MMR:

Measles, mumps, and rubella

BCG:

Bacillus Calmette-Guérin

OPV:

Oral poliovirus vaccine

HIB:

Hemophilus influenzae type-B

CWT:

Continuous wavelet transform

EIIP:

Electron ion interaction potential

PCV10:

Streptococcus pneumonia

NCBI:

National Center for Biotechnology Information

WHO:

World Health Organization

BLAST:

Basic local alignment search tool

Blastp:

Protein blast

References

  1. Zhou P, Yang XL, Wang XG, Hu B, Zhang L, Zhang W et al (2020) A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579(7798):270–273

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Coronaviridae Study Group of the International Committee on Taxonomy of V (2020) The species severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nat Microbiol 5(4):536–44

  3. Li G, De Clercq E (2020) Therapeutic options for the 2019 novel coronavirus (2019-nCoV). Nat Rev Drug Discovery 19(3):149–150

    Article  Google Scholar 

  4. Cui J, Li F, Shi ZL (2019) Origin and evolution of pathogenic coronaviruses. Nat Rev Microbiol 17(3):181–192

    CAS  Article  Google Scholar 

  5. Masters PS, Perlman S (2013) Coronaviridae. Fields Virol 1:825–858

    Google Scholar 

  6. Wu F, Zhao S, Yu B, Chen YM, Wang W, Song ZG et al (2020) A new coronavirus associated with human respiratory disease in China. Nature 579(7798):265–269

    CAS  Article  Google Scholar 

  7. Touati R, Haddad-Boubaker S, Ferchichi I, Messaoudi I, Ouesleti AE, Triki H et al (2020) Comparative genomic signature representations of the emerging COVID-19 coronavirus and other coronaviruses: High identity and possible recombination between Bat and Pangolin coronaviruses. Genomics. 112:4189–4202

    CAS  Article  Google Scholar 

  8. Hedman HD, Krawczyk E, Helmy YA, Zhang L, Varga C (2021) Host diversity and potential transmission pathways of SARS-CoV-2 at the human-animal interface. Pathogens 10(2):180

    CAS  Article  Google Scholar 

  9. WHO. Coronavirus disease (COVID-19) weekly epidemiological update 2020 [22 October 2020]. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports.

  10. Riggioni C, Comberiati P, Giovannini M, Agache I, Akdis M, Alves-Correia M et al (2020) A compendium answering 150 questions on COVID-19 and SARS-CoV-2. Allergy 75(10):2503–2541

    CAS  Article  Google Scholar 

  11. Curtis N, Sparrow A, Ghebreyesus TA, Netea MG (2020) Considering BCG vaccination to reduce the impact of COVID-19. Lancet. https://doi.org/10.1016/S0140-6736(20)31025-4

    Article  PubMed  PubMed Central  Google Scholar 

  12. Hamiel U, Kozer E, Youngster I (2020) SARS-CoV-2 rates in BCG-vaccinated and unvaccinated young adults. JAMA. https://doi.org/10.1001/jama.2020.8189

    Article  PubMed  PubMed Central  Google Scholar 

  13. Escobar LE, Molina-Cruz A, Barillas-Mury C (2020) BCG vaccine protection from severe coronavirus disease 2019 (COVID-19). Proc Natl Acad Sci USA. https://doi.org/10.1073/pnas.2008410117

    Article  PubMed  PubMed Central  Google Scholar 

  14. Haddad-Boubaker S, Othman H, Touati R, Ayouni K, Lakhal M, Mustapha IB et al (2020) In silico comparative study of SARS-CoV-2 proteins and antigenic proteins in BCG, OPV, MMR and other vaccines: evidence of possible putative protective effect

  15. Faust L, Huddart S, MacLean E, Svadzian A. Universal BCG vaccination and protection against COVID-19: critique of an ecological study. April 1, 2020. https://naturemicrobiologycommunity.nature.com/users/36050-emily-maclean/posts/64892-universal-bcgvaccination-and-protection-against-covid-19-critique-of-an-ecological-study (Accessed April 5, 2020).

  16. Mysore V, Cullere X, Settles ML, Ji X, Kattan MW, Desjardins M et al (2021) Protective heterologous T cell immunity in COVID-19 induced by the trivalent MMR and Tdap vaccine antigens. Med 2(9):1050–1071

    Article  Google Scholar 

  17. Lassi ZS, Naseem R, Salam RA, Siddiqui F, Das JK (2021) The impact of the COVID-19 pandemic on immunization campaigns and programs: a systematic review. Int J Environ Res Public Health 18(3):988

    CAS  Article  Google Scholar 

  18. Li WX (2020) Worldwide inverse correlation between Bacille Calmette-Guérin immunization and COVID-19 morbidity and mortality. Research Square

  19. O’Neill LA, Netea MG (2020) BCG-induced trained immunity: can it offer protection against COVID-19? Nat Rev Immunol 20(6):335–337

    Article  Google Scholar 

  20. Sidiq KR, Sabir DK, Ali SM, Kodzius R (2020) Does early childhood vaccination protect against COVID-19? Front Mol Biosci 7:120

    CAS  Article  Google Scholar 

  21. Pawlowski C, Puranik A, Bandi H, Venkatakrishnan AJ, Agarwal V, Kennedy R, O’Horo JC, Gores GJ, Williams AW, Halamka J et al (2020) Exploratory analysis of immunization records highlights decreased SARS-CoV-2 rates in individuals with recent non-COVID-19 vaccinations. medRxiv

  22. Root-Bernstein R (2020) Possible cross-reactivity between SARS-CoV-2 proteins, CRM197 and proteins in pneumococcal vaccines may protect against symptomatic SARS-CoV-2 disease and death. Vaccines 8(4):559

    CAS  Article  Google Scholar 

  23. Pawlowski C, Puranik A, Bandi H, Venkatakrishnan AJ, Agarwal V, Kennedy R et al (2021) Exploratory analysis of immunization records highlights decreased SARS-CoV-2 rates in individuals with recent non-COVID-19 vaccinations. Sci. Rep. 11(1):1–20

    Article  Google Scholar 

  24. Chahed MK, Bellali H, Alaya NB, Ali M, Mahmoudi B (2013) Auditing the quality of immunization data in Tunisia. Asian Pac J Tropic Disease 3(1):65–70

    Article  Google Scholar 

  25. Rao KD, Swamy MNS (2008) Analysis of genomics and proteomics using DSP techniques. IEEE Trans Circuits Syst I Regul Pap 55(1):370–378

    Article  Google Scholar 

  26. Touati R, Messaoudi I, Oueslati AE, Lachiri Z (2019) Distinguishing between intra-genomic helitron families using time-frequency features and random forest approaches. Biomed Signal Process Control 54:101579

    Article  Google Scholar 

  27. Touati R, Tajouri A, Mesaoudi I, Oueslati AE, Lachiri Z, Kharrat M (2021) New methodology for repetitive sequences identification in human X and Y chromosomes. Biomed Signal Process Control 64:102207

    Article  Google Scholar 

  28. Touati R, Messaoudi I, Oueslati AE, Lachiri Z (2019) A combined support vector machine-FCGS classification based on the wavelet transform for Helitrons recognition in C. elegans. Multimed Tools Appl 78(10):13047–13066

    Article  Google Scholar 

  29. Vasilarou M, Alachiotis N, Garefalaki J, Beloukas A, Pavlidis P (2021) Population genomics insights into the first wave of COVID-19. Life 11(2):129

    CAS  Article  Google Scholar 

  30. Baruah C, Devi P, Sharma DK (2020) Sequence analysis and structure prediction of SARS-CoV-2 accessory proteins 9b and ORF14: Evolutionary analysis indicates close relatedness to bat coronavirus. BioMed Res Int 2020:1–13

    Article  Google Scholar 

  31. Kostinov MP, Zhuravlev PI, Gladkova LS, Mashilov KV, Polishchuk VB, Shmitko AD, Zorina VN, Blagovidov DA, Pahomov DV, Vlasenko AE, Ryzhov AA, Khromova EA (2021) Comparative analysis of the measles antibody levels in healthy medical personnel of maternity ward and women in labor. Front Immunol 12:680506. https://doi.org/10.3389/fimmu.2021.680506

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. Sajuni S (2020) Vaksinasi Measles, Mumps, dan Rubella (MMR) Sebagai Prophylaxis Terhadap COVID-19. KELUWIH : Jurnal Kesehatan dan Kedokteran 1(2):25–28

    Google Scholar 

  33. Fidel PL Jr, Noverr MC (2020) Could an unrelated live attenuated vaccine serve as a preventive measure to dampen septic inflammation associated with COVID-19 infection. MBio 11(3):e00907-e920

    Article  Google Scholar 

  34. Franklin R, Young A, Neumann B, Fernandez R, Joannides A, Reyahi A, Modis Y (2020) Homologous protein domains in SARS-CoV-2 and measles, mumps and rubella viruses: preliminary evidence that MMR vaccine might provide protection against COVID-19. MedRxiv

  35. Karadeniz A, Alaşehir EA (2020) Seroepidemiology of hepatitis viruses, measles, mumps, rubella and varicella among healthcare workers and students: should we screen before vaccination. J Infect Public Health 13(4):480–484

    Article  Google Scholar 

  36. Itatani T, Horiike R, Nakai H, Taira K, Honda C, Shirai F, Konishi K (2021) Model comparisons of the cost effectiveness of rubella vaccination method in Japanese adults. Vaccines 9(3):233

    Article  Google Scholar 

  37. Li Y, Tenchov R, Smoot J, Liu C, Watkins S, Zhou Q (2021) A comprehensive review of the global efforts on COVID-19 vaccine development. ACS Cent Sci 7(4):512–533

    Article  Google Scholar 

Download references

Acknowledgements

The authors are thankful to the co-authors for their support to complete the manuscript. We are also thankful to the anonymous reviewers for their perceptive comments on the manuscript.

Author's information

Dr. Rabeb Touati obtained a Ph.D. in electrical engineering from the National Engineering School of Tunisia (ENIT). Currently, she has a Postdoctoral position at the Laboratory of Human Genetics (LR99ES10) at the Faculty of Medicine of Tunis (FMT). Her research interest includes genomic signal processing, bioinformatics, pattern recognition and machine learning.

Dr. Ahmed A. Elngar is Associate Professor of Computer Science at the Faculty of Computers and Artificial Intelligence, Beni-Suef University, Egypt. Dr. AE he is also, Associate Professor of Computer Science at College of Computer Information Technology, American University in the Emirates, United Arab Emirates. Dr. AE is the Founder and Head of Scientific Innovation Research Group (SIRG). Dr. AE is a Director of the Technological and Informatics Studies Center (TISC), Faculty of Computers and Artificial Intelligence, Beni-Suef University. Dr. AE has more than 55 scientific research papers published in prestigious international journals and over 25 books covering such diverse topics as data mining, intelligent systems, social networks and smart environment. Dr. AE is a collaborative researcher He is a member in Egyptian Mathematical Society (EMS) and International Rough Set Society (IRSS). His other research areas include Internet of Things (IoT), Network Security, Intrusion Detection, Machine Learning, Data Mining, Artificial Intelligence. Big Data, Authentication, Cryptology, Healthcare Systems, Automation Systems. He is an Editor and Reviewer of many international journal around the world. Dr. AE won several awards including the Young Researcher in Computer Science Engineering”, from Global Outreach Education Summit and Awards 2019, on 31 January 2019 (Thursday) at Delhi, India. Also, he awards Best Young Researcher Award (Male) (Below 40 years)”, Global Education and Corporate Leadership Awards (GECL-2018), Plot No-8, Shivaji Park, Alwar 301001, Rajasthan.

Funding

This study was funded by the Tunisian Ministry of Higher Education and Scientific Research (Research laboratory: LR99ES10).

Author information

Affiliations

  1. LR99ES10 Human Genetics Laboratory, Faculty of Medicine of Tunis (FMT), University of Tunis El Manar, Tunis, Tunisia

    Rabeb Touati

  2. BIOPOLE Society, 63 Av. Hbib Bourguiba, Tunis, Tunisia

    Rabeb Touati

  3. Faculty of Computers and Artificial Intelligence, Beni-Suef University, Slah Salem Str., 62511, Beni-Suef City, Egypt

    Ahmed A. Elngar

  4. American University in the Emirates, 503000, Dubai Intl. Academic City, Dubai, United Arab Emirates

    Ahmed A. Elngar

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  1. Rabeb Touati
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Contributions

Dr. RT designed the study, Dr. RT and Dr. AE wrote the main text and contributed to carry out analysis and to prepare figures, and Dr. RT validated the study. Dr. RT and Dr. AE read and approved the final manuscript.

Corresponding author

Correspondence to Ahmed A. Elngar.

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This study did not include Human participants or Patient data. Hence no ethical approval and consent to participate is required.

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Not applicable. This study did not include patients.

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The authors declare that they have no competing interests.

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Touati, R., Elngar, A.A. Intelligent system based comparative analysis study of SARS-CoV-2 spike protein and antigenic proteins in different types of vaccines. Beni-Suef Univ J Basic Appl Sci 11, 34 (2022). https://doi.org/10.1186/s43088-022-00216-0

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  • DOI: https://doi.org/10.1186/s43088-022-00216-0

Keywords

  • Coronavirus
  • Vaccines
  • Poliovirus
  • HIB
  • Hepatitis B
  • And PCV10
  • COVID-19
  • Genome signature
  • Genomic coding techniques