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Prediction of Epitope based Peptides for Vaccine Development from Complete Proteome of Novel Corona Virus (SARS-COV-2) Using Immunoinformatics.

ABSTRACT: COVID-19 is an infectious disease caused by a newly discovered corona virus SARS-COV-2. It is the most dangerous epidemic existing currently all over the world. To date, there is no licensed vaccine and not any particular efficient therapeutic agent available to prevent or cure the disease. So development of an effective vaccine is the urgent need of the time. The proposed study aims to identify potential vaccine candidates by screening the complete proteome of SARS-COV-2 using the computational approach. From 14 protein entries in UniProtKB, 4 proteins were screened for epitope prediction based on consensus antigenicity predictions and various physico-chemical criteria like transmembrane domain, allergenicity, GRAVY value, toxicity, stability index. Comprehensive analysis of these 4 antigens revealed that spike protein (P0DTC2) and nucleoprotein (P0DTC9) show the greatest potential for experimental immunogenicity analysis. These 2 proteins have several potential CD4+ and CD8+ T-cell epitopes, as well as high probability of B-cell epitope regions as compared to well-characterized antigen the matrix protein 1 [Influenza A virus (H5N1)]. In addition, the epitope SIIAYTMSL predicted from spike protein (P0DTC2) and epitope SPRWYFYYL predicted from nucleoprotein (P0DTC9) exhibited more than 60% population coverage in the target populations Europe, North America, South Asia, Northeast Asia taken in this study. These epitopes have also been found to exhibit highly significant TCR-pMHC interactions having a joint Z value of 4.51 and 4.37 respectively. Therefore, this analysis suggests that the predicted epitopes might be suitable vaccine candidates and should be subjected to further in-vivo and in-vitro studies.

SUBMITTER: Jain R

PROVIDER: S-EPMC8051835 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe novel coronavirus (SARS-CoV-2) caused lethal infections worldwide during an unprecedented pandemic. Identification of the candidate viral epitopes is the first step in the design of vaccines against the viral infection. Several immunoinformatic approaches were employed to identify the SARS-CoV-2 epitopes that bind specifically with the major histocompatibility molecules class I (MHC-I). We utilized immunoinformatic tools to analyze the whole viral protein sequences, to identify the SARS-CoV-2 epitopes responsible for binding to the most frequent human leukocyte antigen (HLA) alleles in the Egyptian population. These alleles were also found with high frequency in other populations worldwide.ResultsMolecular docking approach showed that using the co-crystallized MHC-I and T cell receptor (TCR) instead of using MHC-I structure only, significantly enhanced docking scores and stabilized the conformation, as well as the binding affinity of the identified SARS-CoV-2 epitopes. Our approach directly predicts 7 potential vaccine subunits from the available SARS-CoV-2 spike and ORF1ab protein sequence. This prediction has been confirmed by published experimentally validated and in silico predicted spike epitope. On the other hand, we predicted novel epitopes (RDLPQGFSA and FCLEASFNY) showing high docking scores and antigenicity response with both MHC-I and TCR. Moreover, antigenicity, allergenicity, toxicity, and physicochemical properties of the predicted SARS-CoV-2 epitopes were evaluated via state-of-the-art bioinformatic approaches, showing high efficacy of the proposed epitopes as a vaccine candidate.ConclusionOur predicted SARS-CoV-2 epitopes can facilitate vaccine development to enhance the immunogenicity against SARS-CoV-2 and provide supportive data for further experimental validation. Our proposed molecular docking approach of exploiting both MHC and TCR structures can be used to identify potential epitopes for most microbial pathogens, provided the crystal structure of MHC co-crystallized with TCR.

Project description:Introduction: The current incidence of the novel coronavirus disease has shown only small reductions of cases and has become a major public health challenge. Development of effective vaccines against the virus is still being encouraged such as multi-epitope vaccines designed from the components of SARS-CoV-2 including its spike, nucleocapsid and ORF1a proteins. Since the addition of adjuvants including HABA protein and L7/L12 ribosomal are considered helpful to increase the effectiveness of the designed vaccine, we proposed to design multiepitope vaccines by two different adjuvants. Methods: We used the IEDB server to predict BCL and TCL epitopes that were characterized using online tools including VaxiJen, AllPred and IL-10 Prediction. The selected epitopes were further constructed into multiepitope vaccines. We also added two different adjuvants to the vaccine components in order to increase the effectiveness of the vaccines. The 3D-structured vaccines were built using trRosetta. They were further docked with different Toll-like-receptors (TLR 3, 4 and 8) and the entry receptor of SARS-CoV-2, ACE2 using ClusPro, PatchDock and refined by FireDock. All structures were visualized by USCF Chimera and PyMOL. Results: In this study, we succeeded in designing two different candidate vaccines by the addition of HABA protein and L7/L12 ribosomal as adjuvants. The two vaccines were almost equally good in terms of their physicochemical properties and characteristics. Likewise, their strong interactions with TLR3 4, 8 and ACE2 show the lowest energy level of both was estimated at more than -1,000. Interactions of vaccines with ACE2 and TLRs are essential for activation of immune responses and production of antibodies. Conclusion: The two designed and constructed multiepitope vaccine have good characteristics and may have the potential to activate humoral and cellular immune responses against SARS-CoV-2. Further research is worth considering to confirm the findings of this study.

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Prediction of Epitope based Peptides for Vaccine Development from Complete Proteome of Novel Corona Virus (SARS-COV-2) Using Immunoinformatics.

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