Using in-silica Analysis and Reverse Vaccinology Approach for COVID-19 Vaccine Development

  1. Ajay Agarwal

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Abstract

Background: The recent pandemic of COVID19 that has struck the world is yet to be battled by a potential cure. Countless lives have been claimed due to the existing pandemic and the societal normalcy has been damaged permanently. As a result, it becomes crucial for academic researchers in the field of bioinformatics to combat the existing pandemic. Materials and Methods: The study involved collecting the virulent strain sequence of SARS-nCoV19 for the country USA against human host through publically available bioinformatics databases. Using in-silica analysis, reverse vaccinology, and 3-D modelling, two leader proteins were identified to be potential vaccine candidates for development of a multi-epitope drug. Results: It was revealed that the two leader proteins ORF1ab MT326102 and MT326715 had the highest extinction coefficient and the lowest score on the GRAVY. Along with the given parameters, these leader proteins were highly stable and were also antigenic in nature. The two selected epitopes were then docked against their respective alleles to obtain the global energy scores, which was the lowest of all possible pairs. Conclusion: The epitopes which displayed the lowest global energy score on docking with the alleles were selected and proposed as successful and potential vaccine candidates for multi-epitope vaccine development. Doi: 10.28991/SciMedJ-2020-02-SI-9 Full Text: PDF

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  1. Version published to 10.28991/scimedj-2020-02-si-9
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    SciScore for 10.1101/2020.06.16.154559: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The reference identification for SARS-CoV-2 is given by RefSeq NC_045512.2 Protein Identification and Retrieval: 14534 viral protein sequences of the SARS-CoV-2 were obtained from the ViPR – Virus Pathogen Database and Analysis. [1].
    RefSeq
    suggested: (RefSeq, RRID:SCR_003496)
    Protein Antigenicity: VaxiJen 2.0 is used to predict the antigenicity of the protein based on the FASTA-files that contain their respective amino acid sequences.
    VaxiJen
    suggested: (VaxiJen, RRID:SCR_018514)
    IEDB is a freely available resource funded by NIAID.
    NIAID
    suggested: (NIAID, RRID:SCR_016598)
    The prediction of transmembrane helices in proteins was determined using the TMHMM Server v2.0 (http://www.cbs.dtu.dk/services/TMHMM/).
    http://www.cbs.dtu.dk/services/TMHMM/
    suggested: (TMHMM Server, RRID:SCR_014935)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • No funding statement was detected.
    • No protocol registration statement was detected.

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  3. Version published to 10.1101/2020.06.16.154559v1 on bioRxiv