Epitope order Matters in multi-epitope-based peptide (MEBP) vaccine design: An in silico study

  1. Muthu Raj Salaikumaran
  2. Prasanna Sudharson Kasamuthu
  3. V L S Prasad Burra

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

With different countries facing multiple waves, with some SARS-CoV-2 variants more deadly and virulent, the COVID-19 pandemic is becoming more dangerous by the day and the world is facing an even more dreadful extended pandemic with exponential positive cases and increasing death rates. There is an urgent need for more efficient and faster methods of vaccine development against SARS-CoV-2. Compared to experimental protocols, the opportunities to innovate are very high in immunoinformatics/ in silico approaches especially with the recent adoption of structural bioinformatics in peptide vaccine design. In recent times, multi-epitope-based peptide vaccine candidates (MEBPVCs) have shown extraordinarily high humoral and cellular responses to immunization. Most of the publications claim that respective reported MEBPVC(s) assembled using a set of in silico predicted epitopes, to be the computationally validated potent vaccine candidate(s) ready for experimental validation. However, in this article, for a given set of predicted epitopes, it is shown that the published MEBPVC is one among the many possible variants and there is high likelihood of finding more potent MEBPVCs than the published candidate. To test the same, a methodology is developed where novel MEBP variants are derived by changing the epitope order of the published MEBPVC. Further, to overcome the limitations of current qualitative methods of assessment of MEBPVC, to enable quantitative comparison, ranking, and the discovery of more potent MEBPVCs, novel predictors, Percent Epitope Accessibility (PEA), Receptor specific MEBP vaccine potency(RMVP), MEBP vaccine potency(MVP) are introduced. The MEBP variants indeed showed varied MVP scores indicating varied immunogenicity. When the MEBP variants were ranked in descending order of their MVP scores, the published MEBPVC had the least MVP score. Further, the MEBP variants with IDs, SPVC_387 and SPVC_206, had the highest MVP scores indicating these variants to be more potent MEBPVCs than the published MEBPVC and hence should be prioritized for experimental testing and validation. Through this method, more vaccine candidates will be available for experimental validation and testing. This study also opens the opportunity to develop new software tools for designing more potent MEBPVCs in less time. The computationally validated top-ranked MEBPVCs must be experimentally tested, validated, and verified. The differences and deviations between experimental results and computational predictions provide an opportunity for improving and developing more efficient algorithms and reliable scoring schemes and software.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2021.06.29.450372: (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
    Sequence alignment: The formatting code, all-to-all sequence alignment among the members of the MEBP dataset was done using BioInt [30], Biobhasha (www.biobhasha.org), and BOSv2.0 (Biological Object-Based Software (BOS): An Integrative Biological Programming Environment).
    BioInt
    suggested: None
    The result from VaxiJen 2.0 categorizes the peptide input into either Probable ANTIGEN or Probable NON-ANTIGEN.
    VaxiJen
    suggested: (VaxiJen, RRID:SCR_018514)
    AllerTop v2.0 server was used to predict the allergenicity of the MEBP variants.
    AllerTop
    suggested: (AllerTop, RRID:SCR_018496)
    The EXPASY ProtParam server was used for predicting the Instability index of the nine MEBPVCs.
    EXPASY
    suggested: None
    Scratch Protein Predictor (http://scratch.proteomics.ics.uci.edu) [47] was used to predict the solvent accessibility of the variants.
    Scratch Protein Predictor
    suggested: None
    For each input sequence, i.e. an MEBP variant in the current context, the SoluProt v1.0 server gives a score in the range of 0 −1.0 where > 0.5 score indicates soluble and < 0.5 indicates insoluble peptide.
    SoluProt
    suggested: None
    To predict the aggregation for the variants, we used the AGGRESCAN server (http://bioinf.uab.es/aggrescan/).
    http://bioinf.uab.es/aggrescan/
    suggested: (Aggrescan: The Hot Spot Finder, RRID:SCR_008403)
    Kyte and Doolittle’s method was used to generate the Hydrophobicity values by using EMBOSS pepwindow server (https://www.ebi.ac.uk/Tools/seqstats/emboss_pepwindow/).
    EMBOSS
    suggested: (EMBOSS, RRID:SCR_008493)

    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: We detected the following sentences addressing limitations in the study:
    This limitation is being effectively addressed through a) combining with adjuvants such as b-defensin 2, HSP70, HBD-2, Matrix-M1, nanoparticles, b) altering the size (molecular weight), and others. Adjuvants have shown to significantly boost immunogenicity but have not matched the current platforms such as RNA, adenovirus vector, and inactivated virus-based platforms [54–56]. It is the fundamental phenomenon that changes in the amino acid order change the structure and function, giving the clue that the earlier reported MEBPVC (REF_SEQ) could have variants if the epitope order changed. A set of ten variants were generated manually to explore if the variants thus generated shall have altered immunogenicity. The variants were analyzed at the sequence, structure, and complex interaction and dosage versus immune response level. In homology modeling, it is a common rule of thumb that for any two sequences, if the sequence identity is > 30%, it is assumed that their 3D structures shall be similar and likely to have identical function[57]. Further, it is also believed that with the increase in the sequence identity, the structural similarity also increases, i.e, RMSD decreases. However, it is interesting to note that the variants show deviations from the rule of thumb. As can be seen from Table 3 and Table 4, there are many pairs that show deviations. There have been studies that proved that 3D structures of 100% identical sequences were having natural conformations that have RMSDs ...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2021.06.29.450372v1 on bioRxiv