Identification of Generalized Peptide Regions for Designing Vaccine Effective for All Significant Mutated Strains of SARS-CoV-2.

BACKGROUND: Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 infection has become a worldwide pandemic and created an utmost crisis across the globe. To mitigate the crisis, the design of vaccine is the crucial solution. The frequent mutation of the virus demands generalized vaccine candidates, which would be effective for all mutated strains at present and for the strains that would evolve due to further new mutations in the virus. OBJECTIVE: The objective of this study is to identify more frequently occurring mutated variants of SARS-CoV-2 and to suggest peptide vaccine candidates effective against the viral strains considered. METHODS: In this study, we have identified all currently prevailing mutated strains of SARS-CoV-2 through 2D Polar plot and Quotient Radius characterization descriptor. Then, by considering the top eight mutation strains, which are significant due to their frequency of occurrence, peptide regions suitable for vaccine design have been identified with the help of a mathematical model, 2D Polygon Representation, followed by the evaluation of epitope potential, ensuring that there is no case of any autoimmune threat. Lastly, in order to verify whether this entire approach is applicable for vaccine design against any other virus in general, we have made a comparative study between the peptide vaccine candidates prescribed for the Zika virus using the current approach and a list of potential vaccine candidates for the same already established in the past. RESULTS: We have finally suggested three generalized peptide regions which would be suitable as sustainable peptide vaccine candidates against SARS-CoV-2 irrespective of its currently prevailing strains as well any other variant of the same that may appear in the future. We also observed that during the comparative study using the case of E protein of Zika virus, the peptide regions suggested using the new approach that matches with the already established results. CONCLUSION: The study, therefore, illustrates an approach that would help in developing peptide vaccine against SARS-CoV-2 by suggesting those peptide regions which can be targeted irrespective of any mutated form of this virus. The consistency with which this entire approach was also able to figure out similar vaccine candidates for Zika virus with utmost accuracy proves that this protocol can be extended for peptide vaccine design against any other viruses in the future.

New Computational Approach for Peptide Vaccine Design Against SARS-COV-2.

The design for vaccines using in silico analysis of genomic data of different viruses has taken many different paths, but lack of any precise computational approach has constrained them to alignment methods and some alignment-free techniques. In this work, a precise computational approach has been established wherein two new mathematical parameters have been suggested to identify the highly conserved and surface-exposed regions which are spread over a large region of the surface protein of the virus so that one can determine possible peptide vaccine candidates from those regions. The first parameter, w, is the sum of the normalized values of the measure of surface accessibility and the normalized measure of conservativeness, and the second parameter is the area of a triangle formed by a mathematical model named 2D Polygon Representation. This method has been, therefore, used to determine possible vaccine targets against SARS-CoV-2 by considering its surface-situated spike glycoprotein. The results of this model have been verified by a parallel analysis using the older approach of manually estimating the graphs describing the variation of conservativeness and surface-exposure across the protein sequence. Furthermore, the working of the method has been tested by applying it to find out peptide vaccine candidates for Zika and Hendra viruses respectively. A satisfactory consistency of the model results with pre-established results for both the test cases shows that this in silico alignment-free analysis proposed by the model is suitable not only to determine vaccine targets against SARS-CoV-2 but also ready to extend against other viruses.

Identification and computational analysis of mutations in SARS-CoV-2.

SARS-CoV-2 infection has become a worldwide pandemic and is spreading rapidly to people across the globe. To combat the situation, vaccine design is the essential solution. Mutation in the virus genome plays an important role in limiting the working life of a vaccine. In this study, we have identified several mutated clusters in the structural proteins of the virus through our novel 2D Polar plot and qR characterization descriptor. We have also studied several biochemical properties of the proteins to explore the dynamics of evolution of these mutations. This study would be helpful to understand further new mutations in the virus and would facilitate the process of designing a sustainable vaccine against the deadly virus.

Computational Methodology for Peptide Vaccine Design for Zika Virus: A Bioinformatics Approach.

With the increasing frequency of viral epidemics, vaccines to augment the human immune response system have been the medium of choice to combat viral infections. The tragic consequences of the Zika virus pandemic in South and Central America a few years ago brought the issues into sharper focus. While traditional vaccine development is time-consuming and expensive, recent advances in information technology, immunoinformatics, genetics, bioinformatics, and related sciences have opened the doors to new paradigms in vaccine design and applications.Peptide vaccines are one group of the new approaches to vaccine formulation. In this chapter, we discuss the various issues involved in the design of peptide vaccines and their advantages and shortcomings, with special reference to the Zika virus for which no drugs or vaccines are as yet available. In the process, we outline our work in this field giving a detailed step-by-step description of the protocol we follow for such vaccine design so that interested researchers can easily follow them and do their own designing. Several flowcharts and figures are included to provide a background of the software to be used and results to be anticipated.

Intercorrelation of Major DNA/RNA Sequence Descriptors - A Preliminary Study.

A large number of alignment-free techniques of graphical representation and numerical characterization (GRANCH) of bio-molecular sequences have been proposed in the recent past years, but the relative efficacy of these methods in determining the degree of similarities and dissimilarities of such sequences have not been ascertained. OBJECTIVE: Our objective is to make an assessment of the relative efficacy of these methods in determining the degree of similarities and dissimilarities of bio-molecular sequences. METHOD: We have chosen 7 published/communicated methods that represent various classes of GRANCH techniques and computed the descriptors that are expected to characterize similarities and dissimilarities in several sets of gene sequences. We critically appraise the different methods and determine which of these yield non-redundant structural information that could be used to compute different properties of the sequences, and which are correlated enough to one another so that using the simplest representative of the group would suffice. We also do a principal component analysis (PCA) to determine how the variances in the calculated sequence descriptors are explained by the computed principal components (PCs). RESULTS: We found that some of the descriptors are strongly correlated implying a commonality of structural information encoded by them while others are distinctly separate. The PCA results show that the first three PC's explain >97% of the variances. CONCLUSION: We found that some mathematical DNA descriptors calculated by a few of these techniques correlate strongly with one another implying a redundancy in the structural information quantified by those descriptors; others are not strongly correlated with one another suggesting that they encode non-redundant sequence information. From this and our PCA results, our recommendation would be to use minimally correlated set of descriptors or orthogonal descriptors like PCs derived from the descriptor set for the characterization of nucleic acid structure and function.