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.

Cluster Analysis of Coronavirus Sequences using Computational Sequence Descriptors: With Applications to SARS, MERS and SARS-CoV-2 (CoVID-19).

INTRODUCTION: Coronaviruses comprise a group of enveloped, positive-sense single-stranded RNA viruses that infect humans as well as a wide range of animals. The study was performed on a set of 573 sequences belonging to SARS, MERS and SARS-CoV-2 (CoVID-19) viruses. The sequences were represented with alignment-free sequence descriptors and analyzed with different chemometric methods: Euclidean/Mahalanobis distances, principal component analysis and self-organizing maps (Kohonen networks). We report the cluster structures of the data. The sequences are well-clustered regarding the type of virus; however, some of them show the tendency to belong to more than one virus type. BACKGROUND: This is a study of 573 genome sequences belonging to SARS, MERS and SARS-- CoV-2 (CoVID-19) coronaviruses. OBJECTIVES: The aim was to compare the virus sequences, which originate from different places around the world. METHODS: The study used alignment free sequence descriptors for the representation of sequences and chemometric methods for analyzing clusters. RESULTS: Majority of genome sequences are clustered with respect to the virus type, but some of them are outliers. CONCLUSION: We indicate 71 sequences, which tend to belong to more than one cluster.

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.