Genome-wide identification and prediction of SARS-CoV-2 mutations show an abundance of variants: Integrated study of bioinformatics and deep neural learning.

Genomic data analysis is a fundamental system for monitoring pathogen evolution and the outbreak of infectious diseases. Based on bioinformatics and deep learning, this study was designed to identify the genomic variability of SARS-CoV-2 worldwide and predict the impending mutation rate. Analysis of 259044 SARS-CoV-2 isolates identify 3334545 mutations (14.01 mutations per isolate), suggesting a high mutation rate. Strains from India showed the highest no. of mutations (48) followed by Scotland, USA, Netherlands, Norway, and France having up to 36 mutations. Besides the most prominently occurring mutations (D416G, F106F, P314L, and UTR:C241T), we identify L93L, A222V, A199A, V30L, and A220V mutations which are in the top 10 most frequent mutations. Multi-nucleotide mutations GGG>AAC, CC>TT, TG>CA, and AT>TA have come up in our analysis which are in the top 20 mutational cohort. Future mutation rate analysis predicts a 17%, 7%, and 3% increment of C>T, A>G, and A>T, respectively in the future. Conversely, 7%, 7%, and 6% decrement is estimated for T>C, G>A, and G>T mutations, respectively. T>G\A, C>G\A, and A>T\C are not anticipated in the future. Since SARS-CoV-2 is evolving continuously, our findings will facilitate the tracking of mutations and help to map the progression of the COVID-19 intensity worldwide.

Temporal landscape of mutation accumulation in SARS-CoV-2 genomes from Bangladesh: possible implications from the ongoing outbreak in Bangladesh

Along with intrinsic evolution, adaptation to selective pressure in new environments might have resulted in the circulatory SARS-CoV-2 strains in response to the geoenvironmental conditions of a country and the demographic profile of its population. Thus the analysis of genomic mutations of these circulatory strains may give an insight into the molecular basis of SARS-CoV-2 pathogenesis and evolution favoring the development of effective treatment and containment strategies. With this target, the current study traced the evolutionary route and mutational frequency of 198 Bangladesh originated SARS-CoV-2 genomic sequences available in the GISAID platform over a period of 13 weeks as of 14 July 2020. The analyses were performed using MEGA 7, Swiss Model Repository, Virus Pathogen Resource and Jalview visualization. Our analysis identified that majority of the circulating strains in the country belong to B and/or L type among cluster A to Z and strikingly differ from both the reference genome and the first sequenced genome from Bangladesh. Mutations in Nonspecific protein 2 (NSP2), NSP3, RNA dependent RNA polymerase (RdRp), Helicase, Spike, ORF3a, and Nucleocapsid (N) protein were common in the circulating strains with varying degrees and the most unique mutations(UM) were found in NSP3 (UM-18). But no or limited changes were observed in NSP9, NSP11, E (Envelope), NSP7a, ORF 6, and ORF 7b suggesting the possible conserved functions of those proteins in SARS-CoV-2 propagation. However, along with D614G mutation, more than 20 different mutations in the Spike protein were detected basically in the S2 domain. Besides, mutations in SR-rich region of N protein and P323L in RDRP were also present. However, the mutation accumulation showed an association with sex and age of the COVID-19 positive cases. So, identification of these mutational accumulation patterns may greatly facilitate drug/ vaccine development deciphering the age and the sex dependent differential susceptibility to COVID-19.

Epitope-based chimeric peptide vaccine design against S, M and E proteins of SARS-CoV-2 etiologic agent of global pandemic COVID-19: an in silico approach

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the ongoing pandemic of coronavirus disease 2019 (COVID-19), a public health emergency of international concern declared by the World Health Organization (WHO). An immuno-informatics approach along with comparative genomic was applied to design a multi-epitope-based peptide vaccine against SARS-CoV-2 combining the antigenic epitopes of the S, M and E proteins. The tertiary structure was predicted, refined and validated using advanced bioinformatics tools. The candidate vaccine showed an average of [≥] 90.0% world population coverage for different ethnic groups. Molecular docking of the chimeric vaccine peptide with the immune receptors (TLR3 and TLR4) predicted efficient binding. Immune simulation predicted significant primary immune response with increased IgM and secondary immune response with high levels of both IgG1 and IgG2. It also increased the proliferation of T-helper cells and cytotoxic T-cells along with the increased INF-{gamma} and IL-2 cytokines. The codon optimization and mRNA secondary structure prediction revealed the chimera is suitable for high-level expression and cloning. Overall, the constructed recombinant chimeric vaccine candidate demonstrated significant potential and can be considered for clinical validation to fight against this global threat, COVID-19.