Background Emergence of new variants mainly variants of concerns (VOC) is caused by mutations in main structural proteins of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Therefore, we aimed to investigate the mutations among structural proteins of SARS-CoV-2 globally. Methods We analyzed samples of amino-acid sequences (AASs) for envelope (E), membrane (M), nucleocapsid (N), and spike (S) proteins from the declaration of the coronavirus 2019 (COVID-19) as pandemic to January 2022. The presence and location of mutations were then investigated by aligning the sequences to the reference sequence and categorizing them based on frequency and continent. Finally, the related human genes with the viral structural genes were discovered, and their interactions were reported. Results The results indicated that the most relative mutations among the E, M, N, and S AASs occurred in the regions of 7 to 14, 66 to 88, 164 to 205, and 508 to 635 AAs, respectively. The most frequent mutations in E, M, N, and S proteins were T9I, I82T, R203M/R203K, and D614G. D614G was the most frequent mutation in all six geographical areas. Following D614G, L18F, A222V, E484K, and N501Y, respectively, were ranked as the most frequent mutations in S protein globally. Besides, A-kinase Anchoring Protein 8 Like (AKAP8L) was shown as the linkage unit between M, E, and E cluster genes. Conclusion Screening the structural protein mutations can help scientists introduce better drug and vaccine development strategies.

Background At the end of December 2019, a novel strain of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) disease (COVID-19) has been identified in Wuhan, a central city in China, and then spread to every corner of the globe. As of October 8, 2022, the total number of COVID-19 cases had reached over 621 million worldwide, with more than 6.56 million confirmed deaths. Since SARS-CoV-2 genome sequences change due to mutation and recombination, it is pivotal to surveil emerging variants and monitor changes for improving pandemic management. Methods 10,287,271 SARS-CoV-2 genome sequence samples were downloaded in FASTA format from the GISAID databases from February 24, 2020, to April 2022. Python programming language (version 3.8.0) software was utilized to process FASTA files to identify variants and sequence conservation. The NCBI RefSeq SARS-CoV-2 genome (accession no. NC_045512.2) was considered as the reference sequence. Results Six mutations had more than 50% frequency in global SARS-CoV-2. These mutations include the P323L (99.3%) in NSP12, D614G (97.6) in S, the T492I (70.4) in NSP4, R203M (62.8%) in N, T60A (61.4%) in Orf9b, and P1228L (50.0%) in NSP3. In the SARS-CoV-2 genome, no mutation was observed in more than 90% of nsp11, nsp7, nsp10, nsp9, nsp8, and nsp16 regions. On the other hand, N, nsp3, S, nsp4, nsp12, and M had the maximum rate of mutations. In the S protein, the highest mutation frequency was observed in aa 508–635(0.77%) and aa 381–508 (0.43%). The highest frequency of mutation was observed in aa 66–88 (2.19%), aa 7–14, and aa 164–246 (2.92%) in M, E, and N proteins, respectively. Conclusion Therefore, monitoring SARS-CoV-2 proteomic changes and detecting hot spots mutations and conserved regions could be applied to improve the SARS‐CoV‐2 diagnostic efficiency and design safe and effective vaccines against emerging variants.

Background: The Coronavirus 2019 (COVID-19) was named by the World Health Organization (WHO) due to its rapid transmittable potential and high mortality rate. Based on the critical role of None Structural Proteins (NSP), NSP3, NSP4, and NSP6 in COVID-19, this study attempts to investigate the superior natural selection mutations and Epistasis among these none structural proteins. Methods: Approximately 6.5 million SARS-CoV-2 protein sequences of each NSP3, NSP4, and NSP6 nonstructural protein were analyzed from January 2020 to January 2022. Python programming language was utilized to preprocess and apply inclusion criteria on the FASTA file to prepare a list of suitable samples. NSP3, NSP4, and NSP6 were aligned to the reference sequence to compare and identify mutation patterns categorized based on frequency, geographical zone distribution, and date. To discover epistasis situations, linear regression between mutation frequency and date among candidate genes was performed to determine correlations. Results: The rate of NSP3, NSP4, and NSP6 mutations in divided geographical areas was different. Based on continental studies, P1228L (54.48%), P1469S (54.41%), and A488S (53.86%) mutations in NSP3, T492I (54.84%), and V167L (52.81%) in NSP4 and T77A (69.85%) mutation in NSP6 increased over time, especially in recent months. For NSP3, Europe had the highest P1228L, P1469S, and A488S mutations. For NSP4, Oceania had the highest T492I and V167L mutations, and for NSP6, Europe had the highest T77A mutation. Hot spot regions for NSP3, NSP4, and NSP6 were 1358 to 1552 AA, 150 to 200 AA, and 58 to 87 AA, respectively. Our results showed a significant correlation and co-occurrence between NSP3, NSP4, and NSP6 mutations. Conclusion: We conclude that the effect of mutations on virus stability and replication can be predicted by examining the amino acid changes of P1228L, P1469S, A488S, T492I, V167L and T77A mutations. Also, these mutations can possibly be effective on the function of proteins and their targets in the host cell. Keywords: NSP4, NSP3, NSP6, Epistasis, SARS-CoV-2, Epidemiology