A continual rise in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection causing coronavirus disease (COVID-19) has become a global threat. The main problem comes when SARS-CoV-2 gets mutated with the rising infection and becomes more lethal for humankind than ever. Mutations in the structural proteins of SARS-CoV-2, i.e., the spike surface glycoprotein (S), envelope (E), membrane (M) and nucleocapsid (N), and replication machinery enzymes, i.e., main protease (Mpro) and RNA-dependent RNA polymerase (RdRp) creating more complexities towards pathogenesis and the available COVID-19 therapeutic strategies. This study analyzes how a minimal variation in these enzymes, especially in S protein at the genomic/proteomic level, affects pathogenesis. The structural variations are discussed in light of the failure of small molecule development in COVID-19 therapeutic strategies. We have performed in-depth sequence- and structure-based analyses of these proteins to get deeper insights into the mechanism of pathogenesis, structure-function relationships, and development of modern therapeutic approaches. Structural and functional consequences of the selected mutations on these proteins and their association with SARS-CoV-2 virulency and human health are discussed in detail in the light of our comparative genomics analysis.

Conserved telomere maintenance component 1 (CTC1) is an important component of the CST (CTC1-STN1-TEN1) complex, involved in maintaining the stability of telomeric DNA. Several non-synonymous single-nucleotide polymorphisms (nsSNPs) in CTC1 have been reported to cause Coats plus syndrome and Dyskeratosis congenital diseases. Here, we have performed sequence and structure analyses of nsSNPs of CTC1 using state-of-the-art computational methods. The structure-based study focuses on the C-terminal OB-fold region of CTC1. There are 11 pathogenic mutations identified, and detailed structural analyses were performed. These mutations cause a significant disruption of noncovalent interactions, which may be a possible reason for CTC1 instability and consequent diseases. To see the impact of such mutations on the protein conformation, all-atom molecular dynamics (MD) simulations of CTC1-wild-type (WT) and two of the selected mutations, R806C and R806L for 200 ns, were carried out. A significant conformational change in the structure of the R806C mutant was observed. This study provides a valuable direction to understand the molecular basis of CTC1 dysfunction in disease progression, including Coats plus syndrome.

Serum and glucocorticoid-regulated kinase 1 (SGK1) is a Ser/Thr protein kinase involved in regulating cell survival, growth, proliferation, and migration. Its elevated expression and dysfunction are reported in breast, prostate, hepatocellular, lung adenoma, and renal carcinomas. We have analyzed the SGK1 mutations to explore their impact at the sequence and structure level by utilizing state-of-the-art computational approaches. Several pathogenic and destabilizing mutations were identified based on their impact on SGK1 and analyzed in detail. Three amino acid substitutions, K127M, T256A, and Y298A, in the kinase domain of SGK1 were identified and incorporated structurally into original coordinates of SGK1 to explore their time evolution impact using all-atom molecular dynamic (MD) simulations for 200 ns. MD results indicate substantial conformational alterations in SGK1, thus its functional loss, particularly upon T256A mutation. This study provides meaningful insights into SGK1 dysfunction upon mutation, leading to disease progression, including cancer, and neurodegeneration.

Single amino-acid substitution in a protein affects its structure and function. These changes are the primary reasons for the advent of many complex diseases. Analyzing single point mutations in a protein is crucial to see their impact and to understand the disease mechanism. This has given many biophysical resources, including databases and web-based tools to explore the effects of mutations on the structure and function of human proteins. For a given mutation, each tool provides a score-based outcomes which indicate deleterious probability. In recent years, developments in existing programs and the introduction of new prediction algorithms have transformed the state-of-the-art protein mutation analysis. In this study, we have performed a systematic study of the most commonly used mutational analysis programs (10 sequence-based and 5 structure-based) to compare their prediction efficiency. We have carried out extensive mutational analyses using these tools for previously known pathogenic single point mutations of five different proteins. These analyses suggested that sequence-based tools, PolyPhen2, PROVEAN, and PMut, and structure-based web tool, mCSM have a better prediction accuracy. This study indicates that the employment of more than one program based on different approaches should significantly improve the prediction power of the available methods.

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