Emerging mutations in envelope protein of SARS-CoV-2 and their effect on thermodynamic properties.

Structural proteins of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are potential drug targets due to their role in the virus life cycle. The envelope (E) protein is one of the structural proteins; plays a critical role in virulency. However, the emergence of mutations oftenly leads to drug resistance and may also play a vital role in virus stabilization and evolution. In this study, we aimed to identify mutations in E proteins that affect the protein stability. About 0.3 million complete whole genome sequences were analyzed to screen mutations in E protein. All these mutations were subjected to stability prediction using the DynaMut server. The most common mutations that were detected at the C-terminal domain, Ser68Phe, Pro71Ser, and Leu73Phe, were examined through molecular dynamics (MD) simulations for a 100ns period. The sequence analysis shows the existence of 259 mutations in E protein. Interestingly, 16 of them were detected in the DFLV amino acid (aa) motif (aa72-aa75) that binds the host PALS1 protein. The results of root mean square deviation, fluctuations, radius of gyration, and free energy landscape show that Ser68Phe, Pro71Ser, and Leu73Phe are exhibiting a more stabilizing effect. However, a more comprehensive experimental study may be required to see the effect on virus pathogenicity. Potential antiviral drugs, and vaccines may be developed used after screening the genomic variations for better management of SARS-CoV-2 infections.

Using Formal Concept Analysis to Identify Negative Correlations in Gene Expression Data.

Recently, many biological studies reported that two groups of genes tend to show negatively correlated or opposite expression tendency in many biological processes or pathways. The negative correlation between genes may imply an important biological mechanism. In this study, we proposed a FCA-based negative correlation algorithm (NCFCA) that can effectively identify opposite expression tendency between two gene groups in gene expression data. After applying it to expression data of cell cycle-regulated genes in yeast, we found that six minichromosome maintenance family genes showed the opposite changing tendency with eight core histone family genes. Furthermore, we confirmed that the negative correlation expression pattern between these two families may be conserved in the cell cycle. Finally, we discussed the reasons underlying the negative correlation of six minichromosome maintenance (MCM) family genes with eight core histone family genes. Our results revealed that negative correlation is an important and potential mechanism that maintains the balance of biological systems by repressing some genes while inducing others. It can thus provide new understanding of gene expression and regulation, the causes of diseases, etc.