Genetic polymorphisms in DNA damage repair and tumor suppressor genes have been associated with increasing the risk of several types of cancer. Analyses of putative functional single nucleotide polymorphisms (SNP) in such genes can greatly improve human health by guiding choice of therapeutics. In this study, we selected nine genes responsible for various cancer types for gene enrichment analysis and found that BRCA1, ATM, and TP53 were more enriched in connectivity. Therefore, we used different computational algorithms to classify the nonsynonymous SNPs which are deleterious to the structure and/or function of these three proteins. The present study showed that the major pathogenic variants (V1687G and V1736G of BRCA1, I2865T and V2906A of ATM, V216G and L194H of TP53) might have a greater impact on the destabilization of the proteins. To stabilize the high-risk SNPs, we performed mutation site-specific molecular docking analysis and validated using molecular dynamics (MD) simulation and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) studies. Additionally, SNPs of untranslated regions of these genes affecting miRNA binding were characterized. Hence, this study will assist in developing precision medicines for cancer types related to these polymorphisms.

Streptomyces coelicolor is a gram-positive soil bacterium which is well known for the production of several antibiotics used in various biotechnological applications. But numerous proteins from its genome are considered hypothetical proteins. Therefore, the present study aimed to reveal the functions of a hypothetical protein from the genome of S. coelicolor. Several bioinformatics tools were employed to predict the structure and function of this protein. Sequence similarity was searched through the available bioinformatics databases to find out the homologous protein. The secondary and tertiary structure were predicted and further validated with quality assessment tools. Furthermore, the active site and the interacting proteins were also explored with the utilization of CASTp and STRING server. The hypothetical protein showed the important biological activity having with two functional domain including POD-like_MBL-fold and rhodanese homology domain. The functional annotation exposed that the selected hypothetical protein could show the hydrolase activity. Furthermore, protein-protein interactions of selected hypothetical protein revealed several functional partners those have the significant role for the bacterial survival. At last, the current study depicts that the annotated hypothetical protein is linked with hydrolase activity which might be of great interest to the further research in bacterial genetics.

Breast cancer (BC) is the second most prevalent cancer worldwide. Estrogen receptor beta (ERβ) is an essential protein of breast cells to suppress estrogen induced uncontrolled proliferation. Thus small molecules that can modulate and enhance ERβ expression would be an effective agent to suppress BC development. Studies showed that cannabinoid (CB), specifically Delta-9-tetrahydrocannabinol (Del9THC), can increase the expression of ERβ and inhibits BC cell proliferation. In this study, less psychoactive and structurally similar analogues of Del9THC were chosen as drug candidates and ERβ was targeted as a therapeutic receptor. Delta-8-tetrahydrocannabinol (Del8THC) and Delta-4-Isotetrahydrocannabinol (Del4isoTHC) were the drug candidates selected on the basis of literature reports, Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) properties, medicinal chemistry profile and physicochemical features. Molecular docking simulations were carried out to determine ligand receptor interactions and binding affinity based on free binding energy. To get a better drug, the structural modification was done on Del8THC and generated a new CB analogue called Cannabinoid A. Finally, molecular interaction analysis revealed that two CBs and one of their analogue interact with the active site residues of ERβ. Therefore, this study revealed a new way to discover novel drug(s) for BC patients.

HMG‐CoA reductase or HMGCR (3‐hydroxy‐3‐methylglutaryl‐CoA reductase) is a rate‐limiting enzyme involved in cholesterol biosynthesis. HMGCR plays an important role in the possible occurrence of hypercholesterolemia leading to atherosclerosis and coronary heart disease. This enzyme is a major target for cholesterol‐lowering drugs such as “statin” which blocks the synthesis of mevalonate, a precursor for cholesterol biosynthesis. This study is aimed at characterizing deleterious mutations and classifying functional single nucleotide polymorphisms (SNPs) of the HMGCR gene through analysis of functional and structural evaluation, domain association, solvent accessibility, and energy minimization studies. The functional and characterization tools such as SIFT, PolyPhen, SNPs and GO, Panther, I‐Mutant, and Pfam along with programming were employed to explore all the available SNPs in the HMGCR gene in the database. Among 6815 SNP entries from different databases, approximately 388 SNPs were found to be missense. Analysis showed that seven missense SNPs are more likely to have deleterious effects. A tertiary model of the mutant protein was constructed to determine the functional and structural effects of the HMGCR mutation. In addition, the location of the mutations suggests that they may have deleterious effects because most of the mutations are residing in the functional domain of the protein. The findings from the analysis predicted that rs147043821 and rs193026499 missense SNPs could cause significant structural and functional instability in the mutated proteins of the HMGCR gene. The findings of the current study will likely be useful in future efforts to uncover the mechanism and cause of hypercholesterolemia. In addition, the identified SNPs of HMGCR gene could set up a strong foundation for further therapeutic discovery.