Docking and Molecular Dynamics Simulation Revealed the Potential Inhibitory Activity of Amygdalin in Triple-Negative Breast Cancer Therapeutics Targeting the BRCT Domain of BARD1 Receptor

Triple-negative breast cancer (TNBC), is diagnosed as the most lethal molecular subtype of breast cancer (BC) preceded by an extremely poor prognosis. For enabling effective TNBC therapy, the identification of novel druggable biomarkers is an earnest need. Multigene paneling and genomewide association studies identify multiple genes with high-to-moderate penetrance in TNBC. Modern computer-aided drug designing techniques, thus aim to design more cost-effective natural small molecule inhibitors for TNBC prevention and diagnosis. Here Amygdalin, a natural glycosidic inhibitor is docked and simulated against three such high-to-moderate penetrance genes identified in TNBC, BARD1, RAD51, and PALB2. The preliminary result of the analysis, reports a highest, intermediate, and least binding energy score of - 6.69 kcal/mol, - 5.09 kcal/mol, and - 4.89 kcal/mol in BARD1, RAD51, and PALB2, respectively. The best-docked protein-ligand complex (BARD1-Amygdalin) was then simulated and compared with an approved drug for TNBC treatment, Olaparib. A comparable binding energy score of - 8.53 kcal/mol was obtained by docking olaparib with BARD1. A 100 ns MD simulation revealed, Amygdalin forms more H-bonds, providing more stable and compact protein-ligand complex with BARD1 than compared to Olaparib. The result was also supported by calculation of solvent accessible surface area and analysis of radius of gyration. Thus, our findings suggest that role of Amygdalin can further be studied in details for TNBC therapeutics, which was found to target the BRCT domain of the BARD1 receptor in stable manner. Please check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary. Name and affiliations are correctly identified.

Antibacterial activity of a novel compound isolated from Bacillus licheniformis for treating bacterial infections in fishes: An in-silico approach

Aeromonas hydrophila is a fish pathogen which is widely associated with diseases related to freshwater fishes. Vibrio parahemolyticus is a major globally emerging marine pathogen. Seven novel compounds were extracted from the ethyl acetate extract of Bacillus licheniformis, a novel marine bacterium isolated from marine actinomycetes. The compounds were identified using Gas Chromatography-Mass Spectroscopy (GC-MS). Only one bioactive compound having potent antibacterial activity was virtually screened to understand its drug-like property according to Lipinski's rule. The core proteins, 3L6E and 3RYL from the pathogens, A. hydrophila and V. parahemolyticus were targeted for drug discovery. In the present in-silico approach, Phenol,2,4-Bis(1,1-Dimethylethyl) a potent bioactive compound present in Bacillus licheniformis was used to prevent the infection due to the two pathogens. Further, using this bioactive compound, molecular docking was done to block their specific target proteins. This bioactive compound satisfied all the five rules of Lipinski. Molecular docking result revealed the best binding efficacy of Phenol,2,4-Bis(1,1-Dimethylethyl) against 3L6E and 3RYL with - 4.24 kcal/mol and - 4.82 kcal/mol, respectively. Molecular dynamics (MD) simulations were also executed to determine the binding modes as well as the stability of the protein-ligand docking complexes in the dynamic structure. The in vitro toxicity analysis of this potent bioactive compound against Artemia salina was carried out, revealing the non-toxic nature of B. licheniformis ethyl acetate extract. Thus, the bioactive compound of B. licheniformis was found to be a potent antibacterial agent against A. hydrophila and V. parahemolyticus.

Network-based gene deletion analysis identifies candidate genes and molecular mechanism involved in clear cell renal cell carcinoma

Human clear cell renal cell carcinoma (ccRCC) is the most common and frequently occurring histological subtype of RCC. Unlike other carcinomas, candidate predictive biomarkers for this type are in need to explore the molecular mechanism of ccRCC and identify candidate target genes for improving disease management. For this, we chose case-control-based studies from the Gene Expression Omnibus and subjected the gene expression microarray data to combined effect size meta-analysis for identifying shared genes signature. Further, we constructed a subnetwork of these gene signatures and evaluated topological parameters during the gene deletion analysis to get to the central hub genes, as they form the backbone of the network and its integrity. Parallelly, we carried out functional enrichment analysis using gene ontology and Elsevier disease pathway collection. We also performed microRNAs target gene analysis and constructed a regulatory network. We identified a total of 577 differentially expressed genes (DEGs), where 146 overexpressed and 431 underexpressed with a significant threshold of adjusted P values <0.05. Enrichment analysis of these DEGs' functions showed a relation to metabolic and cellular pathways like metabolic reprogramming in cancer, proteins with altered expression in cancer metabolic reprogramming, and glycolysis activation in cancer (Warburg effect). Our analysis revealed the potential role of PDHB and ATP5C1 in ccRCC by altering metabolic pathways and amyloid beta precursor protein (APP) role in altering cell-cycle growth for the tumour progression in ccRCC conditions. Identification of these candidate predictive genes paves the way for the development of biomarker-based methods for this carcinoma.

Residue centrality in alpha helical polytopic transmembrane protein structures

Transmembrane proteins serve as receptors, transporters or as enzymes. They mediate a broad range of fundamental cellular activities including signal transduction, cell trafficking and photosynthesis. In this study, we analyzed the significance of central residues in the polytopic transmembrane proteins. Each protein is represented as an undirected graph, where residues represent nodes and inter-residue interactions as the edges. Residue centrality was calculated by removing the nodes and its corresponding edges from the protein contact network. Results revealed that 80% of the predicted central residues had normalized conservation values below the mean since they were slowly evolving conserved sites. We also found that 56% of amino acids were interacting with the ligand molecules and metal ions. Predicted central residues in the polytopic transmembrane proteins were found to account for 84% of binding and active site amino acids. From mutation sensitivity analysis, it was observed that 89% of central residues had deleterious mutations whose probabilities were greater than their mean value. Interestingly, we find that z-score values of each amino acid positively correlate with the conservation scores and also with the degrees of each node. Results show that 87% of central residues are hub residues.