Picloram binds to the h1 and h4 helices of HSA domain IIIA at drug binding site 2.

Picloram (PC) is a systemic herbicide that controls herbaceous weeds and woody plants. HSA, the most abundant protein in human physiology, binds to all exogenic and endogenic ligands. PC is a stable molecule (t1/2∼157-513 days) and a potential threat to human health via the food chain. HSA and PC binding study has been done to decipher the location and thermodynamics of binding. It has been studied with prediction tools like autodocking and MD simulation and then confirmed with fluorescence spectroscopy. HSA fluorescence was quenched by PC at pH 7.4 (N state), pH 3.5 (F state), and pH 7.4 with 4.5 M urea (I state) at temperatures 283 K, 297 K, and 303 K. The location of binding was found to be interdomain between II and III which overlaps with drug binding site 2. The binding was spontaneous, and entropy-driven that show a noticeable increase in binding with the increase in temperature. No secondary structure change at the native state has been observed due to binding. The binding results are important to understand the physiological assimilation of PC. In silico predictions and the results of spectroscopic studies unambiguously indicate the locus and nature of the binding.

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.

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.

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.