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Aflatoxin B1 (AFB1), the natural polyketide produced by Aspergillus flavus, has a potent carcinogenic effect on humans as well as animals. In the present study, the antifungal and anti-aflatoxigenic B1 activity of chemically characterized Zingiber officinale essential oil (ZOEO) was investigated via in vitro analysis aided with molecular dynamics (MD) approaches. The GC–MS results revealed verbenol (52.41%) as the major component of oil. The antifungal and anti-aflatoxigenic activity of ZOEO was found to be 0.6 µl/ml and 0.5 µl/ml respectively. In-vitro analysis targeting the cell membrane, mitochondria and carbohydrate catabolism elucidated the probable antifungal mode of action. Further, docking and MD simulation results confirmed the inhibitory action of verbenol on the structural gene products (Nor-1, Omt-1, and Vbs) of aflatoxin biosynthetic machinery. Biochemical assays revealed the fungitoxic potential of the ZOEO while, computational results infers the stabilizing effects on the gene products upon verbenol binding leads to the impairment in its functionality. This is the first attempt to assess the multi-regime anti-AFB1 mechanism of verbenol chemotype-ZOEO targeting the Nor-1, Omt-1, and Vbs via computational approaches.

By-products of fatty acid degradation are extensively utilized by Mycobacterium tuberculosis (Mtb) for lipid synthesis and energy production during the infection phase. Cholesterol from host is scavenged by Mtb to fulfill its metabolic requirements, evade host immunity and invade macrophages. Blocking cholesterol catabolic pathways leads to bacteriostasis. FadA5 (Acetyl-CoA acetyltransferase), a thiolase encoded by fadA5 (Rv3546) gene in Mtb, plays a crucial role in cholesterol aliphatic chain degradation. Hence, FadA5 is a potential target for designing antitubercular inhibitors. In this study, 60,284 anti-tuberculosis (bioactive) compounds from ChEMBL database and analogous library from ZINC database of commercially available compounds have been screened against FadA5 active site to identify compounds having inhibitory potential against both the apo (state I) and the intermediate (state II) states of FadA5. Altogether, this study reports 7 potential inhibitors against two functional states of FadA5, which can be further taken for invitro studies.

GenerationE of huge “omics” data necessitates the development and application of computational methods to annotate the data in terms of biological features. In the context of DNA sequence, it is important to unravel the hidden physicochemical signatures. For this purpose, we have considered various sequence elements such as promoter, ACS, LTRs, telomere, and retrotransposon of the model organism Saccharomyces cerevisiae. Contributions due to di-nucleotides play a major role in studying the DNA conformation profile. The physicochemical parameters used are hydrogen bonding energy, stacking energy and solvation energy per base pair. Our computational study shows that all sequence elements in this study have distinctive physicochemical signatures and the same can be exploited for prediction experiments. The order that we see in a DNA sequence is dictated by biological regions and hence, there exists role of dependency in the sequence makeup, keeping this in mind we are proposing two computational schemes (a) using a windowing block size procedure and (b) using di-nucleotide transitions. We obtained better discriminating profile when we analyzed the sequence data in windowing manner. In the second novel approach, we introduced the di-nucleotide transition probability matrix (DTPM) to study the hidden layer of information embedded in the sequences. DTPM has been used as weights for scanning and predictions. This proposed computational scheme incorporates the memory property which is more realistic to study the physicochemical properties embedded in DNA sequences. Our analysis shows that the DTPM scheme performs better than the existing method in this applied region. Characterization of these elements will be a key to genome editing applications and advanced machine learning approaches may also require such distinctive profiles as useful input features.

It is known that crowded molecular environment affects the structure, thermodynamics, and dynamics of macromolecules. Most of the previous works on molecular crowding have majorly focused on the behavior of the macromolecule with less emphasis on the behavior of the crowder and water molecules. In the current study, we have precisely focused on the behavior of the crowder, (ethylene glycol (EG)), salt ions, and water in the presence of a DNA with the increase of the EG concentration. We have probed the behavior of water and crowder using molecular dynamics (MD) simulation and by calculating localized thermodynamic properties. Our results show an interesting competition between EG and water molecules to make hydrogen bonds (H-bond) with DNA. Although the total number of H-bonds involving DNA with both EG and water remains essentially same irrespective of the increase in EG concentration, there is a proportional change in the H-bonding pattern between water-water, EG-EG, and EG-water near DNA and in bulk. At low concentrations of EG, the displacement of water molecules near DNA is relatively easy. However, the displacement of water becomes more difficult as the concentration of EG increases. The density of Na+ (Cl-) near DNA increases (decreases) as the concentration of EG is increased. The density of Cl- near Na+ increases with the increase in EG concentration. It was also found that the average free energy per water in the first solvation shell increases with the increase in EG concentration. Putting all these together, a microscopic picture of EG, water, salt interaction in the presence of DNA, as a function of EG concentration, has emerged.