Development of Estrogen Receptor Beta Binding Prediction Model Using Large Sets of Chemicals

We developed an ERβ binding prediction model to facilitate identification of chemicals specifically bind ERβ or ERα together with our previously developed ERα binding model. Decision Forest was used to train ERβ binding prediction model based on a large set of compounds obtained from EADB. Model performance was estimated through 1000 iterations of 5-fold cross validations. Prediction confidence was analyzed using predictions from the cross validations. Informative chemical features for ERβ binding were identified through analysis of the frequency data of chemical descriptors used in the models in the 5-fold cross validations. 1000 permutations were conducted to assess the chance correlation. The average accuracy of 5-fold cross validations was 93.14% with a standard deviation of 0.64%. Prediction confidence analysis indicated that the higher the prediction confidence the more accurate the predictions. Permutation testing results revealed that the prediction model is unlikely generated by chance. Eighteen informative descriptors were identified to be important to ERβ binding prediction. Application of the prediction model to the data from ToxCast project yielded very high sensitivity of 90-92%. Our results demonstrated ERβ binding of chemicals could be accurately predicted using the developed model. Coupling with our previously developed ERα prediction model, this model could be expected to facilitate drug development through identification of chemicals that specifically bind ERβ or ERα

Removal and degradation of mixed dye pollutants by integrated adsorption-photocatalysis technique using 2-D MoS2/TiO2 nanocomposite

Two-dimensional (2D) Molybdenum disulfide (MoS2) has become one of the most exciting areas of research for adsorbents due to its high surface area and abundant active sites. Mainly, 2D MoS2 show promising removal of textile dye pollutants by adsorption process, but it show high affinity for anionic type of dyes, that limits its performance in mixed dye pollutants treatment. Herein, we demonstrate an integrated approach to remove mixed dye pollutants (anionic and cationic) concurrently by combining adsorption and photocatalysis process. We synthesize MoS2/TiO2 nanocomposites for different weight percentages 2.5, 5, 10, 20, 30 and 50 wt% of pre-synthesized flower-like MoS2 nanoparticle by a two-step hydrothermal method. We demonstrate a new process of two-stage adsorption/photocatalysis using high wt% of MoS2 (Stage-I) and low wt% of MoS2 (Stage-II) nanocomposites. The proposed two-stage integrated adsorption and photocatalysis process using 50% and 2.5% of MoS2 coated TiO2, respectively showed complete removal of methylene blue dye ∼5 times faster than conventional single-stage (adsorption or photocatalysis) water treatment process. Furthermore, the feasibility of the proposed two-stage method in mixed dye pollutants removal (anionic and cationic) testified, which showed excellent performance even in doubling the dye pollutant concentration. This work brings a deeper insight into understanding the morphology and concentration of 2-D MoS2 in MoS2/TiO2 nanocomposite in tackling mixed dye pollutants and the possibilities of applying in textile dyeing industries wastewater treatment plants

Ultrafast epitaxial growth of CuO nanowires using atmospheric pressure plasma with enhanced electrocatalytic and photocatalytic activities

This work reports an environment friendly alternative to epitaxially grow copper oxide nanowires (NWs) on copper substrates using single step atmospheric pressure plasma jet assisted oxidation. NWs of average length 300 nm are grown rapidly in 5 minutes along with transforming the surface to superhydrophilic. This method introduces defects in the nanowire structure which is otherwise difficult to achieve due to the highly isotropic nature of nanowire growth. High resolution transmission electron microscopy reveals vacancies and structural defects such as lattice twinning and kinks. Theoretical investigations using density functional theory calculations indicated that oxygen vacancies reduces the adsorption energy of methanol molecules onto the CuO (111) surface and shifts the Fermi level towards conduction band. During electrocatalysis, these defect-rich nanowires exhibit twice the catalytic activity toward oxygen evolution reaction (OER) and methanol oxidation reaction (MOR) in comparison to the traditionally thermally grown nanowires. Moreover, retreating the electrodes after each stability test drops the contact resistance similar to the prisitine sample. Additionally, these NW photocathodes demonstrate an exceptional photocurrent of 2.2 mAcm–2 and have an excellent degradation activity towards organic pollutants namely phenol and paracetamol. This facile growth method can be used to engineer nanowires of other transition metals with enhanced activities

Bias-force guided simulations combined with experimental validations towards GPR17 modulators identification

Glioblastoma Multiforme (GBM) is known to be by far the most aggressive brain tumor to affect adults. The median survival rate of GBM patient's is < 15 months, while the GBM cells aggressively develop resistance to chemo- and radiotherapy with their self-renewal capacity which suggests the pressing need to develop novel preventative measures. We have recently proved that GPR17 —an orphan G protein-coupled receptor— is highly expressed on the GBM cell surface and it has a vital role to play in the disease progression. Despite the progress made on GBM downregulation, there still remain difficulties in developing a promising modulator for GPR17, till date. Here, we have performed robust virtual screening combined with biased-force pulling molecular dynamic (MD) simulations to predict high-affinity GPR17 modulators followed by experimental validation. Initially, the database containing 1379 FDA-approved drugs were screened against the orthosteric binding pocket of the GPR17. The external bias-potentials were then applied to the screened hits during the MD simulations which enabled to predict a spectrum of rupture peak force values that were used to select four approved drugs –ZINC000003792417 (Sacubitril), ZINC000014210457 (Victrelis), ZINC000001536109 (Pralatrexate) and ZINC000003925861 (Vorapaxar)– as top hits. The hits selected turns out to demonstrate unique dissociation pathways, interaction pattern, and change in polar network over time. Subsequently the selected hits with GPR17 were measured by inhibiting the forskolin-stimulated cAMP accumulation in GBM cell lines, LN229 and SNB19. The ex vivo validations shows that Sacubitril drug can act as a full agonist, while Vorapaxar functions as a partial agonist for GPR17. The pEC50 of Sacubitril was identified as 4.841 and 4.661 for LN229 and SNB19, respectively. Small interference of the RNA (siRNA)– silenced the GPR17 to further validate the targeted binding of Sacubitril with GPR17. In the current investigation, we have identified new repurposable GPR17 specific drugs which are likely to increase the opportunity to treat orphan deadly diseases.publishedVersionPeer reviewe

Selectins—The Two Dr. Jekyll and Mr. Hyde Faces of Adhesion Molecules—A Review

Selectins belong to a group of adhesion molecules that fulfill an essential role in immune and inflammatory responses and tissue healing. Selectins are glycoproteins that decode the information carried by glycan structures, and non-covalent interactions of selectins with these glycan structures mediate biological processes. The sialylated and fucosylated tetrasaccharide sLex is an essential glycan recognized by selectins. Several glycosyltransferases are responsible for the biosynthesis of the sLex tetrasaccharide. Selectins are involved in a sequence of interactions of circulated leukocytes with endothelial cells in the blood called the adhesion cascade. Recently, it has become evident that cancer cells utilize a similar adhesion cascade to promote metastases. However, like Dr. Jekyll and Mr. Hyde&rsquo;s two faces, selectins also contribute to tissue destruction during some infections and inflammatory diseases. The most prominent function of selectins is associated with the initial stage of the leukocyte adhesion cascade, in which selectin binding enables tethering and rolling. The first adhesive event occurs through specific non-covalent interactions between selectins and their ligands, with glycans functioning as an interface between leukocytes or cancer cells and the endothelium. Targeting these interactions remains a principal strategy aimed at developing new therapies for the treatment of immune and inflammatory disorders and cancer. In this review, we will survey the significant contributions to and the current status of the understanding of the structure of selectins and the role of selectins in various biological processes. The potential of selectins and their ligands as therapeutic targets in chronic and acute inflammatory diseases and cancer will also be discussed. We will emphasize the structural characteristic of selectins and the catalytic mechanisms of glycosyltransferases involved in the biosynthesis of glycan recognition determinants. Furthermore, recent achievements in the synthesis of selectin inhibitors will be reviewed with a focus on the various strategies used for the development of glycosyltransferase inhibitors, including substrate analog inhibitors and transition state analog inhibitors, which are based on knowledge of the catalytic mechanism

Consensus Modeling for Prediction of Estrogenic Activity of Ingredients Commonly Used in Sunscreen Products

Sunscreen products are predominantly regulated as over-the-counter (OTC) drugs by the US FDA. The “active” ingredients function as ultraviolet filters. Once a sunscreen product is generally recognized as safe and effective (GRASE) via an OTC drug review process, new formulations using these ingredients do not require FDA review and approval, however, the majority of ingredients have never been tested to uncover any potential endocrine activity and their ability to interact with the estrogen receptor (ER) is unknown, despite the fact that this is a very extensively studied target related to endocrine activity. Consequently, we have developed an in silico model to prioritize single ingredient estrogen receptor activity for use when actual animal data are inadequate, equivocal, or absent. It relies on consensus modeling to qualitatively and quantitatively predict ER binding activity. As proof of concept, the model was applied to ingredients commonly used in sunscreen products worldwide and a few reference chemicals. Of the 32 chemicals with unknown ER binding activity that were evaluated, seven were predicted to be active estrogenic compounds. Five of the seven were confirmed by the published data. Further experimental data is needed to confirm the other two predictions