No abstract

We prepared the first sigma-bonded metal complexes of widely utilized organic dyes, perylene tetracarboxylic acid diimides (PDIs). These 1,7-dipalladium PDI complexes were synthesized by C-Br oxidative addition of 1,7-dibromo-N,N'-dicyclohexyl PDI (Br2PDI) to Pd(0) phosphine complexes bearing triphenylphosphine and bischelating 1,2-bis(diphenylphosphino)ethane (dppe). The structures of Pd-PDI complexes were elucidated by single-crystal X-ray analysis. Surprisingly, despite direct attachement of two late transition metal centers, Pd-PDI systems are highly fluorescent (Phi=0.65 and 0.22 for triphenylphosphine and dppe systems, respectively). This is rationalized in terms of weak electronic interactions between the metal centers and PDI pi-system, as revealed by TD-DFT calculations.

Increased availability of nanoparticle-based products will, inevitably, expose the environment to these materials. Engineered nanoparticles (ENPs) may thus find their way into the soil environment via wastewater, dumpsters and other anthropogenic sources; metallic oxide nanoparticles comprise one group of ENPs that could potentially be hazardous for the environment. Because the soil bacterial community is a major service provider for the ecosystem and humankind, it is critical to study the effects of ENP exposure on soil bacteria. These effects were evaluated by measuring bacterial community activity, composition and size following exposure to copper oxide (CuO) and magnetite (Fe3O4) nanosized (<50 nm) particles. Two different soil types were examined: a sandy loam (Bet-Dagan) and a sandy clay loam (Yatir), under two ENP concentrations (1%, 0.1%). Results indicate that the bacterial community in Bet-Dagan soil was more susceptible to change due to exposure to these ENPs, relative to Yatir soil. More specifically, CuO had a strong effect on bacterial hydrolytic activity, oxidative potential, community composition and size in Bet-Dagan soil. Few effects were noted in the Yatir soil, although 1% CuO exposure did cause a significant decreased oxidative potential and changes to community composition. Fe3O4 changed the hydrolytic activity and bacterial community composition in Bet-Dagan soil but did not affect the Yatir soil bacterial community. Furthermore, in Bet-Dagan soil, abundance of bacteria annotated to OTUs from the Bacilli class decreased after addition of 0.1% CuO but increased with 1% CuO, while in Yatir soil their abundance was reduced with 1% CuO. Other important soil bacterial groups, including Rhizobiales and Sphingobacteriaceae, were negatively affected by CuO addition to soil. These results indicate that both ENPs are potentially harmful to soil environments. Furthermore, it is suggested that the clay fraction and organic matter in different soils interact with the ENPs and reduce their toxicity.

No abstract

No abstract

Identifying active compounds (hits) that bind to biological targets of pharmaceutical relevance is the cornerstone of drug design efforts. Structure based virtual screening, namely, the in silico evaluation of binding energies and geometries between a protein and its putative ligands, has emerged over the past few years as a promising approach in this field. The success of the method relies on the availability of reliable 3-dimensional (3D) structures of the target protein and its candidate ligands (the screening library), a reliable docking method that can fit the different ligands into the protein's binding site, and an accurate scoring function that can rank the resulting binding modes in accord with their binding affinities. This last requirement is arguably the most difficult to meet due to the complexity of the binding process. A potential solution to this so-called scoring problem is the usage of multiple scoring functions in an approach known as consensus scoring. Several consensus scoring methods were suggested in the literature and have generally demonstrated an improved ranking of screening libraries relative to individual scoring functions. Nevertheless, current consensus scoring strategies suffer from several shortcomings, in particular, strong dependence on the initial parameters and an incomplete treatment of inactive compounds. In this work we present a new consensus scoring algorithm (SeleX-Consensus Scoring abbreviated to SeleX-CS) specifically designed to address these limitations: (i) A subset of the initial set of the scoring functions is allowed to form the consensus score, and this subset is optimized via a Monte Carlo/Simulated Annealing procedure. (ii) Rank redundancy between the members of the screening library is removed. (iii) The method explicitly considers the presence of inactive compounds. The new algorithm was applied to the ranking of screening libraries targeting two G-protein coupled receptors (GPCR). Excellent enrichment factors were obtained in both cases: For the cannabinoid receptor 1 (CB1), SeleX-CS outperformed the best single score and afforded an enrichment factor of 41 at 1% of the screening library compared with the best single score value of 15 (GOLD_Fitness). For the chemokine receptor type 2 (CCR2) SeleX-CS afforded an enrichment factor of 72 (again at 1% of the screening library) once more outperforming any single score (enrichment factor of 20 by GSCORE). Moreover, SeleX-CS demonstrated success rates of 67% (CCR2) and 73% (CB1) when applied to ranking an external test set. In both cases, the new algorithm also afforded good derichment of inactive compounds (i.e., the ability to push inactive compounds to the bottom of the ranked library). The method was then extended to rank a lead optimization series targeting the Kv4.3 potassium ion channel, resulting in a Spearman's correlation coefficient, p = 0.63 (n = 40), between the SeleX-CS-based rank and the actual pKi values. These results suggest that SeleX-CS is a powerful method for ranking screening libraries in th...

No abstract