Routine inclusion of a global disability item in surveys is recommended, and specific suggestions are made to aid its design.

BackgroundLigand-based in silico target fishing can be used to identify the potential interacting target of bioactive ligands, which is useful for understanding the polypharmacology and safety profile of existing drugs. The underlying principle of the approach is that known bioactive ligands can be used as reference to predict the targets for a new compound.ResultsWe tested a pipeline enabling large-scale target fishing and drug repositioning, based on simple fingerprint similarity rankings with data fusion. A large library containing 533 drug relevant targets with 179,807 active ligands was compiled, where each target was defined by its ligand set. For a given query molecule, its target profile is generated by similarity searching against the ligand sets assigned to each target, for which individual searches utilizing multiple reference structures are then fused into a single ranking list representing the potential target interaction profile of the query compound. The proposed approach was validated by 10-fold cross validation and two external tests using data from DrugBank and Therapeutic Target Database (TTD). The use of the approach was further demonstrated with some examples concerning the drug repositioning and drug side-effects prediction. The promising results suggest that the proposed method is useful for not only finding promiscuous drugs for their new usages, but also predicting some important toxic liabilities.ConclusionsWith the rapid increasing volume and diversity of data concerning drug related targets and their ligands, the simple ligand-based target fishing approach would play an important role in assisting future drug design and discovery.

One of the key shortcomings in the field of nanotechnology risk assessment is the lack of techniques capable of source tracing of nanoparticles (NPs). Silica is the most-produced engineered nanomaterial and also widely present in the natural environment in diverse forms. Here we show that inherent isotopic fingerprints offer a feasible approach to distinguish the sources of silica nanoparticles (SiO 2 NPs). We find that engineered SiO 2 NPs have distinct Si–O two-dimensional (2D) isotopic fingerprints from naturally occurring SiO 2 NPs, due probably to the Si and O isotope fractionation and use of isotopically different materials during the manufacturing process of engineered SiO 2 NPs. A machine learning model is developed to classify the engineered and natural SiO 2 NPs with a discrimination accuracy of 93.3%. Furthermore, the Si–O isotopic fingerprints are even able to partly identify the synthetic methods and manufacturers of engineered SiO 2 NPs.

Fast and accurate predicting of the binding affinities of large sets of diverse protein−ligand complexes is an important, yet extremely challenging, task in drug discovery. The development of knowledge-based scoring functions exploiting structural information of known protein−ligand complexes represents a valuable contribution to such a computational prediction. In this study, we report a scoring function named IPMF that integrates additional experimental binding affinity information into the extracted potentials, on the assumption that a scoring function with the "enriched" knowledge base may achieve increased accuracy in binding affinity prediction. In our approach, the functions and atom types of PMF04 were inherited to implicitly capture binding effects that are hard to model explicitly, and a novel iteration device was designed to gradually tailor the initial potentials. We evaluated the performance of the resultant IPMF with a diverse set of 219 protein-ligand complexes and compared it with seven scoring functions commonly used in computer-aided drug design, including GLIDE, AutoDock4, VINA, PLP, LUDI, PMF, and PMF04. While the IPMF is only moderately successful in ranking native or near native conformations, it yields the lowest mean error of 1.41 log K(i)/K(d) units from measured inhibition affinities and the highest Pearson's correlation coefficient of R(p)2 0.40 for the test set. These results corroborate our initial supposition about the role of "enriched" knowledge base. With the rapid growing volume of high-quality structural and interaction data in the public domain, this work marks a positive step toward improving the accuracy of knowledge-based scoring functions in binding affinity prediction.

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Diaphorina citri Kuwayama is the vector of citrus “huanglongbing”, a citrus disease which poses a significant threat to the global citrus industry. Trehalose-6-phosphate synthase (TPS) plays an important role in the regulation of trehalose levels of insects, while its functions in D. citri are unclear. In this study, full-length cDNA sequences of the TPS gene from D. citri (DcTPS) were cloned and its expression patterns at various developmental stages were investigated. The results indicated that DcTPS mRNA was expressed at each developmental stage and the highest DcTPS expression was found in the fifth-instar nymphs of D. citri. Additionally, mortality and deformity of D. citri were observed after 24 and 48 h by feeding with three different dsRNA concentrations (20, 100 and 500 ng/μL). The results indicated that DcTPS expression was declined, and mortality and malformation in nymphs were increased via feeding with dsDcTPS. Moreover, the enzyme and trehalose content were decreased, while the content of glucose was significantly higher than that of untreated (control) individuals. This suggests that DcTPS might be vital for the growth and development of D. citri and further studies of the genes should be related to molting and metabolism for controlling D. citri.

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