Ursolic acid ameliorates stress and reactive oxygen species in C. elegans knockout mutants by the dopamine Dop1 and Dop3 receptors.

BACKGROUND: Depression and stress-related disorders are leading causes of death worldwide. Standard treatments elevating serotonin or noradrenaline levels are not sufficiently effective and cause adverse side effects. A connection between dopamine pathways and stress-related disorders has been suggested. Compounds derived from herbal medicine could be a promising alternative. We examined the neuroprotective effects of ursolic acid (UA) by focusing on dopamine signalling. METHODS: Trolox equivalent capacity assay was used to determine the antioxidant activities of UA in vitro. C. elegans N2 wildtype and dopamine receptor-knockout mutants (dop1-deficient RB665 and dop3-deficient LX703 strains) were used as in vivo models. H2DCFDA and acute juglone assays were applied to determine the antioxidant activity in dependency of dopamine pathways in vivo. Stress was assessed by heat and acute osmotic stress assays. The influence of UA on overall survival was analyzed by a life span assay. The dop1 and dop3 mRNA expression was determined by real time RT-PCR. We also examined the binding affinity of UA towards C. elegans Dop1 and Dop3 receptors as well as human dopamine receptors D1 and D3 by molecular docking. RESULTS: Antioxidant activity assays showed that UA exerts strong antioxidant activity. UA increased resistance towards oxidative, osmotic and heat stress. Additionally, UA increased life span of nematodes. Moreover, dop1 and dop3 gene expression was significantly enhanced upon UA treatment. Docking analysis revealed stronger binding affinity of UA to C. elegans and human dopamine receptors than the natural ligand, dopamine. Binding to Dop1 was stronger than to Dop3. CONCLUSION: UA reduced stress-dependent ROS generation and acted through Dop1 and to a lesser extent through Dop3 to reduce stress and prolong life span in C. elegans. These results indicate that UA could be a promising lead compound for the development of new antidepressant medications.

Evaluation of QSAR Equations for Virtual Screening.

Quantitative Structure Activity Relationship (QSAR) models can inform on the correlation between activities and structure-based molecular descriptors. This information is important for the understanding of the factors that govern molecular properties and for designing new compounds with favorable properties. Due to the large number of calculate-able descriptors and consequently, the much larger number of descriptors combinations, the derivation of QSAR models could be treated as an optimization problem. For continuous responses, metrics which are typically being optimized in this process are related to model performances on the training set, for example, R2 and QCV2. Similar metrics, calculated on an external set of data (e.g., QF1/F2/F32), are used to evaluate the performances of the final models. A common theme of these metrics is that they are context -" ignorant". In this work we propose that QSAR models should be evaluated based on their intended usage. More specifically, we argue that QSAR models developed for Virtual Screening (VS) should be derived and evaluated using a virtual screening-aware metric, e.g., an enrichment-based metric. To demonstrate this point, we have developed 21 Multiple Linear Regression (MLR) models for seven targets (three models per target), evaluated them first on validation sets and subsequently tested their performances on two additional test sets constructed to mimic small-scale virtual screening campaigns. As expected, we found no correlation between model performances evaluated by "classical" metrics, e.g., R2 and QF1/F2/F32 and the number of active compounds picked by the models from within a pool of random compounds. In particular, in some cases models with favorable R2 and/or QF1/F2/F32 values were unable to pick a single active compound from within the pool whereas in other cases, models with poor R2 and/or QF1/F2/F32 values performed well in the context of virtual screening. We also found no significant correlation between the number of active compounds correctly identified by the models in the training, validation and test sets. Next, we have developed a new algorithm for the derivation of MLR models by optimizing an enrichment-based metric and tested its performances on the same datasets. We found that the best models derived in this manner showed, in most cases, much more consistent results across the training, validation and test sets and outperformed the corresponding MLR models in most virtual screening tests. Finally, we demonstrated that when tested as binary classifiers, models derived for the same targets by the new algorithm outperformed Random Forest (RF) and Support Vector Machine (SVM)-based models across training/validation/test sets, in most cases. We attribute the better performances of the Enrichment Optimizer Algorithm (EOA) models in VS to better handling of inactive random compounds. Optimizing an enrichment-based metric is therefore a promising strategy for the derivation of QSAR models for classification and virtual screening.

Dopamine D1 receptor agonist and D2 receptor antagonist effects of the natural product (-)-stepholidine: molecular modeling and dynamics simulations.

(-)-Stepholidine (SPD), an active ingredient of the Chinese herb Stephania, is the first compound found to have dual function as a dopamine receptor D1 agonist and D2 antagonist. Insights into dynamical behaviors of D1 and D2 receptors and their interaction modes with SPD are crucial in understanding the structural and functional characteristics of dopamine receptors. In this study a computational approach, integrating protein structure prediction, automated molecular docking, and molecular dynamics simulations were employed to investigate the dual action mechanism of SPD on the D1 and D2 receptors, with the eventual aim to develop new drugs for treating diseases affecting the central nervous system such as schizophrenia. The dynamics simulations revealed the surface features of the electrostatic potentials and the conformational "open-closed" process of the binding entrances of two dopamine receptors. Potential binding conformations of D1 and D2 receptors were obtained, and the D1-SPD and D2-SPD complexes were generated, which are in good agreement with most of experimental data. The D1-SPD structure shows that the K-167_EL-2-E-302_EL-3 (EL-2: extracellular loop 2; EL-3: extracellular loop 3) salt bridge plays an important role for both the conformational change of the extracellular domain and the binding of SPD. Based on our modeling and simulations, we proposed a mechanism of the dual action of SPD and a subsequent signal transduction model. Further mutagenesis and biophysical experiments are needed to test and improve our proposed dual action mechanism of SPD and signal transduction model.