Within the last few years a considerable amount of evaluative studies has been published that investigate the performance of 3D virtual screening approaches. Thereby, in particular assessments of protein-ligand docking are facing remarkable interest in the scientific community. However, comparing virtual screening approaches is a non-trivial task. Several publications, especially in the field of molecular docking, suffer from shortcomings that are likely to affect the significance of the results considerably. These quality issues often arise from poor study design, biasing, by using improper or inexpressive enrichment descriptors, and from errors in interpretation of the data output. In this review we analyze recent literature evaluating 3D virtual screening methods, with focus on molecular docking. We highlight problematic issues and provide guidelines on how to improve the quality of computational studies. Since 3D virtual screening protocols are in general assessed by their ability to discriminate between active and inactive compounds, we summarize the impact of the composition and preparation of test sets on the outcome of evaluations. Moreover, we investigate the significance of both classic enrichment parameters and advanced descriptors for the performance of 3D virtual screening methods. Furthermore, we review the significance and suitability of RMSD as a measure for the accuracy of protein-ligand docking algorithms and of conformational space sub sampling algorithms.

Shape-based molecular similarity approaches have been established as important and popular virtual screening techniques. Recent applications have shown successful screening campaigns using different parameters and query selection. It is common sense that pure volume overlap scoring (or "shape-based screening") under-represents chemical or pharmacophoric information of a molecule. Using the "Directory of Useful Decoys" (DUD) as a benchmark set, we systematically evaluate how (i) the choice of query conformations, (ii) the selection of the active compound to be used as a query structure, and (iii) the inclusion of chemical information (i.e., the pharmacophoric properties of the query molecule) affect screening performance. Varying these parameters bears remarkable potential for improvements and delivers the best screening performance reported using these tools so far. From these insights, guidelines on how to reach optimum performance during virtual screening are developed.

TMEM132D is a candidate gene, where risk genotypes have been associated with anxiety severity along with higher mRNA expression in the frontal cortex of panic disorder patients. Concurrently, in a high (HAB) and low (LAB) trait anxiety mouse model, Tmem132d was found to show increased expression in the anterior cingulate cortex (aCC) of HAB as compared to LAB mice. To understand the molecular underpinnings underlying the differential expression, we sequenced the gene and found two single-nucleotide polymorphisms (SNPs) in the promoter differing between both lines which could explain the observed mRNA expression profiles using gene reporter assays. In addition, there was no difference in basal DNA methylation in the CpG Island that encompasses the HAB vs. LAB Tmem132d promoter region. Furthermore, we found significantly higher binding of RNA polymerase II (POLR2A) to the proximal HAB-specific SNP (rs233264624) than the corresponding LAB locus in an oligonucleotide pull-down assay, suggesting increased transcription. Virus mediated overexpression of Tmem132d in the aCC of C57BL/6 J mice could confirm its role in mediating an anxiogenic phenotype. To model gene–environmental interactions, HAB mice exposed to enriched environment (HAB-EE) responded with decreased anxiety levels but, had enhanced Tmem132d mRNA expression as compared to standard-housed HAB (HAB-SH) mice. While LAB mice subjected to unpredictable chronic mild stress (LAB-UCMS) exhibited higher anxiety levels and had lower mRNA expression compared to standard-housed LAB (LAB-SH) mice. Chromatin immunoprecipitation revealed significantly higher binding of POLR2A to rs233264624 in HAB-EE, while LAB-UCMS had lower POLR2A binding at this locus, thus explaining the enhanced or attenuated expression of Tmem132d compared to their respective SH controls. To further investigate gene–environment interactions, DNA methylation was assessed using Illumina 450 K BeadChip in 74 panic disorder patients. Significant methylation differences were observed in two CpGs (cg26322591 and cg03283235) located in TMEM132D depending on the number of positive life events supporting the results of an influence of positive environmental cues on regulation of Tmem132d expression in mice.

The identification of targets whose interaction is likely to result in the successful treatment of a disease is of growing interest for natural product scientists. In the current study we performed an exemplary application of a virtual parallel screening approach to identify potential targets for 16 secondary metabolites isolated and identified from the aerial parts of the medicinal plant Ruta graveolens L. Low energy conformers of the isolated constituents were simultaneously screened against a set of 2208 pharmacophore models generated in-house for the in silico prediction of putative biological targets, i. e., target fishing. Based on the predicted ligand-target interactions, we focused on three biological targets, namely acetylcholinesterase (AChE), the human rhinovirus (HRV) coat protein and the cannabinoid receptor type-2 (CB 2 ). For a critical evaluation of the applied parallel screening approach, virtual hits and non-hits were assayed on the respective targets. For AChE the highest scoring virtual hit, arborinine, showed the best inhibitory in vitro activity on AChE (IC 50 34.7 μM). Determination of the anti-HRV-2 effect revealed 6,7,8-trimethoxycoumarin and arborinine to be the most active antiviral constituents with IC 50 values of 11.98 μM and 3.19 μM, respectively. Of these, arborinine was predicted virtually. Of all the molecules subjected to parallel screening, one virtual CB 2 ligand was obtained, i.e., rutamarin. Interestingly, in experimental studies only this compound showed a selective activity to the CB 2 receptor (Ki of 7.4 μM) by using a radioligand displacement assay. The applied parallel screening paradigm with constituents of R. graveolens on three different proteins has shown promise as an in silico tool for rational target fishing and pharmacological profiling of extracts and single chemical entities in natural product research.

Peroxisome proliferator-activated receptor gamma (PPAR␥) agonists are used for the treatment of type 2 diabetes and metabolic syndrome. However, the currently used PPAR␥ agonists display serious side effects, which has led to a great interest in the discovery of novel ligands with favorable properties. The aim of our study was to identify new PPAR␥ agonists by a PPAR␥ pharmacophore-based virtual screening of 3D natural product libraries. This in silico approach led to the identification of several neolignans predicted to bind the receptor ligand binding domain (LBD). To confirm this prediction, the neolignans dieugenol, tetrahydrodieugenol, and magnolol were isolated from the respective natural source or synthesized and subsequently tested for PPAR␥ receptor binding. The neolignans bound to the PPAR␥ LBD with EC 50 values in the nanomolar range, exhibiting a binding pattern highly similar to the clinically used agonist pioglitazone. In intact cells, dieugenol and tetrahydrodieugenol selectively activated human PPAR␥-mediated, but not human PPAR␣-or -␤/␦-mediated luciferase reporter expression, with a pattern suggesting partial PPAR␥ agonism. The coactivator recruitment study also demonstrated partial agonism of the tested neolignans. Dieugenol, tetrahydrodieugenol, and magnolol but not the structurally related eugenol induced 3T3-L1 preadipocyte differentiation, confirming effectiveness in a cell model with endogenous PPAR␥ expression. In conclusion, we identified neolignans as novel ligands for PPAR␥, which exhibited interesting activation profiles, recommending them as potential pharmaceutical leads or dietary supplements.Western lifestyle with a high intake of simple sugars, saturated fat, and physical inactivity promotes pathologic conditions such as type 2 diabetes, obesity, and metabolic syndrome, which are currently taking a devastating epidemical spread worldwide. Compounds that are activating PPAR␥ may help to fight these pathological conditions (Cho and Momose, 2008).PPARs are ligand-activated transcription factors belonging to the nuclear receptor superfamily, and their main function relates to the regulation of genes involved in glucose and lipid metabolism (Tenenbaum et al., 2003;Desvergne et al., 2006). Three isoforms of this nuclear receptor have been identified so far: PPAR␣, PPAR␤/␦, and PPAR␥. PPAR␣ is highly expressed in skeletal muscle, liver, kidney, heart, and the vascular wall, and it was shown to be mainly involved in the regulation of lipid catabolism (Fruchart, 2009). PPAR␥ is

We describe the generation and validation of pharmacophore models for PPARs, as well as a large scale validation of the parallel screening approach by screening PPAR ligands against a large database of structure-based models. A large test set of 357 PPAR ligands was screened against 48 PPAR models to determine the best models for agonists of PPAR-alpha, PPAR-delta, and PPAR-gamma. Afterwards, a parallel screen was performed using the 357 PPAR ligands and 47 structure-based models for PPARs, which were integrated into a 1537 models comprising in-house pharmacophore database, to assess the enrichment of PPAR ligands within the PPAR hypotheses. For these purposes, we categorized the 1537 database models into 181 protein targets and developed a score that ranks the retrieved targets for each ligand. Thus, we tried to find out if the concept of parallel screening is able to predict the correct pharmacological target for a set of compounds. The PPAR target was ranked first more often than any other target. This confirms the ability of parallel screening to forecast the pharmacological active target for a set of compounds.

The continuum of physiological anxiety up to psychopathology is not merely dependent on genes, but is orchestrated by the interplay of genetic predisposition, gene x environment and epigenetic interactions. Accordingly, inborn anxiety is considered a polygenic, multifactorial trait, likely to be shaped by environmentally driven plasticity at the genomic level. We here took advantage of the extreme genetic predisposition of the selectively bred high (HAB) and low anxiety (LAB) mouse model exhibiting high vs low anxiety-related behavior and tested whether and how beneficial (enriched environment) vs detrimental (chronic mild stress) environmental manipulations are capable of rescuing phenotypes from both ends of the anxiety continuum. We provide evidence that (i) even inborn and seemingly rigid behavioral and neuroendocrine phenotypes can bidirectionally be rescued by appropriate environmental stimuli, (ii) corticotropin-releasing hormone receptor 1 (Crhr1), critically involved in trait anxiety, shows bidirectional alterations in its expression in the basolateral amygdala (BLA) upon environmental stimulation, (iii) these alterations are linked to an increased methylation status of its promoter and, finally, (iv) binding of the transcription factor Yin Yang 1 (YY1) to the Crhr1 promoter contributes to its gene expression in a methylation-sensitive manner. Thus, Crhr1 in the BLA is critically involved as plasticity gene in the bidirectional epigenetic rescue of extremes in trait anxiety.

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