Abstract: Drug-induced phospholipidosis (PLD) is a lysosomal storage disorder characterized by the accumulation of phospholipids within the lysosome. This adverse drug effect can occur in various tissues and is suspected to impact cellular viability. Therefore, it is important to test chemical compounds for their potential to induce PLD during the drug design process. PLD has been reported to be a side effect of many commonly used drugs, especially those with cationic amphiphilic properties. To predict drug-induced PLD in silico, we established a high-throughput cell-culture-based method to quantitatively determine the induction of PLD by chemical compounds. Using this assay, we tested 297 drug-like compounds at two different concentrations (2.5 muM and 5.0 muM). We were able to identify 28 previously unknown PLD-inducing agents. Furthermore, our experimental results enabled the development of a binary classification model to predict PLD-inducing agents based on their molecular properties. This random forest prediction system yields a bootstrapped validated accuracy of 86 %. PLD-inducing agents overlap with those that target similar biological processes; a high degree of concordance with PLD-inducing agents was identified for cationic amphiphilic compounds, small molecules that inhibit acid sphingomyelinase, compounds that cross the blood-brain barrier, and compounds that violate Lipinski's rule of five. Furthermore, we were able to show that PLD-inducing compounds applied in combination additively induce PLD.

Abstract: This work demonstrates that Acinetobacter radioresistens strain S13 during the growth on medium supplemented with long-chain alkanes as the sole energy source expresses almA gene coding for a Baeyer-Villiger monooxygenase (BVMO) involved in alkanes subterminal oxidation. Phylogenetic analysis placed the sequence of this novel BVMO in the same clade of the prodrug activator ethionamide monooxygenase (EtaA) and it bears only a distant relation to the other known class I BVMO proteins. In silico analysis of the 3D model of the S13 BVMO generated by homology modelling also supports the similarities with EtaA by binding ethionamide to the active site. In vitro experiments carried out with the purified enzyme confirm that this novel BVMO is indeed capable of typical Baeyer-Villiger reactions as well as oxidation of the prodrug ethionamide.

Abstract: BACKGROUND: Previous studies have noted that drug targets appear to be associated with higher-degree or higher-centrality proteins in interaction networks. These studies explicitly or tacitly make choices of different source databases, data integration strategies, representation of proteins and complexes, and data reliability assumptions. Here we examined how the use of different data integration and representation techniques, or different notions of reliability, may affect the efficacy of degree and centrality as features in drug target prediction. RESULTS: Fifty percent of drug targets have a degree of less than nine, and ninety-five percent have a degree of less than ninety. We found that drug targets are over-represented in higher degree bins - this relationship is only seen for the consolidated interactome and it is not dependent on n-ary interaction data or its representation. Degree acts as a weak predictive feature for drug-target status and using more reliable subsets of the data does not increase this performance. However, performance does increase if only cancer-related drug targets are considered. We also note that a protein's membership in pathway records can act as a predictive feature that is better than degree and that high-centrality may be an indicator of a drug that is more likely to be withdrawn. CONCLUSIONS: These results show that protein interaction data integration and cleaning is an important consideration when incorporating network properties as predictive features for drug-target status. The provided scripts and data sets offer a starting point for further studies and cross-comparison of methods.

Abstract: The solute carrier 6 (SLC6) is a family of ion-dependent transporters that mediate uptake into the cell of osmolytes such as neurotransmitters and amino acids. Four SLC6 members transport GABA, a key neurotransmitter that triggers inhibitory signaling pathways via various receptors (e.g., GABA(A)). The GABA transporters (GATs) regulate the concentration of GABA available for signaling and are thus targeted by a variety of anticonvulsant and relaxant drugs. Here, we characterize GAT-2, a transporter that plays a role in peripheral GABAergic mechanisms, by constructing comparative structural models based on crystallographic structures of the leucine transporter LeuT. Models of GAT-2 in two different conformations were constructed and experimentally validated, using site-directed mutagenesis. Computational screening of 594,166 compounds including drugs, metabolites, and fragment-like molecules from the ZINC database revealed distinct ligands for the two GAT-2 models. 31 small molecules, including high scoring compounds and molecules chemically related to known and predicted GAT-2 ligands, were experimentally tested in inhibition assays. Twelve ligands were found, six of which were chemically novel (e.g., homotaurine). Our results suggest that GAT-2 is a high selectivity/low affinity transporter that is resistant to inhibition by typical GABAergic inhibitors. Finally, we compared the binding site of GAT-2 with those of other SLC6 members, including the norepinephrine transporter and other GATs, to identify ligand specificity determinants for this family. Our combined approach may be useful for characterizing interactions between small molecules and other membrane proteins, as well as for describing substrate specificities in other protein families.

Abstract: Metabolism has been identified as a defining factor in drug development success or failure because of its impact on many aspects of drug pharmacology, including bioavailability, half-life and toxicity. In this article, we provide an outline and descriptions of the resources for metabolism-related property predictions that are currently either freely or commercially available to the public. These resources include databases with data on, and software for prediction of, several end points: metabolite formation, sites of metabolic transformation, binding to metabolizing enzymes and metabolic stability. We attempt to place each tool in historical context and describe, wherever possible, the data it was based on. For predictions of interactions with metabolizing enzymes, we show a typical set of results for a small test set of compounds. Our aim is to give a clear overview of the areas and aspects of metabolism prediction in which the currently available resources are useful and accurate, and the areas in which they are inadequate or missing entirely.

Abstract: Drug design is crucial for the effective discovery of anti-cancer drugs. The success or failure of drug design often depends on the leading compounds screened in pre-clinical studies. Many efforts, such as in vivo animal experiments and in vitro drug screening, have improved this process, but these methods are usually expensive and laborious. In the post-genomics era, it is possible to seek leading compounds for large-scale candidate small-molecule screening with multiple OMICS datasets. In the present study, we developed a computational method of prioritizing small molecules as leading compounds by integrating transcriptomics and toxicogenomics data. This method provides priority lists for the selection of leading compounds, thereby reducing the time required for drug design. We found 11 known therapeutic small molecules for breast cancer in the top 100 candidates in our list, 2 of which were in the top 10. Furthermore, another 3 of the top 10 small molecules were recorded as closely related to cancer treatment in the DrugBank database. A comparison of the results of our approach with permutation tests and shared gene methods demonstrated that our OMICS data-based method is quite competitive. In addition, we applied our method to a prostate cancer dataset. The results of this analysis indicated that our method surpasses both the shared gene method and random selection. These analyses suggest that our method may be a valuable tool for directing experimental studies in cancer drug design, and we believe this time- and cost-effective computational strategy will be helpful in future studies in cancer therapy.

Abstract: BACKGROUND: Allostery is one of the most powerful and common ways of regulation of protein activity. However, for most allosteric proteins identified to date the mechanistic details of allosteric modulation are not yet well understood. Uncovering common mechanistic patterns underlying allostery would allow not only a better academic understanding of the phenomena, but it would also streamline the design of novel therapeutic solutions. This relatively unexplored therapeutic potential and the putative advantages of allosteric drugs over classical active-site inhibitors fuel the attention allosteric-drug research is receiving at present. A first step to harness the regulatory potential and versatility of allosteric sites, in the context of drug-discovery and design, would be to detect or predict their presence and location. In this article, we describe a simple computational approach, based on the effect allosteric ligands exert on protein flexibility upon binding, to predict the existence and position of allosteric sites on a given protein structure. RESULTS: By querying the literature and a recently available database of allosteric sites, we gathered 213 allosteric proteins with structural information that we further filtered into a non-redundant set of 91 proteins. We performed normal-mode analysis and observed significant changes in protein flexibility upon allosteric-ligand binding in 70% of the cases. These results agree with the current view that allosteric mechanisms are in many cases governed by changes in protein dynamics caused by ligand binding. Furthermore, we implemented an approach that achieves 65% positive predictive value in identifying allosteric sites within the set of predicted cavities of a protein (stricter parameters set, 0.22 sensitivity), by combining the current analysis on dynamics with previous results on structural conservation of allosteric sites. We also analyzed four biological examples in detail, revealing that this simple coarse-grained methodology is able to capture the effects triggered by allosteric ligands already described in the literature. CONCLUSIONS: We introduce a simple computational approach to predict the presence and position of allosteric sites in a protein based on the analysis of changes in protein normal modes upon the binding of a coarse-grained ligand at predicted cavities. Its performance has been demonstrated using a newly curated non-redundant set of 91 proteins with reported allosteric properties. The software developed in this work is available upon request from the authors.

Abstract: Pain and inflammation are major therapeutic areas for drug discovery. Current drugs for these pathologies have limited efficacy, however, and often cause a number of unwanted side effects. In the present study, we identify the nonsteroidal anti-inflammatory drug carprofen as a multitarget-directed ligand that simultaneously inhibits cyclooxygenase-1 (COX-1), COX-2, and fatty acid amide hydrolase (FAAH). Additionally, we synthesized and tested several derivatives of carprofen, sharing this multitarget activity. This may result in improved analgesic efficacy and reduced side effects (Naidu et al. J. Pharmacol. Exp. Ther.2009, 329, 48-56; Fowler, C. J.; et al. J. Enzyme Inhib. Med. Chem.2012, in press; Sasso et al. Pharmacol. Res.2012, 65, 553). The new compounds are among the most potent multitarget FAAH/COX inhibitors reported so far in the literature and thus may represent promising starting points for the discovery of new analgesic and anti-inflammatory drugs.

Abstract: There has been growing interest in disrupting bacterial virulence mechanisms as a form of infectious disease control through the use of 'anti-infective' drugs. Pseudomonas aeruginosa is an opportunistic pathogen noted for its intrinsic antibiotic resistance that causes serious infections requiring new therapeutic options. In this study, an analysis of the P. aeruginosa PAO1 deduced proteome was performed to identify pathogen-associated proteins. A computational screening approach was then used to discover drug repurposing opportunities, i.e. identifying approved drugs that bind and potentially disrupt the pathogen-associated protein targets. The selective oestrogen receptor modulator raloxifene, a drug currently used in the prevention of osteoporosis and/or invasive breast cancer in post-menopausal women, was predicted from this screen to bind P. aeruginosa PhzB2. PhzB2 is involved in production of the blue pigment pyocyanin produced via the phenazine biosynthesis pathway. Pyocyanin is toxic to eukaryotic cells and has been shown to play a role in infection in a mouse model, making it an attractive target for anti-infective drug discovery. Raloxifene was found to strongly attenuate P. aeruginosa virulence in a Caenorhabditis elegans model of infection. Treatment of P. aeruginosa wild-type strains PAO1 and PA14 with raloxifene resulted in a dose-dependent reduction in pyocyanin production in vitro; pyocyanin production and virulence were also reduced for a phzB2 insertion mutant. These results suggest that raloxifene may be suitable for further development as a therapeutic for P. aeruginosa infection and that such already approved drugs may be computationally screened and potentially repurposed as novel anti-infective/anti-virulence agents.

Abstract: The immediate-early gene early growth response 3 (Egr3) is associated with schizophrenia and expressed at reduced levels in postmortem patients' brains. We have previously reported that Egr3-deficient (Egr3(-/-)) mice display reduced sensitivity to the sedating effects of clozapine compared with wild-type (WT) littermates, paralleling the heightened tolerance of schizophrenia patients to antipsychotic side effects. In this study, we have used a pharmacological dissection approach to identify a neurotransmitter receptor defect in Egr3(-/-) mice that may mediate their resistance to the locomotor suppressive effects of clozapine. We report that this response is specific to second-generation antipsychotic agents (SGAs), as first-generation medications suppress the locomotor activity of Egr3(-/-) and WT mice to a similar degree. Further, in contrast to the leading theory that sedation by clozapine results from anti-histaminergic effects, we show that H1 histamine receptors are not responsible for this effect in C57BL/6 mice. Instead, selective serotonin 2A receptor (5HT(2A)R) antagonists ketanserin and MDL-11939 replicate the effect of SGAs, repressing the activity in WT mice at a dosage that fails to suppress the activity of Egr3(-/-) mice. Radioligand binding revealed nearly 70% reduction in 5HT(2A)R expression in the prefrontal cortex of Egr3(-/-) mice compared with controls. Egr3(-/-) mice also exhibit a decreased head-twitch response to 5HT(2A)R agonist 1-(2,5-dimethoxy 4-iodophenyl)-2-amino propane (DOI). These findings provide a mechanism to explain the reduced sensitivity of Egr3(-/-) mice to the locomotor suppressive effects of SGAs, and suggest that 5HT(2A)Rs may also contribute to the sedating properties of these medications in humans. Moreover, as the deficit in cortical 5HT(2A)R in Egr3(-/-) mice aligns with numerous studies reporting decreased 5HT(2A)R levels in the brains of schizophrenia patients, and the gene encoding the 5HT(2A)R is itself a leading schizophrenia candidate gene, these findings suggest a potential mechanism by which putative dysfunction in EGR3 in humans may influence risk for schizophrenia.