HitPickV2: a web server to predict targets of chemical compounds.

MOTIVATION: The identification of protein targets of novel compounds is essential to understand compounds' mechanisms of action leading to biological effects. Experimental methods to determine these protein targets are usually slow, costly and time consuming. Computational tools have recently emerged as cheaper and faster alternatives that allow the prediction of targets for a large number of compounds. RESULTS: Here, we present HitPickV2, a novel ligand-based approach for the prediction of human druggable protein targets of multiple compounds. For each query compound, HitPickV2 predicts up to 10 targets out of 2739 human druggable proteins. To that aim, HitPickV2 identifies the closest, structurally similar compounds in a restricted space within a vast chemical-protein interaction area, until 10 distinct protein targets are found. Then, HitPickV2 scores these 10 targets based on three parameters of the targets in such space: the Tanimoto coefficient (Tc) between the query and the most similar compound interacting with the target, a target rank that considers Tc and Laplacian-modified naïve Bayesian target models scores and a novel parameter introduced in HitPickV2, the number of compounds interacting with each target (occur). We present the performance results of HitPickV2 in cross-validation as well as in an external dataset. AVAILABILITY AND IMPLEMENTATION: HitPickV2 is available in www.hitpickv2.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Novel Drug-Mouse Phenotypic Similarity Method Detects Molecular Determinants of Drug Effects.

The molecular mechanisms that translate drug treatment into beneficial and unwanted effects are largely unknown. We present here a novel approach to detect gene-drug and gene-side effect associations based on the phenotypic similarity of drugs and single gene perturbations in mice that account for the polypharmacological property of drugs. We scored the phenotypic similarity of human side effect profiles of 1,667 small molecules and biologicals to profiles of phenotypic traits of 5,384 mouse genes. The benchmarking with known relationships revealed a strong enrichment of physical and indirect drug-target connections, causative drug target-side effect links as well as gene-drug links involved in pharmacogenetic associations among phenotypically similar gene-drug pairs. The validation by in vitro assays and the experimental verification of an unknown connection between oxandrolone and prokineticin receptor 2 reinforces the ability of this method to provide new molecular insights underlying drug treatment. Thus, this approach may aid in the proposal of novel and personalized treatments.

Organ system heterogeneity DB: a database for the visualization of phenotypes at the organ system level.

Perturbations of mammalian organisms including diseases, drug treatments and gene perturbations in mice affect organ systems differently. Some perturbations impair relatively few organ systems while others lead to highly heterogeneous or systemic effects. Organ System Heterogeneity DB (http://mips.helmholtz-muenchen.de/Organ_System_Heterogeneity/) provides information on the phenotypic effects of 4865 human diseases, 1667 drugs and 5361 genetically modified mouse models on 26 different organ systems. Disease symptoms, drug side effects and mouse phenotypes are mapped to the System Organ Class (SOC) level of the Medical Dictionary of Regulatory Activities (MedDRA). Then, the organ system heterogeneity value, a measurement of the systemic impact of a perturbation, is calculated from the relative frequency of phenotypic features across all SOCs. For perturbations of interest, the database displays the distribution of phenotypic effects across organ systems along with the heterogeneity value and the distance between organ system distributions. In this way, it allows, in an easy and comprehensible fashion, the comparison of the phenotypic organ system distributions of diseases, drugs and their corresponding genetically modified mouse models of associated disease genes and drug targets. The Organ System Heterogeneity DB is thus a platform for the visualization and comparison of organ system level phenotypic effects of drugs, diseases and genes.

Unveiling new biological relationships using shared hits of chemical screening assay pairs.

MOTIVATION: Although the integration and analysis of the activity of small molecules across multiple chemical screens is a common approach to determine the specificity and toxicity of hits, the suitability of these approaches to reveal novel biological information is less explored. Here, we test the hypothesis that assays sharing selective hits are biologically related. RESULTS: We annotated the biological activities (i.e. biological processes or molecular activities) measured in assays and constructed chemical hit profiles with sets of compounds differing on their selectivity level for 1640 assays of ChemBank repository. We compared the similarity of chemical hit profiles of pairs of assays with their biological relationships and observed that assay pairs sharing non-promiscuous chemical hits tend to be biologically related. A detailed analysis of a network containing assay pairs with the highest hit similarity confirmed biological meaningful relationships. Furthermore, the biological roles of predicted molecular targets of the shared hits reinforced the biological associations between assay pairs. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Systematic analysis of gene properties influencing organ system phenotypes in mammalian perturbations.

MOTIVATION: Diseases and adverse drug reactions are frequently caused by disruptions in gene functionality. Gaining insight into the global system properties governing the relationships between genotype and phenotype is thus crucial to understand and interfere with perturbations in complex organisms such as diseases states. RESULTS: We present a systematic analysis of phenotypic information of 5047 perturbations of single genes in mice, 4766 human diseases and 1666 drugs that examines the relationships between different gene properties and the phenotypic impact at the organ system level in mammalian organisms. We observe that while single gene perturbations and alterations of nonessential, tissue-specific genes or those with low betweenness centrality in protein-protein interaction networks often show organ-specific effects, multiple gene alterations resulting e.g. from complex disorders and drug treatments have a more widespread impact. Interestingly, certain cellular localizations are distinctly associated to systemic effects in monogenic disease genes and mouse gene perturbations, such as the lumen of intracellular organelles and transcription factor complexes, respectively. In summary, we show that the broadness of the phenotypic effect is clearly related to certain gene properties and is an indicator of the severity of perturbations. This work contributes to the understanding of gene properties influencing the systemic effects of diseases and drugs.

HitPick: a web server for hit identification and target prediction of chemical screenings.

MOTIVATION: High-throughput phenotypic assays reveal information about the molecules that modulate biological processes, such as a disease phenotype and a signaling pathway. In these assays, the identification of hits along with their molecular targets is critical to understand the chemical activities modulating the biological system. Here, we present HitPick, a web server for identification of hits in high-throughput chemical screenings and prediction of their molecular targets. HitPick applies the B-score method for hit identification and a newly developed approach combining 1-nearest-neighbor (1NN) similarity searching and Laplacian-modified naïve Bayesian target models to predict targets of identified hits. The performance of the HitPick web server is presented and discussed. AVAILABILITY: The server can be accessed at http://mips.helmholtz-muenchen.de/proj/hitpick. CONTACT: monica.campillos@helmholtz-muenchen.de.

Systematic identification of proteins that elicit drug side effects.

Side effect similarities of drugs have recently been employed to predict new drug targets, and networks of side effects and targets have been used to better understand the mechanism of action of drugs. Here, we report a large-scale analysis to systematically predict and characterize proteins that cause drug side effects. We integrated phenotypic data obtained during clinical trials with known drug-target relations to identify overrepresented protein-side effect combinations. Using independent data, we confirm that most of these overrepresentations point to proteins which, when perturbed, cause side effects. Of 1428 side effects studied, 732 were predicted to be predominantly caused by individual proteins, at least 137 of them backed by existing pharmacological or phenotypic data. We prove this concept in vivo by confirming our prediction that activation of the serotonin 7 receptor (HTR7) is responsible for hyperesthesia in mice, which, in turn, can be prevented by a drug that selectively inhibits HTR7. Taken together, we show that a large fraction of complex drug side effects are mediated by individual proteins and create a reference for such relations.

Characterization of drug-induced transcriptional modules: towards drug repositioning and functional understanding.

In pharmacology, it is crucial to understand the complex biological responses that drugs elicit in the human organism and how well they can be inferred from model organisms. We therefore identified a large set of drug-induced transcriptional modules from genome-wide microarray data of drug-treated human cell lines and rat liver, and first characterized their conservation. Over 70% of these modules were common for multiple cell lines and 15% were conserved between the human in vitro and the rat in vivo system. We then illustrate the utility of conserved and cell-type-specific drug-induced modules by predicting and experimentally validating (i) gene functions, e.g., 10 novel regulators of cellular cholesterol homeostasis and (ii) new mechanisms of action for existing drugs, thereby providing a starting point for drug repositioning, e.g., novel cell cycle inhibitors and new modulators of α-adrenergic receptor, peroxisome proliferator-activated receptor and estrogen receptor. Taken together, the identified modules reveal the conservation of transcriptional responses towards drugs across cell types and organisms, and improve our understanding of both the molecular basis of drug action and human biology.

Novel biomarkers for pre-diabetes identified by metabolomics.

Type 2 diabetes (T2D) can be prevented in pre-diabetic individuals with impaired glucose tolerance (IGT). Here, we have used a metabolomics approach to identify candidate biomarkers of pre-diabetes. We quantified 140 metabolites for 4297 fasting serum samples in the population-based Cooperative Health Research in the Region of Augsburg (KORA) cohort. Our study revealed significant metabolic variation in pre-diabetic individuals that are distinct from known diabetes risk indicators, such as glycosylated hemoglobin levels, fasting glucose and insulin. We identified three metabolites (glycine, lysophosphatidylcholine (LPC) (18:2) and acetylcarnitine) that had significantly altered levels in IGT individuals as compared to those with normal glucose tolerance, with P-values ranging from 2.4×10(-4) to 2.1×10(-13). Lower levels of glycine and LPC were found to be predictors not only for IGT but also for T2D, and were independently confirmed in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort. Using metabolite-protein network analysis, we identified seven T2D-related genes that are associated with these three IGT-specific metabolites by multiple interactions with four enzymes. The expression levels of these enzymes correlate with changes in the metabolite concentrations linked to diabetes. Our results may help developing novel strategies to prevent T2D.

SIREs: searching for iron-responsive elements.

The iron regulatory protein/iron-responsive element regulatory system plays a crucial role in the post-transcriptional regulation of gene expression and its disruption results in human disease. IREs are cis-acting regulatory motifs present in mRNAs that encode proteins involved in iron metabolism. They function as binding sites for two related trans-acting factors, namely the IRP-1 and -2. Among cis-acting RNA regulatory elements, the IRE is one of the best characterized. It is defined by a combination of RNA sequence and structure. However, currently available programs to predict IREs do not show a satisfactory level of sensitivity and fail to detect some of the functional IREs. Here, we report an improved software for the prediction of IREs implemented as a user-friendly web server tool. The SIREs web server uses a simple data input interface and provides structure analysis, predicted RNA folds, folding energy data and an overall quality flag based on properties of well characterized IREs. Results are reported in a tabular format and as a schematic visual representation that highlights important features of the IRE. The SIREs (Search for iron-responsive elements) web server is freely available on the web at http://ccbg.imppc.org/sires/index.html.

STITCH 2: an interaction network database for small molecules and proteins.

Over the last years, the publicly available knowledge on interactions between small molecules and proteins has been steadily increasing. To create a network of interactions, STITCH aims to integrate the data dispersed over the literature and various databases of biological pathways, drug-target relationships and binding affinities. In STITCH 2, the number of relevant interactions is increased by incorporation of BindingDB, PharmGKB and the Comparative Toxicogenomics Database. The resulting network can be explored interactively or used as the basis for large-scale analyses. To facilitate links to other chemical databases, we adopt InChIKeys that allow identification of chemicals with a short, checksum-like string. STITCH 2.0 connects proteins from 630 organisms to over 74,000 different chemicals, including 2200 drugs. STITCH can be accessed at http://stitch.embl.de/.

A side effect resource to capture phenotypic effects of drugs.

The molecular understanding of phenotypes caused by drugs in humans is essential for elucidating mechanisms of action and for developing personalized medicines. Side effects of drugs (also known as adverse drug reactions) are an important source of human phenotypic information, but so far research on this topic has been hampered by insufficient accessibility of data. Consequently, we have developed a public, computer-readable side effect resource (SIDER) that connects 888 drugs to 1450 side effect terms. It contains information on frequency in patients for one-third of the drug-side effect pairs. For 199 drugs, the side effect frequency of placebo administration could also be extracted. We illustrate the potential of SIDER with a number of analyses. The resource is freely available for academic research at http://sideeffects.embl.de.

Drug-induced regulation of target expression.

Drug perturbations of human cells lead to complex responses upon target binding. One of the known mechanisms is a (positive or negative) feedback loop that adjusts the expression level of the respective target protein. To quantify this mechanism systems-wide in an unbiased way, drug-induced differential expression of drug target mRNA was examined in three cell lines using the Connectivity Map. To overcome various biases in this valuable resource, we have developed a computational normalization and scoring procedure that is applicable to gene expression recording upon heterogeneous drug treatments. In 1290 drug-target relations, corresponding to 466 drugs acting on 167 drug targets studied, 8% of the targets are subject to regulation at the mRNA level. We confirmed systematically that in particular G-protein coupled receptors, when serving as known targets, are regulated upon drug treatment. We further newly identified drug-induced differential regulation of Lanosterol 14-alpha demethylase, Endoplasmin, DNA topoisomerase 2-alpha and Calmodulin 1. The feedback regulation in these and other targets is likely to be relevant for the success or failure of the molecular intervention.

A drug screen with approved compounds identifies amlexanox as a novel Wnt/ß-catenin activator inducing lung epithelial organoid formation.

BACKGROUND AND PURPOSE: Emphysema is an incurable disease characterized by loss of lung tissue leading to impaired gas exchange. Wnt/ß-catenin signalling is reduced in emphysema, and exogenous activation of the pathway in experimental models in vivo and in human ex vivo lung tissue improves lung function and structure. We sought to identify a pharmaceutical able to activate Wnt/ß-catenin signalling and assess its potential to activate lung epithelial cells and repair. EXPERIMENTAL APPROACH: We screened 1216 human-approved compounds for Wnt/ß-catenin signalling activation using luciferase reporter cells and selected candidates based on their computationally predicted protein targets. We further performed confirmatory luciferase reporter and metabolic activity assays. Finally, we studied the regenerative potential in murine adult epithelial cell-derived lung organoids and in vivo using a murine elastase-induced emphysema model. KEY RESULTS: The primary screen identified 16 compounds that significantly induced Wnt/ß-catenin-dependent luciferase activity. Selected compounds activated Wnt/ß-catenin signalling without inducing cell toxicity or proliferation. Two compounds were able to promote organoid formation, which was reversed by pharmacological Wnt/ß-catenin inhibition, confirming the Wnt/ß-catenin-dependent mechanism of action. Amlexanox was used for in vivo evaluation, and preventive treatment resulted in improved lung function and structure in emphysematous mouse lungs. Moreover, gene expression of Hgf, an important alveolar repair marker, was increased, whereas disease marker Eln was decreased, indicating that amlexanox induces pro-regenerative signalling in emphysema. CONCLUSION AND IMPLICATIONS: Using a drug screen based on Wnt/ß-catenin activity, organoid assays and a murine emphysema model, amlexanox was identified as a novel potential therapeutic agent for emphysema.

STITCH: interaction networks of chemicals and proteins.

The knowledge about interactions between proteins and small molecules is essential for the understanding of molecular and cellular functions. However, information on such interactions is widely dispersed across numerous databases and the literature. To facilitate access to this data, STITCH ('search tool for interactions of chemicals') integrates information about interactions from metabolic pathways, crystal structures, binding experiments and drug-target relationships. Inferred information from phenotypic effects, text mining and chemical structure similarity is used to predict relations between chemicals. STITCH further allows exploring the network of chemical relations, also in the context of associated binding proteins. Each proposed interaction can be traced back to the original data sources. Our database contains interaction information for over 68,000 different chemicals, including 2200 drugs, and connects them to 1.5 million genes across 373 genomes and their interactions contained in the STRING database. STITCH is available at http://stitch.embl.de/.

SuperTarget and Matador: resources for exploring drug-target relationships.

The molecular basis of drug action is often not well understood. This is partly because the very abundant and diverse information generated in the past decades on drugs is hidden in millions of medical articles or textbooks. Therefore, we developed a one-stop data warehouse, SuperTarget that integrates drug-related information about medical indication areas, adverse drug effects, drug metabolization, pathways and Gene Ontology terms of the target proteins. An easy-to-use query interface enables the user to pose complex queries, for example to find drugs that target a certain pathway, interacting drugs that are metabolized by the same cytochrome P450 or drugs that target the same protein but are metabolized by different enzymes. Furthermore, we provide tools for 2D drug screening and sequence comparison of the targets. The database contains more than 2500 target proteins, which are annotated with about 7300 relations to 1500 drugs; the vast majority of entries have pointers to the respective literature source. A subset of these drugs has been annotated with additional binding information and indirect interactions and is available as a separate resource called Matador. SuperTarget and Matador are available at http://insilico.charite.de/supertarget and http://matador.embl.de.

Computational characterization of multiple Gag-like human proteins.

In a genome-wide analysis, we have identified 85 human genes encoding 103 protein isoforms that resemble retroviral Gag proteins. These genes were domesticated from retrotransposons in at least five independent events during vertebrate evolution and were subsequently duplicated further in mammals. Structural insights into the mammalian proteins can be inferred by homology to Gag from viruses such as HIV; in turn, the cellular roles of the mammalian Gag homologs, such as apoptosis-related functions and binding to ubiquitin ligases, might hint at further functionality of viral Gag itself.

Large-scale prediction of drug-target relationships.

The rapidly increasing amount of publicly available knowledge in biology and chemistry enables scientists to revisit many open problems by the systematic integration and analysis of heterogeneous novel data. The integration of relevant data does not only allow analyses at the network level, but also provides a more global view on drug-target relations. Here we review recent attempts to apply large-scale computational analyses to predict novel interactions of drugs and targets from molecular and cellular features. In this context, we quantify the family-dependent probability of two proteins to bind the same ligand as function of their sequence similarity. We finally discuss how phenotypic data could help to expand our understanding of the complex mechanisms of drug action.

Systematic pharmacological screens uncover novel pathways involved in cerebral cavernous malformations.

Cerebral cavernous malformations (CCMs) are vascular lesions in the central nervous system causing strokes and seizures which currently can only be treated through neurosurgery. The disease arises through changes in the regulatory networks of endothelial cells that must be comprehensively understood to develop alternative, non-invasive pharmacological therapies. Here, we present the results of several unbiased small-molecule suppression screens in which we applied a total of 5,268 unique substances to CCM mutant worm, zebrafish, mouse, or human endothelial cells. We used a systems biology-based target prediction tool to integrate the results with the whole-transcriptome profile of zebrafish CCM2 mutants, revealing signaling pathways relevant to the disease and potential targets for small-molecule-based therapies. We found indirubin-3-monoxime to alleviate the lesion burden in murine preclinical models of CCM2 and CCM3 and suppress the loss-of-CCM phenotypes in human endothelial cells. Our multi-organism-based approach reveals new components of the CCM regulatory network and foreshadows novel small-molecule-based therapeutic applications for suppressing this devastating disease in patients.

Protein coding potential of retroviruses and other transposable elements in vertebrate genomes.

We suggest an annotation strategy for genes encoded by retroviruses and transposable elements (RETRA genes) based on a set of marker protein domains. Usually RETRA genes are masked in vertebrate genomes prior to the application of automated gene prediction pipelines under the assumption that they provide no selective advantage to the host. Yet, we show that about 1000 genes in four vertebrate gene sets analyzed contain at least one RETRA gene marker domain. Using the conservation of genomic neighborhood (synteny), we were able to discriminate between RETRA genes with putative functionality in the vertebrates and those that probably function only in the context of mobile elements. We identified 35 such genes in human, along with their corresponding mouse and rat orthologs; which included almost all known human genes with similarity to mobile elements. The results also imply that the vast majority of the remaining RETRA genes in current gene sets are unlikely to encode vertebrate functions. To automatically annotate RETRA genes in other vertebrate genomes, we provide as a tool a set of marker protein domains and a manually refined list of domesticated or ancestral RETRA genes for rescuing genes with vertebrate functions.