Abstract: Inhibitors of DNA methyltransferase (DNMT) are attractive compounds not only as potential therapeutic agents for the treatment of cancer and other diseases, but also as research tools to investigate the role of DNMTs in epigenetic events. Recent advances in high-throughput screening (HTS) for epigenetic targets and the availability of the first crystallographic structure of human DNMT1 encourage the integration of research strategies to uncover and optimize the activity of DNMT inhibitors. Herein, we present a binding model of a novel small-molecule DNMT1 inhibitor obtained by HTS, recently released in a public database. The docking model is in agreement with key interactions previously identified for established inhibitors using extensive computational studies including molecular dynamics and structure-based pharmacophore modeling. Based on the chemical structure of the novel inhibitor, a sequential computational screening of five chemical databases was performed to identify candidate compounds for testing. Similarity searching followed by molecular docking of chemical databases such as approved drugs, natural products, a DNMT-focused library, and a general screening collection, identified at least 108 molecules with promising DNMT inhibitory activity. The chemical structures of all hit compounds are disclosed to encourage the research community working on epigenetics to test experimentally the enzymatic and demethylating activity in vivo. Five candidate hits are drugs approved for other indications and represent potential starting points of a drug repurposing strategy.

Abstract: BACKGROUND: Herbal medicine has long been viewed as a valuable asset for potential new drug discovery and herbal ingredients' metabolites, especially the in vivo metabolites were often found to gain better pharmacological, pharmacokinetic and even better safety profiles compared to their parent compounds. However, these herbal metabolite information is still scattered and waiting to be collected. DESCRIPTION: HIM database manually collected so far the most comprehensive available in-vivo metabolism information for herbal active ingredients, as well as their corresponding bioactivity, organs and/or tissues distribution, toxicity, ADME and the clinical research profile. Currently HIM contains 361 ingredients and 1104 corresponding in-vivo metabolites from 673 reputable herbs. Tools of structural similarity, substructure search and Lipinski's Rule of Five are also provided. Various links were made to PubChem, PubMed, TCM-ID (Traditional Chinese Medicine Information database) and HIT (Herbal ingredients' targets databases). CONCLUSIONS: A curated database HIM is set up for the in vivo metabolites information of the active ingredients for Chinese herbs, together with their corresponding bioactivity, toxicity and ADME profile. HIM is freely accessible to academic researchers at http://www.bioinformatics.org.cn/.

Abstract: The scale of tumor genomic profiling is rapidly outpacing human cognitive capacity to make clinical decisions without the aid of tools. New frameworks are needed to help researchers and clinicians process the information emerging from the explosive growth in both the number of tumor genetic variants routinely tested and the respective knowledge to interpret their clinical significance. We review the current state, limitations, and future trends in methods to support the clinical analysis and interpretation of cancer genomes. This includes the processes of genome-scale variant identification, including tools for sequence alignment, tumor-germline comparison, and molecular annotation of variants. The process of clinical interpretation of tumor variants includes classification of the effect of the variant, reporting the results to clinicians, and enabling the clinician to make a clinical decision based on the genomic information integrated with other clinical features. We describe existing knowledge bases, databases, algorithms, and tools for identification and visualization of tumor variants and their actionable subsets. With the decreasing cost of tumor gene mutation testing and the increasing number of actionable therapeutics, we expect the methods for analysis and interpretation of cancer genomes to continue to evolve to meet the needs of patient-centered clinical decision making. The science of computational cancer medicine is still in its infancy; however, there is a clear need to continue the development of knowledge bases, best practices, tools, and validation experiments for successful clinical implementation in oncology.

Abstract: Purine nucleotides are ubiquitous molecules that play vital roles in all kingdoms of life, not only as components of nucleic acids, but also participating in signaling and energy storage. Cellular pools of purines are maintained by the tight control of several complementary and sometimes competing processes including de novo biosynthesis, salvage and catabolism of nucleotides. While great strides have been made over the past sixty years in understanding the biosynthesis of purines, we are experiencing a renaissance in this field. In this feature article we discuss the most recent discoveries relating to purine biosynthesis, with particular emphasis upon the dynamic multi-protein complex called the purinosome. In particular we highlight advances made towards understanding the assembly, control and function of this protein complex and the attempts made to exploit this knowledge for drug discovery.

Abstract: MicroRNAs (miRNAs) are evolutionary conserved noncoding molecules that regulate gene expression. They influence a number of diverse biological functions, such as development and differentiation. However, their dysregulation has been shown to be associated with disease states, such as cancer. Genes and pathways regulating their biogenesis remain unknown and are highly sought after. For this purpose, we have validated a multiplexed high-content assay strategy to screen for such modulators. Here, we describe its implementation that makes use of a cell-based gain-of-function reporter assay monitoring enhanced green fluorescent protein expression under the control of miRNA 21 (miR-21); combined with measures of both cell metabolic activities through the use of Alamar Blue and cell death through imaged Hoechst-stained nuclei. The strategy was validated using a panel of known genes and enabled us to successfully progress to and complete an arrayed genome-wide short interfering RNA (siRNA) screen against the Ambion Silencer Select v4.0 library containing 64,755 siRNA duplexes covering 21,565 genes. We applied a high-stringency hit analysis method, referred to as the Bhinder-Djaballah analysis method, leading to the nomination of 1,273 genes as candidate inhibitors of the miR-21 biogenesis pathway; after several iterations eliminating those genes with only one active duplex and those enriched in seed sequence mediated off-target effects. Biological classifications revealed four major control junctions among them vesicular transport via clathrin-mediated endocytosis. Altogether, our screen has uncovered a number of novel candidate regulators that are potentially good druggable targets allowing for the discovery and development of small molecules for regulating miRNA function.

Abstract: The bacteria that colonize the gastrointestinal tracts of mammals represent a highly selected microbiome that has a profound influence on human physiology by shaping the host's metabolic and immune system activity. Despite the recent advances on the biological principles that underlie microbial symbiosis in the gut of mammals, mechanistic understanding of the contributions of the gut microbiome and how variations in the metabotypes are linked to the host health are obscure. Here, we mapped the entire metabolic potential of the gut microbiome based solely on metagenomics sequencing data derived from fecal samples of 124 Europeans (healthy, obese and with inflammatory bowel disease). Interestingly, three distinct clusters of individuals with high, medium and low metabolic potential were observed. By illustrating these results in the context of bacterial population, we concluded that the abundance of the Prevotella genera is a key factor indicating a low metabolic potential. These metagenome-based metabolic signatures were used to study the interaction networks between bacteria-specific metabolites and human proteins. We found that thirty-three such metabolites interact with disease-relevant protein complexes several of which are highly expressed in cells and tissues involved in the signaling and shaping of the adaptive immune system and associated with squamous cell carcinoma and bladder cancer. From this set of metabolites, eighteen are present in DrugBank providing evidence that we carry a natural pharmacy in our guts. Furthermore, we established connections between the systemic effects of non-antibiotic drugs and the gut microbiome of relevance to drug side effects and health-care solutions.

Abstract: PURPOSE: Membrane transporters mediate many biological effects of chemicals and play a major role in pharmacokinetics and drug resistance. The selection of viable drug candidates among biologically active compounds requires the assessment of their transporter interaction profiles. METHODS: Using public sources, we have assembled and curated the largest, to our knowledge, human intestinal transporter database (>5,000 interaction entries for >3,700 molecules). This data was used to develop thoroughly validated classification Quantitative Structure-Activity Relationship (QSAR) models of transport and/or inhibition of several major transporters including MDR1, BCRP, MRP1-4, PEPT1, ASBT, OATP2B1, OCT1, and MCT1. RESULTS: QSAR models have been developed with advanced machine learning techniques such as Support Vector Machines, Random Forest, and k Nearest Neighbors using Dragon and MOE chemical descriptors. These models afforded high external prediction accuracies of 71-100% estimated by 5-fold external validation, and showed hit retrieval rates with up to 20-fold enrichment in the virtual screening of DrugBank compounds. CONCLUSIONS: The compendium of predictive QSAR models developed in this study can be used for virtual profiling of drug candidates and/or environmental agents with the optimal transporter profiles.

Abstract: Cryptosporidiosis, a diarrheal disease usually caused by Cryptosporidium parvum or Cryptosporidium hominis in humans, can result in fulminant diarrhea and death in AIDS patients and chronic infection and stunting in children. Nitazoxanide, the current standard of care, has limited efficacy in children and is no more effective than placebo in patients with advanced AIDS. Unfortunately, the lack of financial incentives and the technical difficulties associated with working with Cryptosporidium parasites have crippled efforts to develop effective treatments. In order to address these obstacles, we developed and validated (Z' score = 0.21 to 0.47) a cell-based high-throughput assay and screened a library of drug repurposing candidates (the NIH Clinical Collections), with the hopes of identifying safe, FDA-approved drugs to treat cryptosporidiosis. Our screen yielded 21 compounds with confirmed activity against C. parvum growth at concentrations of <10 muM, many of which had well-defined mechanisms of action, making them useful tools to study basic biology in addition to being potential therapeutics. Additional work, including structure-activity relationship studies, identified the human 3-hydroxy-3-methyl-glutaryl-coenzyme A (HMG-CoA) reductase inhibitor itavastatin as a potent inhibitor of C. parvum growth (50% inhibitory concentration [IC(50)] = 0.62 muM). Bioinformatic analysis of the Cryptosporidium genomes indicated that the parasites lack all known enzymes required for the synthesis of isoprenoid precursors. Additionally, itavastatin-induced growth inhibition of C. parvum was partially reversed by the addition of exogenous isopentenyl pyrophosphate, suggesting that itavastatin reduces Cryptosporidium growth via on-target inhibition of host HMG-CoA reductase and that the parasite is dependent on the host cell for synthesis of isoprenoid precursors.

Abstract: The recently developed field of ligand homology modeling (LHM) that extends the ideas of protein homology modeling to the prediction of ligand binding sites and for use in virtual ligand screening has emerged as a powerful new approach. Unlike traditional docking methodologies, LHM can be applied to low-to-moderate resolution predicted as well as experimental structures with little if any diminution in performance; thereby enabling approximately 75% of an average proteome to have potentially significant virtual screening predictions. In large scale benchmarking, LHM is able to predict off-target ligand binding. Thus, despite the widespread belief to the contrary, low-to-moderate resolution predicted structures have considerable utility for biochemical function prediction.

Abstract: BACKGROUND: There is a growing body of literature describing the properties of marketed drugs, the concept of drug-likeness and the vastness of chemical space. In that context, enumerative combinatorics with simple atomic components may be useful in the conception and design of structurally novel compounds for expanding and enhancing high-throughput screening (HTS) libraries. RESULTS: A random combination of mono- and diatomic carbon, hydrogen, nitrogen, and oxygen containing components in the absence of molecular weight constraints but with the ability to form rings affords virtual compounds that fall in bulk physicochemical space typically associated with drugs, but whose ring assemblies fall in new or under-represented areas of chemical shape space. When compared against compounds in the ChEMBL_14, MDDR, Drug Bank and Dictionary of Natural Products, the percentage of virtual compounds with a Tanimoto index of 1.0 (ECFP_4) was found to be as high as 0.21. Depending on therapeutic target, this value may be in range of what might be expected from an experimental HTS campaign in terms of a true hit rate. CONCLUSION: Virtual compounds derived through enumerative combinatorics of simple atomic components have drug-like properties with ring assemblies that fall in new or under-represented areas of shape space. Structures derived in this manner could provide the starting point or inspiration for the design of structurally novel scaffolds in an unbiased fashion.