in Silico analysis of Escherichia coli polyphosphate kinase (PPK) as a novel antimicrobial drug target and its high throughput virtual screening against PubChem library

Multiple drug resistance (MDR) in bacteria is a global health challenge that needs urgent attention. The 2011 outbreak caused by Escherichia coli O104:H4 in Europe has exposed the inability of present antibiotic arsenal to tackle the problem of antimicrobial infections. It has further posed a tremendous burden on entire pharmaceutical industry to find novel drugs and/or drug targets. Polyphosphate kinase (PPK) in bacteria plays a crucial role in helping latter to adapt to stringent conditions of low nutritional availability thus making it a good target for antibacterials. In spite of this critical role, to best of our knowledge no in-silico work has been carried out to develop PPK as an antibiotic target. In the present study, virtual screening of PPK was carried out against all the 3D compounds with pharmacological action present in PubChem database. Our screening results were further refined by interaction maps to eliminate the false positive data respectively. From our results, compound number 5281927 (PubChem ID) has been found to have significant affinity towards affinity towards PPK active ATP-binding site indicating its therapeutic relevance.     

Determining the Degree of Promiscuity of Extensively Assayed Compounds

In the context of polypharmacology, an emerging concept in drug discovery, promiscuity is rationalized as the ability of compounds to specifically interact with multiple targets. Promiscuity of drugs and bioactive compounds has thus far been analyzed computationally on the basis of activity annotations, without taking assay frequencies or inactivity records into account. Most recent estimates have indicated that bioactive compounds interact on average with only one to two targets, whereas drugs interact with six or more. In this study, we have further extended promiscuity analysis by identifying the most extensively assayed public domain compounds and systematically determining their promiscuity. These compounds were tested in hundreds of assays against hundreds of targets. In our analysis, assay promiscuity was distinguished from target promiscuity and separately analyzed for primary and confirmatory assays. Differences between the degree of assay and target promiscuity were surprisingly small and average and median degrees of target promiscuity of 2.6 to 3.4 and 2.0 were determined, respectively. Thus, target promiscuity remained at a low level even for most extensively tested active compounds. These findings provide further evidence that bioactive compounds are less promiscuous than drugs and have implications for pharmaceutical research. In addition to a possible explanation that drugs are more extensively tested for additional targets, the results would also support a “promiscuity enrichment model” according to which promiscuous compounds might be preferentially selected for therapeutic efficacy during clinical evaluation to ultimately become drugs.     

Chemotography for multi-target SAR analysis in the context of biological pathways

The increasing amount of chemogenomics data, that is, activity measurements of many compounds across a variety of biological targets, allows for better understanding of pharmacology in a broad biological context. Rather than assessing activity at individual biological targets, today understanding of compound interaction with complex biological systems and molecular pathways is often sought in phenotypic screens. This perspective poses novel challenges to structure-activity relationship (SAR) assessment. Today, the bottleneck of drug discovery lies in the understanding of SAR of rich datasets that go beyond single targets in the context of biological pathways, potential off-targets, and complex selectivity profiles. To aid in the understanding and interpretation of such complex SAR, we introduce Chemotography (chemotype chromatography), which encodes chemical space using a color spectrum by combining clustering and multidimensional scaling. Rich biological data in our approach were visualized using spatial dimensions traditionally reserved for chemical space. This allowed us to analyze SAR in the context of target hierarchies and phylogenetic trees, two-target activity scatter plots, and biological pathways. Chemotography, in combination with the Kyoto Encyclopedia of Genes and Genomes (KEGG), also allowed us to extract pathway-relevant SAR from the ChEMBL database. We identified chemotypes showing polypharmacology and selectivity-conferring scaffolds, even in cases where individual compounds have not been tested against all relevant targets. In addition, we analyzed SAR in ChEMBL across the entire Kinome, going beyond individual compounds. Our method combines the strengths of chemical space visualization for SAR analysis and graphical representation of complex biological data. Chemotography is a new paradigm for chemogenomic data visualization and its versatile applications presented here may allow for improved assessment of SAR in biological context, such as phenotypic assay hit lists.     

Tools and methodologies capable of isolating and identifying a target molecule for a bioactive compound

Elucidating the mechanism of action of bioactive compounds, such as commonly used pharmaceutical drugs and biologically active natural products, in the cells and the living body is important in drug discovery research. To this end, isolation and identification of target protein(s) for the bioactive compound are essential in understanding its function fully. And, development of reliable tools and methodologies capable of addressing efficiently identification and characterization of the target proteins based on the bioactive compounds accelerates drug discovery research. Affinity-based isolation and identification of target molecules for the bioactive compounds is a classic, but still powerful approach. This paper introduces recent progress on affinity chromatography system, focusing on development of practical affinity matrices and useful affinity-based methodologies on target identification. Beneficial affinity chromatography systems with using practical tools and useful methodologies facilitate chemical biology and drug discovery research.     

Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks

A major challenge in drug discovery is to distinguish the molecular targets of a bioactive compound from the hundreds to thousands of additional gene products that respond indirectly to changes in the activity of the targets. Here, we present an integrated computational-experimental approach for computing the likelihood that gene products and associated pathways are targets of a compound. This is achieved by filtering the mRNA expression profile of compound-exposed cells using a reverse-engineered model of the cell's gene regulatory network. We apply the method to a set of 515 whole-genome yeast expression profiles resulting from a variety of treatments (compounds, knockouts and induced expression), and correctly enrich for the known targets and associated pathways in the majority of compounds examined. We demonstrate our approach with PTSB, a growth inhibitory compound with a previously unknown mode of action, by predicting and validating thioredoxin and thioredoxin reductase as its target.     

Target Prediction for an Open Access Set of Compounds Active against Mycobacterium tuberculosis

Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), infects an estimated two billion people worldwide and is the leading cause of mortality due to infectious disease. The development of new anti-TB therapeutics is required, because of the emergence of multi-drug resistance strains as well as co-infection with other pathogens, especially HIV. Recently, the pharmaceutical company GlaxoSmithKline published the results of a high-throughput screen (HTS) of their two million compound library for anti-mycobacterial phenotypes. The screen revealed 776 compounds with significant activity against the M. tuberculosis H37Rv strain, including a subset of 177 prioritized compounds with high potency and low in vitro cytotoxicity. The next major challenge is the identification of the target proteins. Here, we use a computational approach that integrates historical bioassay data, chemical properties and structural comparisons of selected compounds to propose their potential targets in M. tuberculosis. We predicted 139 target - compound links, providing a necessary basis for further studies to characterize the mode of action of these compounds. The results from our analysis, including the predicted structural models, are available to the wider scientific community in the open source mode, to encourage further development of novel TB therapeutics.     

Database on pharmacophore analysis of active principles, from medicinal plants

Plants continue to be a major source of medicines, as they have been throughout human history. In the present days, drug discovery from plants involves a multidisciplinary approach combining ethnobotanical, phytochemical and biological techniques to provide us new chemical compounds (lead molecules) for the development of drugs against various pharmacological targets, including cancer, diabetes and its secondary complications. In view of this need in current drug discovery from medicinal plants, here we describe another web database containing the information of pharmacophore analysis of active principles possessing antidiabetic, antimicrobial, anticancerous and antioxidant properties from medicinal plants. The database provides the botanical, taxonomic classification, biochemical as well as pharmacological properties of medicinal plants. Data on antidiabetic, antimicrobial, anti oxidative, anti tumor and anti inflammatory compounds, and their physicochemical properties, SMILES Notation, Lipinski's properties are included in our database. One of the proposed features in the database is the predicted ADMET values and the interaction of bioactive compounds to the target protein. The database alphabetically lists the compound name and also provides tabs separating for anti microbial, antitumor, antidiabetic, and antioxidative compounds. AVAILABILITY: http://www.hccbif.info /     

Diversity & tractability revisited in collaborative small molecule phenotypic screening library design

Identification of purposeful chemical matter on a broad range of drug targets is of high importance to the pharmaceutical industry. However, disease-relevant but more complex hit-finding plans require flexibility regarding the subset of the compounds that we screen. Herein we describe a strategy to design high-quality small molecule screening subsets of two different sizes to cope with a rapidly changing early discovery portfolio. The approach taken balances chemical tractability, chemical diversity and biological target coverage. Furthermore, using surveys, we actively involved chemists within our company in the selection process of the diversity decks to ensure current medicinal chemistry principles were incorporated. The chemist surveys revealed that not all published PAINS substructure alerts are considered productive by the medicinal chemistry community and in agreement with previously published results from other institutions, QED scores tracked quite well with chemists’ notions of chemical attractiveness.     

An integrated database of flavonoids

Flavonoids are polyphenolic compounds that occur ubiquitously in foods of plant origin. Some of these molecules exhibit various physiological activities. Among existing drugs, there are a huge number of compounds bearing a flavonoid-related skeleton. Because of the relevance for pharmaceutical research, it would be beneficial to collect these compounds into a database. Recently, various databases of chemicals were compiled to help biological and/or chemical research, but no comprehensive database of flavonoids with chemical structures and physicochemical parameters, supposedly related to their activity, is available yet. The aim of this research was to merge the information about flavonoids of plant origin and flavonoids used as medicines into a database. Moreover, predictions of activities against various targets were performed using a virtual screening procedure to demonstrate a possible application of the database for pharmaceutical research.     

药物靶标蛋白在蛋白网络中的分布

药物靶标发现是目前生物学研究领域的热点和难点问题。从已有药物靶标中寻找规律可以为新靶标的发现总结规律,提供依据。随着功能基因组学的发展,这种组学数据的积累为这一问题的研究提供了契机。本文研究了已有靶标在蛋白网络中的分布,并分析了它们的蛋白功能域组成情况。结果显示靶标基因倾向位于网络的核心区域,并且集中在一些特定蛋白家族中。这些规律的总结将对药物研发过程中药物靶点的选择提供一定的帮助。     

Structure of MurF from Streptococcus pneumoniae co-crystallized with a small molecule inhibitor exhibits interdomain closure

In a broad genomics analysis to find novel protein targets for antibiotic discovery, MurF was identified as an essential gene product for Streptococcus pneumonia that catalyzes a critical reaction in the biosynthesis of the peptidoglycan in the formation of the cell wall. Lacking close relatives in mammalian biology, MurF presents attractive characteristics as a potential drug target. Initial screening of the Abbott small-molecule compound collection identified several compounds for further validation as pharmaceutical leads. Here we report the integrated efforts of NMR and X-ray crystallography, which reveal the multidomain structure of a MurF-inhibitor complex in a compact conformation that differs dramatically from related structures. The lead molecule is bound in the substrate-binding region and induces domain closure, suggestive of the domain arrangement for the as yet unobserved transition state conformation for MurF enzymes. The results form a basis for directed optimization of the compound lead by structure-based design to explore the suitability of MurF as a pharmaceutical target.     

Development of a high-content screening assay panel to accelerate mechanism of action studies for oncology research

Efficient elucidation of the biological mechanism of action of novel compounds remains a major bottleneck in the drug discovery process. To address this need in the area of oncology, we report the development of a multiparametric high-content screening assay panel at the level of single cells to dramatically accelerate understanding the mechanism of action of cell growth-inhibiting compounds on a large scale. Our approach is based on measuring 10 established end points associated with mitochondrial apoptosis, cell cycle disruption, DNA damage, and cellular morphological changes in the same experiment, across three multiparametric assays. The data from all of the measurements taken together are expected to help increase our current understanding of target protein functions, constrain the list of possible targets for compounds identified using phenotypic screens, and identify off-target effects. We have also developed novel data visualization and phenotypic classification approaches for detailed interpretation of individual compound effects and navigation of large collections of multiparametric cellular responses. We expect this general approach to be valuable for drug discovery across multiple therapeutic areas.     

Survey of phosphorylation near drug binding sites in the Protein Data Bank (PDB) and their effects

While it is currently estimated that 40 to 50% of eukaryotic proteins are phosphorylated, little is known about the frequency and local effects of phosphorylation near pharmaceutical inhibitor binding sites. In this study, we investigated how frequently phosphorylation may affect the binding of drug inhibitors to target proteins. We examined the 453 non‐redundant structures of soluble mammalian drug target proteins bound to inhibitors currently available in the Protein Data Bank (PDB). We cross‐referenced these structures with phosphorylation data available from the PhosphoSitePlus database. Three hundred twenty‐two of 453 (71%) of drug targets have evidence of phosphorylation that has been validated by multiple methods or labs. For 132 of 453 (29%) of those, the phosphorylation site is within 12 Å of the small molecule‐binding site, where it would likely alter small molecule binding affinity. We propose a framework for distinguishing between drug‐phosphorylation site interactions that are likely to alter the efficacy of drugs versus those that are not. In addition we highlight examples of well‐established drug targets, such as estrogen receptor alpha, for which phosphorylation may affect drug affinity and clinical efficacy. Our data suggest that phosphorylation may affect drug binding and efficacy for a significant fraction of drug target proteins. Proteins 2015; 83:25–36. © 2014 Wiley Periodicals, Inc.     

TCM Database@Taiwan: the world's largest traditional Chinese medicine database for drug screening in silico

Rapid advancing computational technologies have greatly speeded up the development of computer-aided drug design (CADD). Recently, pharmaceutical companies have increasingly shifted their attentions toward traditional Chinese medicine (TCM) for novel lead compounds. Despite the growing number of studies on TCM, there is no free 3D small molecular structure database of TCM available for virtual screening or molecular simulation. To address this shortcoming, we have constructed TCM Database@Taiwan (http://tcm.cmu.edu.tw/) based on information collected from Chinese medical texts and scientific publications. TCM Database@Taiwan is currently the world's largest non-commercial TCM database. This web-based database contains more than 20,000 pure compounds isolated from 453 TCM ingredients. Both cdx (2D) and Tripos mol2 (3D) formats of each pure compound in the database are available for download and virtual screening. The TCM database includes both simple and advanced web-based query options that can specify search clauses, such as molecular properties, substructures, TCM ingredients, and TCM classification, based on intended drug actions. The TCM database can be easily accessed by all researchers conducting CADD. Over the last eight years, numerous volunteers have devoted their time to analyze TCM ingredients from Chinese medical texts as well as to construct structure files for each isolated compound. We believe that TCM Database@Taiwan will be a milestone on the path towards modernizing traditional Chinese medicine.     

DNA patenting: the end of an era?

Debates on patenting DNA must evolve to reflect the global decline in filings and regional disparities in patenting activity.     

Displacement of protein-bound aptamers with small molecules screened by fluorescence polarization

Small molecule inhibitors of proteins are invaluable tools in research and as starting points for drug development. However, their screening can be tedious, as most screening methods have to be tailored to the corresponding drug target. Here, we describe a detailed protocol for a modular and generally applicable assay for the identification of small organic compounds that displace an aptamer complexed to its target protein. The method relies on fluorescence-labeled aptamers and the increase of fluorescence polarization upon their binding to the target protein. The assay has high Z'-factors, making it compatible with high-throughput screening. It allows easy automation, making fluorescence readout the time-limiting step. As aptamers can be generated for virtually any protein target, the assay allows identification of small molecule inhibitors for targets or individual protein domains for which no functional screen is available. We provide the step-by-step protocol to screen for antagonists of the cytohesin class of small guanosine exchange factors.     

The use of transgenic systems in pharmaceutical research.

Those pharmaceutical companies whose goal is to generate novel innovative drugs are faced with the challenge that only a fraction of the compounds tested in clinical trials eventually become a registered drug. This problem of attrition is compounded by the fact that the clinical trial or development stage is by far the most costly phase of bringing a new drug to market, consuming around 80 per cent of the total spend. Transgenic technology represents an attractive approach to reducing the attrition rate of compounds entering clinical trials by increasing the quality of the target and compound combinations making the transition from discovery into development. Transgenic technology can impact at many points in the discovery process, including target identification and target validation, and provides models designed to alert researchers early to potential problems with drug metabolism and toxicity, as well as providing better models for human diseases. In target identification, transgenic animals harbouring large DNA fragments can be used to narrow down genetic regions. Genetic studies often result in the identification of large genomic regions and one way to decrease the region size is to do complementation studies in transgenic animals using, for example, inserts from bacterial artificial chromosome (BAC) clones. In target validation, transgenic animals can be used for in vivo validation of a specific target. Considerable efforts are being made to establish new, rapid and robust tools with general utility for in vivo validation, but, so far, only transgenic animals work reliably on a wide range of targets. Transgenic animals can also be used to generate better disease models. Predictive animal models to test new compounds and targets will significantly speed up the drug discovery process and, more importantly, increase the quality of the compounds taken further in the research and development process. Humanised transgenic animals harbouring the human target molecule can be used to understand the effect of a compound acting on the human target in vivo. Also, models mimicking human drug metabolism will provide a means of assessing the effect of human-specific metabolites and of understanding the pharmacokinetic properties of potential drugs. In toxicology studies, transgenic animals are providing more predictive models. A good example of this are those models routinely used to look for carcinogenicity associated with new compounds.