Dissecting glucose signalling with diversity-oriented synthesis and small-molecule microarrays

Large-scale Direct Targeting for Drug Repositioning and Discovery

A system-level identification of drug-target direct interactions is vital to drug repositioning and discovery. However, the biological means on a large scale remains challenging and expensive even nowadays. The available computational models mainly focus on predicting indirect interactions or direct interactions on a small scale. To address these problems, in this work, a novel algorithm termed weighted ensemble similarity (WES) has been developed to identify drug direct targets based on a large-scale of 98,327 drug-target relationships. WES includes: (1) identifying the key ligand structural features that are highly-related to the pharmacological properties in a framework of ensemble; (2) determining a drug’s affiliation of a target by evaluation of the overall similarity (ensemble) rather than a single ligand judgment; and (3) integrating the standardized ensemble similarities (Z score) by Bayesian network and multi-variate kernel approach to make predictions. All these lead WES to predict drug direct targets with external and experimental test accuracies of 70% and 71%, respectively. This shows that the WES method provides a potential in silico model for drug repositioning and discovery.

In-silico prediction of drug targets, biological activities, signal pathways and regulating networks of dioscin based on bioinformatics

Background

Inverse docking technology has been a trend of drug discovery, and bioinformatics approaches have been used to predict target proteins, biological activities, signal pathways and molecular regulating networks affected by drugs for further pharmacodynamic and mechanism studies.

Methods

In the present paper, inverse docking technology was applied to screen potential targets from potential drug target database (PDTD). Then, the corresponding gene information of the obtained drug-targets was applied to predict the related biological activities, signal pathways and processes networks of the compound by using MetaCore platform. After that, some most relevant regulating networks were considered, which included the nodes and relevant pathways of dioscin.

Results

71 potential targets of dioscin from humans, 7 from rats and 8 from mice were screened, and the prediction results showed that the most likely targets of dioscin were cyclin A2, calmodulin, hemoglobin subunit beta, DNA topoisomerase I, DNA polymerase lambda, nitric oxide synthase and UDP-N-acetylhexosamine pyrophosphorylase, etc. Many diseases including experimental autoimmune encephalomyelitis of human, temporal lobe epilepsy of rat and ankylosing spondylitis of mouse, may be inhibited by dioscin through regulating immune response alternative complement pathway, G-protein signaling RhoB regulation pathway and immune response antiviral actions of interferons, etc. The most relevant networks (5 from human, 3 from rat and 5 from mouse) indicated that dioscin may be a TOP1 inhibitor, which can treat cancer though the cell cycle– transition and termination of DNA replication pathway. Dioscin can down regulate EGFR and EGF to inhibit cancer, and also has anti-inflammation activity by regulating JNK signaling pathway.

Conclusions

The predictions of the possible targets, biological activities, signal pathways and relevant regulating networks of dioscin provide valuable information to guide further investigation of dioscin on pharmacodynamics and molecular mechanisms, which also suggests a practical and effective method for studies on the mechanism of other chemicals.

Construction and application of a photo-cross-linked chemical array

Chemical array technology is a powerful tool for high-throughput screening of small-molecule ligand-protein interactions. A chemical array is a collection of small-molecule compounds spotted and immobilized on a glass slide surface, providing a multiplex platform to identify small-molecule compounds binding to a protein of interest in high-throughput screening. Several research groups have developed a variety of methods for the immobilization of small molecules onto a solid matrix. We have developed a unique photo-cross-linked chemical array for immobilizing small molecules in a functional-group-independent manner. In this chapter, we describe in detail a protocol for the construction of a photo-cross-linked chemical array and its application for ligand screening by using a tag-fused protein.

Universal screening platform using three-dimensional small molecule microarray based on surface plasmon resonance imaging

Herein we report a potent methodology for drug screening on a three-dimensional (3D) surface using a carbene based photo-cross-linking reaction.

Integrated analysis identifies interaction patterns between small molecules and pathways

Previous studies have indicated that the downstream proteins in a key pathway can be potential drug targets and that the pathway can play an important role in the action of drugs. So pathways could be considered as targets of small molecules. A link map between small molecules and pathways was constructed using gene expression profile, pathways, and gene expression of cancer cell line intervened by small molecules and then we analysed the topological characteristics of the link map. Three link patterns were identified based on different drug discovery implications for breast, liver, and lung cancer. Furthermore, molecules that significantly targeted the same pathways tended to treat the same diseases. These results can provide a valuable reference for identifying drug candidates and targets in molecularly targeted therapy.

Identification of biologically active, HIV TAR RNA-binding small molecules using small molecule microarrays

Identifying small molecules that selectively bind to structured RNA motifs remains an important challenge in developing potent and specific therapeutics. Most strategies to find RNA-binding molecules have identified highly charged compounds or aminoglycosides that commonly have modest selectivity. Here we demonstrate a strategy to screen a large unbiased library of druglike small molecules in a microarray format against an RNA target. This approach has enabled the identification of a novel chemotype that selectively targets the HIV transactivation response (TAR) RNA hairpin in a manner not dependent on cationic charge. Thienopyridine 4 binds to and stabilizes the TAR hairpin with a Kd of 2.4 μM. Structure-activity relationships demonstrate that this compound achieves activity through hydrophobic and aromatic substituents on a heterocyclic core, rather than cationic groups typically required. Selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) analysis was performed on a 365-nucleotide sequence derived from the 5' untranslated region (UTR) of the HIV-1 genome to determine global structural changes in the presence of the molecule. Importantly, the interaction of compound 4 can be mapped to the TAR hairpin without broadly disrupting any other structured elements of the 5' UTR. Cell-based anti-HIV assays indicated that 4 inhibits HIV-induced cytopathicity in T lymphocytes with an EC50 of 28 μM, while cytotoxicity was not observed at concentrations approaching 1 mM.

Exploring the ligand-protein networks in traditional chinese medicine: current databases, methods and applications

While the concept of "single component-single target" in drug discovery seems to have come to an end, "Multi-component-multi-target" is considered to be another promising way out in this field. The Traditional Chinese Medicine (TCM), which has thousands of years' clinical application among China and other Asian countries, is the pioneer of the "Multi-component-multi-target" and network pharmacology. Hundreds of different components in a TCM prescription can cure the diseases or relieve the patients by modulating the network of potential therapeutic targets. Although there is no doubt of the efficacy, it is difficult to elucidate convincing underlying mechanism of TCM due to its complex composition and unclear pharmacology. Without thorough investigation of its potential targets and side effects, TCM is not able to generate large-scale medicinal benefits, especially in the days when scientific reductionism and quantification are dominant. The use of ligand-protein networks has been gaining significant value in the history of drug discovery while its application in TCM is still in its early stage. This article firstly surveys TCM databases for virtual screening that have been greatly expanded in size and data diversity in recent years. On that basis, different screening methods and strategies for identifying active ingredients and targets of TCM are outlined based on the amount of network information available, both on sides of ligand bioactivity and the protein structures. Furthermore, applications of successful in silico target identification attempts are discussed in details along with experiments in exploring the ligand-protein networks of TCM. Finally, it will be concluded that the prospective application of ligand-protein networks can be used not only to predict protein targets of a small molecule, but also to explore the mode of action of TCM.

TCMSP: a database of systems pharmacology for drug discovery from herbal medicines

Background

Modern medicine often clashes with traditional medicine such as Chinese herbal medicine because of the little understanding of the underlying mechanisms of action of the herbs. In an effort to promote integration of both sides and to accelerate the drug discovery from herbal medicines, an efficient systems pharmacology platform that represents ideal information convergence of pharmacochemistry, ADME properties, drug-likeness, drug targets, associated diseases and interaction networks, are urgently needed.

Description

The traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) was built based on the framework of systems pharmacology for herbal medicines. It consists of all the 499 Chinese herbs registered in the Chinese pharmacopoeia with 29,384 ingredients, 3,311 targets and 837 associated diseases. Twelve important ADME-related properties like human oral bioavailability, half-life, drug-likeness, Caco-2 permeability, blood-brain barrier and Lipinski’s rule of five are provided for drug screening and evaluation. TCMSP also provides drug targets and diseases of each active compound, which can automatically establish the compound-target and target-disease networks that let users view and analyze the drug action mechanisms. It is designed to fuel the development of herbal medicines and to promote integration of modern medicine and traditional medicine for drug discovery and development.

Conclusions

The particular strengths of TCMSP are the composition of the large number of herbal entries, and the ability to identify drug-target networks and drug-disease networks, which will help revealing the mechanisms of action of Chinese herbs, uncovering the nature of TCM theory and developing new herb-oriented drugs. TCMSP is freely available at http://sm.nwsuaf.edu.cn/lsp/tcmsp.php.

A liquid array platform for the multiplexed analysis of synthetic molecule-protein interactions

Synthetic molecule microarrays, consisting of many different compounds spotted onto a planar surface such as modified glass or cellulose, have proven to be useful tools for the multiplexed analysis of small molecule- and peptide-protein interactions. However, these arrays are technically difficult to manufacture and use with high reproducibility and require specialized equipment. Here we report a more convenient alternative composed of color-encoded beads that display a small molecule protein ligand on the surface. Quantitative, multiplexed assay of protein binding to up to 24 different ligands can be achieved using a common flow cytometer for the readout. This technology should be useful for evaluating hits from library screening efforts, the determination of structure activity relationships, and certain types of serological analyses.

Recent advances in microarray technologies for proteomics

Proteins are fundamental components of all living systems and critical drivers of biological functions. The large-scale study of proteins, their structures and functions, is defined as proteomics. This systems-wide analysis leads to a more comprehensive view of the intricate signaling transduction pathways that proteins engage in and improves the overall understanding of the complex processes supporting the living systems. Over the last two decades, the development of high-throughput analytical tools, such as microarray technologies, capable of rapidly analyzing thousands of protein-functioning and protein-interacting events, has fueled the growth of this important field. Herein, we review the most recent advancements in microarray technologies, with a special focus on peptide microarray, small molecule microarray, and protein microarray. These technologies have become prominent players in proteomics and have made significant changes to the landscape of life science and biomedical research. We will elaborate on their performance, advantages, challenges, and future directions.

Recent discoveries and applications involving small-molecule microarrays

High-throughput and unbiased binding assays have proven useful in probe discovery for a myriad of biomolecules, including targets of unknown structure or function and historically challenging target classes. Over the past decade, a number of novel formats for executing large-scale binding assays have been developed and used successfully in probe discovery campaigns. Here we review the use of one such format, the small-molecule microarray (SMM), as a tool for discovering protein-small molecule interactions. This review will briefly highlight selected recent probe discoveries using SMMs as well as novel uses of SMMs in profiling applications.

Predicting drug–target interaction networks of human diseases based on multiple feature information

Aim: Drug–target interaction is crucial in the drug design process. Predicting the drug–target interaction networks of important human diseases can provide valuable clues for the characterization of the mechanism of action of diseases. Materials & methods: A new graph-based semisupervised learning (GBSSL) method is proposed to predict the drug–target interaction networks involved in 13 types of diseases. According to the method, each drug–target pair is initially described with different biological features including sequence, structure, function and network topology information. Then, the optimal feature selection procedures based on the relief and minimum redundancy maximum relevance are executed, respectively. Finally, unknown drug–target interactions can be predicted by the GBSSL method effectively. Results: The proposed method can effectively predict drug–target interactions (with a receiver operating characteristic score of 94.8% and a precision-recall score of 76.5%). Conclusion: Compared with the existing methods, the GBSSL method provides an efficient means of generating optimal features obtained from the combination of multiple sources of feature information.

Original submitted 22 April 2013; Revision submitted 14 August 2013.

Small molecule probes to target the human Mediator complex

The human Mediator complex is a central integrator for transcription and represents a primary interface that allows DNA-binding transcription factors to communicate their regulatory signals to the RNA polymerase II enzyme. Because Mediator is dynamic both in terms of subunit composition and structure, it presents challenges as a target for small molecule probes. Moreover, little high-resolution structural information exists for Mediator. Its global requirement for transcription, as well as its distinct, transcription factor specific interaction surfaces, however, suggest that development of probes that bind specific Mediator subunits might enable gene- and pathway-specific modulation of transcription. Here we provide a brief overview of the Mediator complex, highlighting biological and structural features that make it an attractive target for molecular probes. We then outline several chemical strategies that might be effective for targeting the complex.

Exploring the ligand-protein networks in traditional chinese medicine: current databases, methods, and applications

The traditional Chinese medicine (TCM), which has thousands of years of clinical application among China and other Asian countries, is the pioneer of the “multicomponent-multitarget” and network pharmacology. Although there is no doubt of the efficacy, it is difficult to elucidate convincing underlying mechanism of TCM due to its complex composition and unclear pharmacology. The use of ligand-protein networks has been gaining significant value in the history of drug discovery while its application in TCM is still in its early stage. This paper firstly surveys TCM databases for virtual screening that have been greatly expanded in size and data diversity in recent years. On that basis, different screening methods and strategies for identifying active ingredients and targets of TCM are outlined based on the amount of network information available, both on sides of ligand bioactivity and the protein structures. Furthermore, applications of successful in silico target identification attempts are discussed in detail along with experiments in exploring the ligand-protein networks of TCM. Finally, it will be concluded that the prospective application of ligand-protein networks can be used not only to predict protein targets of a small molecule, but also to explore the mode of action of TCM.

A semi-supervised method for drug-target interaction prediction with consistency in networks

Computational prediction of interactions between drugs and their target proteins is of great importance for drug discovery and design. The difficulties of developing computational methods for the prediction of such potential interactions lie in the rarity of known drug-protein interactions and no experimentally verified negative drug-target interaction sample. Furthermore, target proteins need also to be predicted for some new drugs without any known target interaction information. In this paper, a semi-supervised learning method NetCBP is presented to address this problem by using labeled and unlabeled interaction information. Assuming coherent interactions between the drugs ranked by their relevance to a query drug, and the target proteins ranked by their relevance to the hidden target proteins of the query drug, we formulate a learning framework maximizing the rank coherence with respect to the known drug-target interactions. When applied to four classes of important drug-target interaction networks, our method improves previous methods in terms of cross-validation and some strongly predicted interactions are confirmed by the publicly accessible drug target databases, which indicates the usefulness of our method. Finally, a comprehensive prediction of drug–target interactions enables us to suggest many new potential drug–target interactions for further studies.

Diversity-oriented synthesis of inner core oligosaccharides of the lipopolysaccharide of pathogenic Gram-negative bacteria

Lipopolysaccharide (LPS) is a potent virulence factor of pathogenic Gram-negative bacteria. To better understand the role of LPS in host-pathogen interactions and to elucidate the antigenic and immunogenic properties of LPS inner core region, a collection of well-defined L-glycero-D-manno-heptose (Hep) and 3-deoxy-α-D-manno-oct-2-ulosonic acid (Kdo)-containing inner core oligosaccharides is required. To address this need, we developed a diversity-oriented approach based on a common orthogonal protected disaccharide Hep-Kdo. Utilizing this new approach, we synthesized a range of LPS inner core oligosaccharides from a variety of pathogenic bacteria including Y. pestis, H. influenzae, and Proteus that cause plague, meningitis, and severe wound infections, respectively. Rapid access to these highly branched core oligosaccharides relied on elaboration of the disaccharide Hep-Kdo core as basis for the elongation with various flexible modules including unique Hep and 4-amino-4-deoxy-β-L-arabinose (Ara4N) monosaccharides and branched Hep-Hep disaccharides. A regio- and stereoselective glycosylation of Kdo 7,8-diol was key to selective installation of the Ara4N moiety at the 8-hydroxyl group of Kdo moiety of the Hep-Kdo disaccharide. The structure of the LPS inner core oligosaccharides was confirmed by comparison of (1)H NMR spectra of synthetic antigens and isolated fragments. These synthetic LPS core oligosaccharides can be covalently bound to carrier proteins via the reducing end pentyl amine linker, to explore their antigenic and immunogenic properties as well as potential applications such as diagnostic tools and vaccines.

A HaloTag-based small molecule microarray screening methodology with increased sensitivity and multiplex capabilities

Small Molecule Microarrays (SMMs) represent a general platform for screening small molecule-protein interactions independent of functional inhibition of target proteins. In an effort to increase the scope and utility of SMMs, we have modified the SMM screening methodology to increase assay sensitivity and facilitate multiplex screening. Fusing target proteins to the HaloTag protein allows us to covalently prelabel fusion proteins with fluorophores, leading to increased assay sensitivity and an ability to conduct multiplex screens. We use the interaction between FKBP12 and two ligands, rapamycin and ARIAD's "bump" ligand, to show that the HaloTag-based SMM screening methodology significantly increases assay sensitivity. Additionally, using wild type FKBP12 and the FKBP12 F36V mutant, we show that prelabeling various protein isoforms with different fluorophores allows us to conduct multiplex screens and identify ligands to a specific isoform. Finally, we show this multiplex screening technique is capable of identifying ligands selective for a specific PTP1B isoform using a 20,000 compound screening deck.

An gene expression pattern

DNA microarray technology has become a powerful tool in the arsenal of the molecular biologist. Capitalizing on high-precision robotics and the wealth of DNA sequences annotated from the genomes of a large number of organisms, the manufacture of microarrays is now possible for the average academic laboratory with the funds and motivation. Microarray production requires attention to both biological and physical resources, including DNA libraries, robotics, and qualified personnel. Although the fabrication of microarrays is a very labor-intensive process, production of quality microarrays individually tailored on a project-by-project basis will help researchers shed light on future scientific questions.

A system-level investigation into the mechanisms of Chinese Traditional Medicine: Compound Danshen Formula for cardiovascular disease treatment

Compound Danshen Formula (CDF) is a widely used Traditional Chinese Medicine (TCM) which has been extensively applied in clinical treatment of cardiovascular diseases (CVDs). However, the underlying mechanism of clinical administrating CDF on CVDs is not clear. In this study, the pharmacological effect of CDF on CVDs was analyzed at a systemic point of view. A systems-pharmacological model based on chemical, chemogenomics and pharmacological data is developed via network reconstruction approach. By using this model, we performed a high-throughput in silico screen and obtained a group of compounds from CDF which possess desirable pharmacodynamical and pharmacological characteristics. These compounds and the corresponding protein targets are further used to search against biological databases, such as the compound-target associations, compound-pathway connections and disease-target interactions for reconstructing the biologically meaningful networks for a TCM formula. This study not only made a contribution to a better understanding of the mechanisms of CDF, but also proposed a strategy to develop novel TCM candidates at a network pharmacology level.

Prioritizing cancer therapeutic small molecules by integrating multiple OMICS datasets

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.

A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data

In silico prediction of drug-target interactions from heterogeneous biological data can advance our system-level search for drug molecules and therapeutic targets, which efforts have not yet reached full fruition. In this work, we report a systematic approach that efficiently integrates the chemical, genomic, and pharmacological information for drug targeting and discovery on a large scale, based on two powerful methods of Random Forest (RF) and Support Vector Machine (SVM). The performance of the derived models was evaluated and verified with internally five-fold cross-validation and four external independent validations. The optimal models show impressive performance of prediction for drug-target interactions, with a concordance of 82.83%, a sensitivity of 81.33%, and a specificity of 93.62%, respectively. The consistence of the performances of the RF and SVM models demonstrates the reliability and robustness of the obtained models. In addition, the validated models were employed to systematically predict known/unknown drugs and targets involving the enzymes, ion channels, GPCRs, and nuclear receptors, which can be further mapped to functional ontologies such as target-disease associations and target-target interaction networks. This approach is expected to help fill the existing gap between chemical genomics and network pharmacology and thus accelerate the drug discovery processes.

Fluorescence imaging-based forward genetic screens to identify trafficking regulators in plants

Coordinated, subcellular trafficking of proteins is one of the fundamental properties of the multicellular eukaryotic organisms. Trafficking involves a large diversity of compartments, pathways, cargo molecules, and vesicle-sorting events. It is also crucial in regulating the localization and, thus, the activity of various proteins, but the process is still poorly genetically defined in plants. In the past, forward genetics screens had been used to determine the function of genes by searching for a specific morphological phenotype in the organism population in which mutations had been induced chemically or by irradiation. Unfortunately, these straightforward genetic screens turned out to be limited in identifying new regulators of intracellular protein transport, because mutations affecting essential trafficking pathways often lead to lethality. In addition, the use of these approaches has been restricted by functional redundancy among trafficking regulators. Screens for mutants that rely on the observation of changes in the cellular localization or dynamics of fluorescent subcellular markers enable, at least partially, to circumvent these issues. Hence, such image-based screens provide the possibility to identify either alleles with weak effects or components of the subcellular trafficking machinery that have no strong impact on the plant growth.

Drug-target interaction prediction by random walk on the heterogeneous network

Predicting potential drug-target interactions from heterogeneous biological data is critical not only for better understanding of the various interactions and biological processes, but also for the development of novel drugs and the improvement of human medicines. In this paper, the method of Network-based Random Walk with Restart on the Heterogeneous network (NRWRH) is developed to predict potential drug-target interactions on a large scale under the hypothesis that similar drugs often target similar target proteins and the framework of Random Walk. Compared with traditional supervised or semi-supervised methods, NRWRH makes full use of the tool of the network for data integration to predict drug-target associations. It integrates three different networks (protein-protein similarity network, drug-drug similarity network, and known drug-target interaction networks) into a heterogeneous network by known drug-target interactions and implements the random walk on this heterogeneous network. When applied to four classes of important drug-target interactions including enzymes, ion channels, GPCRs and nuclear receptors, NRWRH significantly improves previous methods in terms of cross-validation and potential drug-target interaction prediction. Excellent performance enables us to suggest a number of new potential drug-target interactions for drug development.

Hybrids of privileged structures benzothiazoles and pyrrolo[2,1-c] [1,4]benzodiazepin-5-one, and diversity-oriented synthesis of benzothiazoles

Privileged structures like Benzothiazole and Pyrrolobenzodiazepine offer wonderful opportunity to explore in anti-cancer drug discovery as a mean to counter drug-resistance problem. BT-PBD hybrids and diverse BT derivatives have been synthesized and their in vitro cytotoxic activities were screened against five cancer cell lines have been discussed. The novel compounds showed promising results as compared with the marketed drug etoposide and could well be used in future anti-cancer drug development studies.

Unbiased binding assays for discovering small-molecule probes and drugs

2011 marks the 10-year anniversary of milestone manuscripts describing drafts of the human genome sequence. Over the past decade, a number of new proteins have been linked to disease-many of which fall into classes that have been historically considered challenging from the perspective of drug discovery. Several of these newly associated proteins lack structural information or strong annotation with regard to function, making development of conventional in vitro functional assays difficult. A recent resurgence in the popularity of simple small molecule binding assays has led to new approaches that do not require knowledge of protein structure or function in advance. Here we briefly review selected methods for executing binding assays that have been used successfully to discover small-molecule probes or drug candidates.

Genetic and proteomic approaches to identify cancer drug targets

While target-based small-molecule discovery has taken centre-stage in the pharmaceutical industry, there are many cancer-promoting proteins not easily addressed with a traditional target-based screening approach. In order to address this problem, as well as to identify modulators of biological states in the absence of knowing the protein target of the state switch, alternative phenotypic screening approaches, such as gene expression-based and high-content imaging, have been developed. With this renewed interest in phenotypic screening, however, comes the challenge of identifying the binding protein target(s) of small-molecule hits. Emerging technologies have the potential to improve the process of target identification. In this review, we discuss the application of genomic (gene expression-based), genetic (short hairpin RNA and open reading frame screening), and proteomic approaches to protein target identification.

In silico prediction of drug targets in Vibrio cholerae

Identification of potential drug targets is the first step in the process of modern drug discovery, subjected to their validation and drug development. Whole genome sequences of a number of organisms allow prediction of potential drug targets using sequence comparison approaches. Here, we present a subtractive approach exploiting the knowledge of global gene expression along with sequence comparisons to predict the potential drug targets more efficiently. Based on the knowledge of 155 known virulence and their coexpressed genes mined from microarray database in the public domain, 357 coexpressed probable virulence genes for Vibrio cholerae were predicted. Based on screening of Database of Essential Genes using blastn, a total of 102 genes out of these 357 were enlisted as vitally essential genes, and hence good putative drug targets. As the effective drug target is a protein which is only present in the pathogen, similarity search of these 102 essential genes against human genome sequence led to subtraction of 66 genes, thus leaving behind a subset of 36 genes whose products have been called as potential drug targets. The gene ontology analysis using Blast2GO of these 36 genes revealed their roles in important metabolic pathways of V. cholerae or on the surface of the pathogen. Thus, we propose that the products of these genes be evaluated as target sites of drugs against V. cholerae in future investigations.

The end game of chemical genetics: target identification

The use of classical genetic and molecular biology methods along with the sequencing of many genomes has proven crucial for elucidating complex biological processes. Despite being invaluable tools, their limitations have led to a search for more versatile alternatives and, thus, to the use of small molecules. Chemical genetics is a rapidly emerging field that uses small-molecule techniques to probe biological systems and is composed of three parts: natural product or small-molecule libraries, phenotypic screening and target identification. Currently, the biggest hurdle in the overall process of chemical genetics is target identification. Efforts to overcome this obstacle have led to advances in the areas of affinity chromatography, yeast haploinsufficiency, complementary DNA (cDNA) overexpression, DNA microarray, small-molecule microarray and RNA interference (RNAi) technologies. While these technologies continue to undergo further optimization, they have been integral in the identification and/or confirmation of many cellular targets and have seen an increase in applications to the drug-development process.

Diversity-oriented synthesis of macrocyclic peptidomimetics

Structurally diverse libraries of novel small molecules represent important sources of biologically active agents. In this paper we report the development of a diversity-oriented synthesis strategy for the generation of diverse small molecules based around a common macrocyclic peptidomimetic framework, containing structural motifs present in many naturally occurring bioactive compounds. Macrocyclic peptidomimetics are largely underrepresented in current small-molecule screening collections owing primarily to synthetic intractability; thus novel molecules based around these structures represent targets of significant interest, both from a biological and a synthetic perspective. In a proof-of-concept study, the synthesis of a library of 14 such compounds was achieved. Analysis of chemical space coverage confirmed that the compound structures indeed occupy underrepresented areas of chemistry in screening collections. Crucial to the success of this approach was the development of novel methodologies for the macrocyclic ring closure of chiral α-azido acids and for the synthesis of diketopiperazines using solid-supported N methylmorpholine. Owing to their robust and flexible natures, it is envisaged that both new methodologies will prove to be valuable in a wider synthetic context.

Role of natural product diversity in chemical biology

Through the natural selection process, natural products possess a unique and vast chemical diversity and have been evolved for optimal interactions with biological macromolecules. Owing to their diversity, target affinity, and specificity, natural products have demonstrated enormous potential as modulators of biomolecular function, been an essential source for drug discovery, and provided design principles for combinatorial library development.

The synthetic genetic interaction network reveals small molecules that target specific pathways in Sacchromyces cerevisiae

High-throughput elucidation of synthetic genetic interactions (SGIs) has contributed to a systems-level understanding of genetic robustness and fault-tolerance encoded in the genome. Pathway targets of various compounds have been predicted by comparing chemical-genetic synthetic interactions to a network of SGIs. We demonstrate that the SGI network can also be used in a powerful reverse pathway-to-drug approach for identifying compounds that target specific pathways of interest. Using the SGI network, the method identifies an indicator gene that may serve as a good candidate for screening a library of compounds. The indicator gene is selected so that compounds found to produce sensitivity in mutants deleted for the indicator gene are likely to abrogate the target pathway. We tested the utility of the SGI network for pathway-to-drug discovery using the DNA damage checkpoint as the target pathway. An analysis of the compendium of synthetic lethal interactions in yeast showed that superoxide dismutase 1 (SOD1) has significant SGI connectivity with a large subset of DNA damage checkpoint and repair (DDCR) genes in Saccharomyces cerevisiae, and minimal SGIs with non-DDCR genes. We screened a sod1Δ strain against three National Cancer Institute (NCI) compound libraries using a soft agar high-throughput halo assay. Fifteen compounds out of ∼3100 screened showed selective toxicity toward sod1Δ relative to the isogenic wild type (wt) strain. One of these, 1A08, caused a transient increase in growth in the presence of sublethal doses of DNA damaging agents, suggesting that 1A08 inhibits DDCR signaling in yeast. Genome-wide screening of 1A08 against the library of viable homozygous deletion mutants further supported DDCR as the relevant targeted pathway of 1A08. When assayed in human HCT-116 colorectal cancer cells, 1A08 caused DNA-damage resistant DNA synthesis and blocked the DNA-damage checkpoint selectively in S-phase.

Chemical microarrays constructed by selective attachment of hydrazide-conjugated substances to epoxide surfaces and their applications

Microarray technology has received considerable attention for rapid analysis of biomolecular interactions and high-throughput screening to identify binding partners. An efficient and selective immobilization technique of substances on the surface is essential for successful construction of microarrays. Although a variety of immobilization methods have been exploited to prepare microarrays over the past decade, a superior technique needs to be developed for diverse applications. Recently, an efficient and simple method that relies on selective reactions between the hydrazide conjugated to substances and the epoxide derivatized on the solid surface was developed to fabricate chemical microarrays. Reactions between hydrazides with epoxides are highly selective in that they take place even in the presence of other potent nucleophiles such as amines and thiols. This technique is utilized to immobilize various substances such as small molecules, carbohydrates, and peptides to glass surfaces. The microarrays constructed by this immobilization method are used to evaluate protein binding to carbohydrates, peptides, and small molecules. In addition, the microarrays are also employed to determine binding affinities between proteins and binding partners as well as profiling of enzyme activities.

Small molecule microarrays: the first decade and beyond

Molecular Bits and Chips: Profiling and discovering the next generation of small molecule ligands.

Chemical microarrays: a new tool for discovery enzyme inhibitors

Enzymes, the catalytic proteins, are playing pivotal roles in regulating basic cell functions. Drugs that inhibit enzyme activities cover varying aspects of diseases and offer potential cures. One of the major technologies used in the drug discovery industry for finding the enzyme inhibitors is high-throughput screening, which is facing a daunting challenge due to the fast-growing numbers of drug targets arising from genomic and proteomic research and the large chemical libraries generated from high-throughput synthesis. Chemical microarray, as a new technology, could be an excellent alternative for traditional well-based screening, since the technology can screen more compounds against more targets in parallel with a minimum amount of materials, reducing cost and increasing productivity. In this chapter, we have introduced the basic techniques and applications of chemical microarrays, and how to use them routinely for identifying enzyme inhibitors with functional-based assays. Sample assays for kinases, proteases, histone deacetylases, and phosphatases are demonstrated.

PROBING CANCER SIGNALING WITH RESONANT WAVEGUIDE GRATING BIOSENSORS

IMPORTANCE OF THE FIELD: Cancer is a collection of diseases that arise from the progressive accumulation of genetic alterations in somatic cells. Genomic approaches have identified a great variety of genetic abnormalities associated with tumorigenesis, and molecular imaging and quantification assays have further elucidated the complex interactions within or between pathways. It is acknowledged that it is proteins, rather than genes, to fulfill most cellular functions; and signaling proteins largely operate through a large and complex network. To this end, cancer is mostly a pathway dysregulated disease - a small number of core pathways are dominate in aberrant cell growth leading to cancer. Thus, understanding the functional consequences of dysregulated and/or mutant signaling proteins in the context of native signaling networks is the frontier in cancer research. AREAS COVERED IN THIS REVIEW: This article reviews why resonant waveguide grating (RWG) biosensor cellular assays are considered to be integrative in nature, and how RWG biosensor can be used for mining the surface markers of cancer cells, and discovering core pathway(s) of cancer receptor signaling. WHAT THE READER WILL GAIN: The reader will gain an overview of cancer biology from pathway perspective, and have a glimpse of potential implications of integrative cellular assays, as promised by RWG biosensor, in cancer research and diagnosis. TAKE HOME MESSAGE: Successful approaches for developing next-generation anti-cancer therapies and diagnostic protocols should take into account that the dysregulation of oncogenic pathways is central to tumorigenesis. The biosensor cellular assays offer unprecedented advantage in characterizing cancer biology. However, significant challenges are also presented in deconvoluting and validating cellular mechanisms identified in cancer receptor signaling using these assays.

One-step, aid-mediated method for modification of glass surfaces with N-hydroxysuccinimide esters and its application to the construction of microarrays for studies of biomolecular interactions

Microarray technologies have received considerable attention owing to the fact that they serve as powerful tools for the high-throughput analysis of biomolecular interactions and the identification of bioactive substances that bind to biomolecules. Most of the current methods used to construct microarrays rely on the immobilization of substances on properly derivatized surfaces. Among various functional groups used for this purpose, the N-hydroxysuccinimide (NHS) ester group has been largely employed because it can be readily reacted with amine or hydrazide functionalities in substances of interest. However, the NHS ester group is usually introduced onto the surface of a glass slide by employing inconvenient and time-consuming multistep processes. In recent studies, we have developed an efficient, single step method for derivatization of glass surfaces with NHS ester groups that takes advantage of an acid-mediated reaction of NHS ester functionalized dimethallylsilanes with silanols on the glass surface. Conditions for the surface modification procedure that utilize TfOH rather than Sc(OTf)(3) were found to be superior. Protein and RNA-binding experiments show that glass surfaces modified by employing this method are suitable for efficient immobilization of various substances that are appended by amine, hydrazide, and alcohol functionalities. The microarrays, generated in this way, are applicable to procedures for rapid analysis of protein-protein, protein-glycan, protein-small molecule, and peptide-RNA interactions, as well as for profiling enzyme activities. The newly developed acid-mediated, glass surface modification method should be generally applicable to the preparation of various functional group-modified surfaces.

Small molecule microarrays enable the discovery of compounds that bind the Alzheimer's Aβ peptide and reduce its cytotoxicity

The amyloid-β (Aβ) aggregation pathway is a key target in efforts to discover therapeutics that prevent or delay the onset of Alzheimer's disease. Efforts at rational drug design, however, are hampered by uncertainties about the precise nature of the toxic aggregate. In contrast, high-throughput screening of compound libraries does not require a detailed understanding of the structure of the toxic species, and can provide an unbiased method for the discovery of small molecules that may lead to effective therapeutics. Here, we show that small molecule microarrays (SMMs) represent a particularly promising tool for identifying compounds that bind the Aβ peptide. Microarray slides with thousands of compounds immobilized on their surface were screened for binding to fluorescently labeled Aβ. Seventy-nine compounds were identified by the SMM screen, and then assayed for their ability to inhibit the Aβ-induced killing of PC12 cells. Further experiments focused on exploring the mechanism of rescue for one of these compounds: Electron microscopy and Congo red binding showed that the compound enhances fibril formation, and suggest that it may rescue cells by accelerating Aβ aggregation past an early toxic oligomer. These findings demonstrate that the SMM screen for binding to Aβ is effective at identifying compounds that reduce Aβ toxicity, and can reveal potential therapeutic leads without the biases inherent in methods that focus on inhibitors of aggregation.

Extensive in vivo metabolite-protein interactions revealed by large-scale systematic analyses

Natural small compounds comprise most cellular molecules and bind proteins as substrates, products, cofactors, and ligands. However, a large-scale investigation of in vivo protein-small metabolite interactions has not been performed. We developed a mass spectrometry assay for the large-scale identification of in vivo protein-hydrophobic small metabolite interactions in yeast and analyzed compounds that bind ergosterol biosynthetic proteins and protein kinases. Many of these proteins bind small metabolites; a few interactions were previously known, but the vast majority are new. Importantly, many key regulatory proteins such as protein kinases bind metabolites. Ergosterol was found to bind many proteins and may function as a general regulator. It is required for the activity of Ypk1, a mammalian AKT/SGK kinase homolog. Our study defines potential key regulatory steps in lipid biosynthetic pathways and suggests that small metabolites may play a more general role as regulators of protein activity and function than previously appreciated.

Discovery of a quorum sensing modulator pharmacophore by 3D small-molecule microarray screening

The screening of large arrays of drug-like small-molecules was traditionally a time consuming and resource intensive task. New methodology developed within our laboratories provides an attractive low cost, 3D microarray-assisted screening platform that could be used to rapidly assay thousands of compounds. As a proof-of-principle the platform was exploited to screen a number of quorum sensing analogs. Quorum sensing is used by bacterium to initiate and spread infection; in this context its modulation may have significant clinical value. 3D microarray slides were probed with fluorescently labeled ligand-binding domains of the LuxR homolog CarR from Erwinia carotovora subsp. carotovora. The 3D microarray platform was used to discover the biologically active chloro-pyridine pharmacophore, which was validated using a fluorometric ligand binding assay and ITC. Analogs containing the chloro-pyridine pharmacophore were found to be potent inhibitors of N-acyl-homoserine-lactone (AHL) mediated quorum sensing phenotypes in Serratia (IC(50) = ∼5 μM) and Pseudomonas aeruginosa (IC(50) = 10-20 μM).

Gold(I)-catalyzed coupling reactions for the synthesis of diverse small molecules using the build/couple/pair strategy

The build/couple/pair strategy has yielded small molecules with stereochemical and skeletal diversity by using short reaction sequences. Subsequent screening has shown that these compounds can achieve biological tasks considered challenging if not impossible (‘undruggable’) for small molecules. We have developed gold(I)-catalyzed cascade reactions of easily prepared propargyl propiolates as a means to achieve effective intermolecular coupling reactions for this strategy. Sequential alkyne activation of propargyl propiolates by a cationic gold(I) catalyst yields an oxocarbenium ion that we previously showed is trapped by C-based nucleophiles at an extrannular site to yield α-pyrones. Here, we report O-based nucleophiles react by ring opening to afford a novel polyfunctional product. In addition, by coupling suitable building blocks, we subsequently performed intramolecular pairing reactions that yield diverse and complex skeletons. These pairing reactions include one based on a novel aza-Wittig-6π-electrocyclization sequence and others based on ring-closing metathesis reactions.