Tools for target identification and validation

Network pharmacology and in vitro experimental verification unveil glycyrrhizin from glycyrrhiza glabra alleviates acute pancreatitis via modulation of MAPK and STAT3 signaling pathways

Acute pancreatitis (AP) is a severe gastrointestinal inflammatory disease with increasing mortality and morbidity. Glycyrrhiza glabra, commonly known as Liquorice, is a widely used plant containing bioactive compounds like Glycyrrhizin, which possesses diverse medicinal properties such as anti-inflammatory, antioxidant, antiviral, and anticancer activities. The objective of this study is to investigate the active components, relevant targets, and underlying mechanisms of the traditional Chinese medicine Glycyrrhiza glabra in the treatment of AP. Utilizing various computational biology methods, we explored the potential targets and molecular mechanisms through Glycyrrhizin supplementation. Computational results indicated that Glycyrrhizin shows promising pharmacological potential, particularly with mitogen-activated protein kinase 3 (MAPK3) protein (degree: 70), forming stable complexes with Glycyrrhizin through ionic and hydrogen bonding interactions, with a binding free energy (ΔGbind) of -33.01 ± 0.08 kcal/mol. Through in vitro experiments, we validated that Glycyrrhizin improves primary pancreatic acinar cell injury by inhibiting the MAPK/STAT3/AKT signaling pathway. Overall, MAPK3 emerges as a reliable target for Glycyrrhizin's therapeutic effects in AP treatment. This study provides novel insights into the active components and potential targets and molecular mechanisms of natural products.

Target Identification-Based Analysis of Mechanism of Betulinic Acid-Induced Cells Apoptosis of Cervical Cancer SiHa

Cervical cancer is the fourth most common female malignancy with high morbidity and mortality, which urgently needs novel anti-cancer drugs. Accumulating investigations have focused on the antitumor activity of betulinic acid (BA), which is a natural compound with low toxicity and high efficiency. Although the effect of BA on SiHa cells is obvious, the specific mechanism is seldom studied. Target identification is an important part of research on the internal mechanism of action. In this current study, an integrated method based on literature collection, target prediction, enrichment analysis, network analysis, and western blotting experiments was performed to identify the potential key targets of BA-induced apoptosis. Then, combined with the identified potential key targets, the specific mechanism of BA-induced cervical cancer SiHa cells apoptosis was elucidated. Our present study demonstrated that BA significantly reduces the viability of cervical cancer SiHa cells in a dose- and time-dependent manner. In addition, 8 potential key targets (AKT1, CASP8, LMNA, TNF, BCL2, CASP3, PARP1, and XIAP) were obtained through our integrated target identification method. Meanwhile, western blotting showed that within a certain concentration range, the expression of cleaved-caspase 3, cleaved-PARP, and cytochrome c increased with the BA concentration, while XIAP was almost unchanged. Therefore, the effect of BA on cervical cancer is noticeable. BA-induced SiHa cells apoptosis is a multi-molecule coordinated process. In this process, BA is not only a participant in either the extrinsic or intrinsic pathways, but also a regulator of apoptosis effector molecules of the CASP3/PARP1 axis.

Discovery of potential asthma targets based on the clinical efficacy of Traditional Chinese Medicine formulas

ETHNOPHARMACOLOGICAL RELEVANCE

Standard therapy for asthma, a highly heterogeneous disease, is primarily based on bronchodilators and immunosuppressive drugs, which confer short-term symptomatic relief but not a cure. It is difficult to discover novel bronchodilators, although potential new targets are emerging. Traditional Chinese Medicine (TCM) formulas have been used to treat asthma for more than 2000 years, forming the basis for representative asthma treatments.

AIM OF THE STUDY

Based on the efficacy of TCM formulas, anti-asthmatic herbal compounds bind proteins are potential targets for asthma therapy. This analysis will provide new drug targets and discovery strategies for asthma therapy.

MATERIALS AND METHODS

A list of candidate herbs for asthma was selected from the classical formulas (CFs) of TCM for the treatment of wheezing or dyspnea recorded in Treatise on Cold Damage and Miscellaneous Diseases (TCDMD) and from modern herbal formulas identified in the SAPHRON TCM Database using the keywords "wheezing" or "dyspnea". Compounds in the selected herbs and compounds that directly bind target proteins were acquired by searching the Herbal Ingredients' Targets Database (HITD), TCM Data Bank (TCMDB) and TCM Integrated Database (TCMID). Therapeutic targets of conventional medicine (CM) for asthma were collected by searching Therapeutic Target Database (TTD), DrugBank and PubMed as supplements. Finally, the enriched gene ontology (GO) terms of the targets were obtained using the Database for Annotation Visualization and Integrated Discovery (DAVID) and protein-protein interactions (PPI) networks were constructed using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). The effects of two selected TCM compounds, kaempferol and ginkgolide A, on cellular resistance in human airway smooth muscle cells (ASMCs) and pulmonary resistance in a mouse model were investigated.

RESULTS

The list of 32 candidate herbs for asthma was selected from 10 CFs for the treatment of wheezing or dyspnea recorded in TCDMD and 1037 modern herbal formulas obtained from the SAPHRON TCM Database. A total of 130 compounds from the 32 selected herbs and 68 herbal compounds directly bind target proteins were acquired from HITD and TCMDB. Eighty-eight therapeutic targets of CM for asthma were collected by searching TTD and PubMed as supplements. DAVID and STRING analyses showed targets of TCM formulas are primarily related to cytochrome P450 (CYP) family, transient receptor potential (TRP) channels, matrix metalloproteinases (MMPs) and ribosomal protein. Both TCM formulas and CM act on the same types of targets or signaling pathways, such as G protein-coupled receptors (GPCRs), steroid hormone receptors (SHRs), and JAK-STAT signaling pathway. The proteins directly targeted by herbal compounds, TRPM8, TRPA1, TRPV3, CYP1B1, CYP2B6, CYP1A2, CYP3A4, CYP1A1, PPARA, PPARD, NR1I2, MMP1, MMP2, ESR1, ESR2, RPLP0, RPLP1 and RPLP2, are potential targets for asthma therapy. In vitro results showed kaempferol (1 × 10^-2 mM) and ginkgolide A (1 × 10^-5 mM) significantly increased the cell index (P < 0.05 vs. histamine, n = 3) and therefore relaxed human ASMCs. In vivo results showed kaempferol (145 μg/kg) and ginkgolide A (205 μg/kg) significantly reduced pulmonary resistance (P < 0.05 vs. methacholine, n = 6).

CONCLUSION

Potential target discovery for asthma treatment based on the clinical effectiveness of TCM is a feasible strategy.

New directions in antimalarial target validation

Introduction: Malaria is one of the most prevalent human infections worldwide with over 40% of the world's population living in malaria-endemic areas. In the absence of an effective vaccine, emergence of drug-resistant strains requires urgent drug development. Current methods applied to drug target validation, a crucial step in drug discovery, possess limitations in malaria. These constraints require the development of techniques capable of simplifying the validation of Plasmodial targets.Areas covered: The authors review the current state of the art in techniques used to validate drug targets in malaria, including our contribution - the protein interference assay (PIA) - as an additional tool in rapid in vivo target validation.Expert opinion: Each technique in this review has advantages and disadvantages, implying that future validation efforts should not focus on a single approach, but integrate multiple approaches. PIA is a significant addition to the current toolset of antimalarial validation. Validation of aspartate metabolism as a druggable pathway provided proof of concept of how oligomeric interfaces can be exploited to control specific activity in vivo. PIA has the potential to be applied not only to other enzymes/pathways of the malaria parasite but could, in principle, be extrapolated to other infectious diseases.

Interpretable machine learning as a tool for scientific discovery in chemistry

There has been an upsurge of interest in applying machine learning to chemistry, and impressive predictive accuracies have been achieved, but this has been done without providing any insight into what has been learnt from the training data.

Natural radioprotectors and their impact on cancer drug discovery

Cancer is a complex multifaceted illness that affects different patients in discrete ways. For a number of cancers the use of chemotherapy has become standard practice. Chemotherapy is a use of cytostatic drugs to cure cancer. Cytostatic agents not only affect cancer cells but also affect the growth of normal cells; leading to side effects. Because of this, radiotherapy gained importance in treating cancer. Slaughtering of cancerous cells by radiotherapy depends on the radiosensitivity of the tumor cells. Efforts to improve the therapeutic ratio have resulted in the development of compounds that increase the radiosensitivity of tumor cells or protect the normal cells from the effects of radiation. Amifostine is the only chemical radioprotector approved by the US Food and Drug Administration (FDA), but due to its side effect and toxicity, use of this compound was also failed. Hence the use of herbal radioprotectors bearing pharmacological properties is concentrated due to their low toxicity and efficacy. Notably, in silico methods can expedite drug discovery process, to lessen the compounds with unfavorable pharmacological properties at an early stage of drug development. Hence a detailed perspective of these properties, in accordance with their prediction and measurement, are pivotal for a successful identification of radioprotectors by drug discovery process.

Prediction of Human Drug Targets and Their Interactions Using Machine Learning Methods: Current and Future Perspectives

Identification of drug targets and drug target interactions are important steps in the drug-discovery pipeline. Successful computational prediction methods can reduce the cost and time demanded by the experimental methods. Knowledge of putative drug targets and their interactions can be very useful for drug repurposing. Supervised machine learning methods have been very useful in drug target prediction and in prediction of drug target interactions. Here, we describe the details for developing prediction models using supervised learning techniques for human drug target prediction and their interactions.

Non-clinical studies required for new drug development - Part I: early in silico and in vitro studies, new target discovery and validation, proof of principles and robustness of animal studies

This review presents a historical overview of drug discovery and the non-clinical stages of the drug development process, from initial target identification and validation, through in silico assays and high throughput screening (HTS), identification of leader molecules and their optimization, the selection of a candidate substance for clinical development, and the use of animal models during the early studies of proof-of-concept (or principle). This report also discusses the relevance of validated and predictive animal models selection, as well as the correct use of animal tests concerning the experimental design, execution and interpretation, which affect the reproducibility, quality and reliability of non-clinical studies necessary to translate to and support clinical studies. Collectively, improving these aspects will certainly contribute to the robustness of both scientific publications and the translation of new substances to clinical development.

Gene expression profiling of selenophosphate synthetase 2 knockdown in Drosophila melanogaster

Selenium (Se) is an important trace element for many organisms and is incorporated into selenoproteins as selenocysteine (Sec). In eukaryotes, selenophosphate synthetase SPS2 is essential for Sec biosynthesis. In recent years, genetic disruptions of both Sec biosynthesis genes and selenoprotein genes have been investigated in different animal models, which provide important clues for understanding the Se metabolism and function in these organisms. However, a systematic study on the knockdown of SPS2 has not been performed in vivo. Herein, we conducted microarray experiments to study the transcriptome of fruit flies with knockdown of SPS2 in larval and adult stages. Several hundred differentially expressed genes were identified in each stage. In spite that the expression levels of other Sec biosynthesis genes and selenoprotein genes were not significantly changed, it is possible that selenoprotein translation might be reduced without impacting the mRNA level. Functional enrichment and network-based analyses revealed that although different sets of differentially expressed genes were obtained in each stage, they were both significantly enriched in the carbohydrate metabolism and redox processes. Furthermore, protein-protein interaction (PPI)-based network clustering analysis implied that several hub genes detected in the top modules, such as Nimrod C1 and regucalcin, could be considered as key regulators that are responsible for the complex responses caused by SPS2 knockdown. Overall, our data provide new insights into the relationship between Se utilization and several fundamental cellular processes as well as diseases.

Study on Wangzaozin-A-Inducing Cancer Apoptosis and Its Theoretical Protein Targets

Wangzaozin A is the most representative cytotoxic C-20-nonoxide compound of Isodon plants of Labiatae. The protein targets of the 2 isomers (X and X') of Wangzaozin A are, respectively, extracted from target fishing dock Web site. Each isomer has 23 targets with good energy scores. The binding modes of each isomer and its targets are, respectively, analyzed by Dock. The energy score of the binding mode between the isomer X and the inositol-1(or 4)-monophosphatase is the smallest. The apoptosis, antiproliferative, and lethal effects of human gastric cancer cells induced by Wangzaozin A are assessed by trypan blue exclusion, hoechst33258 stain in SGC-7901, and flow cytometric measurement. The mechanism of Wangzaozin-A-inducing cancer apoptosis is analyzed. Wangzaozin A inhibits the growth of human gastric cancer SGC-7901 cell lines at the lower concentration (<4.0 µmol/L) and results in the lethal effect at the relative higher concentration (>8.0 µmol/L).

Advances in phage display technology for drug discovery

INTRODUCTION

Over the past decade, several library-based methods have been developed to discover ligands with strong binding affinities for their targets. These methods mimic the natural evolution for screening and identifying ligand-target interactions with specific functional properties. Phage display technology is a well-established method that has been applied to many technological challenges including novel drug discovery.

AREAS COVERED

This review describes the recent advances in the use of phage display technology for discovering novel bioactive compounds. Furthermore, it discusses the application of this technology to produce proteins and peptides as well as minimize the use of antibodies, such as antigen-binding fragment, single-chain fragment variable or single-domain antibody fragments like VHHs.

EXPERT OPINION

Advances in screening, manufacturing and humanization technologies demonstrate that phage display derived products can play a significant role in the diagnosis and treatment of disease. The effects of this technology are inevitable in the development pipeline for bringing therapeutics into the market, and this number is expected to rise significantly in the future as new advances continue to take place in display methods. Furthermore, a widespread application of this methodology is predicted in different medical technological areas, including biosensing, monitoring, molecular imaging, gene therapy, vaccine development and nanotechnology.

Training set selection for the prediction of essential genes

Various computational models have been developed to transfer annotations of gene essentiality between organisms. However, despite the increasing number of microorganisms with well-characterized sets of essential genes, selection of appropriate training sets for predicting the essential genes of poorly-studied or newly sequenced organisms remains challenging. In this study, a machine learning approach was applied reciprocally to predict the essential genes in 21 microorganisms. Results showed that training set selection greatly influenced predictive accuracy. We determined four criteria for training set selection: (1) essential genes in the selected training set should be reliable; (2) the growth conditions in which essential genes are defined should be consistent in training and prediction sets; (3) species used as training set should be closely related to the target organism; and (4) organisms used as training and prediction sets should exhibit similar phenotypes or lifestyles. We then analyzed the performance of an incomplete training set and an integrated training set with multiple organisms. We found that the size of the training set should be at least 10% of the total genes to yield accurate predictions. Additionally, the integrated training sets exhibited remarkable increase in stability and accuracy compared with single sets. Finally, we compared the performance of the integrated training sets with the four criteria and with random selection. The results revealed that a rational selection of training sets based on our criteria yields better performance than random selection. Thus, our results provide empirical guidance on training set selection for the identification of essential genes on a genome-wide scale.

Role and challenges of proteomics in pharma and biotech: technical, scientific and commercial perspective

Contemporary proteomics, currently in its exponential growth phase, is a bewildering array of tools. Proteomic methods are the result of a convergence of rapidly improving mass spectrometry technologies, protein chemistry and separation sciences, genomics and bioinformatics. Strides in improving proteomics technologies to map and measure proteomes and subproteomes are being made. However, no single proteomic platform appears ideally suited to address all research needs or accomplish ambitious goals satisfactorily. However, proteomics is in a unique position to contribute to protein discovery and to public health in terms of better biomarkers, diagnostics and treatment of disease. While the potential is great, many challenges and issues remain to be solved. Fundamental issues, such as biological variability, pre-analytic factors and analytical reproducibility, remain to be resolved. Neither an all-genetic approach nor an all-proteomic approach will solve biological complexity. Proteomics will be the foundation for constructing and extracting useful knowledge to pharma and biotech depicted in the following path: data --> structured data --> information --> information architecture --> knowledge --> useful knowledge.

A new computational strategy for predicting essential genes

Background

Determination of the minimum gene set for cellular life is one of the central goals in biology. Genome-wide essential gene identification has progressed rapidly in certain bacterial species; however, it remains difficult to achieve in most eukaryotic species. Several computational models have recently been developed to integrate gene features and used as alternatives to transfer gene essentiality annotations between organisms.

Results

We first collected features that were widely used by previous predictive models and assessed the relationships between gene features and gene essentiality using a stepwise regression model. We found two issues that could significantly reduce model accuracy: (i) the effect of multicollinearity among gene features and (ii) the diverse and even contrasting correlations between gene features and gene essentiality existing within and among different species. To address these issues, we developed a novel model called feature-based weighted Naïve Bayes model (FWM), which is based on Naïve Bayes classifiers, logistic regression, and genetic algorithm. The proposed model assesses features and filters out the effects of multicollinearity and diversity. The performance of FWM was compared with other popular models, such as support vector machine, Naïve Bayes model, and logistic regression model, by applying FWM to reciprocally predict essential genes among and within 21 species. Our results showed that FWM significantly improves the accuracy and robustness of essential gene prediction.

Conclusions

FWM can remarkably improve the accuracy of essential gene prediction and may be used as an alternative method for other classification work. This method can contribute substantially to the knowledge of the minimum gene sets required for living organisms and the discovery of new drug targets.

The eradication of leprosy: molecular modeling techniques for novel drug discovery

INTRODUCTION

Leprosy is a slowly progressing bacterial infection caused by Mycobacterium leprae. The World Health Organization recommended multidrug therapy (MDT) which is extremely effective and halts the progress of the disease. Even though the objective of eliminating leprosy as a public health problem has been achieved successfully, leprosy is not yet eradicated. Furthermore, the long-term use of MDT results in single- and multidrug resistance. Therefore, there is still a need for new drug discovery for leprosy.

AREAS COVERED

The authors explain the importance of discovery of new drug to leprosy and the significance of homology modeling to drug discovery. This review highlights the principle steps, applications, and the resources of homology modeling. Finally, the authors emphasize the application of different structure-based drug design (SBDD) approaches to design novel therapeutics for leprosy.

EXPERT OPINION

MDT has proved to be effective in controlling infection, with prevalence of leprosy now predominantly isolated to the developing countries. The emergence of single- and multidrug-resistant strains of M. leprae has, however, provided some concern with the need for newer antibacterial agents. Drug resistance can be overcome by multi-targeted therapy. SBDD approaches, which reported many successful drugs, depend predominantly on the three-dimensional (3D) structure of drug targets. As of 2013, only very few experimental structures are available for M. leprae proteins. Hence, SBDD, in leprosy research, relies heavily on homology modeling to predict the 3D structure of drug targets and to design better therapeutics.

Small molecules present large opportunities in plant biology

Since the introduction of chemical genomics to plant biology as a tool for basic research, the field has advanced significantly. There are now examples of important basic discoveries that demonstrate the power and untapped potential of this approach. Given the combination of protein and small-molecule complexity, new phenotypes can be described through the perturbation of cellular functions that can be linked to growth and developmental phenotypes. There are now clear examples of overcoming functional redundancy in plants to dissect molecular mechanisms or critical pathways such as hormone signaling and dynamic intracellular processes. Owing to ongoing advances, including more sophisticated high-content screening and rapid approaches for target identification, the field is beginning to move forward. However, there are also challenges to improve automation, imaging, and analysis and provide chemical biology resources to the broader plant biology community.

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.

Identification of a lacosamide binding protein using an affinity bait and chemical reporter strategy: 14-3-3 ζ

We have advanced a useful strategy to elucidate binding partners of ligands (drugs) with modest binding affinity. Key to this strategy is attaching to the ligand an affinity bait (AB) and a chemical reporter (CR) group, where the AB irreversibly attaches the ligand to the receptor upon binding and the CR group is employed for receptor detection and isolation. We have tested this AB&CR strategy using lacosamide ((R)-1), a low-molecular-weight antiepileptic drug. We demonstrate that using a (R)-lacosamide AB&CR agent ((R)-2) 14-3-3 ζ in rodent brain soluble lysates is preferentially adducted, adduction is stereospecific with respect to the AB&CR agent, and adduction depends upon the presence of endogenous levels of the small molecule metabolite xanthine. Substitution of lacosamide AB agent ((R)-5) for (R)-2 led to the identification of the 14-3-3 ζ adduction site (K120) by mass spectrometry. Competition experiments using increasing amounts of (R)-1 in the presence of (R)-2 demonstrated that (R)-1 binds at or near the (R)-2 modification site on 14-3-3 ζ. Structure-activity studies of xanthine derivatives provided information concerning the likely binding interaction between this metabolite and recombinant 14-3-3 ζ. Documentation of the 14-3-3 ζ-xanthine interaction was obtained with isothermal calorimetry using xanthine and the xanthine analogue 1,7-dimethylxanthine.

Applications of genetically modified tools to safety assessment in drug development

The process of new drug development consists of several stages; after identifying potential candidate compounds, preclinical studies using animal models link the laboratory and human clinical trials. Among many steps in preclinical studies, toxicology and safety assessments contribute to identify potential adverse events and provide rationale for setting the initial doses in clinical trials. Gene modulation is one of the important tools of modern biology, and is commonly employed to examine the function of genes of interest. Advances in new drug development have been achieved by exploding information on target selection and validation using genetically modified animal models as well as those of cells. In this review, a recent trend of genetically modified methods is discussed with reference to safety assessments, and the exemplary applications of gene-modulating tools to the tests in new drug development were summarized.

Dataset of potential targets for Mycobacterium tuberculosis H37Rv through comparative genome analysis

Mycobacterium tuberculosis is the causative agent of the disease, tuberculosis and H37Rv is the most studied clinical strain. We use comparative genome analysis of Mycobacterium tuberculosis H37Rv and human for the identification of potential targets dataset. We used DEG (Database of Essential Genes) to identify essential genes in the H37Rv strain. The analysis shows that 628 of the 3989 genes in Mycobacterium tuberculosis H37Rv were found to be essential of which 324 genes lack similarity to the human genome. Subsequently hypothetical proteins were removed through manual curation. This further resulted in a dataset of 135 proteins with essential function and no homology to human.

Computational prediction of essential genes in an unculturable endosymbiotic bacterium, Wolbachia of Brugia malayi

BACKGROUND

Wolbachia (wBm) is an obligate endosymbiotic bacterium of Brugia malayi, a parasitic filarial nematode of humans and one of the causative agents of lymphatic filariasis. There is a pressing need for new drugs against filarial parasites, such as B. malayi. As wBm is required for B. malayi development and fertility, targeting wBm is a promising approach. However, the lifecycle of neither B. malayi nor wBm can be maintained in vitro. To facilitate selection of potential drug targets we computationally ranked the wBm genome based on confidence that a particular gene is essential for the survival of the bacterium.

RESULTS

wBm protein sequences were aligned using BLAST to the Database of Essential Genes (DEG) version 5.2, a collection of 5,260 experimentally identified essential genes in 15 bacterial strains. A confidence score, the Multiple Hit Score (MHS), was developed to predict each wBm gene's essentiality based on the top alignments to essential genes in each bacterial strain. This method was validated using a jackknife methodology to test the ability to recover known essential genes in a control genome. A second estimation of essentiality, the Gene Conservation Score (GCS), was calculated on the basis of phyletic conservation of genes across Wolbachia's parent order Rickettsiales. Clusters of orthologous genes were predicted within the 27 currently available complete genomes. Druggability of wBm proteins was predicted by alignment to a database of protein targets of known compounds.

CONCLUSION

Ranking wBm genes by either MHS or GCS predicts and prioritizes potentially essential genes. Comparison of the MHS to GCS produces quadrants representing four types of predictions: those with high confidence of essentiality by both methods (245 genes), those highly conserved across Rickettsiales (299 genes), those similar to distant essential genes (8 genes), and those with low confidence of essentiality (253 genes). These data facilitate selection of wBm genes for entry into drug design pipelines.

Identification of the cellular targets of bioactive small organic molecules using affinity reagents

The elucidation of molecular targets of bioactive small organic molecules remains a significant challenge in modern biomedical research and drug discovery. This tutorial review summarizes strategies for the derivatization of bioactive small molecules and their use as affinity probes to identify cellular binding partners. Special emphasis is placed on logistical concerns as well as common problems encountered during such target identification experiments. The roadmap provided is a guide through the process of affinity probe selection, target identification, and downstream target validation.

Exploring functional genomics for drug target and therapeutics discovery in Plasmodia

Functional genomics approaches are indispensable tools in the drug discovery arena and have recently attained increased attention in antibacterial drug discovery research. However, the application of functional genomics to post-genomics research of Plasmodia is still in comparatively early stages. Nonetheless, with this genus having the most species sequenced of any eukaryotic organism so far, the Plasmodia could provide unique opportunities for the study of intracellular eukaryotic pathogens. This review presents the status quo of functional genomics of the malaria parasite including descriptions of the transcriptome, proteome and interactome. We provide examples for the in silico mining of the X-ome data sets and illustrate how X-omic data from drug challenged parasites might be used in elucidating amongst others, the mode-of-action of inhibitory compounds, validate potential targets and discover novel targets/therapeutics.

Quantitative analysis on the characteristics of targets with FDA approved drugs

Accumulated knowledge of genomic information, systems biology, and disease mechanisms provide an unprecedented opportunity to elucidate the genetic basis of diseases, and to discover new and novel therapeutic targets from the wealth of genomic data. With hundreds to a few thousand potential targets available in the human genome alone, target selection and validation has become a critical component of drug discovery process. The explorations on quantitative characteristics of the currently explored targets (those without any marketed drug) and successful targets (targeted by at least one marketed drug) could help discern simple rules for selecting a putative successful target. Here we use integrative in silico (computational) approaches to quantitatively analyze the characteristics of 133 targets with FDA approved drugs and 3120 human disease genes (therapeutic targets) not targeted by FDA approved drugs. This is the first attempt to comparatively analyze targets with FDA approved drugs and targets with no FDA approved drug or no drugs available for them. Our results show that proteins with 5 or fewer number of homologs outside their own family, proteins with single-exon gene architecture and proteins interacting with more than 3 partners are more likely to be targetable. These quantitative characteristics could serve as criteria to search for promising targetable disease genes.

Microarray detection of duplex and triplex DNA binders with DNA-modified gold nanoparticles

We have designed a chip-based assay, using microarray technology, for determining the relative binding affinities of duplex and triplex DNA binders. This assay combines the high discrimination capabilities afforded by DNA-modified Au nanoparticles with the high-throughput capabilities of DNA microarrays. The detection and screening of duplex DNA binders are important because these molecules, in many cases, are potential anticancer agents as well as toxins. Triplex DNA binders are also promising drug candidates. These molecules, in conjunction with triplex-forming oligonucleotides, could potentially be used to achieve control of gene expression by interfering with transcription factors that bind to DNA. Therefore, the ability to screen for these molecules in a high-throughput fashion could dramatically improve the drug screening process. The assay reported here provides excellent discrimination between strong, intermediate, and weak duplex and triplex DNA binders in a high-throughput fashion.

Tamoxifen-based probes for the study of estrogen receptor-mediated transcription

The nuclear receptors are ideal targets to control the expression of specific genes with small molecules. Estrogen receptor can activate or repress transcription though a number of different pathways. As part of an effort to develop reagents that selectively target specific transcriptional regulatory pathways, analogs of 4-hydroxytamoxifen were synthesized with variations in the basic side chain. In vitro binding assays and cell-based luciferase reporter gene assays confirm that all the derivatives have high affinity for the receptor and high potency at repressing direct estrogen receptor-mediated transcription.

Support vector machines approach for predicting druggable proteins: recent progress in its exploration and investigation of its usefulness

Identification and validation of viable targets is an important first step in drug discovery and new methods, and integrated approaches are continuously explored to improve the discovery rate and exploration of new drug targets. An in silico machine learning method, support vector machines, has been explored as a new method for predicting druggable proteins from amino acid sequence independent of sequence similarity, thereby facilitating the prediction of druggable proteins that exhibit no or low homology to known targets.

Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?

The organization and mining of malaria genomic and post-genomic data is important to significantly increase the knowledge of the biology of its causative agents, and is motivated, on a longer term, by the necessity to predict and characterize new biological targets and new drugs. Biological targets are sought in a biological space designed from the genomic data from Plasmodium falciparum, but using also the millions of genomic data from other species. Drug candidates are sought in a chemical space containing the millions of small molecules stored in public and private chemolibraries. Data management should, therefore, be as reliable and versatile as possible. In this context, five aspects of the organization and mining of malaria genomic and post-genomic data were examined: 1) the comparison of protein sequences including compositionally atypical malaria sequences, 2) the high throughput reconstruction of molecular phylogenies, 3) the representation of biological processes, particularly metabolic pathways, 4) the versatile methods to integrate genomic data, biological representations and functional profiling obtained from X-omic experiments after drug treatments and 5) the determination and prediction of protein structures and their molecular docking with drug candidate structures. Recent progress towards a grid-enabled chemogenomic knowledge space is discussed.

Peptide deformylase is a potential target for anti-Helicobacter pylori drugs: reverse docking, enzymatic assay, and X-ray crystallography validation

Colonization of human stomach by the bacterium Helicobacter pylori is a major causative factor for gastrointestinal illnesses and gastric cancer. However, the discovery of anti-H. pylori agents is a difficult task due to lack of mature protein targets. Therefore, identifying new molecular targets for developing new drugs against H. pylori is obviously necessary. In this study, the in-house potential drug target database (PDTD, http://www.dddc.ac.cn/tarfisdock/) was searched by the reverse docking approach using an active natural product (compound 1) discovered by anti-H. pylori screening as a probe. Homology search revealed that, among the 15 candidates discovered by reverse docking, only diaminopimelate decarboxylase (DC) and peptide deformylase (PDF) have homologous proteins in the genome of H. pylori. Enzymatic assay demonstrated compound 1 and its derivative compound 2 are the potent inhibitors against H. pylori PDF (HpPDF) with IC50 values of 10.8 and 1.25 microM, respectively. X-ray crystal structures of HpPDF and the complexes of HpPDF with 1 and 2 were determined for the first time, indicating that these two inhibitors bind well with HpPDF binding pocket. All these results indicate that HpPDF is a potential target for screening new anti-H. pylori agents. In addition, compounds 1 and 2 were predicted to bind to HpPDF with relatively high selectivity, suggesting they can be used as leads for developing new anti-H. pylori agents. The results demonstrated that our strategy, reverse docking in conjunction with bioassay and structural biology, is effective and can be used as a complementary approach of functional genomics and chemical biology in target identification.

Gene expression analysis of cardiovascular diseases: novel insights into biology and clinical applications

Although the contribution of genetics to complex cardiovascular diseases such as atherosclerosis has been accepted for quite some time, full and detailed knowledge of the individual causative genes has been elusive. With the advent of genomic technologies and methods, the necessary tools are now available to begin pinpointing the genes that contribute to disease susceptibility and progression. One approach being applied extensively in candidate gene discovery is gene expression analysis of human and animal tissues using microarrays. The genes identified by these genomic studies provide valuable insight into disease biology and represent the initial steps toward the development of diagnostic tests and therapeutic strategies that will substantially improve human health. This paper highlights the progress that has been made in using gene expression analysis cardiovascular genomic research and the potential for applying these findings in clinical medicine.

Prioritizing genomic drug targets in pathogens: application to Mycobacterium tuberculosis

We have developed a software program that weights and integrates specific properties on the genes in a pathogen so that they may be ranked as drug targets. We applied this software to produce three prioritized drug target lists for Mycobacterium tuberculosis, the causative agent of tuberculosis, a disease for which a new drug is desperately needed. Each list is based on an individual criterion. The first list prioritizes metabolic drug targets by the uniqueness of their roles in the M. tuberculosis metabolome (“metabolic chokepoints”) and their similarity to known “druggable” protein classes (i.e., classes whose activity has previously been shown to be modulated by binding a small molecule). The second list prioritizes targets that would specifically impair M. tuberculosis, by weighting heavily those that are closely conserved within the Actinobacteria class but lack close homology to the host and gut flora. M. tuberculosis can survive asymptomatically in its host for many years by adapting to a dormant state referred to as “persistence.” The final list aims to prioritize potential targets involved in maintaining persistence in M. tuberculosis. The rankings of current, candidate, and proposed drug targets are highlighted with respect to these lists. Some features were found to be more accurate than others in prioritizing studied targets. It can also be shown that targets can be prioritized by using evolutionary programming to optimize the weights of each desired property. We demonstrate this approach in prioritizing persistence targets.

The search for drugs to prevent or treat infections remains an urgent focus in infectious disease research. A new software program has been developed by the authors of this article that can be used to rank genes as potential drug targets in pathogens. Traditional prioritization approaches to drug target identification, such as searching the literature and trying to mentally integrate varied criteria, can quickly become overwhelming for the drug discovery researcher. Alternatively, one can computationally integrate different criteria to create a ranking function that can help to identify targets. The authors demonstrate the applicability of this approach on the genome of Mycobacterium tuberculosis, the organism that causes tuberculosis (TB), a disease for which new drug treatments are especially needed because of emerging drug-resistant strains. The experiences gained from this work will be useful for both wet-lab and informatics scientists working in infectious disease research; first, it demonstrates that ample public data already exist on the M. tuberculosis genome that can be tuned effectively for prioritizing drug targets. Second, the output from numerous freely available bioinformatics tools can be pushed to achieve these goals. Third, the methodology can easily be extended to other pathogens of interest. Currently studied TB targets are also highlighted in terms of the authors' ranking system, which should be useful for researchers focusing on TB drug discovery.

Target identification for a promising anti-lupus drug

Target identification is one of the more difficult aspects of modern chemical genetics. In this issue of Chemistry & Biology, Johnson et al. provide a compelling case for going through the trouble. As a byproduct of their identification of the cellular target of an anti-lupus compound, they have uncovered a powerful tool for the characterization of a fascinating molecular machine, the F1F0-ATP synthase.