Protein network prediction and topological analysis in <Emphasis Type="Italic">Leishmania major</Emphasis> as a tool for drug target selection

Background  

Leishmaniasis is a virulent parasitic infection that causes a worldwide disease burden. Most treatments have toxic side-effects and efficacy has decreased due to the emergence of resistant strains. The outlook is worsened by the absence of promising drug targets for this disease. We have taken a computational approach to the detection of new drug targets, which may become an effective strategy for the discovery of new drugs for this tropical disease.     

Chemical proteomics and its application to drug discovery

The completion of the human genome sequencing project has provided a flood of new information that is likely to change the way scientists approach the study of complex biological systems. A major challenge lies in translating this information into new and better ways to treat human disease. The multidisciplinary science of chemical proteomics can be used to distill this flood of new information. This approach makes use of synthetic small molecules that can be used to covalently modify a set of related enzymes and subsequently allow their purification and/or identification as valid drug targets. Furthermore, such methods enable rapid biochemical analysis and small-molecule screening of targets thereby accelerating the often difficult process of target validation and drug discovery.     

Microarray analysis of pathogens and their interaction with hosts

Microarrays are a promising technique for elucidating and interpreting the mechanistic roles of genes in the pathogenesis of infectious disease. Microarrays have been used to analyse the genetic polymorphisms of specific loci associated with resistance to antimicrobial agents, to explore the distribution of genes among isolates from the same and similar species, to understand the evolutionary relationship between closely related species and to integrate the clinical and genomic data. This technique has also been used to study host–pathogen interactions, mainly by identifying genes from pathogens that may be involved in pathogenicity and by surveying the scope of the host response to infection. The RNA expression profile of pathogens has been used to identify regulatory mechanisms that ensure gene expression in the appropriate environment, to hypothesize functions of hundreds of uncharacterized genes and to identify virulence genes that promote colonization or tissue damage. This information also has the potential to identify targets for drug design. Furthermore, microarrays have been used to investigate the mechanism of drug action and to delineate and predict adverse effects of new drugs. In this paper, we review the use of spotted and high-density oligonucleotide arrays to study the genetic polymorphisms of pathogens, host–pathogen interactions and whole-genome expression profiles of pathogens, as well as their use for drug discovery.     

NMR screening in drug discovery

NMR methods in drug discovery have traditionally been used to obtain structural information for drug targets or target-ligand complexes. Recently, it has been shown that NMR may be used as an alternative approach for identification of ligands that bind to protein drug targets, shifting the emphasis of many NMR laboratories towards screening and design of potential drug molecules, rather than structural characterization.     

A systematic analysis of FDA-approved anticancer drugs

Background

The discovery of novel anticancer drugs is critical for the pharmaceutical research and development, and patient treatment. Repurposing existing drugs that may have unanticipated effects as potential candidates is one way to meet this important goal. Systematic investigation of efficient anticancer drugs could provide valuable insights into trends in the discovery of anticancer drugs, which may contribute to the systematic discovery of new anticancer drugs.

Results

In this study, we collected and analyzed 150 anticancer drugs approved by the US Food and Drug Administration (FDA). Based on drug mechanism of action, these agents are divided into two groups: 61 cytotoxic-based drugs and 89 target-based drugs. We found that in the recent years, the proportion of targeted agents tended to be increasing, and the targeted drugs tended to be delivered as signal drugs. For 89 target-based drugs, we collected 102 effect-mediating drug targets in the human genome and found that most targets located on the plasma membrane and most of them belonged to the enzyme, especially tyrosine kinase. From above 150 drugs, we built a drug-cancer network, which contained 183 nodes (150 drugs and 33 cancer types) and 248 drug-cancer associations. The network indicated that the cytotoxic drugs tended to be used to treat more cancer types than targeted drugs. From 89 targeted drugs, we built a cancer-drug-target network, which contained 214 nodes (23 cancer types, 89 drugs, and 102 targets) and 313 edges (118 drug-cancer associations and 195 drug-target associations). Starting from the network, we discovered 133 novel drug-cancer associations among 52 drugs and 16 cancer types by applying the common target-based approach. Most novel drug-cancer associations (116, 87%) are supported by at least one clinical trial study.

Conclusions

In this study, we provided a comprehensive data source, including anticancer drugs and their targets and performed a detailed analysis in term of historical tendency and networks. Its application to identify novel drug-cancer associations demonstrated that the data collected in this study is promising to serve as a fundamental for anticancer drug repurposing and development.

     

In silico identification of common putative drug targets in <Emphasis Type="Italic">Leptospira interrogans</Emphasis>

Infectious diseases are the leading causes of death worldwide. Hence, there is a need to develop new antimicrobial agents. Traditional method of drug discovery is time consuming and yields a few drug targets with little intracellular information for guiding target selection. Thus, focus in drug development has been shifted to computational comparative genomics for identifying novel drug targets. Leptospirosis is a worldwide zoonosis of global concern caused by Leptospira interrogans. Availability of L. interrogans serovars and human genome sequences facilitated to search for novel drug targets using bioinformatics tools. The genome sequence of L. interrogans serovar Copenhageni has 5,124 genes while that of serovar Lai has 4,727 genes. Through subtractive genomic approach 218 genes in serovar Copenhageni and 158 genes in serovar Lai have been identified as putative drug targets. Comparative genomic approach had revealed that 88 drug targets were common to both the serovars. Pathway analysis using the Kyoto Encyclopaedia of Genes and Genomes revealed that 66 targets are enzymes and 22 are non-enzymes. Sixty two common drug targets were predicted to be localized in cytoplasm and 16 were surface proteins. The identified potential drug targets form a platform for further investigation in discovery of novel therapeutic compounds against Leptospira.     

Making drugs on proteins: site-directed ligand discovery for fragment-based lead assembly

Rapid progress in genomics and proteomics has provided a wealth of new targets for the pharmaceutical industry, even as many older targets still remain challenging for small-molecule drug discovery. Fragment-based lead discovery, in which leads are built progressively by expanding or combining small fragments, is a rapidly growing field that offers potential advantages over traditional lead-discovery processes. However, identifying and assembling the fragments themselves can be challenging. Here, we review the concept of site-directed ligand discovery, in which a covalent bond is used to stabilize the interaction between a low-affinity fragment and a target protein. We also describe how this technique can facilitate fragment-based lead discovery and help overcome some of the limitations of traditional screening methods.     

膜片钳阵列技术及其应用于药物筛选的研究进展

离子通道是一类重要的药物作用耙点。膜片钳技术是目前进行离子通道研究和影响离子通道药物研究的最好方法。但膜片钳技术通量低,成为应用该法进行药物筛选的最大障碍。膜片钳阵列技术是在普通膜片钳技术基础上发展起来的高通量技术,包括平面膜片钳阵列技术和微管自动化膜片钳技术,已经在药物筛选中得到应用。本文仅就这2种方法当前的研究进展及其在药物筛选中的应用做简单的介绍。     

Identification of potential targets in biological signalling systems through network perturbation analysis

The network-based representation and analysis of biological systems contributes to a greater understanding of their structures and functions at different levels of complexity. These techniques can also be used to identify potential novel therapeutic targets based on the characterisation of vulnerable or highly influential network components. There is a need to investigate methods for estimating the impact of molecular perturbations. The prediction of high-impact or critical targets can aid in the identification of novel strategies for controlling the level of activation of specific, therapeutically relevant genes or proteins. Here, we report a new computational strategy for the analysis of the vulnerability of cellular signalling networks based on the quantitative assessment of the impact of large-scale, dynamic perturbations. To show the usefulness of this methodology, two complex signalling networks were analysed: the caspase-3 and the adenosine-regulated calcium signalling systems. This allowed us to estimate and rank the perturbation impact of the components defining these networks. Testable hypotheses about how these targets could modify the dynamic operation of the systems are provided. In the case of the caspase-3 system, the predictions and rankings were in line with results obtained from previous experimental validations of computational predictions generated by a relatively more computationally complex technique. In the case of the adenosine-regulated calcium system, we offer new testable predictions on the potential effect of different targets on the control of calcium flux. Unlike previous methods, the proposed approach provides perturbation-specific scores for each network component. The proposed perturbation assessment methodology may be applied to other systems to gain a deeper understanding of their dynamic operation and to assist the discovery of new therapeutic targets and strategies.     

Lipidomics analysis in drug discovery and development

Despite being a relatively new addition to the Omics' landscape, lipidomics is increasingly being recognized as an important tool for the identification of druggable targets and biochemical markers. In this review we present recent advances of lipid analysis in drug discovery and development. We cover current state of the art technologies which are constantly evolving to meet demands in terms of sensitivity and selectivity. A careful selection of important examples is then provided, illustrating the versatility of lipidomics analysis in the drug discovery and development process. Integration of lipidomics with other omics’, stem-cell technologies, and metabolic flux analysis will open new avenues for deciphering pathophysiological mechanisms and the discovery of novel targets and biomarkers.     

Metabolic pathway analysis of S. pneumoniae: an in silico approach towards drug-design

The emergence of multidrug resistant varieties of Streptococcus pneumoniae (S. pneumoniae) has led to a search for novel drug targets. An in silico comparative analysis of metabolic pathways of the host Homo sapiens (H. sapiens) and the pathogen S. pneumoniae have been performed. Enzymes from the biochemical pathways of S. pneumoniae from the KEGG metabolic pathway database were compared with proteins from the host H. sapiens, by performing a BLASTp search against the non-redundant database restricted to the H. sapiens subset. The e-value threshold cutoff was set to 0.005. Enzymes, which do not show similarity to any of the host proteins, below this threshold, were filtered out as potential drug targets. Five pathways unique to the pathogen S. pneumoniae when compared to the host H. sapiens have been identified. Potential drug targets from these pathways could be useful for the discovery of broad-spectrum drugs. Potential drug targets were also identified from pathways related to lipid metabolism, carbohydrate metabolism, amino acid metabolism, energy metabolism, vitamin and cofactor biosynthetic pathways and nucleotide metabolism. Of the 161 distinct targets identified from these pathways, many are in various stages of progress at the Microbial Genome Database. However, 44 of the targets are new and can be considered for rational drug design. The study was successful in listing out potential drug targets from the S. pneumoniae proteome involved in vital aspects of the pathogen's metabolism, persistence, virulence and cell wall biosynthesis. This systematic evaluation of metabolic pathways of host and pathogen through reliable and conventional bioinformatics approach can be extended to other pathogens of clinical interest.     

Integrative genome‐scale metabolic analysis of Vibrio vulnificus for drug targeting and discovery

Although the genomes of many microbial pathogens have been studied to help identify effective drug targets and novel drugs, such efforts have not yet reached full fruition. In this study, we report a systems biological approach that efficiently utilizes genomic information for drug targeting and discovery, and apply this approach to the opportunistic pathogen Vibrio vulnificus CMCP6. First, we partially re‐sequenced and fully re‐annotated the V. vulnificus CMCP6 genome, and accordingly reconstructed its genome‐scale metabolic network, VvuMBEL943. The validated network model was employed to systematically predict drug targets using the concept of metabolite essentiality, along with additional filtering criteria. Target genes encoding enzymes that interact with the five essential metabolites finally selected were experimentally validated. These five essential metabolites are critical to the survival of the cell, and hence were used to guide the cost‐effective selection of chemical analogs, which were then screened for antimicrobial activity in a whole‐cell assay. This approach is expected to help fill the existing gap between genomics and drug discovery.     

Selecting targets for therapeutic validation through differential protein expression using chromatography-mass spectrometry

The identification of potential targets for therapeutic intervention can be accomplished on a systematic basis by a variety of techniques that include quantitative analysis of gene-specific mRNA levels and expressed proteins in normal and diseased cells. Differences in the expression levels of nucleic acid and protein gene products could suggest protein drug targets that are directly causative of disease, or reveal biochemical pathways that could be modulated by therapeutic molecules. Any effort based on mRNA or protein expression level comparisons could be confounded by a number of factors: level in steady-state may not be correlated with actual encoded protein levels; differentially expressed protein levels might be a result of disease process, and not causative of the process, and therapeutic intervention based on such a difference will be unproductive and the differential expression of mRNA or protein may be the result of biological variation unrelated to the disease process under study. In order to address these possibly confounding factors, it is necessary to validate potential targets by establishing their firm association with disease, and their minimal distribution in non-diseased tissues of any type. This requirement suggests that emphasis on true and reproducible quantitation of protein expression levels in a variety of samples will be an effective and highly efficient method of generating drug targets with a high degree of utility. To achieve this aim, we have established an industrial-scale proteomics-based discovery platform consisting of cell biology, protein chemistry, and mass spectrometry technical groups together with bioinformatics groups. The analytical method used for quantitation employs isotope labeling for differential analysis (ICATTM, Applied Biosystems, Inc.). With this technique, tryptic peptides are generated from labeled proteins that have been specifically captured from various subcellular locations or protein families. The resulting peptides are identified and quantified by mass spectrometry. To evaluate this approach on a large-scale, we have applied it to a study of continuous cell lines derived from human pancreatic adenocarcinomas. We have been able to establish processes for target discovery for small molecule drug targets as well as therapeutic antibody target identification for cell surface proteins. In addition, we have developed a process for identification of serum markers of this disease based upon standardized fractionation procedures. The results of these analyses will be presented together with the some of the issues from both the wet and dry (computational) lab that need to be addressed in such an undertaking.     

Turning a Drug Target into a Drug Candidate: A New Paradigm for Neurological Drug Discovery?

The conventional paradigm for developing new treatments for disease mainly involves either the discovery of new drug targets, or finding new, improved drugs for old targets. However, an ion channel found only in invertebrates offers the potential of a completely new paradigm in which an established drug target can be re-engineered to serve as a new candidate therapeutic agent. The L-glutamate-gated chloride channels (GluCls) of invertebrates are absent from vertebrate genomes, offering the opportunity to introduce this exogenous, inhibitory, L-glutamate receptor into vertebrate neuronal circuits either as a tool with which to study neural networks, or a candidate therapy. Epileptic seizures can involve L-glutamate-induced hyper-excitation and toxicity. Variant GluCls, with their inhibitory responses to L-glutamate, when engineered into human neurons, might counter the excitotoxic effects of excess L-glutamate. In reviewing recent studies on model organisms, it appears that this approach might offer a new paradigm for the development of candidate therapeutics for epilepsy.     

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.     

NMR techniques for characterization of ligand binding: utility for lead generation and optimization in drug discovery.

Over the last ten years, nmr spectroscopy has evolved into an important discipline in drug discovery. Initially, nmr was most useful as a technique to provide structural information regarding protein drug targets and target-ligand interactions. More recently, it has been shown that nmr may be used as an alternative method for identification of small molecule ligands that bind to protein drug targets. High throughput implementation of these experiments to screen small molecule libraries may lead to identification of potent and novel lead compounds. In this review, we will use examples from our own research to illustrate how nmr experiments to characterize ligand binding may be used to both screen for novel compounds during the process of lead generation, as well as provide structural information useful for lead optimization during the latter stages of a discovery program.     

UPA, a universal protein array system for quantitative detection of protein-protein, protein-DNA, protein-RNA and protein-ligand interactions

Protein-protein interactions have been widely used to study gene expression pathways and may be considered as a new approach to drug discovery. Here I report the development of a universal protein array (UPA) system that provides a sensitive, quantitative, multi-purpose, effective and easy technology to determine not only specific protein-protein interactions, but also specific interactions of proteins with DNA, RNA, ligands and other small chemicals. (i) Since purified proteins are used, the results can be easily interpreted. (ii) UPA can be used multiple times for different targets, making it economically affordable for most laboratories, hospitals and biotechnology companies. (iii) Unlike DNA chips or DNA microarrays, no additional instrumentation is required. (iv) Since the UPA uses active proteins (without denaturation and renaturation), it is more sensitive compared with most existing methods. (v) Because the UPA can analyze hundreds (even thousands on a protein microarray) of proteins in a single experiment, it is a very effective method to screen proteins as drug targets in cancer and other human diseases.     

Design of small molecule epigenetic modulators

The field of epigenetics has expanded rapidly to reveal multiple new targets for drug discovery. The functional elements of the epigenomic machinery can be categorized as writers, erasers and readers, and together these elements control cellular gene expression and homeostasis. It is increasingly clear that aberrations in the epigenome can underly a variety of diseases, and thus discovery of small molecules that modulate the epigenome in a specific manner is a viable approach to the discovery of new therapeutic agents. In this Digest, the components of epigenetic control of gene expression will be briefly summarized, and efforts to identify small molecules that modulate epigenetic processes will be described.     

UPA, a universal protein array system for quantitative detection of protein–protein, protein–DNA, protein–RNA and protein–ligand interactions

Protein–protein interactions have been widely used to study gene expression pathways and may be considered as a new approach to drug discovery. Here I report the development of a universal protein array (UPA) system that provides a sensitive, quantitative, multi-purpose, effective and easy technology to determine not only specific protein–protein interactions, but also specific interactions of proteins with DNA, RNA, ligands and other small chemicals. (i) Since purified proteins are used, the results can be easily interpreted. (ii) UPA can be used multiple times for different targets, making it economically affordable for most laboratories, hospitals and biotechnology companies. (iii) Unlike DNA chips or DNA microarrays, no additional instrumentation is required. (iv) Since the UPA uses active proteins (without denaturation and renaturation), it is more sensitive compared with most existing methods. (v) Because the UPA can analyze hundreds (even thousands on a protein microarray) of proteins in a single experiment, it is a very effective method to screen proteins as drug targets in cancer and other human diseases.