Fluorine nuclear magnetic resonance-based assay in living mammalian cells

Nuclear magnetic resonance (NMR)-based screening has been recognized as a powerful approach for the identification and characterization of molecules interacting with pharmaceutical targets. Indeed, several NMR methods have been developed and successfully applied to many drug discovery projects. Whereas most of these approaches have targeted isolated biomolecular receptors, very few cases are reported with the screening performed in intact cells and cell extracts. Here we report the first successful application of the fluorine NMR-based assay n-FABS (n-fluorine atoms for biochemical screening) in living mammalian cells expressing the membrane protein fatty acid amide hydrolase (FAAH). This method allows the identification of both weak and potent inhibitors and the measurement of their potency in a physiological environment.     

Human microRNA target identification by RRSM

MicroRNAs (miRNAs) are small endogenously expressed non-coding RNAs that regulate target messenger RNAs in various biological processes. In recent years, there have been many studies concentrated on the discovery of new miRNAs and identification of their mRNA targets. Although researchers have identified many miRNAs, few miRNA targets have been identified by actual experimental methods. To expedite the identification of miRNA targets for experimental verification, in the literature approaches based on the sequence or microarray expression analysis have been established to discover the potential miRNA targets. In this study, we focus on the human miRNA target prediction and propose a generalized relative R2 method (RRSM) to find many high-confidence targets. Many targets have been confirmed from previous studies. The targets for several miRNAs discovered by the HITS-CLIP method in a recent study have also been selected by our study.     

High-throughput screening of biocatalytic activity: applications in drug discovery

Enzymes catalyze a diverse set of reactions that propel life's processes and hence serve as valuable therapeutic targets. High-throughput screening methods have become essential for sifting through large chemical libraries in search of drug candidates, and several sensitive and reliable analytical techniques have been specifically adapted to high-throughput measurements of biocatalytic activity. High-throughput biocatalytic assay platforms thus enable rapid screening against enzymatic targets, and have vast potential to impact various stages of the drug discovery process, including lead identification and optimization, and ADME/Tox assessment. These advances are paving the way for the adoption of high-throughput biocatalytic assays as an indispensable tool for the pharmaceutical industry.     

Yeast "N"-hybrid systems for protein-protein and drug-protein interaction discovery

The majority of small molecule drugs act on protein targets to exert a therapeutic function. It has become apparent in recent years that many small molecule drugs act on more than one particular target and consequently, approaches which profile drugs to uncover their target binding spectrum have become increasingly important. Classical yeast two-hybrid systems have mainly been used to discover and characterize protein-protein interactions, but recent modifications and improvements have opened up new routes towards screening for small molecule-protein interactions. Such yeast "n"-hybrid systems hold great promise for the development of drugs which interfere with protein-protein interactions and for the discovery of drug-target interactions. In this review, we discuss several yeast two-hybrid based approaches with applications in drug discovery and describe a protocol for yeast three-hybrid screening of small molecules to identify their direct targets.     

Advances in systems biology are enhancing our understanding of disease and moving us closer to novel disease treatments

With tens of billions of dollars spent each year on the development of drugs to treat human diseases, and with fewer and fewer applications for investigational new drugs filed each year despite this massive spending, questions now abound on what changes to the drug discovery paradigm can be made to achieve greater success. The high rate of failure of drug candidates in clinical development, where the great majority of these drugs fail due to lack of efficacy, speak directly to the need for more innovative approaches to study the mechanisms of disease and drug discovery. Here we review systems biology approaches that have been devised over the last several years to understand the biology of disease at a more holistic level. By integrating a diversity of data like DNA variation, gene expression, protein–protein interaction, DNA–protein binding, and other types of molecular phenotype data, more comprehensive networks of genes both within and between tissues can be constructed to paint a more complete picture of the molecular processes underlying physiological states associated with disease. These more integrative, systems-level methods lead to networks that are demonstrably predictive, which in turn provides a deeper context within which single genes operate such as those identified from genome-wide association studies or those targeted for therapeutic intervention. The more comprehensive views of disease that result from these methods have the potential to dramatically enhance the way in which novel drug targets are identified and developed, ultimately increasing the probability of success for taking new drugs through clinical development. We highlight a number of the integrative approaches via examples that have resulted not only in the identification of novel genes for diabetes and cardiovascular disease, but in more comprehensive networks as well that describe the context in which the disease genes operate.     

Recent Advances in Function-based Metagenomic Screening

Metagenomes from uncultured microorganisms are rich resources for novel enzyme genes. The methods used to screen the metagenomic libraries fall into two categories, which are based on sequence or function of the enzymes. The sequence-based approaches rely on the known sequences of the target gene families. In contrast, the function-based approaches do not involve the incorporation of metagenomic sequencing data and, therefore, may lead to the discovery of novel gene sequences with desired functions. In this review, we discuss the function-based screening strategies that have been used in the identification of enzymes from metagenomes. Because of its simplicity, agar plate screening is most commonly used in the identification of novel enzymes with diverse functions. Other screening methods with higher sensitivity are also employed, such as microtiter plate screening. Furthermore, several ultra-high-throughput methods were developed to deal with large metagenomic libraries. Among these are the FACS-based screening, droplet-based screening, and the in vivo reporter-based screening methods. The application of these novel screening strategies has increased the chance for the discovery of novel enzyme genes.     

Towards high-throughput characterization of small molecule mechanisms of action

Drug discovery is hampered by the lack of general strategies to characterize the mechanisms of action and intracellular targets of bioactive small molecules. Genomics and proteomics promise to aid in this process. Genome-wide approaches in yeast have proven useful to infer the targets and target pathways of small molecules. These approaches are being systematically transferred into mammalian cell culture systems in order to interrogate more complex pathways in a more relevant setting. Advances in proteomics and in vivo genetic screening in multicellular model organism systems are also becoming increasingly powerful and amenable to high-throughput. Current methodologies and technologies are discussed, including how these global approaches complement affinity-based target identification strategies.     

Sweating the small stuff: microRNA discovery in plants

The class of small RNAs known as microRNAs (miRNAs) has a demonstrated role in the negative regulation of gene expression in both plants and animals. These small molecules have been shown to play a critical role in a wide range of developmental and physiological pathways. Although hundreds of different miRNAs have now been identified using cloning and computational approaches, characterization of their targets and biological roles has been more limited. New sequencing technologies promise to accelerate the sequencing of small RNAs and additional genetic and genomic strategies are being applied to assess their regulatory function on RNA targets. These technologies will enable the identification of large numbers of small RNAs from diverse species, and comparative genomics approaches based on these data are likely to identify additional miRNAs. Combined with bioinformatics and experimental approaches to separate miRNAs from short-interfering RNAs (siRNAs), the pace of miRNA discovery is likely to accelerate, leading to an improved understanding of miRNA function and biological significance.     

整合分析多组学数据筛选疾病靶点的精准医学策略

随着高通量测序技术的不断发展与完善,对于不同层次和类型的生物组学数据的获取及分析方法也日趋成熟与完善。基于单组学数据的疾病研究已经发现了诸多新的疾病相关因子,而整合多组学数据研究疾病靶点的工作方兴未艾。生命体是一个复杂的调控系统,疾病的发生与发展涉及基因变异、表观遗传改变、基因表达异常以及信号通路紊乱等诸多层次的复杂调控机制,利用单一组学数据分析致病因子的局限性愈发显著。通过对多种层次和来源的高通量组学数据的整合分析,系统地研究临床发病机理、确定最佳疾病靶点已经成为精准医学研究的重要发展方向,将为疾病研究提供新的思路,并对疾病的早期诊断、个体化治疗和指导用药等提供新的理论依据。本文详细介绍了基因组、转录组和表观组等系统组学研究在疾病靶点筛选方面出现的新技术手段和研究进展,并对它们之间的整合分析新策略和优势进行了讨论。     

Combining bioinformatics and MS-based proteomics: clinical implications

Clinical proteomics research aims at i) discovery of protein biomarkers for screening, diagnosis and prognosis of disease, ii) discovery of protein therapeutic targets for improvement of disease prevention, treatment and follow-up, and iii) development of mass spectrometry (MS)-based assays that could be implemented in clinical chemistry, microbiology or hematology laboratories. MS has been increasingly applied in clinical proteomics studies for the identification and quantification of proteins. Bioinformatics plays a key role in the exploitation of MS data in several aspects such as the generation and curation of protein sequence databases, the development of appropriate software for MS data treatment and integration with other omics data and the establishment of adequate standard files for data sharing. In this article, we discuss the main MS approaches and bioinformatics solutions that are currently applied to accomplish the objectives of clinical proteomic research.     

Structural biology and bioinformatics in drug design: opportunities and challenges for target identification and lead discovery

Impressive progress in genome sequencing, protein expression and high-throughput crystallography and NMR has radically transformed the opportunities to use protein three-dimensional structures to accelerate drug discovery, but the quantity and complexity of the data have ensured a central place for informatics. Structural biology and bioinformatics have assisted in lead optimization and target identification where they have well established roles; they can now contribute to lead discovery, exploiting high-throughput methods of structure determination that provide powerful approaches to screening of fragment binding.     

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.     

Conserved fungal genes as potential targets for broad-spectrum antifungal drug discovery

The discovery of novel classes of antifungal drugs depends to a certain extent on the identification of new, unexplored targets that are essential for growth of fungal pathogens. Likewise, the broad-spectrum capacity of future antifungals requires the target gene(s) to be conserved among key fungal pathogens. Using a genome comparison (or concordance) tool, we identified 240 conserved genes as candidates for potential antifungal targets in 10 fungal genomes. To facilitate the identification of essential genes in Candida albicans, we developed a repressible C. albicans MET3 (CaMET3) promoter system capable of evaluating gene essentiality on a genome-wide scale. The CaMET3 promoter was found to be highly amenable to controlled gene expression, a prerequisite for use in target-based whole-cell screening. When the expression of the known antifungal target C. albicans ERG1 was reduced via down-regulation of the CaMET3 promoter, the CaERG1 conditional mutant strain became hypersensitive, specifically to its inhibitor, terbinafine. Furthermore, parallel screening against a small compound library using the CaERG1 conditional mutant under normal and repressed conditions uncovered several hypersensitive compound hits. This work therefore demonstrates a streamlined process for proceeding from selection and validation of candidate antifungal targets to screening for specific inhibitors.     

Crystal and Solution Studies of the “Plus-C” Odorant-binding Protein 48 from Anopheles gambiae: CONTROL OF BINDING SPECIFICITY THROUGH THREE-DIMENSIONAL DOMAIN SWAPPING*

Much physiological and behavioral evidence has been provided suggesting that insect odorant-binding proteins (OBPs) are indispensable for odorant recognition and thus are appealing targets for structure-based discovery and design of novel host-seeking disruptors. Despite the fact that more than 60 putative OBP-encoding genes have been identified in the malaria vector Anopheles gambiae, the crystal structures of only six of them are known. It is therefore clear that OBP structure determination constitutes the bottleneck for structure-based approaches to mosquito repellent/attractant discovery. Here, we describe the three-dimensional structure of an A. gambiae “Plus-C” group OBP (AgamOBP48), which exhibits the second highest expression levels in female antennae. This structure represents the first example of a three-dimensional domain-swapped dimer in dipteran species. A combined binding site is formed at the dimer interface by equal contribution of each monomer. Structural comparisons with the monomeric AgamOBP47 revealed that the major structural difference between the two Plus-C proteins localizes in their N- and C-terminal regions, and their concerted conformational change may account for monomer-swapped dimer conversion and furthermore the formation of novel binding pockets. Using a combination of gel filtration chromatography, differential scanning calorimetry, and analytical ultracentrifugation, we demonstrate the AgamOBP48 dimerization in solution. Eventually, molecular modeling calculations were used to predict the binding mode of the most potent synthetic ligand of AgamOBP48 known so far, discovered by ligand- and structure-based virtual screening. The structure-aided identification of multiple OBP binders represents a powerful tool to be employed in the effort to control transmission of the vector-borne diseases.     

Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction

In pharmaceutical sciences, a crucial step of the drug discovery process is the identification of drug-target interactions. However, only a small portion of the drug-target interactions have been experimentally validated, as the experimental validation is laborious and costly. To improve the drug discovery efficiency, there is a great need for the development of accurate computational approaches that can predict potential drug-target interactions to direct the experimental verification. In this paper, we propose a novel drug-target interaction prediction algorithm, namely neighborhood regularized logistic matrix factorization (NRLMF). Specifically, the proposed NRLMF method focuses on modeling the probability that a drug would interact with a target by logistic matrix factorization, where the properties of drugs and targets are represented by drug-specific and target-specific latent vectors, respectively. Moreover, NRLMF assigns higher importance levels to positive observations (i.e., the observed interacting drug-target pairs) than negative observations (i.e., the unknown pairs). Because the positive observations are already experimentally verified, they are usually more trustworthy. Furthermore, the local structure of the drug-target interaction data has also been exploited via neighborhood regularization to achieve better prediction accuracy. We conducted extensive experiments over four benchmark datasets, and NRLMF demonstrated its effectiveness compared with five state-of-the-art approaches.     

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.     

RNA as a small molecule druggable target

Small molecule drugs have readily been developed against many proteins in the human proteome, but RNA has remained an elusive target for drug discovery. Increasingly, we see that RNA, and to a lesser extent DNA elements, show a persistent tertiary structure responsible for many diverse and complex cellular functions. In this digest, we have summarized recent advances in screening approaches for RNA targets and outlined the discovery of novel, drug-like small molecules against RNA targets from various classes and therapeutic areas. The link of structure, function, and small-molecule Druggability validates now for the first time that RNA can be the targets of therapeutic agents.