Analysis of multiple compound–protein interactions reveals novel bioactive molecules

The discovery of novel bioactive molecules advances our systems‐level understanding of biological processes and is crucial for innovation in drug development. For this purpose, the emerging field of chemical genomics is currently focused on accumulating large assay data sets describing compound–protein interactions (CPIs). Although new target proteins for known drugs have recently been identified through mining of CPI databases, using these resources to identify novel ligands remains unexplored. Herein, we demonstrate that machine learning of multiple CPIs can not only assess drug polypharmacology but can also efficiently identify novel bioactive scaffold‐hopping compounds. Through a machine‐learning technique that uses multiple CPIs, we have successfully identified novel lead compounds for two pharmaceutically important protein families, G‐protein‐coupled receptors and protein kinases. These novel compounds were not identified by existing computational ligand‐screening methods in comparative studies. The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.     

Proteomic methods for drug target discovery

The field of drug target discovery is currently very popular with a great potential for advancing biomedical research and chemical genomics. Innovative strategies have been developed to aid the process of target identification, either by elucidating the primary mechanism-of-action of a drug, by understanding side effects involving unanticipated 'off-target' interactions, or by finding new potential therapeutic value for an established drug. Several promising proteomic methods have been introduced for directly isolating and identifying the protein targets of interest that are bound by active small molecules or for visualizing enzyme activities affected by drug treatment. Significant progress has been made in this rapidly advancing field, speeding the clinical validation of drug candidates and the discovery of the novel targets for lead compounds developed using cell-based phenotypic screens. Using these proteomic methods, further insight into drug activity and toxicity can be ascertained.     

Herbogenomics: from traditional Chinese medicine to novel therapeutics

Traditional Chinese medicine (TCM) has a long history of development and application and has demonstrated on evidence basis its efficacy in the treatment of many diseases affecting multiple organ systems. In particular, TCM is effective in the prevention and treatment of chronic diseases and metabolic syndromes. However, the value of TCM has not been fully recognized worldwide due to the lack of definitive information of active ingredients in almost any TCM preparation. Novel functional genomics and proteomics approaches provide alternate perspectives on the mechanism of action of TCM. The target molecules on which TCM either activates or inactivates can be identified by functional genomics and proteomics, thus the affected critical signaling pathway cascades leading to effective recovery of chronic diseases can be studied. Several TCM preparations have been available for the treatment of liver fibrosis and cirrhosis, even advanced liver cirrhosis that has been shown to be irreversible and has no US-FDA approved therapy. In the TCM-treated livers with fibrosis and cirrhosis, some critical molecules that are significantly involved in the recovery can be identified through functional genomics and proteomics studies. These molecules become novel targets for drug discovery and development and candidates for the development of gene therapy. Gene therapy developed based on this strategy for the treatment of advanced liver fibrosis and cirrhosis in animal models has obtained promising results. This process thus establishes a herbogenomics approach to understand mechanisms of action of TCM and to identify effective molecular targets for the discovery and development of novel therapeutics.     

Miniaturization of a Mammalian Cell-Based Assay: Luciferase Reporter Gene Readout in a 3 Microliter 1536-Well Plate

The combined efforts of the fields of combinatorial chemistry and genomics have significantly increased the number of compounds and therapeutic targets available for screening. The number of compounds will reach into the million range in the near future and provide vast chemical diversity for drug discovery. However, this reservoir of chemical diversity creates downstream hurdles for any screening effort. Properly examining this number of compounds increases investments dramatically, both in the number of dollars spent and amount of limited reagents depleted. Traditional HTS techniques, such as the use of 96-well microtiter plates, have paved the way for faster processing speeds, but are being rapidly overwhelmed by screening demands. Miniaturization of such assays will allow for greater throughput, while concurrently reducing cost. To date, miniaturization efforts have been most successfully applied to bacterial and soluble protein based assays. Questions about the ability to deliver microquantities of mammalian cells without disruption of the cell membrane and/or activation of stress responses have been raised. An assay has been developed in which a human T-cell screen has been adapted to a 1536-well plate format. Through the use of a luciferase reporter gene system, it is shown that a mammalian cell-based assay may be successfully performed in 3 μl and potent inhibitors of the target of interest identified.     

后基因组时代微生物天然产物高效发掘体系设计与展望

微生物天然产物具有丰富的化学结构多样性和诱人的生物活性,持续启迪着创新医药和农药的发现。近年来,随着高通量测序技术的快速发展,巨大的微生物基因组数据揭示了多样生物合成和新颖天然产物的潜能远高于以前的认知。然而,如何高效地激活隐性的生物合成基因簇 (BGCs) 并识别相应的化合物,以及避免已知代谢产物的重复发现等挑战依然严峻。本文描述了面对这些问题时基因组学、生物信息学、机器学习、代谢组学、基因编辑和合成生物学等新技术在发现药用先导化合物过程中提供的机遇;总结并论述了在潜力菌株优选、BGCs的生物信息学预测、沉默 BGCs的高效激活以及目标产物的识别和跟踪方面的新见解;提出了基于潜力菌株选择和多组学挖掘技术从微生物天然产物中高效发现先导结构的系统线路 (SPLSD),并讨论了未来天然产物药用先导发现的机遇和挑战。     

Affinity Selection and Mass Spectrometry-Based Strategies to Identify Lead Compounds in Combinatorial Libraries

The screening of diverse libraries of small molecules created by combinatorial synthetic methods is a recent development which has the potential to accelerate the identification of lead compounds in drug discovery. We have developed a direct and rapid method to identify lead compounds in libraries involving affinity selection and mass spectrometry. In our strategy, the receptor or target molecule of interest is used to isolate the active components from the library physically, followed by direct structural identification of the active compounds bound to the target molecule by mass spectrometry. In a drug design strategy, structurally diverse libraries can be used for the initial identification of lead compounds. Once lead compounds have been identified, libraries containing compounds chemically similar to the lead compound can be generated and used to optimize the binding characteristics. These strategies have also been adopted for more detailed studies of protein–ligand interactions.     

Structural genomics—Impact on biomedicine and drug discovery

The field of structural genomics emerged as one of many 'omics disciplines more than a decade ago, and a multitude of large scale initiatives have been launched across the world. Development and implementation of methods for high-throughput structural biology represents a common denominator among different structural genomics programs. From another perspective a distinction between “biology-driven” versus “structure-driven” approaches can be made. This review outlines the general themes of structural genomics, its achievements and its impact on biomedicine and drug discovery. The growing number of high resolution structures of known and potential drug target proteins is expected to have tremendous value for future drug discovery programs. Moreover, the availability of large numbers of purified proteins enables generation of tool reagents, such as chemical probes and antibodies, to further explore protein function in the cell.     

Structural genomics

Structural genomics can be defined as structural biology on a large number of target proteins in parallel. This approach plays an important role in modern structure-based drug design. Although a number of structural genomics initiatives have been initiated, relatively few are associated with integral membrane proteins. This indicates the difficulties in expression, purification, and crystallization of membrane proteins, which has also been confirmed by the existence of some 100 high-resolution structures of membrane proteins among the more than 30,000 entries in public databases. Paradoxically, membrane proteins represent 60–70% of current drug targets and structural knowledge could both improve and speed up the drug discovery process. In order to improve the sucess rate for structure resolution of membrane proteins structural genomics networks have been established.     

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.     

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.     

Structural genomics and drug discovery: all in the family

Structural genomics is starting to have an impact on the early stages of drug discovery and target validation through the contribution of new structures of known and potential drug targets, their complexes with ligands and protocols and reagents for additional structural work within a drug discovery program. Recent progress includes structures of targets from bacterial, viral and protozoan human pathogens, and human targets from known or potential druggable protein families such as, kinases, phosphatases, dehydrogenases/oxidoreductases, sulfo-, acetyl- and methyl-transferases, and a number of other key metabolic enzymes. Importantly, many of these structures contained ligands in the active sites, including for example, the first structures of target-bound therapeutics. Structural genomics of protein families combined with ligand discovery holds particular promise for advancing early stage discovery programs.     

Improving success rates for lead generation using affinity binding technologies

Affinity technologies have been applied at several stages of the drug discovery process, ranging from target identification and purification to the identification of preclinical candidates. The detection of ligand-macromolecule interactions in lead discovery is the best studied and most powerful of these techniques. Although affinity methods have been in widespread use for about a decade, only recently have many reports emerged on their utility. Primary affinity screens of large libraries of small molecules or fragments have begun to produce results for challenging targets. Furthermore, in secondary assays affinity methods are opening new avenues to tackle important medicinal chemistry tasks.     

A simple assay for detection of small-molecule redox activity

In addition to selecting molecules of pharmacological interest, high-throughput screening campaigns often generate hits of undesirable mechanism, which cannot be exploited for drug discovery as they lead to obvious problems of specificity and developability. Examples of undesirable mechanisms are target alkylation/acylation and compound aggregation. Both types of "promiscuous" mechanisms have been described in the literature, as have methods for their detection. In addition to these mechanisms, compounds can also inhibit by oxidizing susceptible enzyme targets, such as metalloenzymes and cysteine-using enzymes. However, this redox phenomenon has been documented infrequently, and an easy method for detecting this behavior is missing. In this article, the authors describe direct proof of small-molecule oxidation of a cysteine protease by liquid chromatography/tandem mass spectrometry, develop a simple assay to predict this oxidizing behavior by compounds, and show the utility of this assay by demonstrating its ability to distinguish nuisance redox compounds from well-behaved inhibitors in 3 historical GlaxoSmithKline drug discovery efforts.     

New target for inhibition of bacterial RNA polymerase: 'switch region'

A new drug target - the 'switch region' - has been identified within bacterial RNA polymerase (RNAP), the enzyme that mediates bacterial RNA synthesis. The new target serves as the binding site for compounds that inhibit bacterial RNA synthesis and kill bacteria. Since the new target is present in most bacterial species, compounds that bind to the new target are active against a broad spectrum of bacterial species. Since the new target is different from targets of other antibacterial agents, compounds that bind to the new target are not cross-resistant with other antibacterial agents. Four antibiotics that function through the new target have been identified: myxopyronin, corallopyronin, ripostatin, and lipiarmycin. This review summarizes the switch region, switch-region inhibitors, and implications for antibacterial drug discovery.     

High-throughput screening assay for the identification of compounds regulating self-renewal and differentiation in human embryonic stem cells

High-throughput screening (HTS) of chemical libraries has become a critical tool in basic biology and drug discovery. However, its implementation and the adaptation of high-content assays to human embryonic stem cells (hESCs) have been hampered by multiple technical challenges. Here we present a strategy to adapt hESCs to HTS conditions, resulting in an assay suitable for the discovery of small molecules that drive hESC self-renewal or differentiation. Use of this new assay has led to the identification of several marketed drugs and natural compounds promoting short-term hESC maintenance and compounds directing early lineage choice during differentiation. Global gene expression analysis upon drug treatment defines known and novel pathways correlated to hESC self-renewal and differentiation. Our results demonstrate feasibility of hESC-based HTS and enhance the repertoire of chemical compounds for manipulating hESC fate. The availability of high-content assays should accelerate progress in basic and translational hESC biology.     

Computational ligand-based rational design: Role of conformational sampling and force fields in model development

A significant number of drug discovery efforts are based on natural products or high throughput screens from which compounds showing potential therapeutic effects are identified without knowledge of the target molecule or its 3D structure. In such cases computational ligand-based drug design (LBDD) can accelerate the drug discovery processes. LBDD is a general approach to elucidate the relationship of a compound's structure and physicochemical attributes to its biological activity. The resulting structure-activity relationship (SAR) may then act as the basis for the prediction of compounds with improved biological attributes. LBDD methods range from pharmacophore models identifying essential features of ligands responsible for their activity, quantitative structure-activity relationships (QSAR) yielding quantitative estimates of activities based on physiochemical properties, and to similarity searching, which explores compounds with similar properties as well as various combinations of the above. A number of recent LBDD approaches involve the use of multiple conformations of the ligands being studied. One of the basic components to generate multiple conformations in LBDD is molecular mechanics (MM), which apply an empirical energy function to relate conformation to energies and forces. The collection of conformations for ligands is then combined with functional data using methods ranging from regression analysis to neural networks, from which the SAR is determined. Accordingly, for effective application of LBDD for SAR determinations it is important that the compounds be accurately modelled such that the appropriate range of conformations accessible to the ligands is identified. Such accurate modelling is largely based on use of the appropriate empirical force field for the molecules being investigated and the approaches used to generate the conformations. The present chapter includes a brief overview of currently used SAR methods in LBDD followed by a more detailed presentation of issues and limitations associated with empirical energy functions and conformational sampling methods.     

Identification and validation of bioactive small molecule target through phenotypic screening

For effective bioactive small molecule discovery and development into new therapeutic drug, a systematic screening and target protein identification is required. Different from the conventional screening system, herein phenotypic screening in combination with multi-omics-based target identification and validation (MOTIV) is introduced. First, phenotypic screening provides visual effect of bioactive small molecules in the cell or organism level. It is important to know the effect on the cell or organism level since small molecules affect not only a single target but the entire cellular mechanism within a cell or organism. Secondly, MOTIV provides systemic approach to discover the target protein of bioactive small molecule. With the chemical genomics and proteomics approach of target identification methods, various target protein candidates are identified. Then network analysis and validations of these candidates result in identifying the biologically relevant target protein and cellular mechanism. Overall, the combination of phenotypic screening and MOTIV will provide an effective approach to discover new bioactive small molecules and their target protein and mechanism identification.     

Complementary medicinal chemistry-driven strategies toward new antitrypanosomal and antileishmanial lead drug candidates

Trypanosomiases and Leishmaniases are neglected tropical diseases that affect the less developed countries. For this reason, they did not and still do not have high visibility in Western societies. The name neglected diseases also refers to the fact that they often received little interest at the level of public investment, research and development. The drug discovery scenario, however, is changing dramatically. After a period in which different socioeconomic factors have prevented massive research efforts in this field, such efforts have increased considerably in the very recent years, with significant scientific advancements. In this context, we have embarked on a new drug discovery project devoted to identification of new small molecules for the treatment of trypanosomal and leishmanial diseases. Two complementary approaches have been pursued and are reported here. The first deals with a structure-based drug design, and a privileged structure-guided synthesis of quinazoline compounds able to modulate trypanothione reductase activity was accomplished. In the second, a combinatorial library, built on a natural product-based strategy, was synthesized. Using whole parasite assays, different quinones have been identified as promising lead compounds. A combination of both approaches to hopefully overcome some of the challenges of anti-trypanosomatid drug discovery has eventually been proposed.     

Omics based approach for biodiscovery of microbial natural products in antibiotic resistance era

The need for a new antibiotic pipeline to confront threat imposed by resistant pathogens has become a major global concern for human health. To confront the challenge there is a need for discovery and development of new class of antibiotics. Nature which is considered treasure trove, there is re-emerged interest in exploring untapped microbial to yield novel molecules, due to their wide array of negative effects associated with synthetic drugs. Natural product researchers have developed many new techniques over the past few years for developing diverse compounds of biopotential. Taking edge in the advancement of genomics, genetic engineering, in silico drug design, surface modification, scaffolds, pharmacophores and target-based approach is necessary. These techniques have been economically sustainable and also proven efficient in natural product discovery. This review will focus on recent advances in diverse discipline approach from integrated Bioinformatics predictions, genetic engineering and medicinal chemistry for the synthesis of natural products vital for the discovery of novel antibiotics having potential application.