基于化学蛋白质组学的药物靶标鉴定

通过药物靶标鉴定,可以建立药物活性与细胞表型之间的联系,阐明药物的作用机理,还可以发现药物的脱靶效应和耐药性机制,发现治疗药物的新靶点,并在药物开发的早期阶段预测可能存在的毒性,降低药物研发失败的风险。目前虽然科学技术取得了飞速发展,但是鉴定药物靶标依然是一件令人生畏的工作。本文对近10年来药物靶标鉴定方面的研究进展,特别是无化学标记的新技术进行了评述。     

Can an in silico drug-target search method be used to probe potential mechanisms of medicinal plant ingredients?

Medicinal plants have been explored therapeutically in traditional medicines and are a valuable source for drug discovery. Insufficient knowledge about the molecular mechanism of these medicinal plants limits the scope of their application and hinders the effort to design new drugs using the therapeutic principles of herbal medicines. This problem can be partially alleviated if efficient methods for rapid identification of protein targets of herbal ingredients can be introduced. Efforts have been directed at developing efficient computer methods for facilitating target identification. Various methods being explored or under investigation are reviewed here. So far, one computer method, INVDOCK, has been specifically used for automated drug target identification. Its usefulness in the identification of therapeutic targets of medicinal herbal ingredients as well as synthetic chemicals is reviewed. The majority of INVDOCK identified therapeutic targets of several well-known medicinal herbal ingredients have been found to be confirmed or implicated by experiments, which suggests the potential of in silico methods in facilitating the study of molecular mechanism of medicinal plants.     

Applications of Activity‐Based Protein Profiling (ABPP) and Bioimaging in Drug Discovery

Activity‐based protein profiling (ABPP) and bioimaging have been developed in recent years as powerful technologies in drug discovery. Specifically, both approaches can be applied in critical steps of drug development, such as therapy target discovery, high‐throughput drug screening and target identification of bioactive molecules. We have been focused on the development of various strategies that enable simultaneous activity‐based protein profiling and bioimaging studies, thus facilitating an understanding of drug actions and potential toxicities. In this Minireview, we summarize these novel strategies and applications, with the aim of promoting these technologies in drug discovery.     

In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets

Target identification is an important step in drug discovery, and computer-aided drug target identification methods are attracting more attention compared with traditional drug target identification methods, which are time-consuming and costly. Computer-aided drug target identification methods can greatly reduce the searching scope of experimental targets and associated costs by identifying the diseases-related targets and their binding sites and evaluating the druggability of the predicted active sites for clinical trials. In this review, we introduce the principles of computer-based active site identification methods, including the identification of binding sites and assessment of druggability. We provide some guidelines for selecting methods for the identification of binding sites and assessment of druggability. In addition, we list the databases and tools commonly used with these methods, present examples of individual and combined applications, and compare the methods and tools. Finally, we discuss the challenges and limitations of binding site identification and druggability assessment at the current stage and provide some recommendations and future perspectives.     

Modern drug discovery technologies: opportunities and challenges in lead discovery

The identification of promising hits and the generation of high quality leads are crucial steps in the early stages of drug discovery projects. The definition and assessment of both chemical and biological space have revitalized the screening process model and emphasized the importance of exploring the intrinsic complementary nature of classical and modern methods in drug research. In this context, the widespread use of combinatorial chemistry and sophisticated screening methods for the discovery of lead compounds has created a large demand for small organic molecules that act on specific drug targets. Modern drug discovery involves the employment of a wide variety of technologies and expertise in multidisciplinary research teams. The synergistic effects between experimental and computational approaches on the selection and optimization of bioactive compounds emphasize the importance of the integration of advanced technologies in drug discovery programs. These technologies (VS, HTS, SBDD, LBDD, QSAR, and so on) are complementary in the sense that they have mutual goals, thereby the combination of both empirical and in silico efforts is feasible at many different levels of lead optimization and new chemical entity (NCE) discovery. This paper provides a brief perspective on the evolution and use of key drug design technologies, highlighting opportunities and challenges.     

20 years of DNA-encoded chemical libraries

The identification of specific binding molecules is a central problem in chemistry, biology and medicine. Therefore, technologies, which facilitate ligand discovery, may substantially contribute to a better understanding of biological processes and to drug discovery. DNA-encoded chemical libraries represent a new inexpensive tool for the fast and efficient identification of ligands to target proteins of choice. Such libraries consist of collections of organic molecules, covalently linked to a unique DNA tag serving as an amplifiable identification bar code. DNA-encoding enables the in vitro selection of ligands by affinity capture at sub-picomolar concentrations on virtually any target protein of interest, in analogy to established selection methodologies like antibody phage display. Multiple strategies have been investigated by several academic and industrial laboratories for the construction of DNA-encoded chemical libraries comprising up to millions of DNA-encoded compounds. The implementation of next generation high-throughput sequencing enabled the rapid identification of binding molecules from DNA-encoded libraries of unprecedented size. This article reviews the development of DNA-encoded library technology and its evolution into a novel drug discovery tool, commenting on challenges, perspectives and opportunities for the different experimental approaches.     

基于结构的组合库设计策略在新药创制中的应用

合理设计一些容量小的、针对特定靶标的化合物库(称为集中库,focused library),是当前组合库设计中的热点。当靶标的三维结构可以通过X射线衍射或NMR等手段确定后,人们就能采用几种不同的策略来进行组合库的设计。本文讨论了在靶标结合位点的约束下,进行组合库设计的主要方法及程序,同时强调了它们的优点与不足。通过这些方法的成功应用实例,显示了它们在新药创制中的广泛应用前景。     

Discovery of novel targets with high throughput RNA interference screening

High throughput technologies have the potential to affect all aspects of drug discovery. Considerable attention is paid to high throughput screening (HTS) for small molecule lead compounds. The identification of the targets that enter those HTS campaigns had been driven by basic research until the advent of genomics level data acquisition such as sequencing and gene expression microarrays. Large-scale profiling approaches (e.g., microarrays, protein analysis by mass spectrometry, and metabolite profiling) can yield vast quantities of data and important information. However, these approaches usually require painstaking in silico analysis and low-throughput basic wet-lab research to identify the function of a gene and validate the gene product as a potential therapeutic drug target. Functional genomic screening offers the promise of direct identification of genes involved in phenotypes of interest. In this review, RNA interference (RNAi) mediated loss-of-function screens will be discussed and as well as their utility in target identification. Some of the genes identified in these screens should produce similar phenotypes if their gene products are antagonized with drugs. With a carefully chosen phenotype, an understanding of the biology of RNAi and appreciation of the limitations of RNAi screening, there is great potential for the discovery of new drug targets.     

Label-free cell-based assays for GPCR screening

G protein-coupled receptors (GPCRs) have been proven to be the largest family of druggable targets in the human genome. Given the importance of GPCRs as drug targets and the de-orphanization of novel targets, GPCRs are likely to remain the frequent targets of many drug discovery programs. With recent advances in instrumentation and understanding of cellular mechanisms for the signals measured, biosensor-centered label-free cell assay technologies become a very active area for GPCR screening. This article reviews the principles and potential of current label-free cell assay technologies in GPCR drug discovery.     

Carbohydrate chemistry in drug discovery

The multitude of roles that carbohydrates and their glyco-conjugates play in biological processes has stimulated great interest in determining the nature of their interactions in both normal and diseased states. Manipulating such interactions will provide leads for drug discovery. Of the major classes of biomolecule, carbohydrates are the most structurally diverse. This hetereogeneity makes isolation of pure samples, and in sufficient amounts, from biological sources extremely difficult. Chemical synthesis offers the advantage of producing pure and structurally defined oligosaccharides for biological investigations. Although the complex nature of carbohydrates means that this is challenging, recent advances in the field have facilitated access to these molecules. The synthesis and isolation of oligosaccharides combined with progress in glycoarray technology have aided the identification of new carbohydrate-binding drug targets. This review aims to provide an overview of the latest advancements in carbohydrate chemistry and the role of these complex molecules in drug discovery, focusing particularly on synthetic methodologies, glycosaminoglycans, glycoprotein synthesis and vaccine development over the last few years.     

Fragment‐Based Drug Design Facilitated by Protein‐Templated Click Chemistry: Fragment Linking and Optimization of Inhibitors of the Aspartic Protease Endothiapepsin

There is an urgent need for the development of efficient methodologies that accelerate drug discovery. We demonstrate that the strategic combination of fragment linking/optimization and protein‐templated click chemistry is an efficient and powerful method that accelerates the hit‐identification process for the aspartic protease endothiapepsin. The best binder, which inhibits endothiapepsin with an IC₅₀ value of 43 μm , represents the first example of triazole‐based inhibitors of endothiapepsin. Our strategy could find application on a whole range of drug targets.     

Machine Learning Methods in Drug Discovery

The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.     

Repositioning of Quinazolinedione-Based Compounds on Soluble Epoxide Hydrolase (sEH) through 3D Structure-Based Pharmacophore Model-Driven Investigation

The development of new bioactive compounds represents one of the main purposes of the drug discovery process. Various tools can be employed to identify new drug candidates against pharmacologically relevant biological targets, and the search for new approaches and methodologies often represents a critical issue. In this context, in silico drug repositioning procedures are required even more in order to re-evaluate compounds that already showed poor biological results against a specific biological target. 3D structure-based pharmacophoric models, usually built for specific targets to accelerate the identification of new promising compounds, can be employed for drug repositioning campaigns as well. In this work, an in-house library of 190 synthesized compounds was re-evaluated using a 3D structure-based pharmacophoric model developed on soluble epoxide hydrolase (sEH). Among the analyzed compounds, a small set of quinazolinedione-based molecules, originally selected from a virtual combinatorial library and showing poor results when preliminarily investigated against heat shock protein 90 (Hsp90), was successfully repositioned against sEH, accounting the related built 3D structure-based pharmacophoric model. The promising results here obtained highlight the reliability of this computational workflow for accelerating the drug discovery/repositioning processes.     

Fluorescence assays for high-throughput screening of protein kinases

Protein kinases comprise one of the most important group of targets for drug discovery research today. Methods to identify novel kinase inhibitors by high-throughput screening have evolved rapidly in recent years. An important aspect is the availability of fluorescent probes that can be applied in a homogeneous, or mix-and-measure, assay format. Here, we illustrate the application of fluorescence read-out technologies for kinase targets in light of our own experiences in assay development and high-throughput screening.     

MCC/SNAr methodology. Part 2: Novel three-step solution phase access to libraries of benzodiazepines

New developments in the search for novel pharmacological agents over the last decade have focused on the preparation of chemical libraries as sources for new leads for drug discovery. To aid this search a plethora of personal synthesizers and new automation technologies have emerged to help fuel the lead discovery engines of drug discovery organizations. In fact, multi-step solid-phase syntheses of diverse libraries in excess of 10,000 products are now feasible via split and mix techniques. At the same time, a multitude of more efficient, diversity or target oriented solution phase chemical methodologies have appeared in the chemical literature, which have enabled the relatively facile construction of successful lead generation libraries with low FTE input and little capital expenditure. This communication reveals a further application of N-BOC-α-aminoaldehydes in the Ugi condensation reaction, followed by a secondary S_NAr cyclization, accessing arrays of biologically relevant benzodiazepines in good yield and overall purity.     

MASPIT: three-hybrid trap for quantitative proteome fingerprinting of small molecule-protein interactions in mammalian cells

Organic small molecules generally act by perturbing the function of one or more cellular target proteins, the identification of which is essential to an understanding of the molecular basis of drug action. Here we describe the application of methotrexate-linked small molecule ligands to a mammalian three-hybrid interaction trap for proteome-wide identification of small molecule targets, quantification of the targeting potency of unmodified small molecules for such targets in intact cells, and screening for inhibitors of small molecule-protein interactions. During the course of this study we also identified the pyrido[2,3-d]pyrimidine PD173955, a known SRC kinase inhibitor, as a potent inhibitor of several ephrin receptor tyrosine kinases. This finding could perhaps be exploited in the design of inhibitors for this kinase subfamily, members of which have been implicated in the pathogenesis of various diseases, including cancer.     

Signal Amplification Technologies for the Detection of Nucleic Acids: from Cell-Free Analysis to Live-Cell Imaging

Due to their unique properties, such as programmability, ligand-binding capability, and flexibility, nucleic acids can serve as analytes and/or recognition elements for biosensing. To improve the sensitivity of nucleic acid-based biosensing and hence the detection of a few copies of target molecule, different modern amplification methodologies, namely target-and-signal-based amplification strategies, have already been developed. These recent signal amplification technologies, which are capable of amplifying the signal intensity without changing the targets’ copy number, have resulted in fast, reliable, and sensitive methods for nucleic acid detection. Working in cell-free settings, researchers have been able to optimize a variety of complex and quantitative methods suitable for deploying in live-cell conditions. In this study, a comprehensive review of the signal amplification technologies for the detection of nucleic acids is provided. We classify the signal amplification methodologies into enzymatic and non-enzymatic strategies with a primary focus on the methods that enable us to shift away from in vitro detecting to in vivo imaging. Finally, the future challenges and limitations of detection for cellular conditions are discussed.     

Bioluminescence and chemiluminescence in drug screening

Drug screening, that is, the evaluation of the biological activity of candidate drug molecules, is a key step in the drug discovery and development process. In recent years, high-throughput screening assays have become indispensable for early stage drug discovery because of the developments in synthesis technologies, such as combinatorial chemistry and automated synthesis, and the discovery of an increasing number of new pharmacological targets.Bioluminescence and chemiluminescence represent suitable detection techniques for high-throughput screening because they allow rapid and sensitive detection of the analytes and can be applied to small-volume samples. In this paper we report on recent applications of bioluminescence and chemiluminescence in drug screening, both for in vitro and in vivo assays. Particular attention is devoted to the latest and most innovative bioluminescence and chemiluminescence-based technologies for drug screening, such as assays based on genetically modified cells, bioluminescence resonance energy transfer (BRET)-based assays, and in vivo imaging assays using transgenic animals or bioluminescent markers. The possible relevance of bioluminescence and chemiluminescence techniques in the future developments of high-throughput screening technologies is also discussed.     

Using silico methods predicting ligands for orphan GPCRs

The G-protein coupled receptor (GPCR) superfamily is one of the most important drug target classes for the pharmaceutical industry. The completion of the human genome project has revealed that there are more than 300 potential GPCR targets of interest. The identification of their natural ligands can gain significant insights into regulatory mechanisms of cellular signaling networks and provide unprecedented opportunities for drug discovery. Much effort has been directed towards the GPCR ligand discovery study by both academic institutions and pharmaceutical industries. However, the endogenous ligands still remain unknown for about 150 GPCRs in the human genome. It is necessary to develop new strategies to predict candidate ligands for these so-called orphan receptors. Computational techniques are playing an increasingly important role in finding and validating novel ligands for orphan GPCRs (oGPCRs). In this paper, we focus on recent development in applying bioinformatics approaches for the discovery of GPCR ligands. In addition, some of the data resources for ligand identification are also provided.     

Prediction of protein–ligand binding affinity by free energy simulations: assumptions,pitfalls and expectations

Many limitations of current computer-aided drug design arise from the difficulty of reliably predicting the binding affinity of a small molecule to a biological target. There is thus a strong interest in novel computational methodologies that claim predictions of greater accuracy than current scoring functions, and at a throughput compatible with the rapid pace of drug discovery in the pharmaceutical industry. Notably, computational methodologies firmly rooted in statistical thermodynamics have received particular attention in recent years. Yet free energy calculations can be daunting to learn for a novice user because of numerous technical issues and various approaches advocated by experts in the field. The purpose of this article is to provide an overview of the current capabilities of free energy calculations and to discuss the applicability of this technology to drug discovery.