药物靶标配体高通量筛选的亲和质谱技术研究

疾病相关的药物靶标蛋白与小分子化合物的亲和作用研究是当今新药研发的热点领域,基于靶蛋白与配体亲和作用的筛选技术已成为与基于靶蛋白活性高通量筛选技术高度互补的药物先导化合物发现关键技术。本文综述了亲和质谱技术用于筛选和检测指定靶蛋白的小分子配体的基本原理和主要优势,详细介绍了该技术应用于大规模化合物库筛选、分子片段库筛选、天然产物粗提物筛选和蛋白质与胞内代谢物相互作用研究领域的主要进展。     

"Click"化学在药物发现中的应用

“click”化学是最近几年发展起来的一种新技术,它在药物发现的许多方面均有着广泛的应用:快速合成小分子化合物库并从中筛选先导化合物、进行靶标导向的活性小分子合成以及在生物耦联技术等方面的应用。     

鼠兔睾丸特异乳酸脱氢酶抑制物的筛选

乳酸脱氢酶C4(lactate dehydrogenase C4,LDH-C4)被认为是研发节育药物的一个很好的靶蛋白。本研究进行了对鼠兔LDH-C4蛋白有抑制作用的小分子化合物的虚拟筛选和体外酶动力学筛选。筛选出了4个IC_(50)小于100μm的小分子化合物,其ZINC数据库编号分别为ZINC05328871(5μm)、ZINC01699287(36μm)、ZINC17995347(53μm)、ZINC05390471(87μm)。这4个化合物和已报道的LDH-C4抑制物的结构均不相同,属于新的结构支架,可以作为靶向鼠兔LDH-C4的节育药物先导化合物进行进一步的研究。     

基于流感病毒PA_N蛋白的高通量药物筛选

流感病毒的PA N蛋白高度保守,并且具有核酸内切酶活性,是抗流感药物研发的潜在靶点。通过高通量药物筛选体系,从372种化合物中筛选出3种对流感病毒H5N1的PA N蛋白抑制作用较好的化合物。将这3种化合物分别与PA N蛋白进行分子对接模拟,结果显示它们均可以与PA N蛋白活性位点的二价金属离子和氨基酸残基相互作用,从而为抗流感病毒药物的发现提供了先导化合物。     

高通量药物筛选技术进展及新药开发策略

新药研发过程中．通过筛选而获得具有生物活性的先导化合物．是创新药物研究的关键．目前药物筛选模型已经从传统的整体动物、器官和组织水平发展到细胞和分子水平。创新药物的发现都离不开采用适当的药物作用靶点对大量化合物样品进行筛选．而且筛选规模越大,发现新药的机会就越多。随着计算机技术、生物芯片、蛋白质组学、组合化学等的发展．高通量药物筛选技术应运而生。高通量筛选体系在创新药物筛选中的应用是新药开发研究的一个重要领域。     

靶向铜绿假单胞菌(Pseudomonas aeruginosa)粘肽合成酶PBP3的抗菌先导化合物的虚拟筛选及活性研究

【目的】开发出与铜绿假单胞菌粘肽合成酶PBP3有高亲和力,具有全新结构的先导化合物。【方法】以铜绿假单胞菌粘肽合成酶PBP3为靶点,通过分子对接软件DOCK6.5,对含有104万个小分子化合物的数据库进行了大规模虚拟筛选,选取结构相对简单的中选化合物进行合成,得到先导化合物,并验证其抑菌活性。【结果】通过grid score进行第一轮初筛,筛选出grid score分值小于–30 kcal/mol的6万个化合物,接着以amber score进行第二轮筛选,筛出amber score分值小于–20 kcal/mol的化合物约200个。最终,经过观察分析,从中挑选出4种打分高并且结构新颖的小分子化合物作为先导化合物。合成出的先导化合物及其衍生物对铜绿假单胞菌等常见微生物的最小抑菌浓度(MIC)在175–275μg/m L之间,对革兰氏阴性菌和阳性菌均有效,MIC值比作为阳性对照的磺胺嘧啶更低,说明先导化合物具有较好的抗菌活性。【结论】这些先导化合物可以进一步开发为新型抗菌药物,用于解决铜绿假单胞菌的耐药性问题。     

应用化学基因组信息预测小分子化合物的潜在生物靶标的理论方法

在后基因组时代,化学基因组技术在药物作用靶点的确认、小分子化合物对通路的作用,以及小分子先导化合物的识别等方面都有着广泛的应用,为新药研发提供了新的技术方法。本文主要介绍了当前几种基于化学基因组信息来预测小分子化合物潜在生物靶标的理论方法（包括化学相似性搜索方法、反向分子对接方法、数据挖掘方法以及生物活性谱图分析方法）,并分析了这些方法的优缺点以及应用前景。     

新冠病毒主蛋白酶小分子抑制剂荧光共振能量转移高通量筛选模型的优化与应用北大核心CSCD

基于荧光共振能量转移(fluorescence resonance energy transfer, FRET)原理,以新冠病毒主蛋白酶(main protease, Mpro)为靶标,建立并应用Mpro小分子抑制剂FRET高通量筛选模型,以期快速筛选新型Mpro小分子抑制剂。利用大肠杆菌原核表达与分离纯化高活性的Mpro,再以FRET法进行比活力测定。基于FRET原理,以7-甲氧基香豆素-4-乙酸(7-methoxycoumarin-4-acetic acid, MCA)与2,4-二硝基苯酚(2,4-dinitropheno, Dnp)标记的多肽作为Mpro水解底物,通过优化反应缓冲液、Mpro反应浓度、反应温度与时间及DMSO耐受浓度,建立并应用Mpro小分子抑制剂FRET高通量筛选模型进行苗头化合物的筛选。利用大肠杆菌实现了高活性Mpro的原核表达与分离纯化,且比活力不低于40 000 U/mg。通过一系列优化实验,使用0.4μmol/L Mpro与5μmol/L底物建立了Z′因子值为0.79的Mpro小分子抑制剂FRET高通量筛选模型,且反应体系中含有的二硫苏糖醇(1,4-dithiothreitol,DTT)是影响FRET筛选模型可靠性的重要因素。通过对天然产物化合物库进行高通量筛选,发现白花丹素与银杏酸在体外对Mpro酶活性具有良好的抑制作用。本研究建立了基于FRET原理的Mpro小分子抑制剂高通量筛选模型,初步证实了白花丹素与银杏酸是一类新型苗头化合物,为抗新型冠状病毒药物先导化合物的筛选与发现奠定了基础。     

海洋糖类创新药物研究进展

海洋生物资源作为一种可持续利用的再生性资源,为我们提供了丰富的海洋特征化合物,特别是糖类化合物已经成为寻找和发现海洋创新药物的重要源泉。海洋糖类化合物依据其来源可分为:海洋植物来源、动物来源及微生物来源糖类分子,而不同来源的糖类化合物由于其结构存在较大差异,可被用于不同功能的糖类药物研发。综述了海洋来源糖类化合物的结构与活性特征、修饰与衍生方法以及相关糖类药物研发的最新进展。尽管我国有丰富的海洋生物资源,且在海洋糖药物研发方面走在世界前列,然而目前糖类药物开发仍面临巨大挑战,亟需有效解决糖类先导化合物的作用靶点、作用机制、药代动力学性质以及安全性评价等方面问题,进而建立完善的糖类药物研发技术平台,加快推进我国具有自主知识产权的海洋糖类创新药物的研究与开发。     

小分子活性肽筛选方法

小分子活性肽作为疫苗、诊断试剂、药物以及药物先导化合物已成为药物研究领域的新趋势。小分子活性肽有多种筛选方法：基于噬菌体展示肽库、蛋白质降解(酶法．化学法)、MHC-多肽复合物、蛋白质结构、蛋白质结构预测和反义同源盒原理,它们各具特点。基于蛋白质结构的活性肽分子筛选将成为多肽药物筛选的主要方向之一。     

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.     

Electrostatic Similarities between Protein and Small Molecule Ligands Facilitate the Design of Protein-Protein Interaction Inhibitors

One of the underlying principles in drug discovery is that a biologically active compound is complimentary in shape and molecular recognition features to its receptor. This principle infers that molecules binding to the same receptor may share some common features. Here, we have investigated whether the electrostatic similarity can be used for the discovery of small molecule protein-protein interaction inhibitors (SMPPIIs). We have developed a method that can be used to evaluate the similarity of electrostatic potentials between small molecules and known protein ligands. This method was implemented in a software called EleKit. Analyses of all available (at the time of research) SMPPII structures indicate that SMPPIIs bear some similarities of electrostatic potential with the ligand proteins of the same receptor. This is especially true for the more polar SMPPIIs. Retrospective analysis of several successful SMPPIIs has shown the applicability of EleKit in the design of new SMPPIIs.     

A loop-mimetic inhibitor of the HCV-NS3 protease derived from a minibody

We have been interested for some time in establishing a strategy for deriving lead compounds from macromolecule ligands such as minibody variants. A minibody is a minimized antibody variable domain whose two loops are amenable to combinatorial mutagenesis. This approach can be especially useful when dealing with 'difficult' targets. One such target is the NS3 protease of hepatitis C virus (HCV), a human pathogen that is believed to infect about 100 million individuals worldwide and for which an effective therapy is not yet available. Based on known inhibitor specificity (residues P6-P1) of NS3 protease, we screened a number of minibodies from our collection and we were able to identify a competitive inhibitor of this enzyme. We thus validated an aspect of recognition by HCV NS3 protease, namely that an acid anchor is necessary for inhibitor activity. In addition, the characterization of the minibody inhibitor led to the synthesis of a constrained hexapeptide mimicking the bioactive loop of the parent macromolecule. The cyclic peptide is a lead compound prone to rapid optimization through solid phase combinatorial chemistry. We therefore confirmed that the potential of turning a protein ligand into a low molecular weight active compound for lead discovery is achievable and can complement more traditional drug discovery approaches.     

Discovery of novel [1,2,4]triazolo[4,3-a]quinoxaline aminophenyl derivatives as BET inhibitors for cancer treatment

Bromodomain and extra-terminal (BET) proteins, a class of epigenetic reader domains has emerged as a promising new target class for small molecule drug discovery for the treatment of cancer, inflammatory, and autoimmune diseases. Starting from in silico screening campaign, herein we report the discovery of novel BET inhibitors based on [1,2,4]triazolo[4,3-a]quinoxaline scaffold and their biological evaluation. The hit compound was optimized using the medicinal chemistry approach to the lead compound with excellent inhibitory activities against BRD4 in the binding assay. The substantial antiproliferative activities in human cancer cell lines, promising drug-like properties, and the selectivity for the BET family make the lead compound (13) as a novel BRD4 inhibitor motif for anti-cancer drug discovery.     

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.     

Drug discovery in academia

Drug discovery and development is generally done in the commercial rather than the academic realm. Drug discovery involves target discovery and validation, lead identification by high-throughput screening, and lead optimization by medicinal chemistry. Follow-up preclinical evaluation includes analysis in animal models of compound efficacy and pharmacology (ADME: administration, distribution, metabolism, elimination) and studies of toxicology, specificity, and drug interactions. Notwithstanding the high-cost, labor-intensive, and non-hypothesis-driven aspects of drug discovery, the academic setting has a unique and expanding niche in this important area of investigation. For example, academic drug discovery can focus on targets of limited commercial value, such as third-world and rare diseases, and on the development of research reagents such as high-affinity inhibitors for pharmacological "gene knockout" in animal models ("chemical genetics"). This review describes the practical aspects of the preclinical drug discovery process for academic investigators. The discovery of small molecule inhibitors and activators of the cystic fibrosis transmembrane conductance regulator is presented as an example of an academic drug discovery program that has yielded new compounds for physiology research and clinical development. high-throughput screening; drug development; pharmacology; fluorescence; cystic fibrosis transmembrane conductance regulator     

Collation and data-mining of literature bioactivity data for drug discovery

The challenge of translating the huge amount of genomic and biochemical data into new drugs is a costly and challenging task. Historically, there has been comparatively little focus on linking the biochemical and chemical worlds. To address this need, we have developed ChEMBL, an online resource of small-molecule SAR (structure-activity relationship) data, which can be used to support chemical biology, lead discovery and target selection in drug discovery. The database contains the abstracted structures, properties and biological activities for over 700000 distinct compounds and in excess of more than 3 million bioactivity records abstracted from over 40000 publications. Additional public domain resources can be readily integrated into the same data model (e.g. PubChem BioAssay data). The compounds in ChEMBL are largely extracted from the primary medicinal chemistry literature, and are therefore usually 'drug-like' or 'lead-like' small molecules with full experimental context. The data cover a significant fraction of the discovery of modern drugs, and are useful in a wide range of drug design and discovery tasks. In addition to the compound data, ChEMBL also contains information for over 8000 protein, cell line and whole-organism 'targets', with over 4000 of those being proteins linked to their underlying genes. The database is searchable both chemically, using an interactive compound sketch tool, protein sequences, family hierarchies, SMILES strings, compound research codes and key words, and biologically, using a variety of gene identifiers, protein sequence similarity and protein families. The information retrieved can then be readily filtered and downloaded into various formats. ChEMBL can be accessed online at https://www.ebi.ac.uk/chembldb.     

Natural product-like synthetic libraries

There is a paucity of chemical matter suitably poised for effective drug development. Improving the quality and efficiency of research early on in the drug discovery process has been a long standing objective for the drug industry and improvements to the accessibility and quality of compound screening decks might have a significant and positive impact. In the absence of specific molecular information that can be modeled and used predicatively we are far from identifying which small molecules are most relevant to emerging biological targets such as protein-protein interactions. Natural products have been historically successful as an entry point for drug discovery and recently screening libraries are being synthesized to emulate natural product like features.     

High-content single-cell drug screening with phosphospecific flow cytometry

Drug screening is often limited to cell-free assays involving purified enzymes, but it is arguably best applied against systems that represent disease states or complex physiological cellular networks. Here, we describe a high-content, cell-based drug discovery platform based on phosphospecific flow cytometry, or phosphoflow, that enabled screening for inhibitors against multiple endogenous kinase signaling pathways in heterogeneous primary cell populations at the single-cell level. From a library of small-molecule natural products, we identified pathway-selective inhibitors of Jak-Stat and MAP kinase signaling. Dose-response experiments in primary cells confirmed pathway selectivity, but importantly also revealed differential inhibition of cell types and new druggability trends across multiple compounds. Lead compound selectivity was confirmed in vivo in mice. Phosphoflow therefore provides a unique platform that can be applied throughout the drug discovery process, from early compound screening to in vivo testing and clinical monitoring of drug efficacy.     

Ligand binding affinity determined by temperature-dependent circular dichroism: cyclin-dependent kinase 2 inhibitors

To support drug discovery efforts for cyclin-dependent kinase 2 (CDK2), a moderate-throughput binding assay that can rank order or estimate the affinity of lead inhibitors has been developed. The method referred to as temperature-dependent circular dichroism (TdCD) uses the classical temperature-dependent unfolding of proteins by circular dichroism (CD) to measure the degree of protein unfolding in the absence and presence of potential inhibitors. The midpoint of unfolding is the Tm value. Rank ordering the affinity and predictions of the dissociation constant of compounds is obtained by measuring the increase in Tm for different protein-inhibitor complexes. This is the first time an extensive characterization of the TdCD method has been described for characterizing lead inhibitors in a drug discovery mode. The method has several favorable properties. Using the new six-cell Peltier temperature controller for the Jasco 810 spectropolarimeter, one can determine the affinity of 12-18 compounds per day. The method also requires only 20-40 microg protein per sample and can be used to estimate the affinity of compounds with dissociation constants of picomolar to micromolar. An important property of the method for lead discovery is that dissociation constants of approximately 5 microM can be estimated from a single experiment using a low concentration of compound such as 20 microM, which is generally low enough for most small molecules to be soluble for testing. In addition, the method does not require labeling the compound or protein. Although other methods such as isothermal titration calorimetry (ITC) can provide a full thermodynamic characterization of binding, ITC requires 1-2 mg protein per sample, cannot readily determine binding constants below nanomolar values, is most versatile with soluble compounds, and has a throughput of two to three experiments per day. The ITC method is not usually used in a high-throughput drug discovery mode; however, using the thermodynamic information from several ITC experiments can make the TdCD method very robust in determining reliable binding constants. Using the kinase inhibitors BMS-250595, purvalanol B, AG-12275, flavopiridol, and several other compounds, it is demonstrated that one can obtain excellent comparisons between the Kd values of binding to CDK2 obtained by TdCD and ITC.