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。     

含RGD三肽的骨基质非胶原蛋白在骨质疏松症中的研究进展

骨基质非胶原蛋白是对于骨的生长、再生、发育等有重要作用的蛋白质,其中包含一类在结构上都含有RGD(精氨酸-甘氨酸-天冬氨酸)三肽的蛋白.近来研究表明,RGD蛋白在骨基质的形成、矿化,以及介导细胞.细胞、细胞-基质的相互作用中发挥关键作用.本文对骨基质中主要的RGD蛋白的骨生物活性,以及与骨质疏松症关系方面的最新研究进展作一简述.     

基于生物信息学方法发现潜在药物靶标

药物靶点通常是在代谢或信号通路中与特定疾病或病理状态有关的关键分子.通过绑定到特定活动区域抑制这个关键分子进行药物设计.确定特定疾病有关的靶标分子是现代新药开发的基础.在药物靶标发现的过程中,生物信息学方法发挥了不可替代的重要的作用,尤其适用于大规模多组学数据的分析.目前,已涌现了许多与疾病相关的数据库资源,基于生物网络特征、多基因芯片、蛋白质组、代谢组数据等建立了多种生物信息学方法发现潜在的药物靶标,并预测靶标可药性和药物副作用.     

化合物药物-靶标蛋白互作关联预测算法进展分析

药物的使用极大地提高了人类的生存质量。药物的有效性是药物发现研究中的关键环节。药物的有效性通过识别药物与其作用的靶标蛋白来判断。然而,通过高通量筛选的实验方法分析确定化合物药物-靶标蛋白互作关联是一个十分昂贵、耗时且富有挑战性的任务。基于计算方法的化合物药物-靶标蛋白互作关联预测研究具有效率高、成本低的特点,越来越受到人们的重视。相比实验验证方法,化合物药物-靶标蛋白互作关联的计算方法可为药物发现研究后续的生物药学实验提供更为准确的潜在化合物药物-靶标蛋白候选对,达到减少生物实验的时间和成本的目的。本文回顾了近20年来基于计算方法的化合物药物-靶标蛋白互作关联预测算法所涉及的生物医学特征数据、预测方法和技术,并分析研究过程中所面临的生物医学特征数据高维稀疏,以及多源生物医学数据融合程度不高等问题,为进一步研究提供有价值的参考。     

《细胞-代谢》：研究发现戊二酸尿症潜在药物靶标

《细胞-代谢》（Cell Metabolism）报道。中科院上海药物所化学蛋白质组学研究中心科研人员首次发现了与遗传代谢性疾病戊二酸尿症相关的体内蛋白修饰新型通路——赖氨酸戊二酰化,并发现了治疗此疾病的潜在新药物靶标。     

化学蛋白质组学在天然产物分子靶标鉴定中的应用

绿色新农药的开发和利用有利于农业的可持续发展,基于天然产物进行活性先导发现及作用机制研究是重要的新农药创制策略,然而其作用靶标和作用机制难以确定,阻碍了其在新农药中的应用。因此发现化合物新靶点对于新农药创制来说是一项既重要又艰巨的任务。化学蛋白质组学作为后基因组时代的新技术,目前已经成为研究药物靶点的重要手段之一。本文对基于化学蛋白组学的化合物作用分子靶点发现方法和典型案例进行探析,介绍这些技术的主要原理、应用以及各自的优点和局限性,旨在阐述基于化学蛋白质组学发现药物作用靶标的最新方法,并为天然产物靶点及新农药创制研究提供参考。     

药物靶标蛋白筛选的化学蛋白质组学研究进展

药物或生物活性物质通过与靶蛋白结合而发挥功能,研究表明,大多数药物具有多个作用靶点,药物靶标的发现有助于药物前体的筛选和作用机制的研究,同时对其耐药性等副作用的解决方案提供理论指导.基于生物质谱技术的蛋白质组学可对蛋白质进行高通量的定性定量分析,为药物靶标的筛选提供了全新的平台.本文综述了基于固载药物和游离药物模式的药物靶标蛋白筛选相关方法和应用研究的最新进展,为基于生物质谱技术的化学蛋白质组学研究提供参考.     

亲环蛋白在病毒感染中的作用

亲环蛋白家族是一类具有肽酰-脯氨酰顺反异构酶活性的蛋白.亲环蛋白不仅具有帮助蛋白质组装和折叠、参与调节细胞内信号转导通路等多种生物学功能,还在病毒感染及病毒的复制周期中起到重要作用.本文就亲环蛋白在病毒感染过程中的具体分子机制进行综述,旨在更深入的阐明病毒的感染过程并为寻找潜在的抗病毒药物靶标提供新的理论依据及思路.     

化学蛋白质组学技术在药物靶标鉴定中的应用

药物开发过程面临多重挑战,而靶标确证是其中的重要一环。如何运用多种研究方法发现和确认小分子药物的靶标是目前研究人员的主要工作内容之一。化学蛋白质组学整合了细胞生物学、合成化学和生物质谱等多门学科,为药物的靶标筛选提供了新平台。本文对近年来发展的基于生物质谱的化学蛋白质组学药物靶标鉴定技术进行了总结,结合具体应用分析其优缺点,并对该类技术的发展和应用进行总结和展望。     

靶向膜型1基质金属蛋白酶反义肽的虚拟筛选与分子模拟

膜型1基质金属蛋白酶(Membrane type-1 matrix metalloproteinase,MT1-MMP,MMP14)在肿瘤的发生发展及转移中起着重要作用,是肿瘤潜在理想的药物靶标。为了获得MT1-MMP结合肽,我们首先采用生物信息学方法分析MMPs序列,获得MT1-MMP差异大且特异的序列。以此为正义肽靶标,设计反义肽库,然后通过分子对接、分子动力学模拟以及体外细胞实验等多种方法,进行靶向MT1-MMP反义肽的筛选与活性研究。多序列比对确定了位于MT1-MMP环区的特异序列AYIREGHE(简称MT1-loop),并构建1 536条反义肽。经两轮虚拟筛选,选取打分位于前五的反义肽用于后续研究。该五条反义肽与MT1-MMP存在较强的相互作用且能很好地对接于正义肽区域。进一步分析其与MMPs其他家族成员(MMP1-3,MMP7-13,MMP14HPX,MMP16)的亲和力,发现反义肽FVTFPYIR对MT1-MMP具有更强的特异性。分子动力学模拟表明,反义肽FVTFPYIR可能是通过影响受体MT1-MMP的构象稳定性,进而影响其功能活性。体外细胞实验初步确定反义肽FVTFPYIR可选择性地抑制表达MT1-MMP的人成骨肉瘤细胞MG63和乳腺癌MDA-MB-231细胞的增殖。本研究为抗肿瘤反义肽先导药物的研发提供了一种新的思路与途径。     

Serendipity and new drugs for infectious disease

Serendipity, in various shades of semantic legitimacy, is abundantly evident in the history of the chemotherapy of infectious disease. We may be on the threshold of a new era of rational drug design, but most medications for infectious diseases have arisen, and continue to arise, from chance observation, clinical experience, and the empirical search for substances active against pathogens. Chance does not produce drugs; but where chance has played a pivotal role in drug discovery, the event may be considered serendipitous to a greater or lesser degree. In a deliberate search for new drugs, it is often difficult to assess the degree to which any resulting discovery is serendipitous, and the usefulness of the term becomes debatable. Many therapeutic advances emerge from research involving animals, and a triggering "happy accident" may reside in the most basic aspects of animal care or in the most arcane knowledge of animals. The examples discussed in this article deal mostly with parasitic disease and the use of animal models in the discovery of antiparasitic agents. In this area, as in others, chance has laid the groundwork for scientific advancement and practical benefit. Although the applicability of the word serendipity to drug discovery may often be uncertain, the role played by chance should be recognized and welcomed.     

Will genetics really revolutionize the drug discovery process?

Genetics has played only a modest role in drug discovery, but new technologies will radically change this. Whole genome sequencing will identify new drug discovery targets, and emerging methods for the determination of gene function will increase the ability to select robust targets. Detection of single nucleotide polymorphisms and common polymorphisms will enhance the investigation of polygenic diseases and the use of genetics in drug development. Oligonucleotide arraying technologies will allow analysis of gene expression patterns in novel ways.     

Exploiting drug-disease relationships for computational drug repositioning

Finding new uses for existing drugs, or drug repositioning, has been used as a strategy for decades to get drugs to more patients. As the ability to measure molecules in high-throughput ways has improved over the past decade, it is logical that such data might be useful for enabling drug repositioning through computational methods. Many computational predictions for new indications have been borne out in cellular model systems, though extensive animal model and clinical trial-based validation are still pending. In this review, we show that computational methods for drug repositioning can be classified in two axes: drug based, where discovery initiates from the chemical perspective, or disease based, where discovery initiates from the clinical perspective of disease or its pathology. Newer algorithms for computational drug repositioning will likely span these two axes, will take advantage of newer types of molecular measurements, and will certainly play a role in reducing the global burden of disease.     

Bioinformatics approaches for new drug discovery: a review

Prolonged antibiotic therapy for the bacterial infections has resulted in high levels of antibiotic resistance. Initially, bacteria are susceptible to the antibiotics, but can gradually develop resistance. Treating such drug-resistant bacteria remains difficult or even impossible. Hence, there is a need to develop effective drugs against bacterial pathogens. The drug discovery process is time-consuming, expensive and laborious. The traditionally available drug discovery process initiates with the identification of target as well as the most promising drug molecule, followed by the optimization of this, in-vitro, in-vivo and in pre-clinical studies to decide whether the compound has the potential to be developed as a drug molecule. Drug discovery, drug development and commercialization are complicated processes. To overcome some of these problems, there are many computational tools available for new drug discovery, which could be cost effective and less time-consuming. In-silico approaches can reduce the number of potential compounds from hundreds of thousands to the tens of thousands which could be studied for drug discovery and this results in savings of time, money and human resources. Our review is on the various computational methods employed in new drug discovery processes.     

Natural products as probes in pharmaceutical research

From the start of the pharmaceutical research natural products played a key role in drug discovery and development. Over time many discoveries of fundamental new biology were triggered by the unique biological activity of natural products. Unprecedented chemical structures, novel chemotypes, often pave the way to investigate new biology and to explore new pathways and targets. This review summarizes the recent results in the area with a focus on research done in the laboratories of Novartis Institutes for BioMedical Research. We aim to put the technological advances in target identification techniques in the context to the current revival of phenotypic screening and the increasingly complex biological questions related to drug discovery.     

Recent applications of protein crystallography and structure-guided drug design

Technological advances to increase the throughput of purified protein production and co-crystallization of target proteins with small molecules have helped to solidify the role that structure via crystallography has on drug discovery. Visualization of how drug-like molecules bind to the target protein is a key step in driving follow-up or preclinical chemistry to improve characteristics of the molecule. Using structural information to guide small-molecule design and generate new chemical ideas is now a mainstay in the drug discovery process.     

Cryo-EM as a powerful tool for drug discovery

The recent revolution in cryo-EM has produced an explosion of structures at near-atomic or better resolution. This has allowed cryo-EM structures to provide visualization of bound small-molecule ligands in the macromolecules, and these new structures have provided unprecedented insights into the molecular mechanisms of complex biochemical processes. They have also had a profound impact on drug discovery, defining the binding modes and mechanisms of action of well-known drugs as well as driving the design and development of new compounds. This review will summarize and highlight some of these structures. Most excitingly, the latest cryo-EM technology has produced structures at 1.2 Å resolution, further solidifying cryo-EM as a powerful tool for drug discovery. Therefore, cryo-EM will play an ever-increasing role in drug discovery in the coming years.     

Drug discovery for malaria: a very challenging and timely endeavor

The prevalence of resistance to known antimalarial drugs has resulted in the expansion of antimalarial drug discovery efforts. Academic and nonprofit institutions are partnering with the pharmaceutical industry to develop new antimalarial drugs. Several new antimalarial agents are undergoing clinical trials, mainly those resurrected from previous antimalarial drug discovery programs. Novel antimalarials are being advanced through the drug development process, of course, with the anticipated high failure rate typical of drug discovery. Many of these are summarized in this review. Mechanisms for funding antimalarial drug discovery and genomic information to aid drug target selection have never been better. It remains to be seen whether ongoing efforts will be sufficient for reducing malaria burden in the developing world.     

RNA as a target for small molecules

Proteins are folded to form a small binding site for catalysis or ligand recognition and this small binding site is traditionally the target for drug discovery. An alternative target for potential drug candidates is the translational process, which requires a precise reading of the entire mRNA sequence and, therefore, can be interrupted with small molecules that bind to mRNA sequence-specifically. RNA thus presents itself as a new upstream target for drug discovery because of the critical role it plays in the life of pathogens and in the progression of diseases. In this post-genomic era, RNA is becoming increasingly amenable to small-molecule therapy as greater structural and functional information accumulates with regard to important RNA functional domains. The study of aminoglycoside antibiotics and their binding to 16S ribosomal RNA has been a paradigm for our understanding of the ways in which small molecules can be developed to affect the function of RNA.     

A structural perspective on targeting the RTK/Ras/MAP kinase pathway in cancer

Precision oncology is premised on identifying and drugging proteins and pathways that drive tumorigenesis or are required for survival of tumor cells. Across diverse cancer types, the signaling pathway emanating from receptor tyrosine kinases on the cell surface to RAS and the MAP kinase pathway is the most frequent target of oncogenic mutations, and key proteins in this signaling axis including EGFR, SHP2, RAS, BRAF, and MEK have long been a focus in cancer drug discovery. In this review, we provide an overview of historical and recent efforts to develop inhibitors targeting these nodes with an emphasis on the role that an understanding of protein structure and regulation has played in inhibitor discovery and characterization. Beyond its well‐established role in structure‐based drug design, structural biology has revealed mechanisms of allosteric regulation, distinct effects of activating oncogenic mutations, and other vulnerabilities that have opened new avenues in precision cancer drug discovery.