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经典的蛋白质组学研究方法包括IEF/SDS-PAGE双向电泳和质谱技术的联用,但由于IEF的一些不足,限制了其应用范围。对角线电泳是蛋白质组学研究中的一项特殊分离技术,由于其原理与IEF/SDS-PAGE不同,正逐渐成为蛋白质组学中电泳分离技术的重要补充,特别是在膜蛋白和蛋白质相互关系的研究中将起到重要作用。本文综述了对角线双向电泳技术的特点、发展和在蛋白质组学研究中的最新进展,比较了双向电泳和对角线电泳的优缺点,展望了对角线电泳在蛋白质组学研究中的应用前景。 相似文献
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蛋白质组研究中分离新技术与新方法 总被引:6,自引:0,他引:6
对于蛋白质组的研究离不开分析技术的支撑。由于样品及其基质的复杂性,为了实现蛋白质的高通量、高灵敏度、快速分析鉴定,必须发展与之匹配的新技术与新方法。多维高效液相色谱/毛细管电泳技术,部分弥补了传统2D PAGE的不足,近年来,在蛋白质分离鉴定方面取得了最令人瞩目的成绩。本文分别从多维液相色谱分离技术、多维毛细管电泳蛋白质分离平台、微柱液相-毛细管电泳联用技术、极端pH蛋白质的分离分析和蛋白质的在线富集技术等方面对蛋白质组学研究中在新技术与新方法方面近期取得的成果加以系统阐述。 相似文献
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蛋白质组学是研究细胞、组织和器官内所有蛋白质的组成及其动态变化的科学,是在蛋白质水平上定量的、动态的、整体的研究生物体。目前蛋白质组学技术分为样品制备、分离和鉴定3个方面,其新技术主要有激光捕捉显微解剖法、离心超滤法、双向凝胶电泳、同位素亲和标签技术、色谱技术以及质谱技术等。然而,任何一种蛋白质组学研究技术都有其缺陷。因此多种技术的联合应用能使蛋白质组研究更精确和完整,是蛋白质组学的发展趋势。 相似文献
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蛋白质组学是系统鉴定、定量蛋白质及其翻译后修饰形式,并研究这些蛋白质生物学功能的学科。目前,基于质谱的鸟枪法蛋白质组学技术是蛋白质组学研究的主要手段之一,其技术流程是先将蛋白质组样品经位点特异性蛋白酶消化形成肽组,再进行高效液相色谱分离和质谱检测。而位点特异性蛋白酶对蛋白质样品的消化是质谱检测的前提和基础。随着蛋白质组学研究的深入,多种位点特异性蛋白酶被先后开发利用;而切割发生在相应氨基酸的N端,与传统的C端蛋白酶互为镜像的蛋白酶的鉴定、开发、特性研究和广泛使用更是为蛋白质组学研究提供了新的工具。文中对最近发现的胰蛋白酶的镜像酶——赖氨酸精氨酸N端蛋白酶(LysargiNase)的特点及其应用进行综述,为国内外学者更加广泛的使用创造条件。 相似文献
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蛋白质组学是对蛋白质组进行研究的一门新兴学科。它可以揭示细胞内蛋白质组成成分与修饰状态的动态变化.研究蛋白质之间的相互作用.从全局的高度来研究代谢.发育以及调控等复杂的问题。
由于蛋白质组学研究范围十分广泛,所需要的技术手段也多种多样,如双向凝胶电泳(2-DE).色谱.蛋白质芯片、质谱以及生物信息学等。2-DE分离蛋白质.通过生物质谱以及生物信息学手段对蛋白质进行鉴定是目前最常用的一种研究策略。由于该套技术平台中影响因素较多.很多初学者难以快速掌握.所以本实验方法系列讲座将详细介绍这一常规技术平台中的多项经典实验方案(样品制备.2一DE、染色、质谱鉴定等).并着重对一些常见问题与难点进行深入的探讨。此外,本实验方法系列讲座还将涉及近年来刚刚兴起的大规模的蛋白质组自动分析系统——液相色谱-质谱联用.旨在为研究者拓宽实验方法的视野。 相似文献
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Following the completion of genome sequen-cing of model plants,such as rice (Oryza sativa L.) and Arabidopsis thaliana,the era of functional plant genomics has arrived which provides a solid basis for the develop-ment of plant proteomics.We review the background and concepts of proteomics,as well as the key techniques which include:(1) separation techniques such as 2-DE (two-dimensional electrophoresis),RP-HPLC (reverse phase high performance liquid chromatography) and SELDI (surface enhanced laser desorption/ionization) protein chip; (2) mass spectrometry such as MALDI-TOF-MS (matrix assisted laser desorption/ionization-time of flight- mass spectrometry) and ESI-MS/MS (elec-trospray ionization mass spectrometry/mass spectro-metry); (3) Peptide sequence tags; (4) databases related to proteomics; (5) quantitative proteome; (6) TAP (tandem affinity purification) and (7) yeast two-hybrid system.In addition,the challenges and prospects of pro-teomics are also discussed. 相似文献
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蛋白质组学是在基因组学基础上发展起来的新兴学科, 其基本技术包括样品制备、蛋白质分离和蛋白质鉴定分析, 其中的核心技术是双向凝胶电泳技术(2-Dimensional Electrophoresis, 2-DE)和质谱技术(Mass Spectrometry, MS)。近年来, 蛋白质组学技术已应用于结核分枝杆菌的研究领域。应用蛋白质组学技术分离、鉴定、检测结核分枝杆菌致病株的全菌蛋白及分泌蛋白, 分析其蛋白组成, 可深入解析结核分枝杆菌的致病机理和耐药机制。通过对结核分枝杆菌致病株抗原的分析, 为研制预防结核病的新型疫苗拓展了空间。通过对结核分枝杆菌临床分离株的蛋白组成分析还发现了一些有意义的结核病早期诊断标志物。蛋白质组学技术还应用于寻找新的药物靶标, 在研制和筛选新的抗结核药物等方面展示了一些有价值的研究成果, 为更好地开展结核病的预防、早期诊断及治疗打下了基础。 相似文献
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综述了ICP-MS法应用于蛋白质定量技术方面的研究进展.蛋白质定量研究已成为蛋白质组学研究领域的热点,它是解析生物体蛋白质功能的重要途径.基于同位素标记和生物质谱分析技术是蛋白质定量最常用的方法之一,近年来,随着质谱技术的发展,电感耦合等离子体质谱(ICP-MS)技术成为元素测量的重要手段,这使其在蛋白质定量中具一定的应用前景. 相似文献
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植物蛋白质组学研究进展Ⅰ. 蛋白质组关键技术 总被引:10,自引:0,他引:10
随着模式植物拟南芥和水稻基因组测序相继完成, 使植物基因组学研究成功迈入到功能基因组学研究的时代。这为蛋白质组学产生及其发展奠定了坚实的基础。文章重点介绍了蛋白质组学的概念、产生背景和蛋白质组学的关键技术。蛋白质组学的关键技术包括双向电泳、高效液相色谱、蛋白芯片、质谱技术、蛋白质组学的相关数据库、定量蛋白组技术、蛋白复合体标签亲和纯化技术和酵母双杂交系统。同时对当前蛋白质组技术面临的挑战和发展前景进行了讨论。 相似文献
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Proteome analysis requires a comprehensive approach including high-performance separation methods, mass spectrometric analysis, and bioinformatics. While recent advances in mass spectrometry (MS) have led to remarkable improvements in the ability to characterize complex mixtures of biomolecules in proteomics, a proper pre-MS separation step of proteins/peptides is still required. The need of high-performance separation and/or isolation/purification techniques of proteins is increasing, due to the importance of proteins expressed at extremely low levels in proteome samples. In this review, flow field-flow fractionation (F4) is introduced as a complementary pre-analytical separation method for protein separation/isolation, which can be effectively utilized for proteomic research. F4 is a set of elution-based techniques that are capable of separating macromolecules by differences in diffusion coefficient and, therefore, in hydrodynamic size. F4 provides protein separation without surface interaction of the analyte with packing or gel media. Separation is carried out in an open channel structure by a flow stream of a mobile phase of any composition, and it is solely based on the interaction of the analytes with a perpendicularly-applied, secondary flow of the fluid. Therefore, biological analytes such as proteins can be kept under a bio-friendly environment without losing their original structural configuration. Moreover, proteins fractionated on a size/shape basis can be readily collected for further characterization or proteomic analysis by MS using, for instance, either on-line or off-line methods based on electrospray ionization (ESI) or matrix-assisted laser desorption-ionization (MALDI). This review focuses on the advantages of F4 compared to most-assessed separation/isolation techniques for proteomics, and on selected applications based on size-dependent proteome separation. New method developments based on the hyphenation of F4 with on-line or off-line MS, and with other separation methods such as capillary isoelectric focusing (CIEF) are also described. 相似文献
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《Expert review of proteomics》2013,10(6):611-619
Protein profiling using mass spectrometry technology has emerged as a powerful method for analyzing large-scale protein-expression patterns in cells and tissues. However, a number of challenges are present in proteomics research, one of the greatest being the high degree of protein complexity and huge dynamic range of proteins expressed in the complex biological mixtures, which exceeds six orders of magnitude in cells and ten orders of magnitude in body fluids. Since many important signaling proteins have low expression levels, methods to detect the low-abundance proteins in a complex sample are required. This review will focus on the fundamental fractionation and mass spectrometry techniques currently used for large-scale shotgun proteomics research. 相似文献
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The objective of proteomics is to get an overview of the proteins expressed at a given point in time in a given tissue and to identify the connection to the biochemical status of that tissue. Therefore sample throughput and analysis time are important issues in proteomics. The concept of proteomics is to encircle the identity of proteins of interest. However, the overall relation between proteins must also be explained. Classical proteomics consist of separation and characterization, based on two-dimensional electrophoresis, trypsin digestion, mass spectrometry and database searching. Characterization includes labor intensive work in order to manage, handle and analyze data. The field of classical proteomics should therefore be extended to also include handling of large datasets in an objective way. The separation obtained by two-dimensional electrophoresis and mass spectrometry gives rise to huge amount of data. We present a multivariate approach to the handling of data in proteomics with the advantage that protein patterns can be spotted at an early stage and consequently the proteins selected for sequencing can be selected intelligently. These methods can also be applied to other data generating protein analysis methods like mass spectrometry and near infrared spectroscopy and examples of application to these techniques are also presented. Multivariate data analysis can unravel complicated data structures and may thereby relieve the characterization phase in classical proteomics. Traditionally statistical methods are not suitable for analysis of the huge amounts of data, where the number of variables exceed the number of objects. Multivariate data analysis, on the other hand, may uncover the hidden structures present in these data. This study takes its starting point in the field of classical proteomics and shows how multivariate data analysis can lead to faster ways of finding interesting proteins. Multivariate analysis has shown interesting results as a supplement to classical proteomics and added a new dimension to the field of proteomics. 相似文献