蛋白质芯片技术

以前对蛋白质的研究集中在一次研究一种蛋白质 ,通常费时费力 ;而蛋白质芯片技术是研究蛋白质组的新技术 ,是高通量、微型化和自动化的蛋白质分析技术。它可以用来研究蛋白质的亚细胞定位和蛋白质与蛋白质之间的相互作用 ,以及对蛋白质的功能进行生物化学分析 ,将对蛋白质组研究及医学生物学的发展有很大的推动作用。较系统地介绍了蛋白质芯片的概念、制作及检测方法 ;同时也讨论了蛋白质芯片的两种功能形式、存在问题和应用前景。     

Mapping the human protein interactome

Interactions are the essence of all biomolecules because they cannot fulfill their roles without interacting with other molecules. Hence, mapping the interactions of biomolecules can be useful for understanding their roles and functions. Furthermore, the development of molecular based systems biology requires an understanding of the biomolecular interactions. In recent years, the mapping of protein-protein interactions in different species has been reported, but few reports have focused on the large-scale mapping of protein-protein interactions in human. Here, we review the developments in protein interaction mapping and we discuss issues and strategies for the mapping of the human protein interactome.     

Functional protein microarray: an ideal platform for investigating protein binding property

Functional protein microarray is an important tool for high-throughput and large-scale systems biology studies.Besides the progresses that have been made for protein microarray fabrication,significant ...     

Feature-based classification of native and non-native protein-protein interactions: Comparing supervised and semi-supervised learning approaches

Structural knowledge about protein-protein interactions can provide insights to the basic processes underlying cell function. Recent progress in experimental and computational structural biology has led to a rapid growth of experimentally resolved structures and computationally determined near-native models of protein-protein interactions. However, determining whether a protein-protein interaction is physiological or it is the artifact of an experimental or computational method remains a challenging problem. In this work, we have addressed two related problems. The first problem is distinguishing between the experimentally obtained physiological and crystal-packing protein-protein interactions. The second problem is concerned with the classification of near-native and inaccurate docking models. We first defined a universal set of interface features and employed a support vector machines (SVM)-based approach to classify the interactions for both problems, with the accuracy, precision, and recall for the first problem classifier reaching 93%. To improve the classification, we next developed a semi-supervised learning approach for the second problem, using transductive SVM (TSVM). We applied both classifiers to a commonly used protein docking benchmark of 124 complexes. We found that while we reached the classification accuracies of 78.9% for the SVM classifier and 80.3% for the TSVM classifier, improving protein-docking methods by model re-ranking remains a challenging problem.     

Interaction networks for systems biology

Cellular functions are almost always the result of the coordinated action of several proteins, interacting in protein complexes, pathways or networks. Progress made in devising suitable tools for analysis of protein-protein interactions, have recently made it possible to chart interaction networks on a large-scale. The aim of this review is to provide a short overview of the most promising contributions of interaction networks to human biology, structural biology and human genetics.     

Interactome Mapping: Using Protein Microarray Technology to Reconstruct Diverse Protein Networks

A major focus of systems biology is to characterize interactions between cellular components, in order to develop an accurate picture of the intricate networks within biological systems. Over the past decade, protein microarrays have greatly contributed to advances in proteomics and are becoming an important platform for systems biology. Protein microarrays are highly flexible, ranging from large-scale proteome microarrays to smaller customizable microarrays, making the technology amenable for detection of a broad spectrum of biochemical properties of proteins. In this article, we will focus on the numerous studies that have utilized protein microarrays to reconstruct biological networks including protein-DNA interactions, posttranslational protein modifications (PTMs), lectin-glycan recognition, pathogen-host interactions and hierarchical signaling cascades. The diversity in applications allows for integration of interaction data from numerous molecular classes and cellular states, providing insight into the structure of complex biological systems. We will also discuss emerging applications and future directions of protein microarray technology in the global frontier.     

From proteomes to complexomes in the era of systems biology

Protein complexes carry out almost the entire signaling and functional processes in the cell. The protein complex complement of a cell, and its network of complex–complex interactions, is referred to here as the complexome. Computational methods to predict protein complexes from proteomics data, resulting in network representations of complexomes, have recently being developed. In addition, key advances have been made toward understanding the network and structural organization of complexomes. We review these bioinformatics advances, and their discovery‐potential, as well as the merits of integrating proteomics data with emerging methods in systems biology to study protein complex signaling. It is envisioned that improved integration of proteomics and systems biology, incorporating the dynamics of protein complexes in space and time, may lead to more predictive models of cell signaling networks for effective modulation.     

Surface plasmon resonance imaging protein arrays for analysis of triple protein interactions of HPV, E6, E6AP, and p53

We have developed a surface plasmon resonance (SPR)-based protein microarray to study protein-protein interactions in a high-throughput mode. As a model system, triple protein interactions have been explored with human papillomaviral E6 protein, tumor suppressor p53, and ubiquitin ligase E6AP. Human papillomavirus (HPV) is known to be a causative agent of cervical cancer. Upon infection, the viral E6 protein forms a heterotrimeric protein complex with p53 and E6AP. The formation of the complex eventually results in the degradation of p53. In the present study, a GST-fused E6AP protein was layered onto a glutathione (GSH)-modified gold chip surface. The specific binding of GST-E6AP protein onto the gold chip surface was facilitated through the affinity of GST to its specific ligand GSH. The interacting proteins (E6 and/or p53) were then spotted. Detection of the interaction was performed using a SPR imaging (SPRI) technique. The resulting SPRI intensity data showed that the protein-protein interactions of E6AP, E6, and p53 were detected in a concentration-dependent manner, suggesting that the SPRI-based microarray system can be an effective tool to study protein-protein interactions where multiple proteins are involved.     

蛋白质复合体及蛋白质相互作用研究新策略—化学交联结合质谱分析法

近年来化学交联法结合质谱分析法被广泛用于蛋白质复合体结构及蛋白质相互作用的研究。研究表明这两种方法的有机结合为研究蛋白质复合体结构及蛋白质相互作用提供了一条新的途径。文章对不同类型的化学交联剂、质谱分析中的Bottom-up 与Top-down 两种研究策略,以及化学交联法结合质谱分析法在蛋白质复合体结构、蛋白质相互作用研究中的应用进行综述。这两种方法的不断发展与完善,将会极大促进生物大分子复合体结构及蛋白质相互作用的研究。     

Methods for mapping of interaction networks involving membrane proteins

Nearly one-third of all genes in various organisms encode membrane-associated proteins that participate in numerous protein-protein interactions important to the processes of life. However, membrane protein interactions pose significant challenges due to the need to solubilize membranes without disrupting protein-protein interactions. Traditionally, analysis of isolated protein complexes by high-resolution 2D gel electrophoresis has been the main method used to obtain an overall picture of proteome constituents and interactions. However, this method is time consuming, labor intensive, detects only abundant proteins and is limited with respect to the coverage required to elucidate large interaction networks. In this review, we discuss the application of various methods to elucidate interactions involving membrane proteins. These techniques include methods for the direct isolation of single complexes or interactors as well as methods for characterization of entire subcellular and cellular interactomes.     

蛋白质相互作用数据库

随着“蛋白质组学”的蓬勃发展和人类对生物大分子功能机制的知识积累,涌现出海量的蛋白质相互作用数据。随之,研究者开发了300多个蛋白质相互作用数据库,用于存储、展示和数据的重利用。蛋白质相互作用数据库是系统生物学、分子生物学和临床药物研究的宝贵资源。本文将数据库分为3类：（1）综合蛋白质相互作用数据库;（2）特定物种的蛋白质相互作用数据库;（3）生物学通路数据库。重点介绍常用的蛋白质相互作用数据库,包括BioGRID、STRING、IntAct、MINT、DIP、IMEx、HPRD、Reactome和KEGG等。     

A dual luciferase multiplexed high-throughput screening platform for protein-protein interactions

To study the biology of regulators of G-protein signaling (RGS) proteins and to facilitate the identification of small molecule modulators of RGS proteins, the authors recently developed an advanced yeast 2-hybrid (YTH) assay format for GalphaZ and RGS-Z1. Moreover, they describe the development of a multiplexed luciferase-based assay that has been successfully adapted to screen large numbers of small molecule modulators of protein-protein interactions. They generated and evaluated 2 different luciferase reporter gene systems for YTH interactions, a Gal4 responsive firefly luciferase reporter gene and a Gal4 responsive Renilla luciferase reporter gene. Both the firefly and Renilla luciferase reporter genes demonstrated a 40- to 50-fold increase in luminescence in strains expressing interacting YTH fusion proteins versus negative control strains. Because the firefly and Renilla luciferase proteins have different substrate specificity, the assays were multiplexed. The multiplexed luciferase-based YTH platform adds speed, sensitivity, simplicity, quantification, and efficiency to YTH high-throughput applications and therefore greatly facilitates the identification of small molecule modulators of protein-protein interactions as tools or potential leads for drug discovery efforts.     

Protein-protein interaction studies on protein arrays: Effect of detection strategies on signal-to-background ratios

Protein arrays hold great promise for proteome-scale analysis of protein-protein interaction networks, but the technical challenges have hindered their adoption by proteomics researchers. The crucial issue of design and fabrication of protein arrays have been addressed in several studies, but the detection strategies used for identifying protein-protein interactions have received little attention. In this study, we evaluated six different detection strategies to identify four different protein-protein interaction pairs. We discuss each detection approach in terms of signal-to-background (S/B) ratio, ease of use, and adaptability to high-throughput format. Protein arrays for this study were made by expressing both the bait proteins (proteins captured at the surface) and prey proteins (probes) in cell-free rabbit reticulocyte lysate (RRL) systems. Bait proteins were expressed as HaloTag fusions that allow covalent capture on a HaloTag ligand-coated glass without any prior protein purification step. Prey proteins were expressed and modified with either tags (protein or peptides) or labels (fluorescent or radiometric) for detection. This simple method for creating protein arrays in combination with our analyses of several detection strategies should increase the usefulness of protein array technologies.     

Building and analyzing protein interactome networks by cross-species comparisons

Background  

A genomic catalogue of protein-protein interactions is a rich source of information, particularly for exploring the relationships between proteins. Numerous systems-wide and small-scale experiments have been conducted to identify interactions; however, our knowledge of all interactions for any one species is incomplete, and alternative means to expand these network maps is needed. We therefore took a comparative biology approach to predict protein-protein interactions across five species (human, mouse, fly, worm, and yeast) and developed InterologFinder for research biologists to easily navigate this data. We also developed a confidence score for interactions based on available experimental evidence and conservation across species.     

Computational Prediction of Protein–Protein Interaction Networks: Algo-rithms and Resources

Protein interactions play an important role in the discovery of protein functions and pathways in biological processes. This is especially true in case of the diseases caused by the loss of specific protein-protein interactions in the organism. The accuracy of experimental results in finding protein-protein interactions, however, is rather dubious and high throughput experimental results have shown both high false positive beside false negative information for protein interaction. Computational methods have attracted tremendous attention among biologists because of the ability to predict protein-protein interactions and validate the obtained experimental results. In this study, we have reviewed several computational methods for protein-protein interaction prediction as well as describing major databases, which store both predicted and detected protein-protein interactions, and the tools used for analyzing protein interaction networks and improving protein-protein interaction reliability.     

Investigating the validity of current network analysis on static conglomerate networks by protein network stratification

Background  

A molecular network perspective forms the foundation of systems biology. A common practice in analyzing protein-protein interaction (PPI) networks is to perform network analysis on a conglomerate network that is an assembly of all available binary interactions in a given organism from diverse data sources. Recent studies on network dynamics suggested that this approach might have ignored the dynamic nature of context-dependent molecular systems.     

大规模的酵母双杂交系统在蛋白质相互作用组图谱中的应用

蛋白质-蛋白质之间的相互作用是蛋白质发挥其功能的重要途径之一。通过研究蛋白质组中所有蛋白质之间的相互作用做出蛋白质相互作用对图谱是功能基因组时代许多科学家关注的问题,而大规模的酵母双杂交系统是蛋白质相互作用对图谱的研究中应用较为广泛的策略。近两年来该策略最具代表的实例是用它进行酵母中所有蛋白之间相互作用的检查。但是巨大的蛋白质网络比我们想象要大得多,单一的双杂交系统不能解决所有问题,需要同其它的方法有效地结合。