FRET技术及其在蛋白质-蛋白质分子相互作用研究中的应用

简要综述了FRET方法在活细胞生理条件下研究蛋白质-蛋白质间相互作用方面的最新进展.蛋白质-蛋白质间相互作用在整个细胞生命过程中占有重要地位,由于细胞内各种组分极其复杂,因此一些传统研究蛋白质-蛋白质间相互作用的方法,例如酵母双杂交、免疫沉淀等可能会丢失某些重要的信息,无法正确地反映在当时活细胞生理条件下蛋白质-蛋白质间相互作用的动态变化过程.荧光共振能量转移（fluorescence resonance energy transfer, FRET）是近来发展的一项新技术,此项技术的应用,为在活细胞生理条件下对蛋白质-蛋白质间相互作用进行实时的动态研究,提供一个非常便利的条件.     

用FRET方法研究与Mps1蛋白有相互作用的CENP-E蛋白结构域

目的：探索与Mps1蛋白有相互作用的CENP-E蛋白结构域。方法：将重组质粒pEGFP-CENPE2（674~1085位氨基酸）、pEGFP-CENPE3（1200~2134位氨基酸）转染人胚肾293(HEK293)细胞,采用受体漂白荧光共振能量转移方法（FRET方法）,检测EGFP-CENPE2、EGFP-CENPE3和Mps1间的能量转移率(Ef), 进一步用免疫共沉淀方法验证FRET的实验结果。结果：重组质粒转染HEK293细胞后经激光共聚焦显微镜观察重组质粒表达的融合蛋白与Mps1都存在着共定位;FRET检测结果显示EGFP-CENPE3和Mps1间的能量转移率为［(12.63±0.48)%, n=30］,pEGFP-CENPE2和Mps1间的能量转移率为［(3.07±0.21)%, n=30］,与对照组［(2.96±0.27)%, n=30］比较pEGFP-CENPE3和Mps1间的能量转移率差异存在显著性（p<0.05）,免疫共沉淀实验结果显示EGFP-CENPE3与Mps1蛋白间存在相互作用。结论：FRET技术和免疫共沉淀实验证明了EGFP-CENPE3与Mps1间存在着相互作用。     

G蛋白偶联受体与G蛋白相互作用的最新研究进展

传统观念认为,在激动剂作用下,G蛋白偶联受体(GPCRs)能够激活G蛋白的α亚基,从而使Gα亚基与Gβγ亚基分离,被激活的Gα亚基通过信号转导进一步参与细胞的生理过程。但是,最新研究发现GPCRs和G蛋白存在多种偶联关系,GPCRs不仅能够激活Gα亚基,还可以与Gβγ亚基相互靠近,甚至会使G蛋白亚基构象发生重排而不分离,这对于疾病发病机制的研究及新的药物靶点的发现具有重要意义。就GPCRs与G蛋白之间的相互作用以及最新研究技术作一简要综述。     

耐热高活性荧光素酶在单细菌检测中的应用

[目的]开发活性和稳定性提高的荧光素酶突变体,建立单细菌检测方法。[方法]运用定点突变的方法,向荧光素酶LGR中逐步引入提高荧光素酶稳定性的氨基酸位点,构建荧光素酶突变体LGR-A215L、LGR-E354K和LGR-LK,并建立基于单细菌检测的快速无菌检查方法。[结果] LGR-A215L、LGR-E354K和LGR-LK的活性分别提高了9、26和140倍,45℃的稳定性提高了约2.9、1.3和2.7倍;此外,NaCl、培养基和生物制品溶液对LGR-LK的活性影响较小,活性变化在74%～181%之间。LGR-LK与ATP之间的亲和力显著提高,能灵敏的检测到小于10 CFU/mL菌悬液中的细菌,信号/背景值≥200;在48 h以内检测到TSB培养基中的金黄色葡萄球菌和枯草芽孢杆菌,相较于LGR的检测灵敏度提高了约1.5倍。[结论]开发了热稳定高活性荧光素酶突变体LGR-LK,其活性提高了140倍,45℃的热稳定性提高了2.7倍,并且其活性不受生物制品、NaCl和培养基的影响,能灵敏的快速检测单个细菌,可用于生物制品快速无菌检查。     

生物发光共振能量转移技术及其应用

生物发光共振能量转移（BRET）技术是近10年来出现的一种新的检测蛋白质- 蛋白质相互作用的技术.它的最大优势是能在活细胞中实时进行检测,因此能够进行相互作 用动力学的研究.本文系统阐述了BRET的原理和方法,综述了 BRET技术的最新进展,以及该 技术在G蛋白偶联受体（GPCRs）信号转导及药物发现中的应用.     

基于C6流式细胞仪平台应用FRET技术在活细胞中研究蛋白质相互作用

荧光共振能量转移(fluorescence resonance energy transfer,FRET)显微镜技术被广泛应用于在活细胞中研究蛋白质相互作用。随着流式细胞术(fluorescence activated cell sorting,FACS)的发展与应用,FACS-FRET技术不但可以检测活细胞中蛋白质相互作用,还可以进行定量统计分析。由于流式细胞仪价格昂贵、FRET技术对荧光基团发光光谱的特殊要求等原因,目前为止FACS-FRET技术仅仅被应用到一些特殊的科学研究。为了解决这些问题,构建了一对新的FRET荧光基团EGFP-m Cherry,并且在小型流式细胞仪C6上检测了EGFP-m Cherry融合蛋白的FRET信号,最后使用已明确有相互作用关系的p53蛋白和MDM2蛋白做验证,证明了所构建的EGFPm Cherry可以作为检测FRET信号的荧光基团。不仅促进了FACS-FRET技术的发展,还为人类疾病治疗的药物作用靶点研究提供了有利的研究手段。     

荧光素酶研究进展*

荧光素酶 (Luciferase)可以分为萤火虫荧光素酶和细菌荧光素酶两大类。萤火虫荧光素酶是分子量为 60～ 64kD的多肽链 ,在Mg²⁺、ATP、O₂ 存在时 ,催化D 荧光素 (D Luciferin)氧化脱羧 ,发出光 (λ =550～580nm)。细菌荧光素酶是含α、β两个多肽亚基的加单氧酶 ,它催化长链脂肪醛、FMNH₂ 和O₂ 的氧化反应 ,发出绿蓝光 (λ =490nm)。萤火虫荧光素酶和细菌荧光素酶     

计算方法在蛋白质相互作用研究中的应用

计算方法在蛋白质相互作用研究的各个阶段扮演了一个重要的角色。对此,作者将从以下几个方面对计算方法在蛋白质相互作用及相互作用网络研究中的应用做一个概述：蛋白质相互作用数据库及其发展;数据挖掘方法在蛋白质相互作用数据收集和整合中的应用;高通量方法实验结果的验证;根据蛋白质相互作用网络预测和推断未知蛋白质的功能;蛋白质相互作用的预测。     

双杂交扩展技术——报告蛋白片段互补及功能重建技术的应用与展望

报告蛋白片段互补及功能重建技术是对传统的酵母双杂交技术的改进。其原理是将报告蛋白分割成两个没有功能的片段,分别与两个待检测的蛋白质融合,如果待检测的两个蛋白质能够发生相互作用,就可以使报告蛋白的两个片段发生互补,从而使其功能得以重建。这一技术在检测方法和适用的细胞类型上都对酵母双杂交系统进行了扩展。     

Development of a Model Protein Interaction Pair as a Benchmarking Tool for the Quantitative Analysis of 2-Site Protein-Protein Interactions

A significant challenge in the molecular interaction field is to accurately determine the stoichiometry and stepwise binding affinity constants for macromolecules having >1 binding site. The mission of the Molecular Interactions Research Group (MIRG) of the Association of Biomolecular Resource Facilities (ABRF) is to show how biophysical technologies are used to quantitatively characterize molecular interactions, and to educate the ABRF members and scientific community on the utility and limitations of core technologies [such as biosensor, microcalorimetry, or analytic ultracentrifugation (AUC)]. In the present work, the MIRG has developed a robust model protein interaction pair consisting of a bivalent variant of the Bacillus amyloliquefaciens extracellular RNase barnase and a variant of its natural monovalent intracellular inhibitor protein barstar. It is demonstrated that this system can serve as a benchmarking tool for the quantitative analysis of 2-site protein-protein interactions. The protein interaction pair enables determination of precise binding constants for the barstar protein binding to 2 distinct sites on the bivalent barnase binding partner (termed binase), where the 2 binding sites were engineered to possess affinities that differed by 2 orders of magnitude. Multiple MIRG laboratories characterized the interaction using isothermal titration calorimetry (ITC), AUC, and surface plasmon resonance (SPR) methods to evaluate the feasibility of the system as a benchmarking model. Although general agreement was seen for the binding constants measured using solution-based ITC and AUC approaches, weaker affinity was seen for surface-based method SPR, with protein immobilization likely affecting affinity. An analysis of the results from multiple MIRG laboratories suggests that the bivalent barnase-barstar system is a suitable model for benchmarking new approaches for the quantitative characterization of complex biomolecular interactions.     

ModuleRole: A Tool for Modulization,Role Determination and Visualization in Protein-Protein Interaction Networks

Rapidly increasing amounts of (physical and genetic) protein-protein interaction (PPI) data are produced by various high-throughput techniques, and interpretation of these data remains a major challenge. In order to gain insight into the organization and structure of the resultant large complex networks formed by interacting molecules, using simulated annealing, a method based on the node connectivity, we developed ModuleRole, a user-friendly web server tool which finds modules in PPI network and defines the roles for every node, and produces files for visualization in Cytoscape and Pajek. For given proteins, it analyzes the PPI network from BioGRID database, finds and visualizes the modules these proteins form, and then defines the role every node plays in this network, based on two topological parameters Participation Coefficient and Z-score. This is the first program which provides interactive and very friendly interface for biologists to find and visualize modules and roles of proteins in PPI network. It can be tested online at the website http://www.bioinfo.org/modulerole/index.php, which is free and open to all users and there is no login requirement, with demo data provided by “User Guide” in the menu Help. Non-server application of this program is considered for high-throughput data with more than 200 nodes or user’s own interaction datasets. Users are able to bookmark the web link to the result page and access at a later time. As an interactive and highly customizable application, ModuleRole requires no expert knowledge in graph theory on the user side and can be used in both Linux and Windows system, thus a very useful tool for biologist to analyze and visualize PPI networks from databases such as BioGRID.

Availability

ModuleRole is implemented in Java and C, and is freely available at http://www.bioinfo.org/modulerole/index.php. Supplementary information (user guide, demo data) is also available at this website. API for ModuleRole used for this program can be obtained upon request.     

PPIM: A Protein-Protein Interaction Database for Maize

Maize (Zea mays) is one of the most important crops worldwide. To understand the biological processes underlying various traits of the crop (e.g. yield and response to stress), a detailed protein-protein interaction (PPI) network is highly demanded. Unfortunately, there are very few such PPIs available in the literature. Therefore, in this work, we present the Protein-Protein Interaction Database for Maize (PPIM), which covers 2,762,560 interactions among 14,000 proteins. The PPIM contains not only accurately predicted PPIs but also those molecular interactions collected from the literature. The database is freely available at http://comp-sysbio.org/ppim with a user-friendly powerful interface. We believe that the PPIM resource can help biologists better understand the maize crop.Maize (Zea mays) is one of the most important crops in the world. Understanding the molecular mechanisms underlying various traits of maize (e.g. response to drought and salt) is important to improve the quality and yield of the crop. Although the maize genome sequence has unraveled the gene components of the crop, most traits involve complex interactions among molecules. Some protein-protein interactions (PPIs) have been experimentally determined in maize. For example, the CENTRORADIALIS8 protein was found to interact with the floral activator DLF1 protein with yeast two-hybrid assays (Danilevskaya et al., 2008), and barren stalk1 was found to interact with barren inflorescence2 with pull-down assays (Skirpan et al., 2008). Unfortunately, unlike other model organisms, there are very few molecular interactions available for maize. Therefore, a comprehensive maize interactome map is highly demanded.Recently, with more information about maize available, it has become practical to investigate the interactions between maize molecules. For example, with accumulating gene expression data, a gene coexpression network has been built to identify gene modules that play important roles in conditions of interest. With this idea, Downs et al. (2013) constructed a gene coexpression network based on gene expression data from 50 maize tissues and identified some gene modules that are important for development. By comparing the maize and rice (Oryza sativa) coexpression networks, Ficklin and Feltus (2011) identified some conserved gene modules between the two species, indicating their essential roles in crops. With protein abundance and phosphorylation data in different maize tissues across seven developmental stages, Walley et al. (2013) built a protein coexpression network to present kinase-substrate relationships. The metabolic network MaizeCyc (Monaco et al., 2013), containing enzyme catalysts, proteins, and other metabolites, has also been constructed. Focusing on maize kernel development, the expression quantitative trait loci have been investigated with RNA sequencing data (Fu et al., 2013), and the gene regulations underlying endosperm cell differentiation have been identified (Zhan et al., 2015).Despite the above efforts to identify possible interactions between molecules, no comprehensive interactome is available for maize. Most current approaches construct gene coexpression networks; however, these only describe the associations between genes and cannot tell which genes have real interactions. Under these circumstances, we present a comprehensive Protein-Protein Interaction Database for Maize (PPIM), which provides both our predicted physical and functional interactions as well as molecular interactions collected from the literature and public databases. To our knowledge, the PPIM is the most comprehensive database for maize to date. The user-friendly powerful interface accompanying the database can help biologists better explore the database.     

PPI Finder: A Mining Tool for Human Protein-Protein Interactions

Background

The exponential increase of published biomedical literature prompts the use of text mining tools to manage the information overload automatically. One of the most common applications is to mine protein-protein interactions (PPIs) from PubMed abstracts. Currently, most tools in mining PPIs from literature are using co-occurrence-based approaches or rule-based approaches. Hybrid methods (frame-based approaches) by combining these two methods may have better performance in predicting PPIs. However, the predicted PPIs from these methods are rarely evaluated by known PPI databases and co-occurred terms in Gene Ontology (GO) database.

Methodology/Principal Findings

We here developed a web-based tool, PPI Finder, to mine human PPIs from PubMed abstracts based on their co-occurrences and interaction words, followed by evidences in human PPI databases and shared terms in GO database. Only 28% of the co-occurred pairs in PubMed abstracts appeared in any of the commonly used human PPI databases (HPRD, BioGRID and BIND). On the other hand, of the known PPIs in HPRD, 69% showed co-occurrences in the literature, and 65% shared GO terms.

Conclusions

PPI Finder provides a useful tool for biologists to uncover potential novel PPIs. It is freely accessible at http://liweilab.genetics.ac.cn/tm/.     

Protein-Protein Interaction Antagonists as Novel Inhibitors of Non-Canonical Polyubiquitylation

Background

Several pathways that control cell survival under stress, namely RNF8-dependent DNA damage recognition and repair, PCNA-dependent DNA damage tolerance and activation of NF-κB by extrinsic signals, are regulated by the tagging of key proteins with lysine 63-based polyubiquitylated chains, catalyzed by the conserved ubiquitin conjugating heterodimeric enzyme Ubc13-Uev.

Methodology/Principal Findings

By applying a selection based on in vivo protein-protein interaction assays of compounds from a combinatorial chemical library followed by virtual screening, we have developed small molecules that efficiently antagonize the Ubc13-Uev1 protein-protein interaction, inhibiting the enzymatic activity of the heterodimer. In mammalian cells, they inhibit lysine 63-type polyubiquitylation of PCNA, inhibit activation of NF-κB by TNF-α and sensitize tumor cells to chemotherapeutic agents. One of these compounds significantly inhibited invasiveness, clonogenicity and tumor growth of prostate cancer cells.

Conclusions/Significance

This is the first development of pharmacological inhibitors of non-canonical polyubiquitylation that show that these compounds produce selective biological effects with potential therapeutic applications.     

Protein-Protein Interaction Analysis Highlights Additional Loci of Interest for Multiple Sclerosis

Genetic factors play an important role in determining the risk of multiple sclerosis (MS). The strongest genetic association in MS is located within the major histocompatibility complex class II region (MHC), but more than 50 MS loci of modest effect located outside the MHC have now been identified. However, the relative candidate genes that underlie these associations and their functions are largely unknown. We conducted a protein-protein interaction (PPI) analysis of gene products coded in loci recently reported to be MS associated at the genome-wide significance level and in loci suggestive of MS association. Our aim was to identify which suggestive regions are more likely to be truly associated, which genes are mostly implicated in the PPI network and their expression profile. From three recent independent association studies, SNPs were considered and divided into significant and suggestive depending on the strength of the statistical association. Using the Disease Association Protein-Protein Link Evaluator tool we found that direct interactions among genetic products were significantly higher than expected by chance when considering both significant regions alone (p<0.0002) and significant plus suggestive (p<0.007). The number of genes involved in the network was 43. Of these, 23 were located within suggestive regions and many of them directly interacted with proteins coded within significant regions. These included genes such as SYK, IL-6, CSF2RB, FCLR3, EIF4EBP2 and CHST12. Using the gene portal BioGPS, we tested the expression of these genes in 24 different tissues and found the highest values among immune-related cells as compared to non-immune tissues (p<0.001). A gene ontology analysis confirmed the immune-related functions of these genes. In conclusion, loci currently suggestive of MS association interact with and have similar expression profiles and function as those significantly associated, highlighting the fact that more common variants remain to be found to be associated to MS.     

Local Interaction Signal Analysis Predicts Protein-Protein Binding Affinity

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A Protein-Protein Interaction Map of the Trypanosoma brucei Paraflagellar Rod

We have conducted a protein interaction study of components within a specific sub-compartment of a eukaryotic flagellum. The trypanosome flagellum contains a para-crystalline extra-axonemal structure termed the paraflagellar rod (PFR) with around forty identified components. We have used a Gateway cloning approach coupled with yeast two-hybrid, RNAi and 2D DiGE to define a protein-protein interaction network taking place in this structure. We define two clusters of interactions; the first being characterised by two proteins with a shared domain which is not sufficient for maintaining the interaction. The other cohort is populated by eight proteins, a number of which possess a PFR domain and sub-populations of this network exhibit dependency relationships. Finally, we provide clues as to the structural organisation of the PFR at the molecular level. This multi-strand approach shows that protein interactome data can be generated for insoluble protein complexes.     

A Powerful Molecular Engineering Tool Provided Efficient Chlamydomonas Mutants as Bio-Sensing Elements for Herbicides Detection

This study was prompted by increasing concerns about ecological damage and human health threats derived by persistent contamination of water and soil with herbicides, and emerging of bio-sensing technology as powerful, fast and efficient tool for the identification of such hazards. This work is aimed at overcoming principal limitations negatively affecting the whole-cell-based biosensors performance due to inadequate stability and sensitivity of the bio-recognition element. The novel bio-sensing elements for the detection of herbicides were generated exploiting the power of molecular engineering in order to improve the performance of photosynthetic complexes. The new phenotypes were produced by an in vitro directed evolution strategy targeted at the photosystem II (PSII) D1 protein of Chlamydomonas reinhardtii, using exposures to radical-generating ionizing radiation as selection pressure. These tools proved successful to identify D1 mutations conferring enhanced stability, tolerance to free-radical-associated stress and competence for herbicide perception. Long-term stability tests of PSII performance revealed the mutants capability to deal with oxidative stress-related conditions. Furthermore, dose-response experiments indicated the strains having increased sensitivity or resistance to triazine and urea type herbicides with I₅₀ values ranging from 6×10⁻⁸ M to 2×10⁻⁶ M. Besides stressing the relevance of several amino acids for PSII photochemistry and herbicide sensing, the possibility to improve the specificity of whole-cell-based biosensors, via coupling herbicide-sensitive with herbicide-resistant strains, was verified.