Genetic approaches to the study of protein-protein interactions.

This article describes genetic approaches to the study of heterologous protein-protein interactions, focusing on the yeast Saccharomyces cerevisiae as a useful eukaryotic model system. Several methods are described that can be used to search for new interactions, including extragenic suppression, multicopy suppression, synthetic lethality, and transdominant inhibition. Strategies for screening, genetic characterization, and clone identification are described, along with recent examples from the literature. In addition, genetic methods are discussed that can be used to further characterize a newly discovered protein-protein interaction. These include the creation of mutant libraries of a given protein by chemical mutagenesis or polymerase chain reaction, the production of dominant-negative mutants, and strategies for introducing these mutant alleles back into yeast for analysis. Although these genetic methods are quite powerful, they are often just a starting point for further biochemical or cell biological experiments.     

Quorum sensing intervened bacterial signaling: Pursuit of its cognizance and repression

Bacteria communicate within a system by means of a density dependent mechanism known as quorum sensing which regulate the metabolic and behavioral activities of a bacterial community. This sort of interaction occurs through a dialect of chemical signals called as autoinducers synthesized by bacteria. Bacterial quorum sensing occurs through various complex pathways depending upon specious diversity. Therefore the cognizance of quorum sensing mechanism will enable the regulation and thereby constrain bacterial communication. Inhibition strategies of quorum sensing are collectively called as quorum quenching; through which bacteria are incapacitated of its interaction with each other. Many virulence mechanism such as sporulation, biofilm formation, toxin production can be blocked by quorum quenching. Usually quorum quenching mechanisms can be broadly classified into enzymatic methods and non-enzymatic methods. Substantial understanding of bacterial communication and its inhibition enhances the development of novel antibacterial therapeutic drugs. In this review we have discussed the types and mechanisms of quorum sensing and various methods to inhibit and regulate density dependent bacterial communication.     

Systematic identification of common functional modules related to heart failure with different etiologies

The development of heart failure (HF) is a complex process that can be initiated by multiple etiologies. Identifying common functional modules associated with HF is a challenging task. Here, we developed a systems method to identify these common functional modules by integrating multiple expression profiles, protein interactions from four species, gene function annotations, and text information. We identified 1439 consistently differentially expressed genes (CDEGs) across HF with different etiologies by applying three meta-analysis methods to multiple HF-related expression profiles. Using a weighted human interaction network constructed by combining interaction data from multiple species, we extracted 60 candidate CDEG modules. We further evaluated the functional relevance of each module by using expression, interaction network, functional annotations, and text information together. Finally, five functional modules with significant biological relevance were identified. We found that almost half of the genes in these modules are hubs in the weighted network, and that these modules can accurately classify HF patients from healthy subjects. We also identified many significantly enriched biological processes that contribute to the pathophysiology of HF, including two new ones, RNA splicing and vesicle-mediated protein transport. In summary, we proposed a novel framework to analyze common functional modules related to HF with different etiologies. Our findings provide important insights into the complex mechanism of HF. Further biological experimentations should be required to validate these novel biological processes.     

Decipher the connections between proteins and phenotypes

As the outward-most representation of life, phenotype is the fundamental basis with which humans understand life and disease. But with the advent of molecular and sequencing technique and research, a growing portion of science research focuses primarily on the molecular level of life. Our understanding in molecular variations and mechanisms can only be fully utilized when they are translated into the phenotypic level. In this study, we constructed similarity network for phenotype ontology, and then applied network analysis methods to discover phenotype/disease clusters. Then, we used machine learning models to predict protein-phenotype associations. Each protein was characterized by the functional profiles of its interaction neighbors on the protein-protein interaction network. Our methods can not only predict protein-phenotype associations, but also reveal the underlying mechanisms from protein to phenotype.     

Exploiting the co-evolution of interacting proteins to discover interaction specificity

Protein interactions are fundamental to the functioning of cells, and high throughput experimental and computational strategies are sought to map interactions. Predicting interaction specificity, such as matching members of a ligand family to specific members of a receptor family, is largely an unsolved problem. Here we show that by using evolutionary relationships within such families, it is possible to predict their physical interaction specificities. We introduce the computational method of matrix alignment for finding the optimal alignment between protein family similarity matrices. A second method, 3D embedding, allows visualization of interacting partners via spatial representation of the protein families. These methods essentially align phylogenetic trees of interacting protein families to define specific interaction partners. Prediction accuracy depends strongly on phylogenetic tree complexity, as measured with information theoretic methods. These results, along with simulations of protein evolution, suggest a model for the evolution of interacting protein families in which interaction partners are duplicated in coupled processes. Using these methods, it is possible to successfully find protein interaction specificities, as demonstrated for >18 protein families.     

Computational design of protein-protein interactions

Computational protein design strategies have been developed to reengineer protein-protein interfaces in an automated, generalizable fashion. In the past two years, these methods have been successfully applied to generate chimeric proteins and protein pairs with specificities different from naturally occurring protein-protein interactions. Although there are shortcomings in current approaches, both in the way conformational space is sampled and in the energy functions used to evaluate designed conformations, the successes suggest we are now entering an era in which computational methods can be used to modulate, reengineer and design protein-protein interaction networks in living cells.     

Protein interaction: same network, different hubs

Recently, large-scale experiments have provided new insights into the complex protein interaction network in yeast. However, previous analyses have shown that the number of interacting pairs that are common to different methods is extremely low and, therefore, less informative than expected. In this article, we show that comparing the connectivities of individual proteins can reveal that a common tendency between methods has been missed by the pairwise comparison of interactions. We found significant correlations between experimental methods and also between various in silico methods. Exceptionally, a computational method, gene neighbourhood, correlates with both in silico and experimental approaches.     

Pathways for Virus Assembly around Nucleic Acids

Understanding the pathways by which viral capsid proteins assemble around their genomes could identify key intermediates as potential drug targets. In this work, we use computer simulations to characterize assembly over a wide range of capsid protein–protein interaction strengths and solution ionic strengths. We find that assembly pathways can be categorized into two classes, in which intermediates are either predominantly ordered or disordered. Our results suggest that estimating the protein–protein and the protein–genome binding affinities may be sufficient to predict which pathway occurs. Furthermore, the calculated phase diagrams suggest that knowledge of the dominant assembly pathway and its relationship to control parameters could identify optimal strategies to thwart or redirect assembly to block infection. Finally, analysis of simulation trajectories suggests that the two classes of assembly pathways can be distinguished in single-molecule fluorescence correlation spectroscopy or bulk time-resolved small-angle X-ray scattering experiments.     

Interactome data and databases: different types of protein interaction

In recent years, the biomolecular sciences have been driven forward by overwhelming advances in new biotechnological high-throughput experimental methods and bioinformatic genome-wide computational methods. Such breakthroughs are producing huge amounts of new data that need to be carefully analysed to obtain correct and useful scientific knowledge. One of the fields where this advance has become more intense is the study of the network of 'protein-protein interactions', i.e. the 'interactome'. In this short review we comment on the main data and databases produced in this field in last 5 years. We also present a rationalized scheme of biological definitions that will be useful for a better understanding and interpretation of 'what a protein-protein interaction is' and 'which types of protein-protein interactions are found in a living cell'. Finally, we comment on some assignments of interactome data to defined types of protein interaction and we present a new bioinformatic tool called APIN (Agile Protein Interaction Network browser), which is in development and will be applied to browsing protein interaction databases.     

Zeolin is a recombinant storage protein with different solubility and stability properties according to its localization in the endoplasmic reticulum or in the chloroplast

Several strategies have been exploited to maximize heterologous protein accumulation in the plant cell. Recently, it has been shown that a portion of a maize prolamin storage protein, gamma-zein, can be used for the high accumulation of a recombinant protein in novel endoplasmic reticulum (ER)-derived protein bodies of vegetative tissues. In this study, we investigate whether this protein can be expressed in the chloroplast. Our long-term purpose is to use zeolin to produce value-added proteins by fusing these polypeptides with its gamma-zein portion and targeting the recombinant proteins to the ER or to the chloroplast. We show here that zeolin accumulates in the chloroplast to lower levels than in the ER and its stability is compromised by chloroplast proteolytic activity. Co-localization of zeolin and the ER chaperone BiP in the chloroplast does not have a beneficial effect on zeolin accumulation. In this organelle, zeolin is not stored in protein bodies, nor do zeolin polypeptides seem to be linked by inter-chain disulfide bonds, which are usually formed by the six cysteine of the gamma-zein portion, indicating abnormal folding of the recombinant protein. Therefore, it is concluded that to accumulate zeolin in the chloroplast it is necessary to facilitate inter-chain disulfide bond formation.     

Towards a Hierarchical Strategy to Explore Multi-Scale IP/MS Data for Protein Complexes

Protein interaction in cells can be described at different levels. At a low interaction level, proteins function together in small, stable complexes and at a higher level, in sets of interacting complexes. All interaction levels are crucial for the living organism, and one of the challenges in proteomics is to measure the proteins at their different interaction levels. One common method for such measurements is immunoprecipitation followed by mass spectrometry (IP/MS), which has the potential to probe the different protein interaction forms. However, IP/MS data are complex because proteins, in their diverse interaction forms, manifest themselves in different ways in the data. Numerous bioinformatic tools for finding protein complexes in IP/MS data are currently available, but most tools do not provide information about the interaction level of the discovered complexes, and no tool is geared specifically to unraveling and visualizing these different levels. We present a new bioinformatic tool to explore IP/MS datasets for protein complexes at different interaction levels and show its performance on several real–life datasets. Our tool creates clusters that represent protein complexes, but unlike previous methods, it arranges them in a tree–shaped structure, reporting why specific proteins are predicted to build a complex and where it can be divided into smaller complexes. In every data analysis method, parameters have to be chosen. Our method can suggest values for its parameters and comes with adapted visualization tools that display the effect of the parameters on the result. The tools provide fast graphical feedback and allow the user to interact with the data by changing the parameters and examining the result. The tools also allow for exploring the different organizational levels of the protein complexes in a given dataset. Our method is available as GNU-R source code and includes examples at www.bdagroup.nl.     

Endocytosis of different members of the small chondroitin/dermatan sulfate proteoglycan family.

The family of small interstitial chondroitin/dermatan sulfate proteoglycans consists of at least three different molecular species: biglycan (proteoglycan I), decorin (proteoglycan II), and proteoglycan-100, which has a glycosylated core protein of about 100 kDa. The core protein of decorin has been shown to be responsible for receptor-mediated endocytosis of this proteoglycan species by a variety of mesenchymal cells. It is now demonstrated that skin fibroblasts and articular chondrocytes endocytose biglycan with an efficiency similar to that of decorin. Uptake of biglycan is also mediated by its core protein and can be inhibited by decorin in a partially competitive manner. In human fibroblasts, endosomal proteins of 51 and 26 kDa, which are known to bind decorin core protein, also interact with biglycan. This interaction can be inhibited by decorin. Bovine articular chondrocytes contained binding proteins of 48 and 25 kDa. Proteoglycan-100 can be distinguished from biglycan and decorin by its low clearance rate, which however, exceeds the rate of fluid phase endocytosis.     

The interactome as a tree--an attempt to visualize the protein-protein interaction network in yeast

The refinement and high-throughput of protein interaction detection methods offer us a protein–protein interaction network in yeast. The challenge coming along with the network is to find better ways to make it accessible for biological investigation. Visualization would be helpful for extraction of meaningful biological information from the network. However, traditional ways of visualizing the network are unsuitable because of the large number of proteins. Here, we provide a simple but information-rich approach for visualization which integrates topological and biological information. In our method, the topological information such as quasi-cliques or spoke-like modules of the network is extracted into a clustering tree, where biological information spanning from protein functional annotation to expression profile correlations can be annotated onto the representation of it. We have developed a software named PINC based on our approach. Compared with previous clustering methods, our clustering method ADJW performs well both in retaining a meaningful image of the protein interaction network as well as in enriching the image with biological information, therefore is more suitable in visualization of the network.     

The Cartographers toolbox: building bigger and better human protein interaction networks

One of the greatest challenges of the post-genomic era is theconstruction of a more comprehensive human protein interactionmap. While this process may take many years to complete, thedevelopment of stringent high throughput techniques and theemergence of complementary assays mean that the aim of buildinga detailed binary map of the human interactome is now a veryrealistic goal. In particular, methods which facilitate theanalysis of large numbers of membrane-protein interactions meanthat it will be possible to construct more extensive networks,which in turn provide new insights into the functional connectivitybetween intra- and extra-cellular processes. This is importantas many therapeutic strategies are designed to elicit effectsvia ‘tractable’ cell-surface proteins. Therefore,the construction of maps depicting the complexity of trans-cellularcommunication networks will not only improve our understandingof physiological processes, it will also aid the design of rationaltherapeutic strategies, with fewer potential side effects. Thisreview aims to provide a basic insight into the approaches currentlybeing used to construct binary human protein interaction networks,with particular reference to newer techniques, which have thepotential to extend network coverage and aid the conditionalannotation of interactome-scale protein interaction maps.      

Human platelet P-235, a talin-like actin binding protein, binds selectively to mixed lipid bilayers

The interaction of platelet talin (P-235) with mixtures of dimyristoylphosphatidylcholine (DMPC), dimyristoylphosphatidylglycerol (DMPG) and dimyristoylphosphatidylserine (DMPS) as well as with pure lipids was studied in reconstituted lipid bilayers. Incorporation of platelet talin into vesicles was achieved by self-assembly during cycles of freeze-thawing of co-dispersions containing vesicles and the purified protein. The yield of protein incorporation as a function of lipid composition was determined by measuring the protein/lipid ratio using protein assay, phosphate determination and gel electrophoresis in parallel. Protein-lipid interactions are monitored by high sensitive differential scanning calorimetry (DSC) measuring (i) the shifts of transition states delta Ts* and delta Tl*, where Ts represents the solidus line, the onset of lipid chain melting, and Tl the liquidus line, the endpoint of chain melting, and (ii) the heats of transition. Cytoplasmic talin differs from a membrane bound form by its ability and mode of lipid interaction. The latter partially penetrates into the hydrophobic region of the bilayer, which renders a low incorporation rate even into neutral lipids. This interaction is greatly enhanced in the presence of charged lipids: a marked shift of Tl occurs due to a selective electrostatic interaction of the protein with the membrane surface. Evidence for a selective binding is also provided by Fourier transform infrared spectroscopy (FTIR). Right-side-out oriented platelet talin can be cleaved by proteinases, which truncate the extrinsic electrostatic binding domain but not the hydrophobic. In addition, reconstituted platelet talin, like in vivo, can be cleaved by thrombin. The interaction of cytoplasmic platelet talin with lipid bilayers is purely electrostatic. Our data suggest that protein reconstitution by freeze-thawing is an equilibrium process and that the protein distribution between the membrane and water is determined by the Nernst distribution law. Consequently, the work of protein transfer from water into the bilayer can be measured as a function of charged lipids.     

Biochemical approaches for discovering protein-protein interactions

Protein–protein interactions or protein complexes are integral in nearly all cellular processes, ranging from metabolism to structure. Elucidating both individual protein associations and complex protein interaction networks, while challenging, is an essential goal of functional genomics. For example, discovering interacting partners for a 'protein of unknown function' can provide insight into actual function far beyond what is possible with sequence-based predictions, and provide a platform for future research. Synthetic genetic approaches such as two-hybrid screening often reveal a perplexing array of potential interacting partners for any given target protein. It is now known, however, that this type of anonymous screening approach can yield high levels of false-positive results, and therefore putative interactors must be confirmed by independent methods. In vitro biochemical strategies for identifying interacting proteins are varied and time-honored, some being as old as the field of protein chemistry itself. Herein we discuss five biochemical approaches for isolating and characterizing protein–protein interactions in vitro : co-immunoprecipitation, blue native gel electrophoresis, in vitro binding assays, protein cross-linking, and rate-zonal centrifugation. A perspective is provided for each method, and where appropriate specific, trial-tested methods are included.     

Protein complex prediction based on maximum matching with domain–domain interaction

With the development of high-throughput methods for identifying protein–protein interactions, large scale interaction networks are available. Computational methods to analyze the networks to detect functional modules as protein complexes are becoming more important. However, most of the existing methods only make use of the protein–protein interaction networks without considering the structural limitations of proteins to bind together. In this paper, we design a new protein complex prediction method by extending the idea of using domain–domain interaction information. Here we formulate the problem into a maximum matching problem (which can be solved in polynomial time) instead of the binary integer linear programming approach (which can be NP-hard in the worst case). We also add a step to predict domain–domain interactions which first searches the database Pfam using the hidden Markov model and then predicts the domain–domain interactions based on the database DOMINE and InterDom which contain confirmed DDIs. By adding the domain–domain interaction prediction step, we have more edges in the DDI graph and the recall value is increased significantly (at least doubled) comparing with the method of Ozawa et al. (2010) [1] while the average precision value is slightly better. We also combine our method with three other existing methods, such as COACH, MCL and MCODE. Experiments show that the precision of the combined method is improved. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.     

Fluorescence strategies for high-throughput quantification of protein interactions

Advances in high-throughput characterization of protein networks in vivo have resulted in large databases of unexplored protein interactions that occur during normal cell function. Their further characterization requires quantitative experimental strategies that are easy to implement in laboratories without specialized equipment. We have overcome many of the previous limitations to thermodynamic quantification of protein interactions, by developing a series of in-solution fluorescence-based strategies. These methods have high sensitivity, a broad dynamic range, and can be performed in a high-throughput manner. In three case studies we demonstrate how fluorescence (de)quenching and fluorescence resonance energy transfer can be used to quantitatively probe various high-affinity protein-DNA and protein-protein interactions. We applied these methods to describe the preference of linker histone H1 for nucleosomes over DNA, the ionic dependence of the DNA repair enzyme PARP1 in DNA binding, and the interaction between the histone chaperone Nap1 and the histone H2A-H2B heterodimer.     

鉴定cyclic di-GMP效应蛋白：高通量筛选策略与实验验证方法

环二鸟苷单磷酸(cyclic di-GMP或c-di-GMP)是细菌细胞中广泛存在的第二信使,调控细菌生物被膜发育、致病力、运动性、胞外多糖产生及细胞周期在内的诸多重要生理表型。c-di-GMP通过结合多种类型的效应子(包括核糖开关或效应蛋白)来发挥调控功能。由于c-di-GMP分子在构象上具有多变性,其结合的效应子同样具有多样性。新型效应蛋白的筛选、鉴定是当前细菌信号转导领域的研究热点和难点,也是解析c-di-GMP调控机制的首要环节。本文在阐述c-di-GMP结合不同类型的效应蛋白并调控细菌生物被膜发育的基础上,综述了目前筛选c-di-GMP效应蛋白的方法,包括遗传筛选、亲和色谱结合质谱鉴定、DRa CALA系统鉴定以及基于分子对接的预测等。同时,对验证c-di-GMP效应蛋白的技术,如等温微量热滴定、表面等离子共振、微量热泳动在内的多种验证方法进行了总结,对比了这些策略和方法在应用上的优、缺点,为在细菌及其真核宿主基因组水平鉴定c-di-GMP效应蛋白的研究提供参考。     

Protein-protein interaction networks: from interactions to networks

The goal of interaction proteomics that studies the protein-protein interactions of all expressed proteins is to understand biological processes that are strictly regulated by these interactions. The availability of entire genome sequences of many organisms and high-throughput analysis tools has led scientists to study the entire proteome (Pandey and Mann, 2000). There are various high-throughput methods for detecting protein interactions such as yeast two-hybrid approach and mass spectrometry to produce vast amounts of data that can be utilized to decipher protein functions in complicated biological networks. In this review, we discuss recent developments in analytical methods for large-scale protein interactions and the future direction of interaction proteomics.