Combining different 'omics' technologies to map and validate protein-protein interactions in humans.

The mapping of protein-protein interactions is key to understanding biological processes. Many technologies have been reported to map interactions and these have been systematically applied in yeast. To date, the number of reported yeast protein interactions that have been truly validated by at least one other approach is low. The mapping of human protein interaction networks is even more complicated. Thus, it is unreasonable to try to map the human interactome; instead, interaction mapping in human cell lines should be focused along the lines of diseases or changes that can be associated with specific cells. In this paper, an approach for combining different 'omics' technologies to achieve efficient mapping and validation of protein interactions in human cell lines is presented.     

Yeast proteomics and protein microarrays

Our understanding of biological processes as well as human diseases has improved greatly thanks to studies on model organisms such as yeast. The power of scientific approaches with yeast lies in its relatively simple genome, its facile classical and molecular genetics, as well as the evolutionary conservation of many basic biological mechanisms. However, even in this simple model organism, systems biology studies, especially proteomic studies had been an intimidating task. During the past decade, powerful high-throughput technologies in proteomic research have been developed for yeast including protein microarray technology. The protein microarray technology allows the interrogation of protein–protein, protein–DNA, protein–small molecule interaction networks as well as post-translational modification networks in a large-scale, high-throughput manner. With this technology, many groundbreaking findings have been established in studies with the budding yeast Saccharomyces cerevisiae, most of which could have been unachievable with traditional approaches. Discovery of these networks has profound impact on explicating biological processes with a proteomic point of view, which may lead to a better understanding of normal biological phenomena as well as various human diseases.     

Functional analysis of protein kinase networks in living cells: beyond "knock-outs" and "knock-downs"

The identification of over 500 protein kinases encoded by the human genome sequence offers one measure of the importance of protein kinase networks in cell biology. High throughput technologies for inactivating genes are producing an awe-inspiring amount of data on the cellular and organismal effects of reducing the levels of individual protein kinases. Despite these technical advances, our understanding of kinase networks remains imprecise. Major challenges include correctly assigning kinases to particular networks, understanding how they are regulated, and identifying the relevant in vivo substrates. Genetic methods provide a way of addressing these questions, but their application requires understanding the nuances of how different types of mutations can affect protein kinases. The goal of this article is to provide a brief introductory primer into these issues using examples from yeast MAPK cascades and to motivate future systematic genetic analysis focusing on individual residues of protein kinases.     

大规模蛋白质相互作用组实验技术及其应用

大规模蛋白质相互作用研究的主要实验技术包括酵母双杂交技术、串联亲和纯化技术和蛋白质芯片技术,随着这些技术的不断发展和完善,科学家们在模式生物、哺乳动物、病原微生物中展开了大规模的蛋白质相互作用组研究,并进行了药物研发方面的研究,绘制了多种生物的蛋白质相互作用连锁图,揭示了多种蛋白质的新功能,为全面研究蛋白质（群）的分子作用机制、药物研发和疾病的临床预防与治疗等提供了崭新的线索。     

Challenges and rewards of interaction proteomics

The recent explosion of high throughput experimental technologies for characterizing protein interactions has generated large amounts of data describing interactions between thousands of proteins and producing genome scale views of protein assemblies. The systems level views afforded by these data hold great promise of leading to new knowledge but also involve many challenges. Deriving meaningful biological conclusions from these views crucially depends on our understanding of the approximation and biases that enter into deriving and interpreting the data. The challenges and rewards of interaction proteomics are reviewed here using as an example the latest comprehensive high throughput analyses of protein interactions in yeast.     

Constraints imposed by non‐functional protein–protein interactions on gene expression and proteome size

Crowded intracellular environments present a challenge for proteins to form functional specific complexes while reducing non‐functional interactions with promiscuous non‐functional partners. Here we show how the need to minimize the waste of resources to non‐functional interactions limits the proteome diversity and the average concentration of co‐expressed and co‐localized proteins. Using the results of high‐throughput Yeast 2‐Hybrid experiments, we estimate the characteristic strength of non‐functional protein–protein interactions. By combining these data with the strengths of specific interactions, we assess the fraction of time proteins spend tied up in non‐functional interactions as a function of their overall concentration. This allows us to sketch the phase diagram for baker's yeast cells using the experimentally measured concentrations and subcellular localization of their proteins. The positions of yeast compartments on the phase diagram are consistent with our hypothesis that the yeast proteome has evolved to operate closely to the upper limit of its size, whereas keeping individual protein concentrations sufficiently low to reduce non‐functional interactions. These findings have implication for conceptual understanding of intracellular compartmentalization, multicellularity and differentiation.     

Novel protein phosphatases in yeast.

During the last decade several novel yeast genes encoding proteins related to the PPP family of Ser/Thr protein phosphatases have been discovered and their functional characterization initiated. Most of these novel phosphatases display intriguing structural features and/or are involved in a number of important functions, such as cell cycle regulation, protein synthesis and maintenance of cellular integrity. While in some cases these genes appear to be restricted to fungi, in others similar proteins can be found in higher eukaryotes. This review will summarize the latest advances in our understanding about how these phosphatases are regulated and fulfil their functions in the yeast cell.     

The value of high quality protein-protein interaction networks for systems biology

Protein-protein interaction (PPI) networks contain a large amount of useful information for the functional characterization of proteins and promote the understanding of the complex molecular relationships that determine the phenotype of a cell. Recently, large human interaction maps have been generated with high throughput technologies such as the yeast two-hybrid system. However, they are static and incomplete and do not provide immediate clues about the cellular processes that convert genetic information into complex phenotypes. Refined multiple-aspect PPI screening and confirmation strategies will have to be put in place to increase the validity of interaction maps. Integration of interaction data with other qualitative and quantitative information (e.g. protein expression or localization data), will be required to construct networks of protein function that reflect dynamic processes in the cell. In this way, combined PPI networks can become valuable resources for a systems-level understanding of cellular processes and complex phenotypes.     

Categorizing biases in high-confidence high-throughput protein-protein interaction data sets

We characterized and evaluated the functional attributes of three yeast high-confidence protein-protein interaction data sets derived from affinity purification/mass spectrometry, protein-fragment complementation assay, and yeast two-hybrid experiments. The interacting proteins retrieved from these data sets formed distinct, partially overlapping sets with different protein-protein interaction characteristics. These differences were primarily a function of the deployed experimental technologies used to recover these interactions. This affected the total coverage of interactions and was especially evident in the recovery of interactions among different functional classes of proteins. We found that the interaction data obtained by the yeast two-hybrid method was the least biased toward any particular functional characterization. In contrast, interacting proteins in the affinity purification/mass spectrometry and protein-fragment complementation assay data sets were over- and under-represented among distinct and different functional categories. We delineated how these differences affected protein complex organization in the network of interactions, in particular for strongly interacting complexes (e.g. RNA and protein synthesis) versus weak and transient interacting complexes (e.g. protein transport). We quantified methodological differences in detecting protein interactions from larger protein complexes, in the correlation of protein abundance among interacting proteins, and in their connectivity of essential proteins. In the latter case, we showed that minimizing inherent methodology biases removed many of the ambiguous conclusions about protein essentiality and protein connectivity. We used these findings to rationalize how biological insights obtained by analyzing data sets originating from different sources sometimes do not agree or may even contradict each other. An important corollary of this work was that discrepancies in biological insights did not necessarily imply that one detection methodology was better or worse, but rather that, to a large extent, the insights reflected the methodological biases themselves. Consequently, interpreting the protein interaction data within their experimental or cellular context provided the best avenue for overcoming biases and inferring biological knowledge.     

The yeast lifecycle and DNA array technology

The genome variability and meiotic gene expression patterns in two unrelated laboratory yeast strains, SK1 and W303, have been characterized using high-density oligonucleotide arrays. The statistical analysis and comparison of the data has allowed identification of: (1) genes with functional importance to meiosis and sporulation in yeast and (2) genes expressed in a strain-specific manner. The genome-wide data also reveal potential reasons why these strains display significant differences in the ability to make fertile spores. Molecularly tagged yeast deletion strains have been used to determine the contribution of each gene to the execution of the sporulation/germination pathway. The application of genetics and the new genomic technologies have allowed a quantum jump in our understanding of yeast molecular biology. Journal of Industrial Microbiology & Biotechnology (2002) 28, 186–191 DOI: 10.1038/sj/jim/7000200 Received 05 November 2000/ Accepted in revised form 26 June 2001     

Approach of the functional evolution of duplicated genes in Saccharomyces cerevisiae using a new classification method based on protein-protein interaction data

The concept of protein function is widely used and manipulated by biologists. However, the means of the concept and its understanding may vary depending on the level of functionality one considers (molecular, cellular, physiological, etc.). Genomic studies and new high-throughput methods of the post-genomic era provide the opportunity to shed a new light on the concept of protein function: protein-protein interactions can now be considered as pieces of incomplete but still gigantic networks and the analysis of these networks will permit the emergence of a more integrated view of protein function. In this context, we propose a new functional classification method, which, unlike usual methods based on sequence homology, allows the definition of functional classes of protein based on the identity of their interacting partners. An example of such classification will be shown and discussed for a subset of Saccharomyces cerevisiae proteins, accounting for 7% of the yeast proteome. The genome of the budding yeast contains 50% of protein-coding genes that are paralogs, including 457 pairs of duplicated genes coming probably from an ancient whole genome duplication. We will comment on the functional classification of the duplicated genes when using our method and discuss the contribution of these results to the understanding of function evolution for the duplicated genes.     

A secreted protein microarray platform for extracellular protein interaction discovery

Characterization of the extracellular protein interactome has lagged far behind that of intracellular proteins, where mass spectrometry and yeast two-hybrid technologies have excelled. Improved methods for identifying receptor-ligand and extracellular matrix protein interactions will greatly accelerate biological discovery in cell signaling and cellular communication. These technologies must be able to identify low-affinity binding events that are often observed between membrane-bound coreceptor molecules during cell-cell or cell-extracellular matrix contact. Here we demonstrate that functional protein microarrays are particularly well-suited for high-throughput screening of extracellular protein interactions. To evaluate the performance of the platform, we screened a set of 89 immunoglobulin (Ig)-type receptors against a highly diverse extracellular protein microarray with 686 genes represented. To enhance detection of low-affinity interactions, we developed a rapid method to assemble bait Fc fusion proteins into multivalent complexes using protein A microbeads. Based on these screens, we developed a statistical methodology for hit calling and identification of nonspecific interactions on protein microarrays. We found that the Ig receptor interactions identified using our methodology are highly specific and display minimal off-target binding, resulting in a 70% true-positive to false-positive hit ratio. We anticipate that these methods will be useful for a wide variety of functional protein microarray users.     

Functional and topological characterization of protein interaction networks

The elucidation of the cell's large-scale organization is a primary challenge for post-genomic biology, and understanding the structure of protein interaction networks offers an important starting point for such studies. We compare four available databases that approximate the protein interaction network of the yeast, Saccharomyces cerevisiae, aiming to uncover the network's generic large-scale properties and the impact of the proteins' function and cellular localization on the network topology. We show how each database supports a scale-free, topology with hierarchical modularity, indicating that these features represent a robust and generic property of the protein interactions network. We also find strong correlations between the network's structure and the functional role and subcellular localization of its protein constituents, concluding that most functional and/or localization classes appear as relatively segregated subnetworks of the full protein interaction network. The uncovered systematic differences between the four protein interaction databases reflect their relative coverage for different functional and localization classes and provide a guide for their utility in various bioinformatics studies.     

Mammalian cells as biopharmaceutical production hosts in the age of omics

Mammalian cells are important hosts for the production of a wide range of biopharmaceuticals due to their ability to produce correctly folded and glycosylated proteins. Compared to microbes and yeast, however, the productivity of mammalian cells is low because of their comparatively slow growth rate, tendency to undergo apoptosis, and low production capacities. While much effort has been invested in the engineering of mammalian cells with superior production characteristics, the success of these approaches has been limited to date. One factor responsible for this lack of success is our limited understanding of the cellular basis for high productivity, and of how discrete mechanisms within a cell contribute to the overall phenotype. Aiming to measure and characterize all cellular components at different functional levels, omics technologies have the potential to improve our understanding of mammalian cell physiology, elucidating new targets for the generation of a superior host cell line. This review provides a comprehensive analysis of recent examples of omics studies in the context of mammalian cells as production hosts, highlighting both the challenges and successes in the application of these powerful technologies.     

Functional expression of human methionine aminopeptidase type 1 in Saccharomyces cerevisiae

We expressed recombinant human methionine aminopeptidase type 1 (MAP or MetAP) in a map1 null yeast strain to determine the extent of functional complementation between the two proteins. The human MetAP1 protein fully rescued the slow growth phenotype associated with deletion of yeast MetAP1, suggesting that the yeast and human MetAP1 proteins may have similar roles in vivo. Expression of human MetAP1 in yeast has significance in understanding the function of the human protein, studying its in vivo substrate specificity, and developing specific anti-fungal drugs to target yeast MetAP1.     

Dissecting phosphorylation networks: lessons learned from yeast

Protein phosphorylation continues to be regarded as one of the most important post-translational modifications found in eukaryotes and has been implicated in key roles in the development of a number of human diseases. In order to elucidate roles for the 518 human kinases, phosphorylation has routinely been studied using the budding yeast Saccharomyces cerevisiae as a model system. In recent years, a number of technologies have emerged to globally map phosphorylation in yeast. In this article, we review these technologies and discuss how these phosphorylation mapping efforts have shed light on our understanding of kinase signaling pathways and eukaryotic proteomic networks in general.     

Proteomic approaches for generating comprehensive protein interaction maps

The availability of complete genome sequences of numerous model organisms has initiated the development of new approaches in biological research to complement conventional biochemistry and genetics. In this context, high-throughput methods for detecting protein interactions, such as mass spectrometry and yeast two-hybrid assays, have produced vast amounts of data that can be exploited to infer protein function and regulation. In this review, we explore different genome-wide protein interaction studies and comment on their extrapolation towards understanding protein functions. It is likely that improvements of these approaches, together with more sophisticated databases and the invention of novel technologies, will help to decipher the complex interactions among proteins and to integrate interacting proteins into existing and novel cellular pathways.     

Saccharomyces cerevisiae elongation factor 2. Genetic cloning, characterization of expression, and G-domain modeling.

The elongation factor 2 (EF-2) genes of the yeast Saccharomyces cerevisiae have been cloned and characterized with the ultimate goal of gaining a better understanding of the mechanism and control of protein synthesis. Two genes (EFT1 and EFT2) were isolated by screening a bacteriophage lambda yeast genomic DNA library with an oligonucleotide probe complementary to the domain of EF-2 that contains diphthamide, the unique posttranslationally modified histidine that is specifically ADP-ribosylated by diphtheria toxin. Although EFT1 and EFT2 are located on separate chromosomes, the DNA sequences of the two genes differ at only four positions out of 2526 base pairs, and the predicted protein sequences are identical. Genetic deletion of each gene revealed that at least one functional copy of either EFT gene is required for cell viability. Messenger RNA levels of yeast EF-2 parallel cellular growth and peak in mid-log phase cultures. The EF-2 protein sequence is strikingly conserved through evolution. Yeast EF-2 is 66% identical to, and shares over 85% homology with, human EF-2. In addition, yeast and mammalian EF-2 share identical sequences at two critical functional sites: (i) the domain containing the histidine residue that is modified to diphthamide and (ii) the threonine residue that is specifically phosphorylated in vivo in mammalian cells by calmodulin-dependent protein kinase III, also known as EF-2 kinase. Furthermore, yeast EF-2 also contains the Glu-X-X-Arg-X-Ile-Thr-Ile "effector" sequence motif that is conserved among all known elongation factors, and its GTP-binding domain exhibits strong homology to the G-domain of Escherichia coli elongation factor Tu (EF-Tu) and other G-protein family members. Based upon these observations, we have modeled the G-domain of the deduced EF-2 protein sequence to the solved crystallographic structure for EF-Tu.