大规模酵母双杂交技术研究蛋白质相互作用的应用

简介了酵母双杂交技术原理, 总结了酵母双杂交技术大规模筛选蛋白质相互作用的基础、应用及存在的问题。因为大规模酵母双杂交技术结果有大量假阳性及假阴性问题, 因此, 有条件情况下有必要同时开展其他方法的大规模蛋白相互作用研究, 以构建规模更大可信度更高的蛋白质相互作用网络图。     

Roles for the two-hybrid system in exploration of the yeast protein interactome

Comprehensive analysis of protein-protein interactions is a challenging endeavor of functional proteomics and has been best explored in the budding yeast. The yeast protein interactome analysis was achieved first by using the yeast two-hybrid system in a proteome-wide scale and next by large-scale mass spectrometric analysis of affinity-purified protein complexes. While these interaction data have led to a number of novel findings and the emergence of a single huge network containing thousands of proteins, they suffer many false signals and fall short of grasping the entire interactome. Thus, continuous efforts are necessary in both bioinformatics and experimentation to fully exploit these data and to proceed another step forward to the goal. Computational tools to integrate existing biological knowledge buried in literature and various functional genomic data with the interactome data are required for biological interpretation of the huge protein interaction network. Novel experimental methods have to be developed to detect weak, transient interactions involving low abundance proteins as well as to obtain clues to the biological role for each interaction. Since the yeast two-hybrid system can be used for the mapping of the interaction domains and the isolation of interaction-defective mutants, it would serve as a technical basis for the latter purpose, thereby playing another important role in the next phase of protein interactome research.     

Increasing confidence of protein interactomes using network topological metrics

MOTIVATION: Experimental limitations in high-throughput protein-protein interaction detection methods have resulted in low quality interaction datasets that contained sizable fractions of false positives and false negatives. Small-scale, focused experiments are then needed to complement the high-throughput methods to extract true protein interactions. However, the naturally vast interactomes would require much more scalable approaches. RESULTS: We describe a novel method called IRAP* as a computational complement for repurification of the highly erroneous experimentally derived protein interactomes. Our method involves an iterative process of removing interactions that are confidently identified as false positives and adding interactions detected as false negatives into the interactomes. Identification of both false positives and false negatives are performed in IRAP* using interaction confidence measures based on network topological metrics. Potential false positives are identified amongst the detected interactions as those with very low computed confidence values, while potential false negatives are discovered as the undetected interactions with high computed confidence values. Our results from applying IRAP* on large-scale interaction datasets generated by the popular yeast-two-hybrid assays for yeast, fruit fly and worm showed that the computationally repurified interaction datasets contained potentially lower fractions of false positive and false negative errors based on functional homogeneity. AVAILABILITY: The confidence indices for PPIs in yeast, fruit fly and worm as computed by our method can be found at our website http://www.comp.nus.edu.sg/~chenjin/fpfn.     

Signature-tagged mutagenesis: barcoding mutants for genome-wide screens

DNA signature tags (molecular barcodes) facilitate functional screens by identifying mutants in mixed populations that have a reduced or increased adaptation to a particular environment. Many innovative adaptations and refinements in the technology have been described since its original use with Salmonella; they have yielded a wealth of information on a broad range of biological processes--mainly in bacteria, but also in yeast and other fungi, viruses, parasites and, most recently, in mammalian cells. By combining whole-genome microarrays and comprehensive ordered libraries of mutants, high-throughput functional screens can now be achieved on a genomic scale.     

Can simple codon pair usage predict protein-protein interaction?

Deciphering functional interactions between proteins is one of the great challenges in biology. Sequence-based homology-free encoding schemes have been increasingly applied to develop promising protein-protein interaction (PPI) predictors by means of statistical or machine learning methods. Here we analyze the relationship between codon pair usage and PPIs in yeast. We show that codon pair usage of interacting protein pairs differs significantly from randomly expected. This motivates the development of a novel approach for predicting PPIs, with codon pair frequency difference as input to a Support Vector Machine predictor, termed as CCPPI. 10-fold cross-validation tests based on yeast PPI datasets with balanced positive-to-negative ratios indicate that CCPPI performs better than other sequence-based encoding schemes. Moreover, it ranks the best when tested on an unbalanced large-scale dataset. Although CCPPI is subjected to high false positive rates like many PPI predictors, statistical analyses of the predicted true positives confirm that the success of CCPPI is partly ascribed to its capability to capture proteomic co-expression and functional similarities between interacting protein pairs. Our findings suggest that codon pairs of interacting protein pairs evolve in a coordinated manner and consequently they provide additional information beyond amino acids-based encoding schemes. CCPPI has been made freely available at: http://protein.cau.edu.cn/ccppi.     

Approaches to detecting false positives in yeast two-hybrid systems

While many novel associations predicted by two-hybrid library screens reflect actual biological associations of two proteins in vivo, at times the functional co-relevance of two proteins scored as interacting in the two-hybrid system is unlikely. The reason for this positive score remains obscure, which leads to designating such clones as false positives. After investigating the effect of over-expressing a series of putative false positives in yeast, we determined that expression of some of these clones induces an array of biological effects in yeast, including altered growth rate and cell permeability, that bias perceived activity of LacZ reporters. Based on these observations, we identify four simple strategies that can assist in determining whether a protein is likely to have been selected in a two-hybrid screen because of indirect metabolic effects.     

Identification of gene encoding Plasmodium knowlesi phosphatidylserine decarboxylase by genetic complementation in yeast and characterization of in vitro maturation of encoded enzyme

The 23-megabase genome of Plasmodium falciparum, the causative agent of severe human malaria, contains ～5300 genes, most of unknown function or lacking homologs in other organisms. Identification of these gene functions will help in the discovery of novel targets for the development of antimalarial drugs and vaccines. The P. falciparum genome is unusually A+T-rich, which hampers cloning and expressing these genes in heterologous systems for functional analysis. The large repertoire of genetic tools available for Saccharomyces cerevisiae makes this yeast an ideal system for large scale functional complementation analyses of parasite genes. Here, we report the construction of a cDNA library from P. knowlesi, which has a lower A+T content compared with P. falciparum. This library was applied in a yeast complementation assay to identify malaria genes involved in the decarboxylation of phosphatidylserine. Transformation of a psd1Δpsd2Δdpl1Δ yeast strain, defective in phosphatidylethanolamine synthesis, with the P. knowlesi library led to identification of a new parasite phosphatidylserine decarboxylase (PkPSD). Unlike phosphatidylserine decarboxylase enzymes from other eukaryotes that are tightly associated with membranes, the PkPSD enzyme expressed in yeast was equally distributed between membrane and soluble fractions. In vitro studies reveal that truncated forms of PkPSD are soluble and undergo auto-endoproteolytic maturation in a phosphatidylserine-dependent reaction that is inhibited by other anionic phospholipids. This study defines a new system for probing the function of Plasmodium genes by library-based genetic complementation and its usefulness in revealing new biochemical properties of encoded proteins.     

Predicting functional gene links from phylogenetic-statistical analyses of whole genomes

An important element of the developing field of proteomics is to understand protein-protein interactions and other functional links amongst genes. Across-species correlation methods for detecting functional links work on the premise that functionally linked proteins will tend to show a common pattern of presence and absence across a range of genomes. We describe a maximum likelihood statistical model for predicting functional gene linkages. The method detects independent instances of the correlated gain or loss of pairs of proteins on phylogenetic trees, reducing the high rates of false positives observed in conventional across-species methods that do not explicitly incorporate a phylogeny. We show, in a dataset of 10,551 protein pairs, that the phylogenetic method improves by up to 35% on across-species analyses at identifying known functionally linked proteins. The method shows that protein pairs with at least two to three correlated events of gain or loss are almost certainly functionally linked. Contingent evolution, in which one gene's presence or absence depends upon the presence of another, can also be detected phylogenetically, and may identify genes whose functional significance depends upon its interaction with other genes. Incorporating phylogenetic information improves the prediction of functional linkages. The improvement derives from having a lower rate of false positives and from detecting trends that across-species analyses miss. Phylogenetic methods can easily be incorporated into the screening of large-scale bioinformatics datasets to identify sets of protein links and to characterise gene networks.     

Cell wall polysaccharides: before and after autolysis of brewer’s yeast

Brewer’s yeast is used in production of beer since millennia, and it is receiving increased attention because of its distinct fermentation ability and other biological properties. During fermentation, autolysis occurs naturally at the end of growth cycle of yeast. Yeast cell wall provides yeast with osmotic integrity and holds the cell shape upon the cell wall stresses. The cell wall of yeast consists of β-glucans, chitin, mannoproteins, and proteins that cross linked with glycans and a glycolipid anchor. The variation in composition and amount of cell wall polysaccharides during autolysis in response to cell wall stress, laying significant impacts on the autolysis ability of yeast, either benefiting or destroying the flavor of final products. On the other hand, polysaccharides from yeast cell wall show outstanding health effects and are recommended to be used in functional foods. This article reviews the influence of cell wall polysaccharides on yeast autolysis, covering cell wall structure changings during autolysis, and functions and possible applications of cell wall components derived from yeast autolysis.     

Integration of genomic datasets to predict protein complexes in yeast

The ultimate goal of functional genomics is to define the function of all the genes in the genome of an organism. A large body of information of the biological roles of genes has been accumulated and aggregated in the past decades of research, both from traditional experiments detailing the role of individual genes and proteins, and from newer experimental strategies that aim to characterize gene function on a genomic scale.It is clear that the goal of functional genomics can only be achieved by integrating information and data sources from the variety of these different experiments. Integration of different data is thus an important challenge for bioinformatics.The integration of different data sources often helps to uncover non-obvious relationships between genes, but there are also two further benefits. First, it is likely that whenever information from multiple independent sources agrees, it should be more valid and reliable. Secondly, by looking at the union of multiple sources, one can cover larger parts of the genome. This is obvious for integrating results from multiple single gene or protein experiments, but also necessary for many of the results from genome-wide experiments since they are often confined to certain (although sizable) subsets of the genome.In this paper, we explore an example of such a data integration procedure. We focus on the prediction of membership in protein complexes for individual genes. For this, we recruit six different data sources that include expression profiles, interaction data, essentiality and localization information. Each of these data sources individually contains some weakly predictive information with respect to protein complexes, but we show how this prediction can be improved by combining all of them. Supplementary information is available at http://bioinfo.mbb.yale.edu/integrate/interactions/.Abbreviations: TP: true possitive; TN: true negative; FP: false positive; FN: false negative; Y2H: yeast two-hybrid.     

Phenotypic Activation to Discover Biological Pathways and Kinase Substrates

Observing the effects of gene perturbation on cells or organisms has long been a standard strategy in biological research. We developed a genome-wide gene overexpression library as a new tool for large-scale functional analysis in budding yeast. Previous large-scale genetic studies have focused on applications of the deletion mutant collection, which has arguably revolutionized the functional characterization of yeast genes. While extremely powerful, deletion mutant experiments are generally limited to the characterization of loss-of-function phenotypes. We have explored the potential for using the Synthetic Genetic Array (SGA) method, a platform for high-throughput genetic analysis, with a genome-wide “overexpression array”, in which each strain on the array overexpresses a unique yeast gene. The overexpression array enables gain-of-function phenotypes to be examined on a large scale, providing a unique insight into gene function and a novel source of reagents for the global mapping of genetic networks and functional relationships amongst genes and pathways. Understanding the molecular bases of overexpression phenotypes should also shed new light on the nature of genetic dominance.     

Predicting co-complexed protein pairs from heterogeneous data

Proteins do not carry out their functions alone. Instead, they often act by participating in macromolecular complexes and play different functional roles depending on the other members of the complex. It is therefore interesting to identify co-complex relationships. Although protein complexes can be identified in a high-throughput manner by experimental technologies such as affinity purification coupled with mass spectrometry (APMS), these large-scale datasets often suffer from high false positive and false negative rates. Here, we present a computational method that predicts co-complexed protein pair (CCPP) relationships using kernel methods from heterogeneous data sources. We show that a diffusion kernel based on random walks on the full network topology yields good performance in predicting CCPPs from protein interaction networks. In the setting of direct ranking, a diffusion kernel performs much better than the mutual clustering coefficient. In the setting of SVM classifiers, a diffusion kernel performs much better than a linear kernel. We also show that combination of complementary information improves the performance of our CCPP recognizer. A summation of three diffusion kernels based on two-hybrid, APMS, and genetic interaction networks and three sequence kernels achieves better performance than the sequence kernels or diffusion kernels alone. Inclusion of additional features achieves a still better ROC(50) of 0.937. Assuming a negative-to-positive ratio of 600ratio1, the final classifier achieves 89.3% coverage at an estimated false discovery rate of 10%. Finally, we applied our prediction method to two recently described APMS datasets. We find that our predicted positives are highly enriched with CCPPs that are identified by both datasets, suggesting that our method successfully identifies true CCPPs. An SVM classifier trained from heterogeneous data sources provides accurate predictions of CCPPs in yeast. This computational method thereby provides an inexpensive method for identifying protein complexes that extends and complements high-throughput experimental data.     

Virtual identification of essential proteins within the protein interaction network of yeast

Topological analysis of large scale protein-protein interaction networks (PINs) is important for understanding the organizational and functional principles of individual proteins. The number of interactions that a protein has in a PIN has been observed to be correlated with its indispensability. Essential proteins generally have more interactions than the nonessential ones. We show here that the lethality associated with removal of a protein from the yeast proteome correlates with different centrality measures of the nodes in the PIN, such as the closeness of a protein to many other proteins, or the number of pairs of proteins which need a specific protein as an intermediary in their communications, or the participation of a protein in different protein clusters in the PIN. These measures are significantly better than random selection in identifying essential proteins in a PIN. Centrality measures based on graph spectral properties of the network, in particular the subgraph centrality, show the best performance in identifying essential proteins in the yeast PIN. Subgraph centrality gives important structural information about the role of individual proteins, and permits the selection of possible targets for rational drug discovery through the identification of essential proteins in the PIN.     

The cellular robustness by genetic redundancy in budding yeast

The frequent dispensability of duplicated genes in budding yeast is heralded as a hallmark of genetic robustness contributed by genetic redundancy. However, theoretical predictions suggest such backup by redundancy is evolutionarily unstable, and the extent of genetic robustness contributed from redundancy remains controversial. It is anticipated that, to achieve mutual buffering, the duplicated paralogs must at least share some functional overlap. However, counter-intuitively, several recent studies reported little functional redundancy between these buffering duplicates. The large yeast genetic interactions released recently allowed us to address these issues on a genome-wide scale. We herein characterized the synthetic genetic interactions for ～500 pairs of yeast duplicated genes originated from either whole-genome duplication (WGD) or small-scale duplication (SSD) events. We established that functional redundancy between duplicates is a pre-requisite and thus is highly predictive of their backup capacity. This observation was particularly pronounced with the use of a newly introduced metric in scoring functional overlap between paralogs on the basis of gene ontology annotations. Even though mutual buffering was observed to be prevalent among duplicated genes, we showed that the observed backup capacity is largely an evolutionarily transient state. The loss of backup capacity generally follows a neutral mode, with the buffering strength decreasing in proportion to divergence time, and the vast majority of the paralogs have already lost their backup capacity. These observations validated previous theoretic predictions about instability of genetic redundancy. However, departing from the general neutral mode, intriguingly, our analysis revealed the presence of natural selection in stabilizing functional overlap between SSD pairs. These selected pairs, both WGD and SSD, tend to have decelerated functional evolution, have higher propensities of co-clustering into the same protein complexes, and share common interacting partners. Our study revealed the general principles for the long-term retention of genetic redundancy.