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.     

Global approaches to protein-protein interactions

The availability of complete, annotated genome sequences for a variety of eukaryotic organisms has paved the way for a paradigm shift in biomedical research from the 'one gene-one hypothesis' approach to more global, systematic strategies that analyse genes or proteins on a genome- and proteome-wide scale. One daunting task in the post-genome era is to determine how the complement of expressed cellular proteins - the proteome - is organised into functional, higher-order networks, by mapping all constitutive and dynamic protein-protein interactions. Traditionally, reductionist approaches have typically focused on a few, selected gene products and their interactions in a particular physiological context. In contrast, more holistic strategies aim at understanding complex biological systems, for example global protein-protein interaction networks on a cellular or organismal level. Several large-scale proteomics technologies have been developed to generate comprehensive, cellular protein-protein interaction maps.     

Computational approaches to protein-protein interaction

The interactions between proteins allow the cell's life. A number of experimental, genome-wide, high-throughput studies have been devoted to the determination of protein-protein interactions and the consequent interaction networks. Here, the bioinformatics methods dealing with protein-protein interactions and interaction network are overviewed. 1. Interaction databases developed to collect and annotate this immense amount of data; 2. Automated data mining techniques developed to extract information about interactions from the published literature; 3. Computational methods to assess the experimental results developed as a consequence of the finding that the results of high-throughput methods are rather inaccurate; 4. Exploitation of the information provided by protein interaction networks in order to predict functional features of the proteins; and 5. Prediction of protein-protein interactions.     

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.     

Using protein-protein interaction network information to predict the subcellular locations of proteins in budding yeast

The information of protein subcellular localization is vitally important for in-depth understanding the intricate pathways that regulate biological processes at the cellular level. With the rapidly increasing number of newly found protein sequence in the Post-Genomic Age, many automated methods have been developed attempting to help annotate their subcellular locations in a timely manner. However, very few of them were developed using the protein-protein interaction (PPI) network information. In this paper, we have introduced a new concept called "tethering potential" by which the PPI information can be effectively fused into the formulation for protein samples. Based on such a network frame, a new predictor called Yeast-PLoc has been developed for identifying budding yeast proteins among their 19 subcellular location sites. Meanwhile, a purely sequence-based approach, called the "hybrid-property" method, is integrated into Yeast-PLoc as a fall-back to deal with those proteins without sufficient PPI information. The overall success rate by the jackknife test on the 4,683 yeast proteins in the training dataset was 70.25%. Furthermore, it was shown that the success rate by Yeast- PLoc on an independent dataset was remarkably higher than those by some other existing predictors, indicating that the current approach by incorporating the PPI information is quite promising. As a user-friendly web-server, Yeast-PLoc is freely accessible at http://yeastloc.biosino.org/.     

Evolvability of yeast protein-protein interaction interfaces

The functional importance of protein-protein interactions indicates that there should be strong evolutionary constraint on their interaction interfaces. However, binding interfaces are frequently affected by amino acid replacements. Change due to coevolution within interfaces can contribute to variability but is not ubiquitous. An alternative explanation for the ability of surfaces to accept replacements may be that many residues can be changed without affecting the interaction. Candidates for these types of residues are those that make interchain interaction only through the protein main chain, β-carbon, or associated hydrogen atoms. Since almost all residues have these atoms, we hypothesize that this subset of interface residues may be more easily substituted than those that make interactions through other atoms. We term such interactions "residue type independent." Investigating this hypothesis, we find that nearly a quarter of residues in protein interaction interfaces make exclusively interchain residue-type-independent contacts. These residues are less structurally constrained and less conserved than residues making residue-type-specific interactions. We propose that residue-type-independent interactions allow substitutions in binding interfaces while the specificity of binding is maintained.     

Analysing protein-protein interactions with the yeast two-hybrid system

Plant research is moving into the post-genomic era. Proteomic-based strategies are now being developed to study functional aspects of the genes predicted from the various genome-sequencing initiatives. All biological processes depend on interactions formed between proteins and the mapping of such interactions on a global scale is providing interesting functional insights. One of the techniques that has proved itself invaluable in the mapping of protein-protein interactions is the yeast two-hybrid system. This system is a sensitive molecular genetic approach for studying protein-protein interactions in vivo. In this review we will introduce the yeast two-hybrid system, discuss modifications of the system that may be of interest to the plant science community and suggest potential applications of the technology.     

Protein coadaptation and the design of novel approaches to identify protein-protein interactions

Proteins rarely function in isolation but they form part of complex networks of interactions with other proteins within or among cells. The importance of a particular protein for cell viability is directly dependent upon the number of interactions where it participates and the function it performs: the larger the number of interactions of a protein the greater its functional importance is for the cell. With the advent of genome sequencing and "omics" technologies it became feasible conducting large-scale searches for protein interacting partners. Unfortunately, the accuracy of such analyses has been underwhelming owing to methodological limitations and to the inherent complexity of protein interactions. In addition to these experimental approaches, many computational methods have been developed to identify protein-protein interactions by assuming that interacting proteins coevolve resulting from the coadaptation dynamics between the amino acids of their interacting faces. We review the main technological advances made in the field of interactomics and discuss the feasibility of computational methods to identify protein-protein interactions based on the estimation of coevolution. As proof-of-concept, we present a classical case study: the interactions of cell surface proteins (receptors) and their ligands. Finally, we take this discussion one step forward to include interactions between organisms and species to understand the generation of biological complexity. Development of technologies for accurate detection of protein-protein interactions may shed light on processes that go from the fine-tuning of pathways and metabolic networks to the emergence of biological complexity.     

Social behavior of the yeast protein-protein interaction network

Protein-protein interaction networks are useful in contextual annotation of protein function and in general to achieve a system-level understanding of cellular behavior. This work reports on the social behavior of the yeast protein-protein interaction network and concludes that it is non-random. This work, while providing an analysis of organization of genes into functional societies, can potentially be useful in assessing the accuracy of contextual gene annotation based on such interaction networks.     

A network of protein-protein interactions in yeast

A global analysis of 2,709 published interactions between proteins of the yeast Saccharomyces cerevisiae has been performed, enabling the establishment of a single large network of 2,358 interactions among 1,548 proteins. Proteins of known function and cellular location tend to cluster together, with 63% of the interactions occurring between proteins with a common functional assignment and 76% occurring between proteins found in the same subcellular compartment. Possible functions can be assigned to a protein based on the known functions of its interacting partners. This approach correctly predicts a functional category for 72% of the 1,393 characterized proteins with at least one partner of known function, and has been applied to predict functions for 364 previously uncharacterized proteins.     

Development of a high-throughput yeast two-hybrid screening system to study protein-protein interactions in plants

We have developed a high-throughput yeast two-hybrid screening system (HTP-YTH) that incorporates yeast gap-repair cloning, multiple positive ( ADE2, HIS3, lacZ) and negative ( URA3-based) selection schemes to reduce the incidence of negative and false positive clones, and automation of laboratory procedures to increase throughput. This HTP-YTH system has been applied to the study of protein-protein interactions that are involved in rice defense signal transduction pathways. More than 100 genes involved in plant defense responses were selected from DuPont's rice expressed sequence tag (EST) databases as baits for HTP-YTH screening. Results from YTH screening of eight of these rice genes are presented in this paper. Not only have we identified known protein-protein interactions, but we have also discovered novel interactions, which may ultimately reveal the regulatory network of host defense signal transduction pathways. We have demonstrated that our HTP-YTH method can be used to map protein-protein interaction networks and signal transduction pathways in any system. In combination with other approaches, such efficient YTH screens can help us systemically to study the functions of known and unknown genes in the genomics era.     

A systematic study of yeast sterol biosynthetic protein-protein interactions using the split-ubiquitin system

Sterol biosynthesis occurs in the ER and most sterol biosynthetic enzymes have transmembrane domains. However, due to difficulties in characterizing membrane protein-protein interactions, the nature of the sterol biosynthetic complex as well as in vivo interactions between various enzymes have not been described. We employed a split-ubiquitin membrane protein yeast two-hybrid system to characterize interactions between sterol biosynthetic proteins. Fourteen bait constructs were co-transformed into a reporter yeast strain with 14 prey constructs representing all sterol enzymatic reactions beginning with the synthesis of squalene. Our results not only confirmed several previous interactions, but also allowed us to identify novel interactions. Based on these results, ergosterol biosynthetic enzymes display specific protein-protein interactions forming a functional complex we designate, the ergosome. In this complex, Erg11p, Erg25p, Erg27p, and Erg28p appear to form a core center that can interact with other enzymes in the pathway. Also Erg24p and Erg2p, two enzymes that are sensitive to morpholine antifungals, appear to interact with one another; however, the profile of protein interaction partners appears to be unique. Erg2p and Erg3p, two enzymes catalyzing sequential reactions also appear to have different interaction partners. Our results provide a working model as to how sterol biosynthetic enzymes are topologically organized not only in yeast but in plant and animal systems that share many of these biosynthetic reactions.     

Protein bipartivity and essentiality in the yeast protein-protein interaction network

Protein-protein interaction networks (PINs) are structured by means of a few highly connected proteins linked to a large number of less-connected ones. Essential proteins have been found to be more abundant among these highly connected proteins. Here we demonstrate that the likelihood that removal of a protein in a PIN will prove lethal to yeast correlates with the lack of bipartivity of the protein. A protein is bipartite if it can be partitioned in such a way that there are two groups of proteins with intergroup, but not intragroup, interactions. The abundance of essential proteins found among the least bipartite proteins clearly exceeds that found among the most connected ones. For instance, among the top 50 proteins ranked by their lack of bipartivity 62% are essential proteins. However, this percentage is only 38% for proteins ranked according to their number of interactions. Protein bipartivity also surpasses another 5 measures of protein centrality in yeast PIN in identifying essential proteins and doubles the number of essential proteins selected at random. We propose a possible mechanism for the evolution of essential proteins in yeast PIN based on the duplication-divergence scheme. We conclude that a replica protein evolving from a nonbipartite target will also be nonbipartite with high probability. Consequently, these new replicas evolving from nonbipartite (essential) targets will with high probability be essential.     

Integrative features of the yeast phosphoproteome and protein-protein interaction map

Following recent advances in high-throughput mass spectrometry (MS)-based proteomics, the numbers of identified phosphoproteins and their phosphosites have greatly increased in a wide variety of organisms. Although a critical role of phosphorylation is control of protein signaling, our understanding of the phosphoproteome remains limited. Here, we report unexpected, large-scale connections revealed between the phosphoproteome and protein interactome by integrative data-mining of yeast multi-omics data. First, new phosphoproteome data on yeast cells were obtained by MS-based proteomics and unified with publicly available yeast phosphoproteome data. This revealed that nearly 60% of ～6,000 yeast genes encode phosphoproteins. We mapped these unified phosphoproteome data on a yeast protein-protein interaction (PPI) network with other yeast multi-omics datasets containing information about proteome abundance, proteome disorders, literature-derived signaling reactomes, and in vitro substratomes of kinases. In the phospho-PPI, phosphoproteins had more interacting partners than nonphosphoproteins, implying that a large fraction of intracellular protein interaction patterns (including those of protein complex formation) is affected by reversible and alternative phosphorylation reactions. Although highly abundant or unstructured proteins have a high chance of both interacting with other proteins and being phosphorylated within cells, the difference between the number counts of interacting partners of phosphoproteins and nonphosphoproteins was significant independently of protein abundance and disorder level. Moreover, analysis of the phospho-PPI and yeast signaling reactome data suggested that co-phosphorylation of interacting proteins by single kinases is common within cells. These multi-omics analyses illuminate how wide-ranging intracellular phosphorylation events and the diversity of physical protein interactions are largely affected by each other.     

Chromatographic methods to study protein-protein interactions.

Proteins and enzymes are now generally thought to be organized within the cell to form clusters in a dynamic and versatile way, and heterologous protein-protein interactions are believed to be involved in virtually all cellular events. Therefore we need appropriate tools to detect and study such interactions. Chromatographic techniques prove to be well suited for this kind of investigation. Real complexes formed between proteins can be studied by classic gel filtration. When enzymes are studied, active enzyme gel chromatography is a useful alternative. A variant of classic gel filtration is gel filtration equilibrium analysis, which is similar to equilibrium dialysis. When the association formed is only dynamic and equilibrates very rapidly, either the Hummel-Dryer method of equilibrium gel filtration or large-zone equilibrium filtration sometimes allows the interactions to be analyzed, both qualitatively and quantitatively. Very often, however, interactions between enzymes and proteins can only be evidenced in vitro in media that mimic the intracellular situation. Immobilized proteins are excellent tools for this type of research. Several examples are indeed known where the immobilization of an enzyme on a solid support does not affect its real properties, but rather changes its environment in such a way that the diffusion becomes limiting. Affinity chromatography using immobilized proteins allows the analysis of heterologous protein-protein interactions, both qualitatively and quantitatively. A useful alternative appears to be affinity electrophoresis. The latter technique, however, is exclusively qualitative. All these techniques are described and illustrated with examples taken from the literature.     

Detecting functional modules in the yeast protein-protein interaction network

MOTIVATION: Identification of functional modules in protein interaction networks is a first step in understanding the organization and dynamics of cell functions. To ensure that the identified modules are biologically meaningful, network-partitioning algorithms should take into account not only topological features but also functional relationships, and identified modules should be rigorously validated. RESULTS: In this study we first integrate proteomics and microarray datasets and represent the yeast protein-protein interaction network as a weighted graph. We then extend a betweenness-based partition algorithm, and use it to identify 266 functional modules in the yeast proteome network. For validation we show that the functional modules are indeed densely connected subgraphs. In addition, genes in the same functional module confer a similar phenotype. Furthermore, known protein complexes are largely contained in the functional modules in their entirety. We also analyze an example of a functional module and show that functional modules can be useful for gene annotation. CONTACT: yuan.33@osu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Domain-domain interactions underlying herpesvirus-human protein-protein interaction networks

Protein-domains play an important role in mediating protein-protein interactions. Furthermore, the same domain-pairs mediate different interactions in different contexts and in various organisms, and therefore domain-pairs are considered as the building blocks of interactome networks. Here we extend these principles to the host-virus interface and find the domain-pairs that potentially mediate human-herpesvirus interactions. Notably, we find that the same domain-pairs used by other organisms for mediating their interactions underlie statistically significant fractions of human-virus protein inter-interaction networks. Our analysis shows that viral domains tend to interact with human domains that are hubs in the human domain-domain interaction network. This may enable the virus to easily interfere with a variety of mechanisms and processes involving various and different human proteins carrying the relevant hub domain. Comparative genomics analysis provides hints at a molecular mechanism by which the virus acquired some of its interacting domains from its human host.     

Copper-dependent protein-protein interactions studied by yeast two-hybrid analysis

An important step in copper homeostasis is delivery of copper to a specific P-type ATPase in the Golgi apparatus (Ccc2 in yeast, ATP7A and ATP7B in humans) by a small copper chaperone protein (Atx1 in yeast, ATOX1 in humans). Atx1 and ATOX1 both contain an MXCXXC motif that is also present in Ccc2 (two motifs) and ATP7A/B (six motifs). Protein-protein interactions probably require coordination of one Cu(I) by cysteines from both MXCXXC motifs. We applied yeast two-hybrid analysis to screen systematically all possible interactions between MXCXXC-containing domains in these proteins. We demonstrate that ATOX1 and Atx1 preferentially interact with domains 2 and 4 of ATP7B and that Atx1 interacts with both Ccc2 domains. All combinations show a remarkable bell-shaped dependency on copper concentration that is maximal just below normal copper levels. Our results suggest that yeast two-hybrid analysis can be used to study the intracellular copper status of a cell.     

On the number of protein-protein interactions in the yeast proteome

Using two different approaches, we estimated that on average there are about five interacting partners per protein in the proteome of the yeast Saccharomyces cerevisiae. In the first approach, we used a novel method to model sampling overlap by a Bernoulli process, compared the results of two independent yeast two-hybrid interaction screens and tested the robustness of the estimate. The most stable estimate of five interactors per protein was obtained when the three most highly connected nodes in the protein interaction network were removed from the analysis (eight interactors per protein if those nodes were kept). In the second approach, we analysed a published high-confidence subset of putative interaction data obtained from multiple sources, including large-scale two-hybrid screens, complex purifications, synthetic lethals, correlated gene expression, computational predictions and previous annotations. Strikingly, the estimate was again five interactors per protein. These estimates suggest a range of ~16 000–26 000 different interaction pairs in the yeast, excluding homotypic interactions. We also discuss the approaches to estimating the rate of homotypic interactions.