Protein–protein interaction

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Epistasis refers binary genetic interactions genetic interactions in which the mutation of one gene masks the phenotypic effects of a binary genetic interactions at another locus. Examining binary genetic interactions phenotypes resulting from pairs of mutations helps in understanding how the function of these genes intersects.

Binary genetic interactions epistasis an interaction between non- allelic genes is positive in other words, diminishing, antagonistic or buffering when a loss of function mutation of two given genes results in exceeding the fitness predicted from individual effects of deleterious mutations, and it is negative that is, reinforcing, synergistic or aggravating when it decreases fitness. Usually, even in case of positive interactions double mutant has smaller fitness than single mutants.

This aggravated binary genetic interactions arises when genes in compensatory pathways are both knocked out. High-throughput methods of analyzing these types of interactions have been useful in expanding our knowledge of genetic interactions. Synthetic genetic arrays SGAdiploid based synthetic lethality analysis on microarrays dSLAMand epistatic miniarray profiles E-MAP binary genetic interactions three important methods which have been binary genetic interactions for the systematic analysis and mapping of genetic interactions.

This systematic approach to studying epistasis on a genome wide scale has significant implications for functional genomics. By binary genetic interactions the negative and positive interactions between an unknown gene and a set genes within a known pathway, these methods can elucidate the function of previously uncharacterized genes within the context of a metabolic or developmental pathway.

In order to understand how information about binary genetic interactions interactions relates to gene pathways, let us consider a simple example of vulval cell differentiation in C. Cells differentiate from Pn cells to Pn. Mutation of lin [4] blocks differentiation of Pn binary genetic interactions to Pn.

Mutants of lin [5] behave similarly, blocking differentiation at the transition to VP cells. In both cases, the resulting phenotype is marked by an absence of vulval cells as there is an upstream block in the differentiation pathway. A double mutant in which both of these genes have been disrupted exhibits an equivalent phenotype that is no worse than binary genetic interactions single mutant.

The upstream disruption at lin masks the phenotypic effect of a mutation at lin [1] in a classic example of an alleviating epistatic interaction. Aggravating mutations on the other hand give binary genetic interactions to a phenotype which is worse than the cumulative effect of each single mutation. This aggravated phenotype is indicative of two genes in compensatory pathways. In the case of the single mutant a parallel pathway is able to compensate for the loss of the disrupted pathway however, in the case of the double mutant the action of this compensatory pathway is lost as well, resulting in the more dramatic phenotype observed.

This relationship has been significantly easier to detect than the more subtle alleviating phenotypes and has been extensively studied in S.

It should be pointed out that these conclusions from double-mutant analysis, while they apply to many pathways and mutants, are not universal. For example, genes can act in opposite directions in pathways, so that knocking out both produces a near-normal phenotype, while each single mutant is severely affected in opposite directions. A well-studied example occurs during early development in Drosophila, wherein gene products from the hunchback and nanos genes are present in the binary genetic interactions, and act in opposite directions to direct anterior-posterior pattern formation.

Something similar often happens in signal transduction pathways, where knocking out a negative regulator of the pathway causes a hyper-activation phenotype, while knocking out a positively acting component produces an opposite phenotype. In linear pathways with a single "output", when knockout mutations in two oppositely-acting genes are combined in the same individual, the phenotype of the double mutant is typically the same as the phenotype of the single binary genetic interactions whose normal gene product acts downstream in the binary genetic interactions.

Synthetic genetic arrays SGA and diploid based synthetic lethality analysis of microarrays dSLAM are two key methods which have been used to identify synthetic sick lethal mutants and characterize negative epistatic relationships.

Sequencing of the entire yeast genome has made it possible to generate a library of knock-out mutants for nearly every gene in the genome. These molecularly bar-coded mutants greatly facilitate high-throughput epistasis studies, as they can be pooled and used to generate the necessary double mutants.

Microarray profiling is then used to compare the fitness of these single and double mutants. In the case of SGA, the double mutants examined are haploid and collected after mating with a mutant strain followed by several rounds of selection. In the case of dSLAM analysis the fitness of single and double mutants is assessed by microarray analysis of a growth competition assay. In order to binary genetic interactions a richer binary genetic interactions of genetic interactions, experimental approaches are shifting away from this binary classification of phenotypes as wild type or synthetic binary genetic interactions.

The E-MAP approach is particularly compelling because of its ability to highlight both alleviating and aggravating effects and this capacity is what distinguishes this method from others such as SGA and dSLAM.

Furthermore, not only does the E-MAP identify both types of interactions but also recognizes gradations in these interactions and the severity of the masked phenotype, represented by the interaction score applied to each pair of genes. While the method has binary genetic interactions particularly developed for examining epistasis in S.

An E-MAP collates data generated from the systematic generation of double mutant binary genetic interactions for a large clearly defined group of genes.

Each phenotypic response is quantified by imaging colony size to determine growth rate. This fitness score is binary genetic interactions to the predicted fitness for each single mutant, resulting in a genetic interaction score. Hierarchical clustering of this data to group genes with similar interaction profiles allows for the identification of epistatic relationships between genes with and without known function. By sorting the data in this way, genes known to interact will cluster together alongside genes which exhibit a similar pattern of interactions but whose function has not yet been identified.

The E-MAP data is therefore able to place genes into new functions within well characterized pathways. The choice of genes examined within a given E-MAP is critical to achieving fruitful results. It is particularly important that a significant subset of the genes examined have been well established in the literature. These genes are thus able to act as controls for the E-MAP allowing for greater certainty in analyzing the data from uncharacterized genes.

Clusters organized by sub-cellular localization and general cellular processes e. Data binary genetic interactions protein-protein interaction studies can also provide a useful basis for selecting gene groups for E-MAP data. We would expect genes which exhibit physical interactions to also demonstrate interactions at the genetic level and thus these can serve as adequate controls for E-MAP data. If well chosen, these functional binary genetic interactions contain a significantly higher density of genetic interactions than other regions of the genome and thus allows for a higher rate of detection while dramatically decreasing the number of gene binary genetic interactions to be examined.

The generation of libraries of essential gene mutants presents significant difficulties however, as these mutations have a lethal phenotype. Thus, E-MAP studies rely upon strains with intermediate expression levels of these genes. The decreased abundance by messenger RNA perturbation DAmP strategy is particularly common for the high-throughput generation of mutants necessary binary genetic interactions this kind of analysis and allows for the partial disruption of essential genes without loss of viability.

In the case of non-essential genes, deletion strains may be used. Tagging at the deletion sites with molecular barcodes, unique bp sequences, allows for the identification binary genetic interactions study of relative fitness levels in each mutant strain.

From Wikipedia, the free encyclopedia. Retrieved from " https: Views Read Edit View history. This page was last edited on 31 Augustat By using this site, you agree to the Terms of Use and Privacy Policy.

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Protein—protein interactions PPIs are the physical contacts of high specificity established between two or more protein molecules as a result of biochemical events steered by electrostatic forces including the hydrophobic effect. Many are physical contacts with molecular associations between chains that occur in a cell or in a living organism in a specific biomolecular context.

Proteins rarely act alone as their functions tend to be regulated. Many molecular processes within a cell are carried out by molecular machines that are built from a large number of protein components organized by their PPIs. These interactions make up the so-called interactomics of the organism, while aberrant PPIs are the basis of multiple aggregation-related diseases, such as Creutzfeldt—Jakob , Alzheimer's diseases , and may lead to cancer.

PPIs have been studied from different perspectives: In many metabolic reactions, a protein that acts as an electron carrier binds to an enzyme that acts its reductase.

After it receives an electron, it dissociates and then binds to the next enzyme that acts its oxidase i. These interactions between proteins are dependent on highly specific binding between proteins to ensure efficient electron transfer.

In the case of the mitochondrial P systems, the specific residues involved in the binding of the electron transfer protein adrenodoxin to its reductase were identified as two basic Arg residues on the surface of the reductase and two acidic Asp residues on the adrenodoxin. The activity of the cell is regulated by extracellular signals.

The recruitment of signaling pathways through PPIs is called signal transduction and plays a fundamental role in many biological processes and in many diseases including Parkinson's disease and cancer. A protein may be carrying another protein for example, from cytoplasm to nucleus or vice versa in the case of the nuclear pore importins. In many biosynthetic processes enzymes interact with each other to produce small compounds or other macromolecules.

Physiology of muscle contraction involves several interactions. Myosin filaments act as molecular motors and by binding to actin enables filament sliding. To describe the types of protein—protein interactions PPIs it is important to consider that proteins can interact in a "transient" way to produce some specific effect in a short time or to interact with other proteins in a "stable" way to build multiprotein complexes that are molecular machines within the living systems.

A protein complex assembly can result in the formation of homo-oligomeric or hetero-oligomeric complexes. In addition to the conventional complexes, as enzyme-inhibitor and antibody-antigen, interactions can also be established between domain-domain and domain-peptide. Homo-oligomers are macromolecular complexes constituted by only one type of protein subunit. Protein subunits assembly is guided by the establishment of non-covalent interactions in the quaternary structure of the protein.

Disruption of homo-oligomers in order to return to the initial individual monomers often requires denaturation of the complex. Distinct protein subunits interact in hetero-oligomers, which are essential to control several cellular functions. The importance of the communication between heterologous proteins is even more evident during cell signaling events and such interactions are only possible due to structural domains within the proteins as described below.

Stable interactions involve proteins that interact for a long time, taking part of permanent complexes as subunits, in order to carry out structural or functional roles. These are usually the case of homo-oligomers e.

On the other hand, a protein may interact briefly and in a reversible manner with other proteins in only certain cellular contexts — cell type , cell cycle stage , external factors, presence of other binding proteins, etc. These are called transient interactions. MoRFs are transient interactions. Covalent interactions are those with the strongest association and are formed by disulphide bonds or electron sharing. Although being rare, these interactions are determinant in some posttranslational modifications , as ubiquitination and SUMOylation.

Non-covalent bonds are usually established during transient interactions by the combination of weaker bonds, such as hydrogen bonds , ionic interactions, Van der Waals forces , or hydrophobic bonds.

Water molecules play a significant role in the interactions between proteins. The majority of the interface water molecules make hydrogen bonds with both partners of each complex. Some interface amino acid residues or atomic groups of one protein partner engage in both direct and water mediated interactions with the other protein partner.

Doubly indirect interactions, mediated by two water molecules, are more numerous in the homologous complexes of low affinity. The molecular structures of many protein complexes have been unlocked by the technique of X-ray crystallography. Later, nuclear magnetic resonance also started to be applied with the aim of unravelling the molecular structure of protein complexes. One of the first examples was the structure of calmodulin-binding domains bound to calmodulin. Nuclear magnetic resonance is advantageous for characterizing weak PPIs.

Proteins hold structural domains that allow their interaction with and bind to specific sequences on other proteins:. The study of the molecular structure can give fine details about the interface that enables the interaction between proteins. When characterizing PPI interfaces it is important to take into account the type of complex. The great majority of PPI interfaces reflects the composition of protein surfaces, rather than the protein cores, in spite of being frequently enriched in hydrophobic residues, particularly in aromatic residues.

However, much larger interaction interfaces were also observed and were associated with significant changes in conformation of one of the interaction partners. There are a multitude of methods to detect them.

The most conventional and widely used high-throughput methods are yeast two-hybrid screening and affinity purification coupled to mass spectrometry. This system was firstly described in by Fields and Song using Saccharomyces cerevisiae as biological model. The Y2H is based on the functional reconstitution of the yeast transcription factor Gal4 and subsequent activation of a selective reporter such as His3.

To test two proteins for interaction, two protein expression constructs are made: In the assay, yeast cells are transformed with these constructs. Transcription of reporter genes does not occur unless bait DB-X and prey AD-Y interact with each other and form a functional Gal4 transcription factor.

Thus, the interaction between proteins can be inferred by the presence of the products resultant of the reporter gene expression. Despite its usefulness, the yeast two-hybrid system has limitations. It uses yeast as main host system, which can be a problem when studying proteins that contain mammalian-specific post-translational modifications.

The number of PPIs identified is usually low because of a high false negative rate; [31] and, understates membrane proteins , for example. In initial studies that utilized Y2H, proper controls for false positives e. An empirical framework must be implemented to control for these false positives. Affinity purification coupled to mass spectrometry mostly detects stable interactions and thus better indicates functional in vivo PPIs.

One of the most advantageous and widely used method to purify proteins with very low contaminating background is the tandem affinity purification , developed by Bertrand Seraphin and Matthias Mann and respective colleagues.

PPIs can then be quantitatively and qualitatively analysed by mass spectrometry using different methods: Diverse techniques to identify PPIs have been emerging along with technology progression.

These include co-immunoprecipitation, protein microarrays , analytical ultracentrifugation , light scattering , fluorescence spectroscopy , luminescence-based mammalian interactome mapping LUMIER , resonance-energy transfer systems, mammalian protein—protein interaction trap, electro-switchable biosurfaces , protein-fragment complementation assay , as well as real-time label-free measurements by surface plasmon resonance , and calorimetry.

Publicly available information from biomedical documents is readily accessible through the internet and is becoming a powerful resource for collecting known protein-protein interactions PPIs , PPI prediction and protein docking. Text mining is much less costly and time-consuming compared to other high-throughput techniques. Text mining can be implemented in two stages: There are also studies using phylogenetic profiling, basing their functionalities on the theory that proteins involved in common pathways co-evolve in a correlated fashion across species.

Some more complex text mining methodologies use advanced Natural Language Processing NLP techniques and build knowledge networks for example, considering gene names as nodes and verbs as edges. Other developments involve kernel methods to predict protein interactions. These methods use machine learning to distinguish how interacting protein pairs differ from non-interacting protein pairs in terms of pairwise features such as cellular colocalization, gene co-expression, how closely located on a DNA are the genes that encode the two proteins, and so on.

Large scale identification of PPIs generated hundreds of thousands interactions, which were collected together in specialized biological databases that are continuously updated in order to provide complete interactomes.

Databases can be subdivided into primary databases, meta-databases, and prediction databases. Primary databases collect information about published PPIs proven to exist via small-scale or large-scale experimental methods. Meta-databases normally result from the integration of primary databases information, but can also collect some original data. Prediction databases include many PPIs that are predicted using several techniques main article.

Information found in PPIs databases supports the construction of interaction networks. Although the PPI network of a given query protein can be represented in textbooks, diagrams of whole cell PPIs are frankly complex and difficult to generate. One example of a manually produced molecular interaction map is the Kurt Kohn's map of cell cycle control. They used a layered graph drawing method to find an initial placement of the nodes and then improved the layout using a force-based algorithm. Bioinformatic tools have been developed to simplify the difficult task of visualizing molecular interaction networks and complement them with other types of data.

For instance, Cytoscape is an open-source software widely used and lots of plugins are currently available. Identification of functional modules in PPI networks is an important challenge in bioinformatics. Functional modules means a set of proteins that are highly connected to each other in PPI network. It is almost similar problem as community detection in social networks.

There are some methods such as Jactive [51] modules and MoBaS. The awareness of the major roles of PPIs in numerous physiological and pathological processes has been driving the challenge of unravel many interactomes.

Protein-protein relationships are often the result of multiple types of interactions or are deduced from different approaches, including co-localization, direct interaction, suppressive genetic interaction, additive genetic interaction, physical association, and other associations. Protein—protein interactions often result in one of the interacting proteins either being 'activated' or 'repressed'. Such effects can be indicated in a PPI network by "signs" e. Although such attributes have been added to networks for a long time, [57] Vinayagam et al.

Signed networks are often expressed by labeling the interaction as either positive or negative. A positive interaction is one where the interaction results in one of the proteins being activated. Conversely a negative interaction indicates that one of the proteins being inactivated. Protein—protein interaction networks are often constructed as a result of lab experiments such as yeast two hybrid screens or 'affinity purification and subsequent mass spectrometry techniques.

RNA interference RNAi screens repression of individual proteins between transcription and translation are one method that can be utilized in the process of providing signs to the protein-protein interactions. Individual proteins are repressed and the resulting phenotypes are analyzed. A correlating phenotypic relationship i.

Phenotypes that do not correlate i. If protein A is dependent on protein B for activation then the inhibition of either protein A or B will result in a cell losing the service that is provided by protein A and the phenotypes will be the same for the inhibition of either A or B.