Protein–protein interaction

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The horseshoe shaped ribonuclease inhibitor (shown as wireframe) forms a protein–protein interaction with the ribonuclease protein. The contacts between the two proteins are shown as coloured patches.

Protein–protein interactions (PPIs) refer to lasting and specific physical contacts established between two or more proteins as a result of biochemical events and/or electrostatic forces. Commonly they are understood as physical contacts with molecular docking between proteins that occur in a cell or in a living organism in specific biomolecular contexts (De Las Rivas J and Fontanillo C, 2010).[1]

Proteins rarely act alone. 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 are important for the interactomics system of the living cell and aberrant PPIs are the bases of multiple diseases, such as Creutzfeld-Jacob, Alzheimer's disease, and cancer.

PPIs have been studied from different perspectives: biochemistry, quantum chemistry, molecular dynamics, signal transduction, among others.[2] All this information enables the creation of large protein interaction networks – similar to metabolic or genetic/epigenetic networks – that empower the current knowledge on biochemical cascades and disease pathogenesis, as well as provide putative new therapeutic targets.

Examples of protein–protein interactions[edit]

  • Signal transduction
The activity of the cell is regulated by extracellular signals. Signals propagation to inside and/or along the interior of cells depends on PPIs between the various signaling molecules. This process, called signal transduction, plays a fundamental role in many biological processes and in many diseases (e.g. Parkinson's disease and cancer).
  • Transport across membranes
A protein may be carrying another protein (for example, from cytoplasm to nucleus or vice versa in the case of the nuclear pore importins).
  • Cell metabolism
In many biosynthetic processes enzymes interact with each other to produce small compounds or other macromolecules.
  • Muscle contraction
Physiology of muscle contraction involves several interactions. Myosin filaments act as molecular motors and by binding to actin enables filament sliding.[3] Furthermore, members of the skeletal muscle lipid droplet-associated proteins family associate with other proteins, as activator of adipose triglyceride lipase and its coactivator comparative gene identification-58, to regulate lipolysis in skeletal muscle.[4]

Types of protein–protein interactions[edit]

Main article: Multiprotein complex

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. Another important distinction to identify protein-protein interactions is the way they have been determined, since there are techniques that measure direct physical interactions between protein pairs, named “binary” methods, while there are other techniques that measure physical interactions among groups of proteins, without pairwise determination of protein partners, named “co-complex” methods.[1]

Homo-oligomers vs. hetero-oligomers[edit]

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.[5] Several enzymes, carrier proteins, scaffolding proteins, and transcriptional regulatory factors carry out their functions as homo-oligomers. 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 vs. transient interactions[edit]

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.g. cytochrome c), and some hetero-oligomeric proteins, as the subunits of ATPase. 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. – as it happens with most of the proteins involved in biochemical cascades. These are called transient interactions. For example, some G protein-coupled receptors only transiently bind to Gi/o proteins when they are activated by extracellular ligands,[6] while some Gq-coupled receptors, such as muscarinic receptor M3, pre-couple with Gq proteins prior to the receptor-ligand binding.[7]

Covalent vs. non-covalent[edit]

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.[8]

Techniques to study the molecular structure of protein complexes[edit]

Crystal structure of modified Gramicidin S horizontally determined by X-ray crystallography
NMR structure of cytochrome C illustrating its dynamics in solution

The molecular structures of many protein complexes have been unlocked by the technique of X-ray crystallography.[9][10] The first structure to be solved by this method was that of sperm whale myoglobin by Sir John Cowdery Kendrew.[11] In this technique the angles and intensities of a beam of X-rays diffracted by crystalline atoms are detected in a film, thus producing a three-dimensional picture of the density of electrons within the crystal.[12]

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.[10][13] This technique is based on the study of magnetic properties of atomic nuclei, thus determining physical and chemical properties of the correspondent atoms or the molecules. Nuclear magnetic resonance is advantageous for characterizing weak PPIs.[14]

Properties of protein–protein interface[edit]

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.[5]

Parameters evaluated include size (measured in absolute dimensions Å2 or in solvent-accessible surface area (SASA)), shape, complementarity between surfaces, residue interface propensities, hydrophobicity, segmentation and secondary structure, and conformational changes on complex formation.[5]

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.[15] PPI interfaces are dynamic and frequently planar, although they can be globular and protruding as well.[16] Based on three structures – insulin dimer, trypsin-pancreatic trypsin inhibitor complex, and oxyhaemoglobinCyrus Chothia and Joel Janin found that between 1,130 and 1,720 Å2 of surface area was removed from contact with water indicating that hydrophobicity is a major factor of stabilization of PPIs.[17] Later studies refined the buried surface area of the majority of interactions to 1,600±350 Å2. However, much larger interaction interfaces were also observed and were associated with significant changes in conformation of one of the interaction partners.[9] PPIs interfaces exhibit both shape and electrostatic complementarity.[5][18]

Factors that regulate protein–protein interactions[edit]

  • Protein concentration, which in turn are affected by expression levels and degradation rates;
  • Protein affinity for proteins or other binding ligands;
  • Ligands concentrations (substrates, ions, etc.);
  • Presence of other proteins, nucleic acids, and ions;
  • Electric fields around proteins.
  • Occurrence of covalent modifications;

Structural domains involved in protein–protein interactions[edit]

Proteins hold structural domains that allow their interaction with and bind to specific sequences on other proteins:

  • Src homology 2 (SH2) domain
    Main article: SH2 domain
SH2 domains are structurally composed by three-stranded twisted beta sheet sandwiched flanked by two alpha-helices. The existence of a deep binding pocket with high affinity for phosphotyrosine, but not for phosphoserine or phosphothreonine, is essential for the recognition of tyrosine phosphorylated proteins, mainly autophosphorylated growth factor receptors. Growth factor receptor binding proteins and phospholipase Cγ are examples of proteins that have SH2 domains.[19]
  • Src homology 3 (SH3) domain
    Main article: SH3 domain
Structurally, SH3 domains are constituted by a beta barrel formed by two orthogonal beta sheets and three anti-parallel beta strands. These domains recognize proline enriched sequences, as polyproline type II helical structure (PXXP motifs)[verification needed] in cell signaling proteins like protein tyrosine kinases and the growth factor receptor bound protein 2 (Grb2).[19]
  • Phosphotyrosine-binding (PTB) domain
    Main article: PTB domain
PTB domains interact with sequences that contain a phosphotyrosine group. These domains can be found in the insulin receptor substrate.[19]
LIM domains were initially identified in three homeodomain transcription factors (lin11, is11, and mec3). In addition to this homeodomain proteins and other proteins involved in development, LIM domains have also been identified in non-homeodomain proteins with relevant roles in cellular differentiation, association with cytoskeleton and senescence. These domains contain a tandem cysteine-rich Zn2+-finger motif and embrace the consensus sequence CX2CX16-23HX2CX2CX2CX16-21CX2C/H/D. LIM domains bind to PDZ domains, bHLH transcription factors, and other LIM domains.[19]
  • Sterile alpha motif (SAM) domain
    Main article: SAM domain
SAM domains are composed by five helices forming a compact package with a conserved hydrophobic core. These domains, which can be found in the Eph receptor and the stromal interaction molecule (STIM) for example, bind to non-SAM domain-containing proteins and they also appear to have the ability to bind RNA.[19]
PDZ domains were first identified in three guanylate kinases: PSD-95, DlgA and ZO-1. These domains recognize carboxy-terminal tri-peptide motifs (S/TXV), other PDZ domains or LIM domains and bind them through a short peptide sequence that has a C-terminal hydrophobic residue. Some of the proteins identified as having PDZ domains are scaffolding proteins or seem to be involved in ion receptor assembling and receptor-enzyme complexes formation.[19]
FERM domains contain basic residues capable of binding PtdIns(4,5)P2. Talin and focal adhesion kinase (FAK) are two of the proteins that present FERM domains.[19]
CH domains are mainly present in cytoskeletal proteins as parvin.[19]
Pleckstrin homology domains bind to phosphoinositides and acid domains in signaling proteins.
WW domains bind to proline enriched sequences.
  • WSxWS motif
Found in cytokine receptors

Methods to investigate protein–protein interactions[edit]

There are a multitude of methods to detect them.[20] Each of the approaches has its own strengths and weaknesses, especially with regard to the sensitivity and specificity of the method. The most conventional and widely used high-throughput methods are yeast two-hybrid screening and affinity purification coupled to mass spectrometry.[1]

Principles of yeast and mammalian two-hybrid systems

Yeast two-hybrid screening[edit]

Main article: Two-hybrid screening

This system was firstly described in 1989 by Fields and Song using Saccharomyces cerevisiae as biological model.[21] Yeast two hybrid allows the identification of pairwise PPIs (binary method) in vivo, indicating non-specific tendencies towards sticky interactions.[22]

Yeast cells are transfected with two plasmids: the bait (protein of interest fused with the DNA-binding domain of a yeast transcription factor, like Gal4), and the prey (a library of cDNA fragments linked to the activation domain of the transcription factor. Transcription of reporter genes does not occur unless bait and prey interact with each other and form a functional transcription factor. Thus, the interaction between proteins can be inferred by the presence of the products resultant of the reporter gene expression.[8][23]

Despite its usefulness, the yeast two-hybrid system has limitations: specificity is relatively low; uses yeast as main host system, which can be a problem when studying other biological models; the number of PPIs identified is usually low because some transient PPIs are lost during purification steps;[24] and, understates membrane proteins, for example.[25][26] Limitations have been overcoming by the emergence of yeast two-hybrid variants, such as the membrane yeast two-hybrid (MYTH)[26] and the split-ubiquitin system,[23] which are not limited to interactions that occur in the nucleus; and, the bacterial two-hybrid system, performed in bacteria;[27]

Principle of Tandem Affinity Purification

Affinity purification coupled to mass spectrometry[edit]

Main article: Mass spectrometry

Affinity purification coupled to mass spectrometry mostly detects stable interactions and thus better indicates functional in vivo PPIs.[22][23] This method starts by purification of the tagged protein, which is expressed in the cell usually at in vivo concentrations, and its interacting proteins (affinity purification). 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 Mathias Mann and respective colleagues. PPIs can then be quantitatively and qualitatively analysed by mass spectrometry using different methods: chemical incorporation, biological or metabolic incorporation (SILAC), and label-free methods.[5]

Other potential methods[edit]

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, surface plasmon resonance, protein-fragment complementation assay, and calorimetry.[25][26]

Text mining methods[edit]

Text mining protocol.

Publicly available information from biomedical research is readily accessible through the internet and is becoming a powerful resource for predictive protein-protein interactions and protein docking. Text mining is much less time costly and consuming compared to other high-throughput techniques. Currently, these methods generally detect binary relations between interacting protein from individual sentences using machine learning and rule/pattern-based information extraction and machine learning approaches.[28] A wide variety of text mining predicting PPIs applications are available for public use, as well as repositories which often stores manually validated and/or computationally predicted PPIs. The principal stages of text mining divides the implementation into two stages: information retrieval, where literature abstracts containing names of either or both proteins complexes are selected and information extraction, where detecting occurrences of residues are retrieved. The extraction is automated by searching for co-existing sentences, abstracts or paragraphs within textual context.

There are also studies using phylogenetic profiling, basing their functionalities on the theory that proteins involved in common pathways co-evovle in a correlated fashion across large number of species. More complex text mining methodologies use advanced dictionaries and generate networks by Natural Language Processing (NLP) of text, considering gene names as nodes and verbs as edges, other developments involve kernel methods to predict protein interactions.[29]

Protein–protein interaction databases[edit]

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. The first of these databases was the Database of Interacting Proteins (DIP).[30] Since that time, the number of public databases has been increasing. Databases can be subdivided into primary databases, meta-databases, and prediction databases.[31]

  • Primary databases collect information about published PPIs proven to exist via small-scale or large-scale experimental methods. Examples: DIP, Biomolecular Interaction Network Database (BIND), Biological General Repository for Interaction Datasets (BioGRID), Human Protein Reference Database (HPRD), IntAct Molecular Interaction Database, Molecular Interactions Database (MINT), MIPS Protein Interaction Resource on Yeast (MIPS-MPact), and MIPS Mammalian Protein–Protein Interaction Database (MIPS-MPPI).[31]
  • Meta-databases normally result from the integration of primary databases information, but can also collect some original data. Examples: Agile Protein Interaction DataAnalyzer (APID), The Microbial Protein Interaction Database (MPIDB), and Protein Interaction Network Analysis (PINA) platform.,[31] (GPS-Prot)
  • Prediction databases include many PPIs that are predicted using several techniques (main article). Examples: Michigan Molecular Interactions (MiMI), Human Protein–Protein Interaction Prediction Database (PIPs), Online Predicted Human Interaction Database (OPHID), Known and Predicted Protein–Protein Interactions (STRING), and Unified Human Interactome (UniHI).[31]

Protein–protein interaction networks[edit]

Main article: Interactome
Network visualisation of the human interactome where each point represents a protein and each blue line between them is an interaction.

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 1999 map of cell cycle control.[32] Drawing on Kohn's map, Schwikowski et al. in 2000 published a paper on PPIs in yeast, linking together 1,548 interacting proteins determined by two-hybrid screening. 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.[33][34]

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.[31][35] Pajek software is advantageous for the visualization and analysis of very large networks.[36]

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 [37] modules and MoBaS.[38] Jactive modules integrate PPI network and gene expression data where as MoBaS integrate PPI network and Genome Wide association Studies.

The awareness of the major roles of PPIs in numerous physiological and pathological processes has been driving the challenge of unravel many interactomes. Examples of published interactomes are the thyroid specific DREAM interactome[39] and the PP1α interactome in human brain.[40]

Signed Protein-Protein Interaction Networks[edit]

The protein protein interactions are displayed in a signed network that describes what type of interactions that are taking place [41]

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.g. "activation" or "inhibition"). Although such attributes have been added to networks for a long time,[42] Vinayagam et al. (2014) coined the term Signed network for them. 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.[43]

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.[44] However these methods do not provide the layer of information needed in order to determine what type of interaction is present in order to be able to attribute signs to the network diagrams.

RNA Interference Screens[edit]

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.e. where the inhibition of either of two proteins results in the same phenotype) indicates a positive, or activating relationship. Phenotypes that do no correlate (i.e. where the inhibition of either of two proteins results in two different phenotypes) indicate a negative or inactivating relationship. 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. If, however, protein A is inactivated by protein B then the phenotypes will differ depending on which protein is inhibited (inhibit protein B and it can no longer inactivate protein A leaving A active however inactivate A and there is nothing for B to activate since A is inactive and the phenotype changes). Multiple RNAi screens need to be performed in order to reliably appoint a sign to a given protein-protein interaction. Vinayagam et al. who devised this technique state that a minimum of nine RNAi screens are required with confidence increasing as one carries out more screens.[43]

Protein–protein interaction as therapeutic targets[edit]

Modulation of PPI is challenging and is receiving increasing attention by the scientific community.[45] Several properties of PPI such as allosteric sites and hotspots, have been incorporated into drug-design strategies.[46][47] The relevance of PPI as putative therapeutic targets for the development of new treatments is particularly evident in cancer, with several ongoing clinical trials within this area. The consensus among these promising targets is, nonetheless, denoted in the already available drugs on the market to treat a multitude of diseases. Examples are Titrobifan, inhibitor of the glycoprotein IIb/IIIa, used as a cardiovascular drug, and Maraviroc, inhibitor of the CCR5-gp120 interaction, used as anti-HIV drug.[48] Recently,[when?] Amit Jaiswal and others were able to develop 30 peptides using protein–protein interaction studies to inhibit telomerase recruitment towards telomeres.[49][50]

See also[edit]


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