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Studying protein interaction networks of all proteins in an organism ("interactomes") remains one of the major challenges in modern biomedicine. Such information is crucial to understanding cellular pathways and developing effective therapies for the treatment of human diseases. Over the past two decades, diverse biochemical, genetic, and cell biological methods have been developed to map interactomes. In this review, we highlight basic principles of interactome mapping. Specifically, we discuss the strengths and weaknesses of individual assays, how to select a method appropriate for the problem being studied, and provide general guidelines for carrying out the necessary follow-up analyses. In addition, we discuss computational methods to predict, map, and visualize interactomes, and provide a summary of some of the most important interactome resources. We hope that this review serves as both a useful overview of the field and a guide to help more scientists actively employ these powerful approaches in their research.
The MAPPIT (mammalian protein protein interaction trap) method allows high-throughput detection of protein interactions by very simple co-transfection of three plasmids in HEK293T cells, followed by a luciferase readout. MAPPIT detects a large percentage of all protein interactions, including those requiring posttranslational modifications and endogenous or exogenous ligands. Here, we present a straightforward method that allows detailed mapping of interaction interfaces via MAPPIT. The method provides insight into the interaction mechanism and reveals how this is affected by disease-associated mutations. By combining error-prone polymerase chain reaction (PCR) for random mutagenesis, 96-well DNA prepping, Sanger sequencing, and MAPPIT via 384-well transfections, we test the effects of a large number of mutations of a selected protein on its protein interactions. The entire screen takes less than three months and interactions with multiple partners can be studied in parallel. The effect of mutations on the MAPPIT readout is mapped on the protein structure, allowing unbiased identification of all putative interaction sites. We have thus far analysed 6 proteins and mapped their interfaces for 16 different interaction partners. Our method is broadly applicable as the required tools are simple and widely available.
G-protein-coupled receptors (GPCRs) are the largest family of integral membrane receptors with key roles in regulating signaling pathways targeted by therapeutics, but are difficult to study using existing proteomics technologies due to their complex biochemical features. To obtain a global view of GPCR-mediated signaling and to identify novel components of their pathways, we used a modified membrane yeast two-hybrid (MYTH) approach and identified interacting partners for 48 selected full-length human ligand-unoccupied GPCRs in their native membrane environment. The resulting GPCR interactome connects 686 proteins by 987 unique interactions, including 299 membrane proteins involved in a diverse range of cellular functions. To demonstrate the biological relevance of the GPCR interactome, we validated novel interactions of the GPR37, serotonin 5-HT4d, and adenosine ADORA2A receptors. Our data represent the first large-scale interactome mapping for human GPCRs and provide a valuable resource for the analysis of signaling pathways involving this druggable family of integral membrane proteins.
Inhibition of protein-protein interactions (PPIs) is emerging as a promising therapeutic strategy despite the difficulty in targeting such interfaces with drug-like small molecules. PPIs generally feature large and flat binding surfaces as compared to typical drug targets. These features pose a challenge for structural characterization of the surface using geometry-based pocket-detection methods. An attractive mapping strategy--that builds on the principles of fragment-based drug discovery (FBDD)--is to detect the fragment-centric modularity at the protein surface and then characterize the large PPI interface as a set of localized, fragment-targetable interaction regions. Here, we introduce AlphaSpace, a computational analysis tool designed for fragment-centric topographical mapping (FCTM) of PPI interfaces. Our approach uses the alpha sphere construct, a geometric feature of a protein's Voronoi diagram, to map out concave interaction space at the protein surface. We introduce two new features--alpha-atom and alpha-space--and the concept of the alpha-atom/alpha-space pair to rank pockets for fragment-targetability and to facilitate the evaluation of pocket/fragment complementarity. The resulting high-resolution interfacial map of targetable pocket space can be used to guide the rational design and optimization of small molecule or biomimetic PPI inhibitors.
Identification of peptides mediating protein-protein interaction (PPI) is crucial for understanding the function of interlinked proteins in cellular processes and amino acid-associated diseases. Traditional PPI assays are laborious, involving the generation of many truncated proteins. SPOT peptide assay allows high-throughput detection of domains essential for PPI by synthesizing several hundred peptides on a cellulose membrane. Here, we present a rapid SPOT peptide protocol for identifying the binding motifs, which mediate interaction between the chromatin remodeling factors BAF155/BAF170 and the epigenetic factor Kdm6b. For complete details on the use and execution of this protocol, please refer to Narayanan et al. (2015).
Nasopharyngeal carcinoma (NPC), although not very common in many parts of the world, is a major concern in some countries, including Iran. Molecular studies are very helpful to provide essential information regarding underlying carcinogenetic mechanisms. Here, considering NPC proteomic approaches, established biomarkers were designated for protein-protein interaction network construction and analysis with corresponding plug-ins. A network of reported protein markers was constructed and topological and biological process features were investigated. Centrality analysis showed that JUN, CALM1, HSB1, and SOD1 are more important than other differentially expressed proteins in an interacting pattern. What is more, by extending the network, Tp53, PRDM10, AKT1, ALB, HSP90AA1, and EGFR achieved the highest values for NPC network strength. It can be concluded that these proteins as well as their contributing processes, particularly in a second network, may be important for NPC onset and development. Targeting these candidate proteins may allow novel treatment approaches following appropriate validation.
To improve chemical cross-linking of proteins coupled with mass spectrometry (CXMS), we developed a lysine-targeted enrichable cross-linker containing a biotin tag for affinity purification, a chemical cleavage site to separate cross-linked peptides away from biotin after enrichment, and a spacer arm that can be labeled with stable isotopes for quantitation. By locating the flexible proteins on the surface of 70S ribosome, we show that this trifunctional cross-linker is effective at attaining structural information not easily attainable by crystallography and electron microscopy. From a crude Rrp46 immunoprecipitate, it helped identify two direct binding partners of Rrp46 and 15 protein-protein interactions (PPIs) among the co-immunoprecipitated exosome subunits. Applying it to E. coli and C. elegans lysates, we identified 3130 and 893 inter-linked lysine pairs, representing 677 and 121 PPIs. Using a quantitative CXMS workflow we demonstrate that it can reveal changes in the reactivity of lysine residues due to protein-nucleic acid interaction.
One of the crucial steps toward understanding the biological functions of a cellular system is to investigate protein-protein interaction (PPI) networks. As an increasing number of reliable PPIs become available, there is a growing need for discovering PPIs to reconstruct PPI networks of interesting organisms. Some interolog-based methods and homologous PPI families have been proposed for predicting PPIs from the known PPIs of source organisms.
Knowing the manner of protein-protein interactions is vital for understanding biological events. The plant-type [2Fe-2S] ferredoxin (Fd), a well-known small iron-sulfur protein with low redox potential, partitions electrons to a variety of Fd-dependent enzymes via specific protein-protein interactions. Here we have refined the crystal structure of a recombinant plant-type Fd I from the blue green alga Aphanothece sacrum (AsFd-I) at 1.46 Å resolution on the basis of the synchrotron radiation data. Incorporating the revised amino-acid sequence, our analysis corrects the 3D structure previously reported; we identified the short α-helix (67-71) near the active center, which is conserved in other plant-type [2Fe-2S] Fds. Although the 3D structures of the four molecules in the asymmetric unit are similar to each other, detailed comparison of the four structures revealed the segments whose conformations are variable. Structural comparison between the Fds from different sources showed that the distribution of the variable segments in AsFd-I is highly conserved in other Fds, suggesting the presence of intrinsically flexible regions in the plant-type [2Fe-2S] Fd. A few structures of the complexes with Fd-dependent enzymes clearly demonstrate that the protein-protein interactions are achieved through these variable regions in Fd. The results described here will provide a guide for interpreting the biochemical and mutational studies that aim at the manner of interactions with Fd-dependent enzymes.
Protein-DNA damage interactions are critical for understanding the mechanism of DNA repair and damage response. However, due to the relatively random distributions of UV-induced damage and other DNA bulky adducts, it is challenging to measure the interactions between proteins and these lesions across the genome. To address this issue, we developed a new method named Protein-Associated DNA Damage Sequencing (PADD-seq) that uses Damage-seq to detect damage distribution in chromatin immunoprecipitation-enriched DNA fragments. It is possible to delineate genome-wide protein-DNA damage interactions at base resolution with this strategy. Using PADD-seq, we observed that RNA polymerase II (Pol II) was blocked by UV-induced damage on template strands, and the interaction declined within 2 h in transcription-coupled repair-proficient cells. On the other hand, Pol II was clearly restrained at damage sites in the absence of the transcription-repair coupling factor CSB during the same time course. Furthermore, we used PADD-seq to examine local changes in H3 acetylation at lysine 9 (H3K9ac) around cisplatin-induced damage, demonstrating the method's broad utility. In conclusion, this new method provides a powerful tool for monitoring the dynamics of protein-DNA damage interaction at the genomic level, and it encourages comprehensive research into DNA repair and damage response.
Soluble guanylyl cyclase (sGC) is a heterodimeric nitric oxide (NO) receptor that produces cyclic GMP. This signaling mechanism is a key component in the cardiovascular system. NO binds to heme in the β subunit and stimulates the catalytic conversion of GTP to cGMP several hundred fold. Several endogenous factors have been identified that modulate sGC function in vitro and in vivo. In previous work, we determined that protein disulfide isomerase (PDI) interacts with sGC in a redox-dependent manner in vitro and that PDI inhibited NO-stimulated activity in cells. To our knowledge, this was the first report of a physical interaction between sGC and a thiol-redox protein. To characterize this interaction between sGC and PDI, we first identified peptide linkages between sGC and PDI, using a lysine cross-linking reagent and recently developed mass spectrometry analysis. Together with Flag-immunoprecipitation using sGC domain deletions, wild-type (WT) and mutated PDI, regions of sGC involved in this interaction were identified. The observed data were further explored with computational modeling to gain insight into the interaction mechanism between sGC and oxidized PDI. Our results indicate that PDI interacts preferentially with the catalytic domain of sGC, thus providing a mechanism for PDI inhibition of sGC. A model in which PDI interacts with either the α or the β catalytic domain is proposed.
The human papillomavirus 16 (HPV16) has high risk to lead various cancers and afflictions, especially, the cervical cancer. Therefore, investigating the pathogenesis of HPV16 is very important for public health. Protein-protein interaction (PPI) network between HPV16 and human was used as a measure to improve our understanding of its pathogenesis. By adopting sequence and topological features, a support vector machine (SVM) model was built to predict new interactions between HPV16 and human proteins. All interactions were comprehensively investigated and analyzed. The analysis indicated that HPV16 enlarged its scope of influence by interacting with human proteins as much as possible. These interactions alter a broad array of cell cycle progression. Furthermore, not only was HPV16 highly prone to interact with hub proteins and bottleneck proteins, but also it could effectively affect a breadth of signaling pathways. In addition, we found that the HPV16 evolved into high carcinogenicity on the condition that its own reproduction had been ensured. Meanwhile, this work will contribute to providing potential new targets for antiviral therapeutics and help experimental research in the future.
Proximity labeling (PL) coupled with mass spectrometry has emerged as a powerful technique to map proximal protein interactions in living cells. Large-scale sample processing for proximity proteomics necessitates a high-throughput workflow to reduce hands-on time and increase quantitative reproducibility. To address this issue, we developed a scalable and automated PL pipeline, including generation and characterization of monoclonal cell lines, automated enrichment of biotinylated proteins in a 96-well format, and optimization of the quantitative mass spectrometry (MS) acquisition method. Combined with data-independent acquisition (DIA) MS, our pipeline outperforms manual enrichment and data-dependent acquisition (DDA) MS regarding reproducibility of protein identification and quantification. We apply the pipeline to map subcellular proteomes for endosomes, late endosomes/lysosomes, the Golgi apparatus, and the plasma membrane. Moreover, using serotonin receptor (5HT 2A ) as a model, we investigated agonist-induced dynamics in protein-protein interactions. Importantly, the approach presented here is universally applicable for PL proteomics using all biotinylation-based PL enzymes, increasing both throughput and reproducibility of standard protocols.
Interactions between proteins are fundamental for every biological process and especially important in cell signaling pathways. Biochemical techniques that evaluate these protein-protein interactions (PPIs), such as in vitro pull downs and coimmunoprecipitations, have become popular in most laboratories and are essential to identify and validate novel protein binding partners. Most PPIs occur through small domains or motifs, which are challenging and laborious to map by using standard biochemical approaches because they generally require the cloning of several truncation mutants. Moreover, these classical methodologies provide limited resolution of the interacting interface. Here, we describe the development of an alternative technique to overcome these limitations termed "Protein Domain mapping using Yeast 2 Hybrid-Next Generation Sequencing" (DoMY-Seq), which leverages both yeast two-hybrid and next-generation sequencing techniques. In brief, our approach involves creating a library of fragments derived from an open reading frame of interest and enriching for the interacting fragments using a yeast two-hybrid reporter system. Next-generation sequencing is then subsequently employed to read and map the sequence of the interacting fragment, yielding a high-resolution plot of the binding interface. We optimized DoMY-Seq by taking advantage of the well-described and high-affinity interaction between KRAS and CRAF, and we provide high-resolution domain mapping on this and other protein-interacting pairs, including CRAF-MEK1, RIT1-RGL3, and p53-MDM2. Thus, DoMY-Seq provides an unbiased alternative method to rapidly identify the domains involved in PPIs by advancing the use of yeast two-hybrid technology.
Peptides have attracted much attention recently owing to their well-balanced properties as drugs against protein-protein interaction (PPI) surfaces. Molecular simulation-based predictions of binding sites and amino acid residues with high affinity to PPI surfaces are expected to accelerate the design of peptide drugs. Mixed-solvent molecular dynamics (MSMD), which adds probe molecules or fragments of functional groups as solutes to the hydration model, detects the binding hotspots and cryptic sites induced by small molecules. The detection results vary depending on the type of probe molecule; thus, they provide important information for drug design. For rational peptide drug design using MSMD, we proposed MSMD with amino acid residue probes, named amino acid probe-based MSMD (AAp-MSMD), to detect hotspots and identify favorable amino acid types on protein surfaces to which peptide drugs bind. We assessed our method in terms of hotspot detection at the amino acid probe level and binding free energy prediction with amino acid probes at the PPI site for the complex structure that formed the PPI. In hotspot detection, the max-spatial probability distribution map (max-PMAP) obtained from AAp-MSMD detected the PPI site, to which each type of amino acid can bind favorably. In the binding free energy prediction using amino acid probes, ΔGFE obtained from AAp-MSMD roughly estimated the experimental binding affinities from the structure-activity relationship. AAp-MSMD, with amino acid probes, provides estimated binding sites and favorable amino acid types at the PPI site of a target protein.
Inflammatory bowel disease (IBD) is the common name for chronic disorders associated with the inflammation of the gastrointestinal tract. IBD is triggered by environmental factors in genetically susceptible individuals and has a significant number of incidences worldwide. Crohn's disease (CD) and ulcerative colitis (UC) are the two distinct types of IBD. While involvement in ulcerative colitis is limited to the colon, Crohn's disease may involve the whole gastrointestinal tract. Although these two disorders differ in macroscopic inflammation patterns, they share various molecular pathogenesis, yet the diagnosis can remain unclear, and it is important to reveal their molecular signatures in the network level. Improved molecular understanding may reveal disease type-specific and even individual-specific targets. To this aim, we determine the subnetworks specific to UC and CD by mapping transcriptome data to protein-protein interaction (PPI) networks using two different approaches [KeyPathwayMiner (KPM) and stringApp] and perform the functional enrichment analysis of the resulting disease type-specific subnetworks. TP63 was identified as the hub gene in the UC-specific subnet and p63 tumor protein, being in the same family as p53 and p73, has been studied in literature for the risk associated with colorectal cancer and IBD. APP was identified as the hub gene in the CD-specific subnet, and it has an important role in the pathogenesis of Alzheimer's disease (AD). This relation suggests that some similar genetic factors may be effective in both AD and CD. Last, in order to understand the biological meaning of these disease-specific subnets, they were functionally enriched. It is important to note that chemokines-special types of cytokines-and antibacterial response are important in UC-specific subnets, whereas cytokines and antimicrobial responses as well as cancer-related pathways are important in CD-specific subnets. Overall, these findings reveal the differences between IBD subtypes at the molecular level and can facilitate diagnosis for UC and CD as well as provide potential molecular targets that are specific to disease subtypes.
Protein-protein interaction (PPI) data are widely used to generate network models that aim to describe the relationships between proteins in biological systems. The fidelity and completeness of such networks is primarily limited by the paucity of protein interaction information and by the restriction of most of these data to just a few widely studied experimental organisms. In order to extend the utility of existing PPIs, computational methods can be used that exploit functional conservation between orthologous proteins across taxa to predict putative PPIs or 'interologs'. To date most interolog prediction efforts have been restricted to specific biological domains with fixed underlying data sources and there are no software tools available that provide a generalised framework for 'on-the-fly' interolog prediction.
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