Validation Of Protein-protein Interactions Research Articles

Simultaneous Determination and Subcellular Localization of Protein-Protein Interactions in Plant Cells Using Bimolecular Fluorescence Complementation Assay.

The bimolecular fluorescence complementation (BiFC) assay allows the visualization of protein-protein interactions in their native state within living systems. The BiFC assay is based on the in vivo complementation of nonfluorescent component parts of a fluorescent protein through the interaction or proximity target proteins, each fused to a different component of the fluorescent protein. Expansion of the BiFC toolkit with an increasing spectrum of fluorescence markers and catalog of Gateway-compatible vectors for high-throughput screening, has made BiFC an exceedingly powerful tool in discovering new protein interactions or providing backup evidence for known ones. Apart from the validation of protein-protein interactions, BiFC offers the additional benefit of providing information on the subcellular localization of protein interaction complexes. Subcellular localization to a specific subcellular compartment or organelle may be further validated by the coexpression of a fluorescence-labeled protein marker. Here we describe an efficient yet simple protocol for simultaneous determination and subcellular localization of protein-protein interactions in plant cells.

Technologies for the identification and validation of protein-protein interactions.

Proteins are large molecules that play essential roles in all living organisms. In most molecular processes in each cell, proteins usually do not function alone but through physiological interactions with various ligands. The most common interacting molecules for proteins are other proteins, and they act together by protein-protein interactions (PPIs) to create larger protein complexes. The impairment of physiological PPIs or establishing PPIs with pathological proteins often leads to the development of diseases. To bring insights on the knowledge about the physiological functions of proteins in biological processes, and to understand the development and pathogenesis of diseases, numerous qualitative and quantitative methods have been developed. In this review, we summarize the most commonly used methods for studying PPIs, and discuss their advantages and drawbacks.

Validation of protein-protein interactions in databases and resources: the need to identify interaction detection methods that provide binary or indirect experimental evidences

Validation of protein-protein interactions in databases and resources: the need to identify interaction detection methods that provide binary or indirect experimental evidences

Towards improving proximity labeling by the biotin ligase BirA.

The discovery and validation of protein-protein interactions provides a knowledge base that is critical for defining protein networks and how they underpin the biology of the cell. Identification of protein interactions that are highly transient, or sensitive to biochemical disruption, can be very difficult. This challenge has been met by proximity labeling methods which generate reactive species that chemically modify neighboring proteins. The most widely used proximity labeling method is BioID, which features a mutant biotin ligase BirA(Arg118Gly), termed BirA*, fused to a protein of interest. Here, we explore how amino acid substitutions at Arg118 affect the biochemical properties of BirA. We found that relative to wild-type BirA, the Arg118Lys substitution both slightly reduced biotin affinity and increased the release of reactive biotinyl-5'-AMP. BioID using a BirA(Arg118Lys)-Lamin A fusion enabled identification of PCNA as a lamina-proximal protein in HEK293T cells, a finding that was validated by immunofluorescence microscopy. Our data expand on the concept that proximity labeling by BirA fused to proteins of interest can be modulated by amino acid substitutions that affect biotin affinity and the release of biotinyl-5'-AMP.

Exploring the protein-protein interaction landscape in plants.

Protein-protein interactions (PPIs) represent an essential aspect of plant systems biology. Identification of key protein players and their interaction networks provide crucial insights into the regulation of plant developmental processes and into interactions of plants with their environment. Despite the great advance in the methods for the discovery and validation of PPIs, still several challenges remain. First, the PPI networks are usually highly dynamic, and the in vivo interactions are often transient and difficult to detect. Therefore, the properties of the PPIs under study need to be considered to select the most suitable technique, because each has its own advantages and limitations. Second, besides knowledge on the interacting partners of a protein of interest, characteristics of the interaction, such as the spatial or temporal dynamics, are highly important. Hence, multiple approaches have to be combined to obtain a comprehensive view on the PPI network present in a cell. Here, we present the progress in commonly used methods to detect and validate PPIs in plants with a special emphasis on the PPI features assessed in each approach and how they were or can be used for the study of plant interactions with their environment.

Direct Molecular Fishing of New Protein Partners for Human Thromboxane Synthase

Thromboxane synthase (TBXAS1) catalyzes the isomerization reaction of prostaglandin H2 producing thromboxane A2, the autocrine and paracrine factor in many cell types. A high activity and metastability by these arachidonic acid derivatives suggests the existence of supramolecular structures that are involved in the regulation of the biosynthesis and directed translocation of thromboxane to the receptor. The objective of this study was to identify TBXAS1 protein partners from human liver tissue lysate using a complex approach based on the direct molecular fishing technique, LC-MS/MS protein identification, and protein-protein interaction validation by surface plasmon resonance (SPR). As a result, 12 potential TBXAS1 protein partners were identified, including the components regulating cytoskeleton organization (BBIP1 and ANKMY1), components of the coagulation cascade of human blood (SERPINA1, SERPINA3, APOH, FGA, and FN1), and the enzyme involved in the metabolism of xenobiotics and endogenous bioregulators (CYP2E1). SPR validation on the Biacore 3000 biosensor confirmed the effectiveness of the interaction between CYP2E1 (the enzyme that converts prostaglandin H2 to 12-HHT/thromboxane A2 proantagonist) and TBXAS1 (Kd = (4.3 0.4) 10-7 M). Importantly, the TBXAS1CYP2E1 complex formation increases fivefold in the presence of isatin (indole-2,3-dione, a low-molecular nonpeptide endogenous bioregulator, a product of CYP2E1). These results suggest that the interaction between these hemoproteins is important in the regulation of the biosynthesis of eicosanoids.

Прямой молекулярный фишинг новых белков-партнеров тромбоксансинтазы человека

Thromboxane synthase (TBXAS1) catalyzes the isomerization reaction of prostaglandin H2 producing thromboxane A2, the autocrine and paracrine factor in many cell types. A high activity and metastability by these arachidonic acid derivatives suggests the existence of supramolecular structures that are involved in the regulation of the biosynthesis and directed translocation of thromboxane to the receptor. The objective of this study was to identify TBXAS1 protein partners from human liver tissue lysate using a complex approach based on the direct molecular fishing technique, LC-MS/MS protein identification, and protein-protein interaction validation by surface plasmon resonance (SPR). As a result, 12 potential TBXAS1 protein partners were identified, including the components regulating cytoskeleton organization (BBIP1 and ANKMY1), components of the coagulation cascade of human blood (SERPINA1, SERPINA3, APOH, FGA, and FN1), and the enzyme involved in the metabolism of xenobiotics and endogenous bioregulators (CYP2E1). SPR validation on the Biacore 3000 biosensor confirmed the effectiveness of the interaction between CYP2E1 (the enzyme that converts prostaglandin H2 to 12-HHT/thromboxane A2 proantagonist) and TBXAS1 (Kd = (4.3 ± 0.4) × 10-7 M). Importantly, the TBXAS1•CYP2E1 complex formation increases fivefold in the presence of isatin (indole-2,3-dione, a low-molecular nonpeptide endogenous bioregulator, a product of CYP2E1). These results suggest that the interaction between these hemoproteins is important in the regulation of the biosynthesis of eicosanoids.

Alignment of PPI Networks Using Semantic Similarity for Conserved Protein Complex Prediction.

Network alignment is a computational technique to identify topological similarity of graph data by mapping link patterns. In bioinformatics, network alignment algorithms have been applied to protein-protein interaction (PPI) networks to discover evolutionarily conserved substructures at the system level. In particular, local network alignment of PPI networks searches for conserved functional components between species and predicts unknown protein complexes and signaling pathways. In this article, we present a novel approach of local network alignment by semantic mapping. While most previous methods find protein matches between species by sequence homology, our approach uses semantic similarity. Given Gene Ontology (GO) and its annotation data, we estimate functional closeness between two proteins by measuring their semantic similarity. We adopted a new semantic similarity measure, simVICD, which has the best performance for PPI validation and functional match. We tested alignment between the PPI networks of well-studied yeast protein complexes and the genome-wide PPI network of human in order to predict human protein complexes. The experimental results demonstrate that our approach has higher accuracy in protein complex prediction than graph clustering algorithms, and higher efficiency than previous network alignment algorithms.

DULIP: A Dual Luminescence-Based Co-Immunoprecipitation Assay for Interactome Mapping in Mammalian Cells

Mapping of protein–protein interactions (PPIs) is critical for understanding protein function and complex biological processes. Here, we present DULIP, a dual luminescence-based co-immunoprecipitation assay, for systematic PPI mapping in mammalian cells. DULIP is a second-generation luminescence-based PPI screening method for the systematic and quantitative analysis of co-immunoprecipitations using two different luciferase tags. Benchmarking studies with positive and negative PPI reference sets revealed that DULIP allows the detection of interactions with high sensitivity and specificity. Furthermore, the analysis of a PPI reference set with known binding affinities demonstrated that both low- and high-affinity interactions can be detected with DULIP assays. Finally, using the well-characterized interaction between Syntaxin-1 and Munc18, we found that DULIP is capable of detecting the effects of point mutations on interaction strength. Taken together, our studies demonstrate that DULIP is a sensitive and reliable method of great utility for systematic interactome research. It can be applied for interaction screening and validation of PPIs in mammalian cells. Moreover, DULIP permits the specific analysis of mutation-dependent binding patterns.

Identification and validation of protein-protein interactions by combining co-immunoprecipitation, antigen competition, and stable isotope labeling.

Co-immunoprecipitation (coIP) in combination with mass spectrometry (MS) is a powerful tool to identify potential protein-protein interactions. However, unspecifically precipitated proteins usually result in large numbers of false-positive identifications. Here we describe a detailed protocol particularly useful in plant sciences that is based on (15)N stable isotope labeling of cells, (14)N antigen titration, and coIP/MS to distinguish true from false protein-protein interactions.

Investigation of stable and transient protein–protein interactions: Past, present, and future

This article presents an overview of the literature and a review of recent advances in the analysis of stable and transient protein-protein interactions (PPIs) with a focus on their function within cells, organs, and organisms. The significance of PTMs within the PPIs is also discussed. We focus on methods to study PPIs and methods of detecting PPIs, with particular emphasis on electrophoresis-based and MS-based investigation of PPIs, including specific examples. The validation of PPIs is emphasized and the limitations of the current methods for studying stable and transient PPIs are discussed. Perspectives regarding PPIs, with focus on bioinformatics and transient PPIs are also provided.

Proteomics-based methods for discovery, quantification, and validation of protein-protein interactions.

Proteins play a fundamental role in establishing the diversity of cellular processes in health or disease systems. This diversity is accomplished by a vast array of protein functions. In fact, a protein rarely has a single function. The majority of proteins are involved in numerous cellular processes, and these multiple functions are made possible by interactions with other molecules. The complexity of interactions is substantially increased by the spatial and temporal diversity of proteins. For example, proteins can be part of distinct complexes within different subcellular compartments or at different stages of the cell cycle. Posttranslational modifications can regulate and further expand the ability of proteins to establish localization- or temporal-dependent interactions. This complexity and functional divergence of interactions is further increased by the simultaneous presence of stable, transient, direct, and indirect protein interactions. Thus, an understanding of protein functions cannot be fully accomplished without knowledge of its interactions. Characterizing these interactions is therefore critical to understanding the biology of health and disease systems.

Web-Based Computational System for Protein-Protein Interaction Inference

Recently, high-throughput technologies such as the two-hybrid system, protein chip, Mass Spectrometry, and the phage display have furnished a lot of data on protein-protein interactions (PPIs), but the data has not been accurate so far and the quantity has also been limited. In this respect, computational techniques for the prediction and validation of PPIs have been developed. However, existing computational methods do not take into account the fact that a PPI is actually originated from the interactions of domains that each protein contains. So, in this work, the information on domain modules of individual proteins has been employed in order to find out the protein interaction relationship. The system developed here, WASPI (Web-based Assistant System for Protein-protein interaction Inference), has been implemented to provide many functional insights into the protein interactions and their domains. To achieve those objectives, several preprocessing steps have been taken. First, the domain module information of interacting proteins was extracted by taking advantage of the InterPro database, which includes protein families, domains, and functional sites. The InterProScan program was used in this preprocess. Second, the homology comparison with the GO (Gene Ontology) and COG (Clusters of Orthologous Groups) with an E-value of <TEX>$10^{-5}$</TEX>, <TEX>$10^{-3}$</TEX> respectively, was employed to obtain the information on the function and annotation of each interacting protein of a secondary PPI database in the WASPI. The BLAST program was utilized for the homology comparison.

GRIP: A web-based system for constructing Gold Standard datasets for protein-protein interaction prediction

BackgroundInformation about protein interaction networks is fundamental to understanding protein function and cellular processes. Interaction patterns among proteins can suggest new drug targets and aid in the design of new therapeutic interventions. Efforts have been made to map interactions on a proteomic-wide scale using both experimental and computational techniques. Reference datasets that contain known interacting proteins (positive cases) and non-interacting proteins (negative cases) are essential to support computational prediction and validation of protein-protein interactions. Information on known interacting and non interacting proteins are usually stored within databases. Extraction of these data can be both complex and time consuming. Although, the automatic construction of reference datasets for classification is a useful resource for researchers no public resource currently exists to perform this task.ResultsGRIP (Gold Reference dataset constructor from Information on Protein complexes) is a web-based system that provides researchers with the functionality to create reference datasets for protein-protein interaction prediction in Saccharomyces cerevisiae. Both positive and negative cases for a reference dataset can be extracted, organised and downloaded by the user. GRIP also provides an upload facility whereby users can submit proteins to determine protein complex membership. A search facility is provided where a user can search for protein complex information in Saccharomyces cerevisiae.ConclusionGRIP is developed to retrieve information on protein complex, cellular localisation, and physical and genetic interactions in Saccharomyces cerevisiae. Manual construction of reference datasets can be a time consuming process requiring programming knowledge. GRIP simplifies and speeds up this process by allowing users to automatically construct reference datasets. GRIP is free to access at .