Thousands of interactions assemble proteins into modules that impart spatial and functional organization to the cellular proteome. Through affinity-purification mass spectrometry, we have created two ...proteome-scale, cell-line-specific interaction networks. The first, BioPlex 3.0, results from affinity purification of 10,128 human proteins—half the proteome—in 293T cells and includes 118,162 interactions among 14,586 proteins. The second results from 5,522 immunoprecipitations in HCT116 cells. These networks model the interactome whose structure encodes protein function, localization, and complex membership. Comparison across cell lines validates thousands of interactions and reveals extensive customization. Whereas shared interactions reside in core complexes and involve essential proteins, cell-specific interactions link these complexes, “rewiring” subnetworks within each cell’s interactome. Interactions covary among proteins of shared function as the proteome remodels to produce each cell’s phenotype. Viewable interactively online through BioPlexExplorer, these networks define principles of proteome organization and enable unknown protein characterization.
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•Two protein interaction networks built from 15,650 pull-downs in two human cell lines•Extensive network remodeling reflects specialized biology of each cell line•Shared interactions form core complexes with essential, conserved functions•Networks reveal biological context for thousands of uncharacterized proteins
Comparative analysis of large-scale protein-protein interactions across two cell lines highlights context-specific interactions and proteome-scale shifts in how functional networks are arranged.
This mini review focuses on advances in biophysical techniques to study polyphenol interactions with proteins. Polyphenols have many beneficial pharmacological properties, as a result of which they ...have been the subject of intensive studies. The most conventional techniques described here can be divided into three groups: (i) methods used for screening (in-situ methods); (ii) methods used to gain insight into the mechanisms of polyphenol-protein interactions; and (iii) methods used to study protein aggregation and precipitation. All of these methods used to study polyphenol-protein interactions are based on modifications to the physicochemical properties of the polyphenols or proteins after binding/complex formation in solution. To date, numerous review articles have been published in the field of polyphenols. This review will give a brief insight in computational methods and biosensors and cell-based methods, spectroscopic methods including fluorescence emission, UV-vis adsorption, circular dichroism, Fourier transform infrared and mass spectrometry, nuclear magnetic resonance, X-ray diffraction, and light scattering techniques including small-angle X-ray scattering and small-angle neutron scattering, and calorimetric techniques (isothermal titration calorimetry and differential scanning calorimetry), microscopy, the techniques which have been successfully used for polyphenol-protein interactions. At the end the new methods based on single molecule detection with high potential to study polyphenol-protein interactions will be presented. The advantages and disadvantages of each technique will be discussed as well as the thermodynamic, kinetic or structural parameters, which can be obtained. The other relevant biophysical experimental techniques that have proven to be valuable, such electrochemical methods, hydrodynamic techniques and chromatographic techniques will not be described here.
Studies of metabolite–protein interactions: A review Matsuda, Ryan; Bi, Cong; Anguizola, Jeanethe ...
Journal of chromatography. B, Analytical technologies in the biomedical and life sciences,
09/2014, Letnik:
966
Journal Article
Recenzirano
Odprti dostop
•Interactions involving metabolites and proteins as binding agents are discussed.•An overview is given of previous methods used to study these interactions.•Drug-, hormone-, and fatty acid–protein ...interactions are considered.•Some effects of metabolic diseases on protein binding are also examined.
The study of metabolomics can provide valuable information about biochemical pathways and processes at the molecular level. There have been many reports that have examined the structure, identity and concentrations of metabolites in biological systems. However, the binding of metabolites with proteins is also of growing interest. This review examines past reports that have looked at the binding of various types of metabolites with proteins. An overview of the techniques that have been used to characterize and study metabolite–protein binding is first provided. This is followed by examples of studies that have investigated the binding of hormones, fatty acids, drugs or other xenobiotics, and their metabolites with transport proteins and receptors. These examples include reports that have considered the structure of the resulting solute–protein complexes, the nature of the binding sites, the strength of these interactions, the variations in these interactions with solute structure, and the kinetics of these reactions. The possible effects of metabolic diseases on these processes, including the impact of alterations in the structure and function of proteins, are also considered.
Structural information about protein‐protein interactions, often missing at the interactome scale, is important for mechanistic understanding of cells and rational discovery of therapeutics. Protein ...docking provides a computational alternative for such information. However, ranking near‐native docked models high among a large number of candidates, often known as the scoring problem, remains a critical challenge. Moreover, estimating model quality, also known as the quality assessment problem, is rarely addressed in protein docking. In this study, the two challenging problems in protein docking are regarded as relative and absolute scoring, respectively, and addressed in one physics‐inspired deep learning framework. We represent protein and complex structures as intra‐ and inter‐molecular residue contact graphs with atom‐resolution node and edge features. And we propose a novel graph convolutional kernel that aggregates interacting nodes’ features through edges so that generalized interaction energies can be learned directly from 3D data. The resulting energy‐based graph convolutional networks (EGCN) with multihead attention are trained to predict intra‐ and inter‐molecular energies, binding affinities, and quality measures (interface RMSD) for encounter complexes. Compared to a state‐of‐the‐art scoring function for model ranking, EGCN significantly improves ranking for a critical assessment of predicted interactions (CAPRI) test set involving homology docking; and is comparable or slightly better for Score_set, a CAPRI benchmark set generated by diverse community‐wide docking protocols not known to training data. For Score_set quality assessment, EGCN shows about 27% improvement to our previous efforts. Directly learning from 3D structure data in graph representation, EGCN represents the first successful development of graph convolutional networks for protein docking.
Global insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype-phenotype relationships
. Here we present a human ...'all-by-all' reference interactome map of human binary protein interactions, or 'HuRI'. With approximately 53,000 protein-protein interactions, HuRI has approximately four times as many such interactions as there are high-quality curated interactions from small-scale studies. The integration of HuRI with genome
, transcriptome
and proteome
data enables cellular function to be studied within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying the specific subcellular roles of protein-protein interactions. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms that might underlie tissue-specific phenotypes of Mendelian diseases. HuRI is a systematic proteome-wide reference that links genomic variation to phenotypic outcomes.
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•Continuous technical and methodological advances over the last two decades have led to many genome-wide systematically-generated protein–protein interaction (PPI) maps.•To help ...store, visualize, analyze and disseminate these specialized experimental datasets via the web, we developed the freely-available Open-source Protein Interaction Platform (openPIP) as a customizable web portal designed to host experimental PPI maps.•No coding skills are required to set up and customize the database and web portal.•OpenPIP has been used to build the databases and web portals of two major protein interactome maps, the Human and Yeast Reference Protein Interactome maps (HuRI and YeRI, respectively).•OpenPIP is freely available as a ready-to-use Docker container for hosting and sharing PPI data with the scientific community.
Knowing which proteins interact with each other is essential information for understanding how most biological processes at the cellular and organismal level operate and how their perturbation can cause disease. Continuous technical and methodological advances over the last two decades have led to many genome-wide systematically-generated protein–protein interaction (PPI) maps. To help store, visualize, analyze and disseminate these specialized experimental datasets via the web, we developed the freely-available Open-source Protein Interaction Platform (openPIP) as a customizable web portal designed to host experimental PPI maps. Such a portal is often required to accompany a paper describing the experimental data set, in addition to depositing the data in a standard repository. No coding skills are required to set up and customize the database and web portal. OpenPIP has been used to build the databases and web portals of two major protein interactome maps, the Human and Yeast Reference Protein Interactome maps (HuRI and YeRI, respectively). OpenPIP is freely available as a ready-to-use Docker container for hosting and sharing PPI data with the scientific community at http://openpip.baderlab.org/ and the source code can be downloaded from https://github.com/BaderLab/openPIP/.
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.
A practical guide to the fundamentals of protein interaction network mapping, providing information to help researchers make effective use of proteomics approaches. A range of new and well‐established experimental and computational methods and resources are covered.
This protocol describes the use of TurboID and split-TurboID in proximity labeling applications for mapping protein-protein interactions and subcellular proteomes in live mammalian cells. TurboID is ...an engineered biotin ligase that uses ATP to convert biotin into biotin-AMP, a reactive intermediate that covalently labels proximal proteins. Optimized using directed evolution, TurboID has substantially higher activity than previously described biotin ligase-related proximity labeling methods, such as BioID, enabling higher temporal resolution and broader application in vivo. Split-TurboID consists of two inactive fragments of TurboID that can be reconstituted through protein-protein interactions or organelle-organelle interactions, which can facilitate greater targeting specificity than full-length enzymes alone. Proteins biotinylated by TurboID or split-TurboID are then enriched with streptavidin beads and identified by mass spectrometry. Here, we describe fusion construct design and characterization (variable timing), proteomic sample preparation (5-7 d), mass spectrometric data acquisition (2 d), and proteomic data analysis (1 week).
We introduce clustering with overlapping neighborhood expansion (ClusterONE), a method for detecting potentially overlapping protein complexes from protein-protein interaction data. ...ClusterONE-derived complexes for several yeast data sets showed better correspondence with reference complexes in the Munich Information Center for Protein Sequence (MIPS) catalog and complexes derived from the Saccharomyces Genome Database (SGD) than the results of seven popular methods. The results also showed a high extent of functional homogeneity.
As viruses continue to pose risks to global health, having a better understanding of virus⁻host protein⁻protein interactions aids in the development of treatments and vaccines. Here, we introduce ...Viruses.STRING, a protein⁻protein interaction database specifically catering to virus⁻virus and virus⁻host interactions. This database combines evidence from experimental and text-mining channels to provide combined probabilities for interactions between viral and host proteins. The database contains 177,425 interactions between 239 viruses and 319 hosts. The database is publicly available at viruses.string-db.org, and the interaction data can also be accessed through the latest version of the Cytoscape STRING app.
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