Dynamic Protein-protein Interaction Networks Research Articles

Detecting temporal protein complexes from dynamic protein-protein interaction networks.

BackgroundProteins dynamically interact with each other to perform their biological functions. The dynamic operations of protein interaction networks (PPI) are also reflected in the dynamic formations of protein complexes. Existing protein complex detection algorithms usually overlook the inherent temporal nature of protein interactions within PPI networks. Systematically analyzing the temporal protein complexes can not only improve the accuracy of protein complex detection, but also strengthen our biological knowledge on the dynamic protein assembly processes for cellular organization.ResultsIn this study, we propose a novel computational method to predict temporal protein complexes. Particularly, we first construct a series of dynamic PPI networks by joint analysis of time-course gene expression data and protein interaction data. Then a Time Smooth Overlapping Complex Detection model (TS-OCD) has been proposed to detect temporal protein complexes from these dynamic PPI networks. TS-OCD can naturally capture the smoothness of networks between consecutive time points and detect overlapping protein complexes at each time point. Finally, a nonnegative matrix factorization based algorithm is introduced to merge those very similar temporal complexes across different time points.ConclusionsExtensive experimental results demonstrate the proposed method is very effective in detecting temporal protein complexes than the state-of-the-art complex detection techniques.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-335) contains supplementary material, which is available to authorized users.

Directed Network Wiring Identifies a Key Protein Interaction in Embryonic Stem Cell Differentiation

Cell signaling depends on dynamic protein-protein interaction (PPI) networks, often assembled through modular domains each interacting with multiple peptide motifs. This complexity raises a conceptual challenge, namely to define whether a particular cellular response requires assembly of the complete PPI network of interest or can be driven by a specific interaction. To address this issue, we designed variants of the Grb2 SH2 domain ("pY-clamps") whose specificity is highly biased toward a single phosphotyrosine (pY) motif among many potential pYXNX Grb2-binding sites. Surprisingly, directing Grb2 predominantly to a single pY site of the Ptpn11/Shp2 phosphatase, but not other sites tested, was sufficient for differentiation of the essential primitive endoderm lineage from embryonic stem cells. Our data suggest that discrete connections within complex PPI networks can underpin regulation of particular biological events. We propose that this directed wiring approach will be of general utility in functionally annotating specific PPIs.

Dynamic protein interaction network construction and applications

With more dynamic information available, researchers' attention has recently shifted from static properties to dynamic properties of protein-protein interaction networks. To compensate the limited ability of technologies of detecting dynamic protein-protein interactions, dynamic protein interaction networks (DPINs) can be constructed by involving proteomic, genomic, and transcriptome analyses. Two groups of DPIN construction methods are classified based on the different focuses on dynamic information extracted from gene expression data. The dynamics of one kind of DPINs is reflected by the changes in protein presence varying with time, while that of the other kind of DPINs is reflected by the differences of coexpression under different conditions. In this review, the applications on DPINs will be discussed, including protein complexes/functional modules and network organization analysis, biomarkers detection in the progression or prognosis of the disease, and network medicine. We also point out the challenges in DPINs construction and future directions in the research of DPINs at the end of this review.

Co-regulated protein functional modules with varying activities in dynamic PPI networks

Current methods for the detection of differential gene expression focus on finding individual genes that may be responsible for certain diseases or external irritants. However, for common genetic diseases, multiple genes and their interactions should be understood and treated together during the exploration of disease causes and possible drug design. The present study focuses on analyzing the dynamic patterns of co-regulated modules during biological progression and determining those having remarkably varying activities, using the yeast cell cycle as a case study. We first constructed dynamic active protein-protein interaction networks by modeling the activity of proteins and assembling the dynamic co-regulation protein network at each time point. The dynamic active modules were detected using a method based on the Bayesian graphical model and then the modules with the most varied dispersion of clustering coefficients, which could be responsible for the dynamic mechanism of the cell cycle, were identified. Comparison of results from our functional module detection with the state-of-art functional module detection methods and validation of the ranking of activities of functional modules using GO annotations demonstrate the efficacy of our method for narrowing the scope of possible essential responding modules that could provide multiple targets for biologists to further experimentally validate.

Identifying protein complexes and functional modules--from static PPI networks to dynamic PPI networks

Cellular processes are typically carried out by protein complexes and functional modules. Identifying them plays an important role for our attempt to reveal principles of cellular organizations and functions. In this article, we review computational algorithms for identifying protein complexes and/or functional modules from protein-protein interaction (PPI) networks. We first describe issues and pitfalls when interpreting PPI networks. Then based on types of data used and main ideas involved, we briefly describe protein complex and/or functional module identification algorithms in four categories: (i) those based on topological structures of unweighted PPI networks; (ii) those based on characters of weighted PPI networks; (iii) those based on multiple data integrations; and (iv) those based on dynamic PPI networks. The PPI networks are modelled increasingly precise when integrating more types of data, and the study of protein complexes would benefit by shifting from static to dynamic PPI networks.

Construction and application of dynamic protein interaction network based on time course gene expression data

In recent years, researchers have tried to inject dynamic information into static protein interaction networks (PINs). The paper first proposes a three-sigma method to identify active time points of each protein in a cellular cycle, where three-sigma principle is used to compute an active threshold for each gene according to the characteristics of its expression curve. Then a dynamic protein interaction network (DPIN) is constructed, which includes the dynamic changes of protein interactions. To validate the efficiency of DPIN, MCL, CPM, and core attachment algorithms are applied on two different DPINs, the static PIN and the time course PIN (TC-PIN) to detect protein complexes. The performance of each algorithm on DPINs outperforms those on other networks in terms of matching with known complexes, sensitivity, specificity, f-measure, and accuracy. Furthermore, the statistics of three-sigma principle show that 23-45% proteins are active at a time point and most proteins are active in about half of cellular cycle. In addition, we find 94% essential proteins are in the group of proteins that are active at equal or great than 12 timepoints of GSE4987, which indicates the potential existence of feedback mechanisms that can stabilize the expression level of essential proteins and might provide a new insight for predicting essential proteins from dynamic protein networks.

Quantitative proteomics: A strategic ally to map protein interaction networks

Many physiological processes are regulated by dynamic protein interaction networks whose characterization provides valuable information on cell biology. Several strategies can be used to analyze protein-protein interactions. Among them, affinity purification combined with mass spectrometry (AP-MS) is arguably the most widely employed technique, not only owing to its high throughput and sensitivity but also because it can answer critical questions such as where, when, and how protein-protein interactions occur. In AP-MS workflows, both the target protein and its interacting partners are isolated before being identified by MS. The main challenge of this approach is to distinguish bona fide binders from background contaminants. This review focuses on the different strategies designed to circumvent this limitation. In this regard, the combination of quantitative proteomics and affinity purification emerges as one of the most powerful, yet relatively simple, strategies to characterize protein-protein interactions.

Mapping a dynamic innate immunity protein interaction network regulating type I interferon production (108.2)

Abstract We use co-immunoprecipitation and mass spectrometry to identify antiviral signaling networks which regulate innate immune responses. Fifty-eight baits were associated with 260 interacting proteins forming a Human Innate Immunity Interactome of 401 unique interactions; 21% of interactions were remodeled following RNA, DNA, or LPS stimulation. Overexpression and depletion analyses identified 22 novel genes which regulate NF-kB and ISRE reporter activity, viral replication, or virus-induced interferon production. The innate immune interactome provides a dynamic physical and regulatory network which serves as a foundation for mechanistic analysis of antiviral signaling and links innate immunity with other cell processes.

Proteomic and protein interaction network analysis of human T lymphocytes during cell‐cycle entry

Regulating the transition of cells such as T lymphocytes from quiescence (G(0)) into an activated, proliferating state involves initiation of cellular programs resulting in entry into the cell cycle (proliferation), the growth cycle (blastogenesis, cell size) and effector (functional) activation. We show the first proteomic analysis of protein interaction networks activated during entry into the first cell cycle from G(0). We also provide proof of principle that blastogenesis and proliferation programs are separable in primary human T cells. We employed a proteomic profiling method to identify large-scale changes in chromatin/nuclear matrix-bound and unbound proteins in human T lymphocytes during the transition from G(0) into the first cell cycle and mapped them to form functionally annotated, dynamic protein interaction networks. Inhibiting the induction of two proteins involved in two of the most significantly upregulated cellular processes, ribosome biogenesis (eIF6) and hnRNA splicing (SF3B2/SF3B4), showed, respectively, that human T cells can enter the cell cycle without growing in size, or increase in size without entering the cell cycle.

Mapping a Dynamic Innate Immunity Protein Interaction Network Regulating Type I Interferon Production

To systematically investigate innate immune signaling networks regulating production of type I interferon, we analyzed protein complexes formed after microbial recognition. Fifty-eight baits were associated with 260 interacting proteins forming a human innate immunity interactome for type I interferon (HI5) of 401 unique interactions; 21% of interactions were modulated by RNA, DNA, or LPS. Overexpression and depletion analyses identified 22 unique genes that regulated NF-κB and ISRE reporter activity, viral replication, or virus-induced interferon production. Detailed mechanistic analysis defined a role for mind bomb (MIB) E3 ligases in K63-linked ubiquitination of TBK1, a kinase that phosphorylates IRF transcription factors controlling interferon production. Mib genes selectively controlled responses to cytosolic RNA. MIB deficiency reduced antiviral activity, establishing the role of MIB proteins as positive regulators of antiviral responses. The HI5 provides a dynamic physical and regulatory network that serves as a resource for mechanistic analysis of innate immune signaling.

The role of Hepatitis C Virus in the dynamic protein interaction networks of hepatocellular cirrhosis and carcinoma

As an important risk factor for Hepatocellular Carcinoma (HCC), Hepatitis C Virus (HCV) infection can induce cirrhosis and HCC. But, the molecular mechanisms of HCV-induced transformation remain largely unknown. In this study, first, we identified the dysfunctional protein interaction networks in cirrhosis and HCC based on the gene expression profiles of 19 normal, 58 cirrhotic and 47 HCC liver tissues. Then, the relationship between dysfunctional networks and HCV infection was analysed. Our results may help understand the mechanisms of HCV-induced malignant transformation and provide clues to cirrhosis and HCC therapy or prevention.

Towards a matrix mechanics framework for dynamic protein network

Protein–protein interaction networks are currently visualized by software generated interaction webs based upon static experimental data. Current state is limited to static, mostly non-compartmental network and non time resolved protein interactions. A satisfactory mathematical foundation for particle interactions within a viscous liquid state (situation within the cytoplasm) does not exist nor do current computer programs enable building dynamic interaction networks for time resolved interactions. Building mathematical foundation for intracellular protein interactions can be achieved in two increments (a) trigger and capture the dynamic molecular changes for a select subset of proteins using several model systems and high throughput time resolved proteomics and, (b) use this information to build the mathematical foundation and computational algorithm for a compartmentalized and dynamic protein interaction network. Such a foundation is expected to provide benefit in at least two spheres: (a) understanding physiology enabling explanation of phenomenon such as incomplete penetrance in genetic disorders and (b) enabling several fold increase in biopharmaceutical production using impure starting materials.

Molecular Self‐Assembly, Chemical Lithography, and Biochemical Tweezers: A Path for the Fabrication of Functional Nanometer‐Scale Protein Arrays

Exploration of the immensurable gene products of the ∼25000 human genes and mapping of dynamic protein interaction networks are key challenges in current biological research. [1] Highly parallel protein chip technologies deliver promising tools for these challenges even down to the singlemolecule level. [2] Whereas DNA molecules are robust and easy to immobilize in a functional manner—as seen in the triumph of DNA chip technology [3] —proteins are highly sensitive and chemically heterogeneous entities denaturing rapidly in vitro. Using multivalency as a design principle, a chemical recognition unit with exceptionally high affinity for proteins was designed, while maintaining their functionality. [4] These multivalent chelator heads consist of cumulated N-nitrilotriacetic acid (NTA) moieties, which provide chemical recognition for histidine-tagged proteins by complexation of transition metal ions, for example, Ni(II). These strong as well as specific binding capabilities can easily be switched on and off by addition or removal of Ni(II) ions. Such properties make tris-NTA chelators ideal “biochemical tweezers” for attaching proteins to surfaces and releasing them again. A tris-NTAunit coordinates six imidazole moieties and, thus, perfectly matches the coordination demands of a hexahistidine tag with binding affinities in the nanomolar range. [5] For the generation of structured protein arrays, a lithography step is necessary. Both electron-beam lithography (EBL) or extreme UV interference lithography (EUV-IL) are promising for this task. EBL gives the largest variation of pattern shapes and lateral dimensions in a very controlled manner. [6] The application of focused e-beams provides a chance to even generate structures capable of immobilizing single biological molecules. EUV-IL can create highly parallel patterns over large areas in a single processing step with the resolution approaching the EBL limit, [7] which is of great potential for applications in nanotechnology. In this Communication we present a new path for the fabrication of solid templates for the laterally defined and functional immobilization of proteins. The technology is based on the combination of electron-induced chem

An integrated mass spectrometric and computational framework for the analysis of protein interaction networks

Biological systems are controlled by protein complexes that associate into dynamic protein interaction networks. We describe a strategy that analyzes protein complexes through the integration of label-free, quantitative mass spectrometry and computational analysis. By evaluating peptide intensity profiles throughout the sequential dilution of samples, the MasterMap system identifies specific interaction partners, detects changes in the composition of protein complexes and reveals variations in the phosphorylation states of components of protein complexes. We use the complexes containing the human forkhead transcription factor FoxO3A to demonstrate the validity and performance of this technology. Our analysis identifies previously known and unknown interactions of FoxO3A with 14-3-3 proteins, in addition to identifying FoxO3A phosphorylation sites and detecting reduced 14-3-3 binding following inhibition of phosphoinositide-3 kinase. By improving specificity and sensitivity of interaction networks, assessing post-translational modifications and providing dynamic interaction profiles, the MasterMap system addresses several limitations of current approaches for protein complexes.

An Integrated Mass Spectrometry‐Based Proteomics Approach‐QTAX to Decipher the 26S Proteasome Interacting Network

We have developed an integrated proteomics approach-QTAX to decipher in vivo protein-protein interactions in the cellular environment and applied this strategy to globally map the 26S proteasome interaction network. We employed in vivo formaldehyde cross-linking to freeze both stable and transient interactions occurring in intact cells prior to lysis. To achieve high purification efficiency under fully denaturing condition, a new tandem affinity tag consisting of a hexahistidine sequence and an in vivo biotinylation signal was adopted for affinity-based purification of cross-linked products. Tandem-affinity purification after in vivo cross-linking was combined with tandem mass spectrometry coupled with a quantitative SILAC strategy to carry out unambiguous protein identification and quantification of specific protein interactions. With this method, we have captured and identified the full composition of yeast 26S proteasome complex as well as the two known soluble ubiquitin receptors. Quantitative mass spectrometry analysis allowed us to distinguish specific proteasome interacting proteins (PIPs) from background proteins and led to the identification of a total of 64 potential PIPs, of which 43 are novel interactions. The method offers an advanced technical approach to elucidate the proteasome's dynamic protein interaction networks, and can find a wide range of applications in the studies of interaction networks of other macromolecular protein complexes.

An Integrated Mass Spectrometry-based Proteomic Approach

We developed an integrated proteomic approach to decipher in vivo protein-protein interactions and applied this strategy to globally map the 26 S proteasome interaction network in yeast. We termed this approach QTAX for quantitative analysis of tandem affinity purified in vivo cross-linked (X) protein complexes. For this work, in vivo formaldehyde cross-linking was used to freeze both stable and transient interactions occurring in intact cells prior to lysis. To isolate cross-linked protein complexes with high purification efficiency under fully denaturing conditions, a new tandem affinity tag consisting of a hexahistidine sequence and an in vivo biotinylation signal was adopted for affinity-based purification. Tandem affinity purification after in vivo cross-linking was combined with tandem mass spectrometry coupled with a quantitative SILAC (stable isotope labeling of amino acids in cell culture) strategy to carry out unambiguous protein identification and quantification of specific protein interactions. Using this method, we captured and identified the full composition of yeast 26 S proteasome complex as well as the two known ubiquitin receptors, Rad23 and Dsk2. Quantitative mass spectrometry analysis allowed us to distinguish specific proteasome-interacting proteins (PIPs) from background proteins and led to the identification of a total of 64 potential PIPs of which 42 are novel interactions. Among the 64 putative specific PIPs, there are ubiquitin pathway components, ubiquitinated substrates, chaperones, and transcription and translation regulators, demonstrating the efficacy of the developed approach in capturing in vivo protein interactions. The method offers an advanced technical approach to elucidate the dynamic protein interaction networks of the proteasome and can find a wide range of applications in the studies of other macromolecular protein complex interaction networks.

Large-Scale Profiling of Rab GTPase Trafficking Networks: The Membrome

Rab GTPases and SNARE fusion proteins direct cargo trafficking through the exocytic and endocytic pathways of eukaryotic cells. We have used steady state mRNA expression profiling and computational hierarchical clustering methods to generate a global overview of the distribution of Rabs, SNAREs, and coat machinery components, as well as their respective adaptors, effectors, and regulators in 79 human and 61 mouse nonredundant tissues. We now show that this systems biology approach can be used to define building blocks for membrane trafficking based on Rab-centric protein activity hubs. These Rab-regulated hubs provide a framework for an integrated coding system, the membrome network, which regulates the dynamics of the specialized membrane architecture of differentiated cells. The distribution of Rab-regulated hubs illustrates a number of facets that guides the overall organization of subcellular compartments of cells and tissues through the activity of dynamic protein interaction networks. An interactive website for exploring datasets comprising components of the Rab-regulated hubs that define the membrome of different cell and organ systems in both human and mouse is available at http://www.membrome.org/.