Dynamic Protein-protein Interactions Research Articles

Construction of dynamic protein interaction network based on gene expression data and quartile one principle.

Real protein interaction network (PIN) is dynamic. Researchers created dynamic PIN by combining static PIN with gene expression data to explain the dynamicity evolution of protein interactions. However, all available approaches failed to recognize low- or high-expression proteins as active proteins. Therefore, determining an adequate threshold is one of the system's biological challenges. In this study, a quartile one (q-one) method is proposed to determine the active time points for each protein according to its expression value's features and construct dynamic protein interaction networks (DPINs) in which a protein is added to the network if it is active in two successive time points. This leads to reduce the number of DPINs to half. The efficiency of the q-one approach and three-sigma (3-sigma) method in detecting protein complexes is evaluated using Markov cluster method, clique percolation method, and ClusterONE algorithms. In most cases, q-one outperforms the 3-sigma method in recall, precision and F-measure. This is in addition to its ability to reveal the dynamicity within the protein-protein interaction network and identify essential proteins.

Regression of exchangeable relational arrays

Summary Relational arrays represent measures of association between pairs of actors, often in varied contexts or over time. Trade flows between countries, financial transactions between individuals, contact frequencies between school children in classrooms and dynamic protein-protein interactions are all examples of relational arrays. Elements of a relational array are often modelled as a linear function of observable covariates. Uncertainty estimates for regression coefficient estimators, and ideally the coefficient estimators themselves, must account for dependence between elements of the array, e.g., relations involving the same actor. Existing estimators of standard errors that recognize such relational dependence rely on estimating extremely complex, heterogeneous structure across actors. This paper develops a new class of parsimonious coefficient and standard error estimators for regressions of relational arrays. We leverage an exchangeability assumption to derive standard error estimators that pool information across actors, and are substantially more accurate than existing estimators in a variety of settings. This exchangeability assumption is pervasive in network and array models in the statistics literature, but not previously considered when adjusting for dependence in a regression setting with relational data. We demonstrate improvements in inference theoretically, via a simulation study, and by analysis of a dataset involving international trade.

Single-Step Affinity Purification (ssAP) and Mass Spectrometry of Macromolecular Complexes in the Yeast S. cerevisiae.

Cellular functions are mostly defined by the dynamic interactions of proteins within macromolecular networks. Deciphering the composition of macromolecular complexes and their dynamic rearrangements is the key to get a comprehensive picture of cellular behavior and to understand biological systems. In the past two decades, affinity purification coupled to mass spectrometry has become a powerful tool to comprehensively study interaction networks and their assemblies. To overcome initial limitations of the approach, in particular, the effect of protein and RNA degradation, loss of transient interactors, and poor overall yield of intact complexes from cell lysates, various modifications to affinity purification protocols have been devised over the years. In this chapter, we describe a rapid single-step affinity purification method for the efficient isolation of dynamic macromolecular complexes. The technique employs cell lysis by cryo-milling, which ensures nondegraded starting material in the submicron range, and magnetic beads, which allow for dense antibody-conjugation and thus rapid complex isolation, while avoiding loss of transient interactions. The method is epitope tag-independent, and overcomes many of the previous limitations to produce large interactomes with almost no contamination. The protocol as described here has been optimized for the yeast S. cerevisiae.

Characterizing emerging features in cell dynamics using topological data analysis methods.

Filament-motor interactions inside cells play essential roles in many developmental as well as other biological processes. For instance, actin-myosin interactions drive the emergence or closure of ring channel structures during wound healing or dorsal closure. These dynamic protein interactions and the resulting protein organization lead to rich time-series data generated by using fluorescence imaging experiments or by simulating realistic stochastic models. We propose methods based on topological data analysis to track topological features through time in cell biology data consisting of point clouds or binary images. The framework proposed here is based on computing the persistent homology of the data at each time point and on connecting topological features through time using established distance metrics between topological summaries. The methods retain aspects of monomer identity when analyzing significant features in filamentous structure data, and capture the overall closure dynamics when assessing the organization of multiple ring structures through time. Using applications of these techniques to experimental data, we show that the proposed methods can describe features of the emergent dynamics and quantitatively distinguish between control and perturbation experiments.

Targeted Cross-Linking Mass Spectrometry on Single-Step Affinity Purified Molecular Complexes in the Yeast Saccharomyces cerevisiae.

Protein cross-linking mass spectrometry (XL-MS) has been developed into a powerful and robust tool that is now well implemented and routinely used by an increasing number of laboratories. While bulk cross-linking of complexes provides useful information on whole complexes, it is limiting for the probing of specific protein "neighbourhoods," or vicinity interactomes. For example, it is not unusual to find cross-linked peptide pairs that are disproportionately overrepresented compared to the surface areas of complexes, while very few or no cross-links are identified in other regions. When studying dynamic complexes along their pathways, some vicinity cross-links may be of too low abundance in the pool of heterogenous complexes of interest to be efficiently identified by standard XL-MS. In this chapter, we describe a targeted XL-MS approach from single-step affinity purified (ssAP) complexes that enables the investigation of specific protein "neighbourhoods" within molecular complexes in yeast, using a small cross-linker anchoring tag, the CH-tag. One advantage of this method over a general cross-linking strategy is the possibility to significantly enrich for localized anchored-cross-links within complexes, thus yielding a higher sensitivity to detect highly dynamic or low abundance protein interactions within a specific protein "neighbourhood" occurring along the pathway of a selected bait protein. Moreover, many variations of the method can be employed; the ssAP-tag and the CH-tag can either be fused to the same or different proteins in the complex, or the CH-tag can be fused to multiple protein components in the same cell line to explore dynamic vicinity interactions along a pathway.

Centrality Combination Method Based on Feature Selection for Protein Interaction Networks

Essential proteins are important participants in various life activities and play a vital role in the survival and reproduction of life. The network-based centrality methods are a common way to identify essential proteins for protein interaction networks. Due to the differences between the existing centrality methods, it is a feasible approach to improve the identification accuracy of essential proteins by combining centrality methods. In this paper, we propose a centrality combination method based on feature selection. First, the measure values of the 14 classical centrality methods are viewed as feature data. Then, a subset of the relevant features is selected according to the importance of features. Finally, the centrality methods corresponding to the selected features are combined by using the geometric mean method for the identification of essential proteins. To verify the effectiveness of the combination method, we apply the combination method on the original static protein interaction network (SPIN), the dynamic protein interaction network (DPIN) and the refined dynamic protein interaction network (RDPIN), and compare the result with those by each single centrality method (LAC, DC, DMNC, NC, TP, CLC, BC, LC, CC, KC, CR, EC, PR, LR). The experimental results on the identification of essential proteins shows that the combination method achieves better results in prediction performance than the 14 centrality mehtods in terms of the prediction precision, sensitivity, specificity, positive predictive value, negative predictive value, F-measure and accuracy rate. It has been illustrated that the proposed method can help to identify essential proteins more accurately.

A Node Ranking Method Based on Multiple Layers for Dynamic Protein Interaction Networks

Constructing dynamic protein interaction networks (DPIN) is a common way to improve identification accuracy of essential proteins. The existing methods usually aggregate DPIN into a single-layer network and in which all nodes are sorted by their importance. This makes the dynamic information about proteins in multiple layers lost in the single layer, and thus affects the identification accuracy of essential proteins. This paper proposes a node ranking method based on multiple layers for DPIN to address the problem. First, we calculate the centrality values of all nodes for each time-specific layer, then work out the centrality score of each node by dividing the total of its centrality values across all layers by its layer activity, and finally sort the importance of all nodes by their centrality scores. Different from the methods based on single layer, our method makes full use of centrality values of each protein in time-specific layers, and thus can more effectively utilize the dynamic information of proteins than those methods based on single layer. To evaluate the effectiveness of the node ranking method based on multiple layers, we apply ten network-based centrality methods on multiple layers and compare the results with those on a single layer. Then the predictive performance of the ten centrality methods are validated in terms of sensitivity, specificity, positive predictive value, negative predictive value, F-measure and accuracy. The experimental results for the identification of essential proteins show that the node ranking method based on multiple layers is superior to those based on a single layer and can help to identify essential proteins more accurate.

Studying Protein-Protein Interactions at Plasmodesmata by Measuring Förster Resonance Energy Transfer.

Plasmodesmata (PD) provide interconnectivity between plant cells to enable the intercellular transport and communication that is requisite to multicellularity. Being at the interface of the apoplast, plasma membrane (PM), endoplasmic reticulum (ER), and symplast, PD are uniquely positioned to integrate exogenously and endogenously derived signals with plant developmental and physiological responses. The distinct membrane curvature and composition of PD allow them to function as microdomains to facilitate dynamic protein-protein interactions. Förster resonance energy transfer (FRET) combined with fluorescence lifetime imaging microscopy (FLIM) and fluorescence anisotropic decay measurements provides valuable tools to analyze these interactions in vivo and in planta. Here we describe a detailed methodology to perform FRET-FLIM and fluorescence anisotropy measurements to analyze protein-protein interactions at PD in a transient expression system using Nicotiana benthamiana; however this can be adapted to other plant species and subcellular compartments.

A dominant mutation in β-AMYLASE1 disrupts nighttime control of starch degradation in Arabidopsis leaves.

Arabidopsis (Arabidopsis thaliana) leaves possess a mechanism that couples the rate of nighttime starch degradation to the anticipated time of dawn, thus preventing premature exhaustion of starch and nighttime starvation. To shed light on the mechanism, we screened a mutagenized population of a starvation reporter line and isolated a mutant that starved prior to dawn. The mutant had accelerated starch degradation, and the rate was not adjusted to time of dawn. The mutation responsible led to a single amino acid change (S132N) in the starch degradation enzyme BETA-AMYLASE1 (BAM1; mutant allele named bam1-2D), resulting in a dominant, gain-of-function phenotype. Complete loss of BAM1 (in bam1-1) did not affect rates of starch degradation, while expression of BAM1(S132N) in bam1-1 recapitulated the accelerated starch degradation phenotype of bam1-2D. In vitro analysis of recombinant BAM1 and BAM1(S132N) proteins revealed no differences in kinetic or stability properties, but in leaf extracts, BAM1(S132N) apparently had a higher affinity than BAM1 for an established binding partner required for normal rates of starch degradation, LIKE SEX FOUR1 (LSF1). Genetic approaches showed that BAM1(S132N) itself is likely responsible for accelerated starch degradation in bam1-2D and that this activity requires LSF1. Analysis of plants expressing BAM1 with alanine or aspartate rather than serine at position 132 indicated that the gain-of-function phenotype is not related to phosphorylation status at this position. Our results strengthen the view that control of starch degradation in wild-type plants involves dynamic physical interactions of degradative enzymes and related proteins with a central role for complexes containing LSF1.

A Quantitative Systems Approach to Define Novel Effects of Tumour p53 Mutations on Binding Oncoprotein MDM2.

Understanding transient protein interactions biochemically at the proteome scale remains a long-standing challenge. Current tools developed to study protein interactions in high-throughput measure stable protein complexes and provide binary readouts; they do not elucidate dynamic and weak protein interactions in a proteome. The majority of protein interactions are transient and cover a wide range of affinities. Nucleic acid programmable protein arrays (NAPPA) are self-assembling protein microarrays produced by freshly translating full-length proteins in situ on the array surface. Herein, we have coupled NAPPA to surface plasmon resonance imaging (SPRi) to produce a novel label-free platform that measures many protein interactions in real-time allowing the determination of the KDs and rate constants. The developed novel NAPPA-SPRi technique showed excellent ability to study protein-protein interactions of clinical mutants of p53 with its regulator MDM2. Furthermore, this method was employed to identify mutant p53 proteins insensitive to the drug nutlin-3, currently in clinical practice, which usually disrupts the p53-MDM2 interactions. Thus, significant differences in the interactions were observed for p53 mutants on the DNA binding domain (Arg-273-Cys, Arg-273-His, Arg-248-Glu, Arg-280-Lys), on the structural domain (His-179-Tyr, Cys-176-Phe), on hydrophobic moieties in the DNA binding domain (Arg-280-Thr, Pro-151-Ser, Cys-176-Phe) and hot spot mutants (Gly-245-Cys, Arg-273-Leu, Arg-248-Glu, Arg-248-Gly), which signifies the importance of point mutations on the MDM2 interaction and nutlin3 effect, even in molecular locations related to other protein activities.

Split-Luciferase Complementation for Analysis of Virus-Host Protein Interactions.

Productive viral infection entails highly regulated and sequential protein-protein interactions between viral factors and between virus and host factors. Deciphering such interactions is of paramount importance for a better understanding of virus infection cycles and the development of new strategies for virus prevention and control. In this protocol, we describe a split-luciferase complementation (SLC ) assay for the detection of protein-protein interaction in Nicotiana benthamiana leaves following agroinfiltration-mediated transient protein expression. In this assay, the firefly luciferase protein is divided into two halves, each expressed as a fusion to a prey or bait protein, respectively. Interaction of the two candidate proteins brings the two otherwise nonfunctional halves into close proximity to restore the luciferase activity, which catalyzes the substrate D-luciferin to emit luminescence. The SLC assay allows for noninvasive, quantitative measurement of dynamic protein interactions in living cells within their native cellular compartments.

Relation Extraction of Protein Complexes from Dynamic Protein-Protein Interaction Network.

Background: Dynamic protein-protein interaction networks (DPPIN) can confirm the conditional and temporal features of proteins and protein complexes. In addition, the relation of protein complexes in dynamic networks can provide useful information in understanding the dynamic functionality of PPI networks. Objective: In this paper, an algorithm is presented to discover the temporal association rule from the dynamic PPIN dataset.Material and Methods: In this analytical study, the static protein-protein interaction network is transformed into a dynamic network using the gene expression thresholding to extract the protein complex relations. The number of presented proteins of the dynamic network is large at each time point. This number will increase for extraction of multidimensional rules at different times. By mapping the gold standard protein complexes as reference protein complexes, the number of items decreases from active proteins to protein complexes at each transaction. Extracted sub graphs as protein complexes, at each time point, are weighted according to the reference protein complexes similarity degrees. Mega-transactions and extended items are created based on occurrence bitmap matrix of the reference complexes. Rules will be extracted based on Mega-transactions of protein complexes.Results: The proposed method has been evaluated using gold standard protein complex rules. The amount of extracted rules from Biogrid datasets and protein complexes are 281, with support 0.2.Conclusion: The characteristic of the proposed algorithm is the simultaneous extraction of intra-transaction and inter-transaction rules. The results evaluation using EBI data shows the efficiency of the proposed algorithm.

Analysis of dynamic molecular networks: the progression from colorectal adenoma to cancer.

Colorectal cancer (CRC) is one of the deadliest cancers worldwide. It is the fourth most deadly cancer in the world with nearly 900,000 people die every year, the progression of polyps into cancer as one of its most common developmental pathways. This study obtained gene chip data collections from the Gene Expression Omnibus for colorectal adenoma (GSE8671) and colorectal cancer (GSE32323). Differentially expressed genes (DEGs) in normal tissue and different stages of CRC were analyzed for clustering, comparison, and visualization using R software. The Cytoscape plugin DyNetViewer was used to construct a dynamic protein-protein interaction network. Subsequently, through the Database for Annotation, Visualization and Integrated Discovery, the DEGs were functionally annotated and path enriched. Our study found that the matrix metalloprotein family and chemokines were the key regulatory genes that drove CRC progression. The Wnt signaling pathway, chemokine signaling pathway, and CRC pathway were the pathological pathways for CRC. Maintenance played an important role in this process. In addition, the related nodes and pathways at various stages may be potential mechanisms for promoting dynamic CRC progression. Our study provides a better understanding of the dynamic pattern of molecular interaction networks during CRC progression and provides relevant markers for more accurate screening of cancer in polyps.

Firefly Luciferase Complementation-Based Analysis of Dynamic Protein-Protein Interactions Under Diurnal and Circadian Conditions in Arabidopsis.

Split firefly luciferase complementation assay (FLCA) is one of the most widely used sensitive and reliable methods for the analysis of constitutive and dynamic protein-protein interactions (PPIs). Here, we report a method for long-term in vivo detects plant protein-protein interactions in Arabidopsis F1 hybrids via Topcount™ Microplate Scintillation Counter or Deep-Cooled CCD camera. Following these protocols, we successfully detected time-dependent PPIs of EARLY FLOWERING 3 (ELF3) and EARLY FLOWERING 4 (ELF4); both of them with LUX ARRHYTHMO (LUX) belong to an evening complex which has been found to play a key role in circadian rhythms, flowering, and growth.

Deconvoluting complex protein interaction networks through reductionist strategies in peptide biochemistry: Modern approaches and research questions.

Following the decoding of the first human genome, researchers have vastly improved their understanding of cell biology and its regulation. As a result, it has become clear that it is not merely genetic information, but the aberrant changes in the functionality and connectivity of its encoded proteins that drive cell response to periods of stress and external cues. Therefore, proper utilization of refined methods that help to describe protein signalling or regulatory networks (i.e., functional connectivity), can help us understand how change in the signalling landscape effects the cell. However, given the vast complexity in 'how and when' proteins communicate or interact with each other, it is extremely difficult to define, characterize, and understand these interaction networks in a tangible manner. Herein lies the challenge of tackling the functional proteome; its regulation is encoded in multiple layers of interaction, chemical modification and cell compartmentalization. To address and refine simple research questions, modern reductionist strategies in protein biochemistry have successfully used peptide-based experiments; their summation helping to simplify the overall complexity of these protein interaction networks. In this way, peptides are powerful tools used in fundamental research that can be readily applied to comparative biochemical research. Understanding and defining how proteins interact is one of the key aspects towards understanding how the proteome functions. To date, reductionist peptide-based research has helped to address a wide range of proteome-related research questions, including the prediction of enzymes substrates, identification of posttranslational modifications, and the annotation of protein interaction partners. Peptide arrays have been used to identify the binding specificity of reader domains, which are able to recognise the posttranslational modifications; forming dynamic protein interactions that are dependent on modification state. Finally, representing one of the fastest growing classes of inhibitor molecules, peptides are now begin explored as "disruptors" of protein-protein interactions or enzyme activity. Collectively, this review will discuss the use of peptides, peptide arrays, peptide-oriented computational biochemistry as modern reductionist strategies in deconvoluting the functional proteome.

An atlas of protein-protein interactions across mouse tissues

Cellular processes arise from the dynamic organization of proteins in networks of physical interactions. Mapping the interactome has therefore been a central objective of high-throughput biology. However, the dynamics of protein interactions across physiological contexts remain poorly understood. Here, we develop a quantitative proteomic approach combining protein correlation profiling with stable isotope labeling of mammals (PCP-SILAM) to map the interactomes of seven mouse tissues. The resulting maps provide a proteome-scale survey of interactome rewiring across mammalian tissues, revealing more than 125,000 unique interactions at a quality comparable to the highest-quality human screens. We identify systematic suppression of cross-talk between the evolutionarily ancient housekeeping interactome and younger, tissue-specific modules. Rewired proteins are tightly regulated by multiple cellular mechanisms and are implicated in disease. Our study opens up new avenues to uncover regulatory mechanisms that shape invivo interactome responses to physiological and pathophysiological stimuli in mammalian systems.

Optogenetic activation of intracellular signaling based on light-inducible protein-protein homo-interactions.

Dynamic protein-protein interactions are essential for proper cell functioning. Homo-interaction events—physical interactions between the same type of proteins—represent a pivotal subset of protein-protein interactions that are widely exploited in activating intracellular signaling pathways. Capacities of modulating protein-protein interactions with spatial and temporal resolution are greatly desired to decipher the dynamic nature of signal transduction mechanisms. The emerging optogenetic technology, based on genetically encoded light-sensitive proteins, provides promising opportunities to dissect the highly complex signaling networks with unmatched specificity and spatiotemporal precision. Here we review recent achievements in the development of optogenetic tools enabling light-inducible protein-protein homo-interactions and their applications in optical activation of signaling pathways.

Proximity Biotin Labeling Reveals Kaposi's Sarcoma-Associated Herpesvirus Interferon Regulatory Factor Networks.

Studies on "hit-and-run" effects by viral proteins are difficult when using traditional affinity precipitation-based techniques under dynamic conditions, because only proteins interacting at a specific instance in time can be precipitated by affinity purification. Recent advances in proximity labeling (PL) have enabled identification of both static and dynamic protein-protein interactions. In this study, we applied a PL method by generating recombinant Kaposi's sarcoma-associated herpesvirus (KSHV). KSHV, a gammaherpesvirus, uniquely encodes four interferon regulatory factors (IRF-1 to -4) that suppress host interferon responses, and we examined KSHV IRF-1 and IRF-4 neighbor proteins to identify cellular proteins involved in innate immune regulation. PL identified 213 and 70 proteins as neighboring proteins of viral IRF-1 (vIRF-1) and vIRF-4 during viral reactivation, and 47 proteins were shared between the two vIRFs; the list also includes three viral proteins, ORF17, thymidine kinase, and vIRF-4. Functional annotation of respective interacting proteins showed highly overlapping biological roles such as mRNA processing and transcriptional regulation by TP53. Innate immune regulation by these commonly interacting 44 cellular proteins was examined with small interfering RNAs (siRNAs), and the splicing factor 3B family proteins were found to be associated with interferon transcription and to act as suppressors of KSHV reactivation. We propose that recombinant mini-TurboID-KSHV is a powerful tool to probe key cellular proteins that play a role in KSHV replication and that selective splicing factors have a function in the regulation of innate immune responses.IMPORTANCE Viral protein interaction with a host protein shows at least two sides: (i) taking host protein functions for its own benefit and (ii) disruption of existing host protein complex formation to inhibit undesirable host responses. Due to the use of affinity precipitation approaches, the majority of studies have focused on how the virus takes advantage of the newly formed protein interactions for its own replication. Proximity labeling (PL), however, can also highlight transient and negative effects-those interactions which lead to dissociation from the existing protein complex. Here, we highlight the power of PL in combination with recombinant KSHV to study viral host interactions.

PiXie Analysis for Monitoring Dynamic Protein Interaction and Folding in a Living Cell

PiXie Analysis for Monitoring Dynamic Protein Interaction and Folding in a Living Cell

Relation Extraction of Protein Complexes from Dynamic Protein-Protein Interaction Network

Background: Dynamic protein-protein interaction networks (DPPIN) can confirm the conditional and temporal features of proteins and protein complexes. In addition, the relation of protein complexes in dynamic networks can provide useful information in understanding the dynamic functionality of PPI networks. Objective: In this paper, an algorithm is presented to discover the temporal association rule from the dynamic PPIN dataset.Material and Methods: In this analytical study, the static protein-protein interaction network is transformed into a dynamic network using the gene expression thresholding to extract the protein complex relations. The number of presented proteins of the dynamic network is large at each time point. This number will increase for extraction of multidimensional rules at different times. By mapping the gold standard protein complexes as reference protein complexes, the number of items decreases from active proteins to protein complexes at each transaction. Extracted sub graphs as protein complexes, at each time point, are weighted according to the reference protein complexes similarity degrees. Mega-transactions and extended items are created based on occurrence bitmap matrix of the reference complexes. Rules will be extracted based on Mega-transactions of protein complexes. Results: The proposed method has been evaluated using gold standard protein complex rules. The amount of extracted rules from Biogrid datasets and protein complexes are 281, with support 0.2. Conclusion: The characteristic of the proposed algorithm is the simultaneous extraction of intra-transaction and inter-transaction rules. The results evaluation using EBI data shows the efficiency of the proposed algorithm.