Dynamic Protein-protein Interactions Research Articles

USP15 regulates dynamic protein–protein interactions of the spliceosome through deubiquitination of PRP31

Post-translational modifications contribute to the spliceosome dynamics by facilitating the physical rearrangements of the spliceosome. Here, we report USP15, a deubiquitinating enzyme, as a regulator of protein-protein interactions for the spliceosome dynamics. We show that PRP31, a component of U4 snRNP, is modified with K63-linked ubiquitin chains by the PRP19 complex and deubiquitinated by USP15 and its substrate targeting factor SART3. USP15SART3 makes a complex with USP4 and this ternary complex serves as a platform to deubiquitinate PRP31 and PRP3. The ubiquitination and deubiquitination status of PRP31 regulates its interaction with the U5 snRNP component PRP8, which is required for the efficient splicing of chromosome segregation related genes, probably by stabilizing the U4/U6.U5 tri-snRNP complex. Collectively, our data suggest that USP15 plays a key role in the regulation of dynamic protein-protein interactions of the spliceosome.

USP15 regulates dynamic protein-protein interactions of the spliceosome through deubiquitination of PRP31.

Post-translational modifications contribute to the spliceosome dynamics by facilitating the physical rearrangements of the spliceosome. Here, we report USP15, a deubiquitinating enzyme, as a regulator of protein–protein interactions for the spliceosome dynamics. We show that PRP31, a component of U4 snRNP, is modified with K63-linked ubiquitin chains by the PRP19 complex and deubiquitinated by USP15 and its substrate targeting factor SART3. USP15SART3 makes a complex with USP4 and this ternary complex serves as a platform to deubiquitinate PRP31 and PRP3. The ubiquitination and deubiquitination status of PRP31 regulates its interaction with the U5 snRNP component PRP8, which is required for the efficient splicing of chromosome segregation related genes, probably by stabilizing the U4/U6.U5 tri-snRNP complex. Collectively, our data suggest that USP15 plays a key role in the regulation of dynamic protein–protein interactions of the spliceosome.

Structural influence of proteins upon adsorption to MoS2 nanomaterials: comparison of MoS2 force field parameters.

Molybdenum disulfide (MoS2) has recently emerged as a promising nanomaterial in a wide range of applications due to its unique and impressive properties. For example, MoS2 has gained attention in the biomedical field because of its ability to act as an antibacterial and anticancer agent. However, the potential influence of this exciting nanomaterial on biomolecules is yet to be extensively studied. Molecular dynamics (MD) simulations are invaluable tools in the examination of protein interactions with nanomaterials such as MoS2. Previous protein MD studies have employed MoS2 force field parameters which were developed to accurately model bulk crystal structures and thermal heat transport but may not necessarily be amendable to its properties at the interface with biomolecules. By adopting a newly developed MoS2 force field, which was designed to better capture its interaction with water and proteins, we have examined the changes in protein structures between the original and refitted MoS2 force field parameters of three representative proteins, polyalanine (α-helix), YAP65 WW-domains (β-sheet), and HP35 (globular protein) when adsorbed onto MoS2 nanomaterials. We find that the original force field, with much larger van der Waals (vdW) contributions, resulted in more dramatic protein structural damage than the refitted parameters. Importantly, some denaturation of the protein tertiary structure and the local secondary structure persisted with the refitted force field albeit overall less severe MoS2 denaturation capacity was found. This work suggests that the denaturation ability of MoS2 to the protein structure is not as dire as previously reported and provides noteworthy findings on the dynamic interactions of proteins with this emergent material.

Hybrid Methods Reveal Multiple Flexibly Linked DNA Polymerases within the Bacteriophage T7 Replisome

The physical organization of DNA enzymes at a replication fork enables efficient copying of two antiparallel DNA strands, yet dynamic protein interactions within the replication complex complicate replisome structural studies. We employed a combination of crystallographic, native mass spectrometry and small-angle X-ray scattering experiments to capture alternative structures of a model replication system encoded by bacteriophage T7. Two molecules of DNA polymerase bind the ring-shaped primase-helicase in a conserved orientation and provide structural insight into how the acidic C-terminal tail of the primase-helicase contacts the DNA polymerase to facilitate loading of the polymerase onto DNA. Athird DNA polymerase binds the ring in an offset manner that may enable polymerase exchange during replication. Alternative polymerase binding modes are also detected by small-angle X-ray scattering with DNA substrates present. Our collective results unveil complex motions within T7 replisome higher-order structures that are underpinned by multivalent protein-protein interactions with functional implications.

Proximity Ligation Assay (PLA) to Evaluate DISC and MISC Composition.

Distribution of the death receptor CD95 into lipid rafts (aggregation) and/or its internalization may contribute to the implementation of the apoptotic signal at the detriment of the non-apoptotic signaling pathway [1-6]. Also CD95 can form different protein complexes via dynamic protein-protein interactions (PPIs) according to its interaction with soluble or transmembrane CD95L. Therefore, spatiotemporal identification of these PPIs is pivotal to anticipate the signaling pathway implemented in cells stimulated with different forms of CD95L. Also, many disorders result from dysfunctions in terms of PPI subcellular distribution and/or their intensity, rendering evaluation of these features crucial to better understand pathogenesis.In situ proximity ligation assay (PLA) is a methodology that offers the possibility to identify PPIs and to determine where these PPIs occur in subcellular location (Fig. 1). Moreover, based on imaging, this method allows a quantification of PPIs at the cellular level and with a higher specificity than classical immunofluorescence assays. We here describe PLA used to confirm CD95/FADD interaction, a protocol that may serve to highlight other CD95 partners.

Characterizing Dynamic Protein-Protein Interactions Using the Genetically Encoded Split Biosensor Assay Technique Split TEV.

Dynamic protein-protein interactions (PPIs) are fundamental building blocks of cellular signaling and monitoring their regulation promotes the understanding of signaling in health and disease. Genetically encoded split protein biosensor assays, such as the split TEV method, have proved to be highly valuable when studying regulated PPIs in living cells. Split TEV is based on the functional complementation of two previously inactive TEV protease fragments fused to interacting proteins and provides a robust, sensitive and flexible readout to monitor PPIs both at the membrane and in the cytosol. Thus, split TEV can be used to analyze interactomes of receptors, membrane-associated proteins, and cytosolic proteins. In particular, split TEV is useful to assay activities of relevant drug targets, such as receptor tyrosine kinases and G protein-coupled receptors, in compound screens. As split TEV uses genetically encoded readouts, including standard reporters based on fluorescence and luminescence, the technique can also be combined with scalable molecular barcode reporter systems, allowing the integration into multiplexed high-throughput assay approaches. Split TEV can be used in standard heterologous cell lines and primary cell types, including neurons, either in a transient or stably integrated format. When using cell lines, the basic protocol takes 30-96 h to complete, depending on the complexity of the experimental question addressed.

Detecting Membrane Protein-protein Interactions Using the Mammalian Membrane Two-hybrid (MaMTH) Assay.

Protein-protein interactions (PPIs) play an integral role in numerous cellular processes. Membrane protein interactions, in particular, are critical in cellular responses to stresses and stimuli, with dysfunction of these PPIs (e.g., due to aberrant expression and/or mutation of interaction partners) leading to a diverse array of pathological states. Exploration of the interaction space and dynamics of membrane proteins is difficult due to the limitations of current techniques used to study proteins in the biochemically complex environment of biological membranes. In the protocols below, we describe a newly developed membrane protein interaction assay called the Mammalian-Membrane Two-Hybrid (MaMTH), designed specifically for the detection of integral membrane PPIs in the context of living mammalian cells. Prior to using MaMTH, cell lines of interest are genetically modified to encode a reporter of choice. MaMTH "bait" and "prey" constructs of interest are also generated using Gateway cloning technology. The assay is then performed by co-transfection of baits and preys, with bait-prey interaction quantifiably assessed by way of a reporter signal (e.g., light (luciferase), fluorescence (GFP). © 2017 by John Wiley & Sons, Inc.

Predicting Protein Complexes in Weighted Dynamic PPI Networks Based on ICSC

Protein complexes play a critical role in understanding the biological processes and the functions of cellular mechanisms. Most existing protein complex detection algorithms cannot reflect dynamics of protein complexes. In this paper, a novel algorithm named Improved Cuckoo Search Clustering (ICSC) algorithm is proposed to detect protein complexes in weighted dynamic protein-protein interaction (PPI) networks. First, we constructed weighted dynamic PPI networks and detected protein complex cores in each dynamic subnetwork. Then, ICSC algorithm was used to cluster the protein attachments to the cores. The experimental results on both DIP dataset and Krogan dataset demonstrated that ICSC algorithm is more effective in identifying protein complexes than other competing methods.

A Spatial Control for Correct Timing of Gene Expression during the Escherichia coli Cell Cycle.

Temporal transcriptions of genes are achieved by different mechanisms such as dynamic interaction of activator and repressor proteins with promoters, and accumulation and/or degradation of key regulators as a function of cell cycle. We find that the TorR protein localizes to the old poles of the Escherichia coli cells, forming a functional focus. The TorR focus co-localizes with the nucleoid in a cell-cycle-dependent manner, and consequently regulates transcription of a number of genes. Formation of one TorR focus at the old poles of cells requires interaction with the MreB and DnaK proteins, and ATP, suggesting that TorR delivery requires cytoskeleton organization and ATP. Further, absence of the protein–protein interactions and ATP leads to loss in function of TorR as a transcription factor. We propose a mechanism for timing of cell-cycle-dependent gene transcription, where a transcription factor interacts with its target genes during a specific period of the cell cycle by limiting its own spatial distribution.

Multidomain structure and correlated dynamics determined by self-consistent FRET networks.

We present an approach that allows us to simultaneously access structure and dynamics of a multi-domain protein in solution. Dynamic domain arrangements are experimentally determined by combining self-consistent networks of distance distributions with known domain structures. Local structural dynamics are correlated with the global arrangements by analyzing networks of time-resolved single-molecule fluorescence parameters. The strength of this hybrid approach is shown by an application to the flexible multi-domain Hsp90. The average solution structure of Hsp90’s closed state resembles the known x-ray crystal structure with Angstrom precision. The open state is represented by an ensemble of conformations with inter-domain fluctuations of up to 25 Å. The data reveal a state-specific suppression of the sub-millisecond fluctuations by dynamic protein-protein interaction. Finally, the method enables localization and functional characterization of dynamic elements and domain interfaces.

Dynamic Protein Interactions of the Polycomb Repressive Complex 2 during Differentiation of Pluripotent Cells

Polycomb proteins assemble to form complexes with important roles in epigenetic regulation. The Polycomb Repressive Complex 2 (PRC2) modulates the di- and tri-methylation of lysine 27 on histone H3, each of which are associated with gene repression. Although three subunits, EZH1/2, SUZ12, and EED, form the catalytic core of PRC2, a wider group of proteins associate with low stoichiometry. This raises the question of whether dynamic variation of the PRC2 interactome results in alternative forms of the complex during differentiation. Here we compared the physical interactions of PRC2 in undifferentiated and differentiated states of NTERA2 pluripotent embryonic carcinoma cells. Label-free quantitative proteomics was used to assess endogenous immunoprecipitation of the EZH2 and SUZ12 subunits of PRC2. A high stringency data set reflecting the endogenous state of PRC2 was produced that included all previously reported core and associated PRC2 components, and several novel interacting proteins. Comparison of the interactomes obtained in undifferentiated and differentiated cells revealed candidate proteins that were enriched in complexes isolated from one of the two states. For example, SALL4 and ZNF281 associate with PRC2 in pluripotent cells, whereas PCL1 and SMAD3 preferentially associate with PRC2 in differentiating cells. Analysis of the mRNA and protein levels of these factors revealed that their association with PRC2 correlated with their cell state-specific expression. Taken together, we propose that dynamic changes to the PRC2 interactome during differentiation may contribute to directing its activity during cell fate transitions.

Nutrient Starvation Sensitizes Human Ovarian Cancer SKOV3 Cells to BH3 Mimetic via Modulation of Mitochondrial Dynamics.

The aberrant proliferation of tumor cells necessitates compensatory changes in tumor metabolic processes. Previous studies on tumor growth and metabolism have established a relationship between nutrient stress and Bcl-2 anti-apoptotic proteins, although the mechanisms connecting these processes remain unclear. We induced nutrient deprivation in human ovarian cancer SKOV3 cells by culturing cells in Earle's balanced salt solution (EBSS) as a starvation model. We used EBSS treatment with the BH3 domain of Bcl-2 family proteins (BH3) mimetic ABT737, which targets Bcl-2/Bcl-xL, to examine mitochondrial dynamics and the interactive regulatory mechanisms between nutrition and Bcl-2 proteins. We found that EBSS combined with ABT737 can promote SKOV3 cells to undergo apoptosis and convert tubular mitochondria into small, fragmented morphologies. Bcl-2 family proteins participated in the regulation of mitochondrial fusion and fission through apoptosis, and the decrease of Mcl-1 expression was the key to ABT737 sensitization. Our findings showed that nutrient stress could sensitize SKOV3 cells to ABT737 via regulation of the mitochondrial dynamic balance and interaction of Bcl-2 family proteins. Our data suggest that nutrient starvation combined with the BH3 mimetic ABT737 could reduce the required effective dose of ABT737, and that inhibition of Bcl-2 and Mcl-1 together with nutrient starvation could serve as an effective strategy for the treatment of human ovarian cancer. Anat Rec, 300:326-339, 2017. © 2016 Wiley Periodicals, Inc.

An Integrative Analysis of the InR/PI3K/Akt Network Identifies the Dynamic Response to Insulin Signaling

Insulin regulates an essential conserved signaling pathway affecting growth, proliferation, and metabolism. To expand our understanding of the insulin pathway, we combine biochemical, genetic, and computational approaches to build a comprehensive Drosophila InR/PI3K/Akt network. First, we map the dynamic protein-protein interaction network surrounding the insulin core pathway using bait-prey interactions connecting 566 proteins. Combining RNAi screening and phospho-specific antibodies, we find that 47% of interacting proteins affect pathway activity, and, using quantitative phosphoproteomics, we demonstrate that ∼10% of interacting proteins are regulated by insulin stimulation at the level of phosphorylation. Next, we integrate these orthogonal datasets to characterize the structure and dynamics of the insulin network at the level of protein complexes and validate our method by identifying regulatory roles for the Protein Phosphatase 2A (PP2A) and Reptin-Pontin chromatin-remodeling complexes as negative and positive regulators of ribosome biogenesis, respectively. Altogether, our study represents a comprehensive resource for the study of the evolutionary conserved insulin network.

Sugar Influx Sensing by the Phosphotransferase System of Escherichia coli.

The phosphotransferase system (PTS) plays a pivotal role in the uptake of multiple sugars in Escherichia coli and many other bacteria. In the cell, individual sugar-specific PTS branches are interconnected through a series of phosphotransfer reactions, thus creating a global network that not only phosphorylates incoming sugars but also regulates a number of cellular processes. Despite the apparent importance of the PTS network in bacterial physiology, the holistic function of the network in the cell remains unclear. Here we used Förster resonance energy transfer (FRET) to investigate the PTS network in E. coli, including the dynamics of protein interactions and the processing of different stimuli and their transmission to the chemotaxis pathway. Our results demonstrate that despite the seeming complexity of the cellular PTS network, its core part operates in a strikingly simple way, sensing the overall influx of PTS sugars irrespective of the sugar identity and distributing this information equally through all studied branches of the network. Moreover, it also integrates several other specific metabolic inputs. The integrated output of the PTS network is then transmitted linearly to the chemotaxis pathway, in stark contrast to the amplification of conventional chemotactic stimuli. Finally, we observe that default uptake through the uninduced PTS network correlates well with the quality of the carbon source, apparently representing an optimal regulatory strategy.

Dopamine Receptor Signaling in MIN6 β-Cells Revealed by Fluorescence Fluctuation Spectroscopy

Insulin secretion defects are central to the development of type II diabetes mellitus. Glucose stimulation of insulin secretion has been extensively studied, but its regulation by other stimuli such as incretins and neurotransmitters is not as well understood. We investigated the mechanisms underlying the inhibition of insulin secretion by dopamine, which is synthesized in pancreatic β-cells from circulating L-dopa. Previous research has shown that this inhibition is mediated primarily by activation of the dopamine receptor D3 subtype (DRD3), even though both DRD2 and DRD3 are expressed in β-cells. To understand this dichotomy, we investigated the dynamic interactions between the dopamine receptor subtypes and their G-proteins using two-color fluorescence fluctuation spectroscopy (FFS) of mouse MIN6 β-cells. We show that proper membrane localization of exogenous G-proteins depends on both the Gβ and Gγ subunits being overexpressed in the cell. Triple transfections of the dopamine receptor subtype and Gβ and Gγ subunits, each labeled with a different-colored fluorescent protein (FP), yielded plasma membrane expression of all three FPs and permitted an FFS evaluation of interactions between the dopamine receptors and the Gβγ complex. Upon dopamine stimulation, we measured a significant decrease in interactions between DRD3 and the Gβγ complex, which is consistent with receptor activation. In contrast, dopamine stimulation did not cause significant changes in the interactions between DRD2 and the Gβγ complex. These results demonstrate that two-color FFS is a powerful tool for measuring dynamic protein interactions in living cells, and show that preferential DRD3 signaling in β-cells occurs at the level of G-protein release.

AAV Vectors for FRET-Based Analysis of Protein-Protein Interactions in Photoreceptor Outer Segments

Fluorescence resonance energy transfer (FRET) is a powerful method for the detection and quantification of stationary and dynamic protein-protein interactions. Technical limitations have hampered systematic in vivo FRET experiments to study protein-protein interactions in their native environment. Here, we describe a rapid and robust protocol that combines adeno-associated virus (AAV) vector-mediated in vivo delivery of genetically encoded FRET partners with ex vivo FRET measurements. The method was established on acutely isolated outer segments of murine rod and cone photoreceptors and relies on the high co-transduction efficiency of retinal photoreceptors by co-delivered AAV vectors. The procedure can be used for the systematic analysis of protein-protein interactions of wild type or mutant outer segment proteins in their native environment. Conclusively, our protocol can help to characterize the physiological and pathophysiological relevance of photoreceptor specific proteins and, in principle, should also be transferable to other cell types.

Neighbor affinity based algorithm for discovering temporal protein complex from dynamic PPI network

Detection of temporal protein complexes would be a great aid in furthering our knowledge of the dynamic features and molecular mechanism in cell life activities. Most existing clustering algorithms for discovering protein complexes are based on static protein interaction networks in which the inherent dynamics are often overlooked. We propose a novel algorithm DPC-NADPIN (Discovering Protein Complexes based on Neighbor Affinity and Dynamic Protein Interaction Network) to identify temporal protein complexes from the time course protein interaction networks. Inspired by the idea of that the tighter a protein’s neighbors inside a module connect, the greater the possibility that the protein belongs to the module, DPC-NADPIN algorithm first chooses each of the proteins with high clustering coefficient and its neighbors to consolidate into an initial cluster, and then the initial cluster becomes a protein complex by appending its neighbor proteins according to the relationship between the affinity among neighbors inside the cluster and that outside the cluster. In our experiments, DPC-NADPIN algorithm is proved to be reasonable and it has better performance on discovering protein complexes than the following state-of-the-art algorithms: Hunter, MCODE, CFinder, SPICI, and ClusterONE; Meanwhile, it obtains many protein complexes with strong biological significance, which provide helpful biological knowledge to the related researchers. Moreover, we find that proteins are assembled coordinately to form protein complexes with characteristics of temporality and spatiality, thereby performing specific biological functions.

Identifying essential proteins based on dynamic protein-protein interaction networks and RNA-Seq datasets

The identification of essential proteins is not only important for understanding organism structure on the molecular level, but also beneficial to drug-target detection and genetic disease prevention. Traditional methods often employ various centrality indices of static protein-protein interaction (PPI) networks and/or gene expression profiles to predict essential proteins. However, the prediction accuracy of most methods still has room to be further improved. In this study, we propose a strategy to increase the prediction accuracy of essential protein identification in three ways. Firstly, RNA-Seq datasets are employed to construct integrated dynamic PPI networks. Using a RNA-Seq dataset is expected to give more accurate predictions than using microarray gene expression profiles. Secondly, a novel integrated dynamic PPI network is constructed by considering both the co-expression pattern and the co-expression level of the RNA-Seq data. Thirdly, a novel two-step strategy is proposed to identify essential proteins from two known centrality indices. Numerical experiments have shown that the proposed strategy can increase the prediction accuracy dramatically, which can be generalized to many existing methods and centrality indices.

Quantitative Analysis of Dynamic Protein Interactions during Transcription Reveals a Role for Casein Kinase II in Polymerase-associated Factor (PAF) Complex Phosphorylation and Regulation of Histone H2B Monoubiquitylation

Using affinity purification MS approaches, we have identified a novel role for casein kinase II (CKII) in the modification of the polymerase associated factor complex (PAF-C). Our data indicate that the facilitates chromatin transcription complex (FACT) interacts with CKII and may facilitate PAF complex phosphorylation. Posttranslational modification analysis of affinity-isolated PAF-C shows extensive CKII phosphorylation of all five subunits of PAF-C, although CKII subunits were not detected as interacting partners. Consistent with this, recombinant CKII or FACT-associated CKII isolated from cells can phosphorylate PAF-C in vitro, whereas no intrinsic kinase activity was detected in PAF-C samples. Significantly, PAF-C purifications combined with stable isotope labeling in cells (SILAC) quantitation for PAF-C phosphorylation from wild-type and CKII temperature-sensitive strains (cka1Δ cka2-8) showed that PAF-C phosphorylation at consensus CKII sites is significantly reduced in cka1Δ cka2-8 strains. Consistent with a role of CKII in FACT and PAF-C function, we show that decreased CKII function in vivo results in decreased levels of histone H2B lysine 123 monoubiquitylation, a modification dependent on FACT and PAF-C. Taken together, our results define a coordinated role of CKII and FACT in the regulation of RNA polymerase II transcription through chromatin via phosphorylation of PAF-C.

Construction of dynamic probabilistic protein interaction networks for protein complex identification.

BackgroundRecently, high-throughput experimental techniques have generated a large amount of protein-protein interaction (PPI) data which can construct large complex PPI networks for numerous organisms. System biology attempts to understand cellular organization and function by analyzing these PPI networks. However, most studies still focus on static PPI networks which neglect the dynamic information of PPI.ResultsThe gene expression data under different time points and conditions can reveal the dynamic information of proteins. In this study, we used an active probability-based method to distinguish the active level of proteins at different active time points. We constructed dynamic probabilistic protein networks (DPPN) to integrate dynamic information of protein into static PPI networks. Based on DPPN, we subsequently proposed a novel method to identify protein complexes, which could effectively exploit topological structure as well as dynamic information of DPPN. We used three different yeast PPI datasets and gene expression data to construct three DPPNs. When applied to three DPPNs, many well-characterized protein complexes were accurately identified by this method.ConclusionThe shift from static PPI networks to dynamic PPI networks is essential to accurately identify protein complex. This method not only can be applied to identify protein complex, but also establish a framework to integrate dynamic information into static networks for other applications, such as pathway analysis.