Dynamic Protein-protein Interaction Networks Research Articles

Identifying Protein Complexes from Dynamic Temporal Interval Protein-Protein Interaction Networks.

Identification of protein complex is very important for revealing the underlying mechanism of biological processes. Many computational methods have been developed to identify protein complexes from static protein-protein interaction (PPI) networks. Recently, researchers are considering the dynamics of protein-protein interactions. Dynamic PPI networks are closer to reality in the cell system. It is expected that more protein complexes can be accurately identified from dynamic PPI networks. In this paper, we use the undulating degree above the base level of gene expression instead of the gene expression level to construct dynamic temporal PPI networks. Further we convert dynamic temporal PPI networks into dynamic Temporal Interval Protein Interaction Networks (TI-PINs) and propose a novel method to accurately identify more protein complexes from the constructed TI-PINs. Owing to preserving continuous interactions within temporal interval, the constructed TI-PINs contain more dynamical information for accurately identifying more protein complexes. Our proposed identification method uses multisource biological data to judge whether the joint colocalization condition, the joint coexpression condition, and the expanding cluster condition are satisfied; this is to ensure that the identified protein complexes have the features of colocalization, coexpression, and functional homogeneity. The experimental results on yeast data sets demonstrated that using the constructed TI-PINs can obtain better identification of protein complexes than five existing dynamic PPI networks, and our proposed identification method can find more protein complexes accurately than four other methods.

Identification of dynamic molecular networks in peripheral blood mononuclear cells in type 1 diabetes mellitus.

BackgroundType 1 diabetes mellitus (T1DM) is an autoimmune disease caused by the immune destruction of islet β cells. Gene expression in peripheral blood mononuclear cells (PBMCs) could offer new disease and treatment markers in T1DM. The objective of this study was to explore the coexpression and dynamic molecular networks in PBMCs of T1DM patients.MethodsDataset GSE9006 contains PBMC samples of healthy volunteers, newly diagnosed T1DM patients, T1DM patients after insulin treatment, and newly diagnosed type 2 diabetes mellitus (T2DM) patients. Weighted correlation network analysis (WGCNA) was used to generate coexpression networks in T1DM and T2DM. Functional pathways in highly correlated modules of T1DM were enriched by gene set enrichment analysis (GSEA). We next filtered the differentially expressed genes (DEGs) and revealed their dynamic expression profiles in T1DM with or without insulin treatment. Furthermore, dynamic clusters and dynamic protein–protein interaction networks were identified. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was developed in dynamic clusters.ResultsWGCNA disclosed 12 distinct gene modules, and distinguished between correlated networks in T1DM and T2DM. Two modules were closely associated with T1DM. GSEA showed that the immune response and response to cytokines were enriched in the T1DM highly correlated module. Next, we screened 44 DEGs in newly diagnosed T1DM compared with healthy donors, and 71 DEGs in 1-month and 97 DEGs in 4-month insulin treatment groups compared with newly diagnosed T1DM. Dynamic expression profiles of DEGs indicated the potential targets for T1DM treatment. Moreover, four molecular dynamic clusters were analyzed in newly diagnosed and insulin-treated T1DM. Functional annotation showed that these clusters were mainly enriched in the IL-17 signaling pathway, nuclear factor-ϰB signaling pathway, and tumor necrosis factor signaling pathway.ConclusionThe results indicate potential drug targets or clinical efficacy markers, as well as demonstrating the underlying molecular mechanisms of T1DM treatment.

Writing a wrong: Coupled RNA polymerase II transcription and RNA quality control.

Processing and maturation of precursor RNA species is coupled to RNA polymerase II transcription. Co‐transcriptional RNA processing helps to ensure efficient and proper capping, splicing, and 3′ end processing of different RNA species to help ensure quality control of the transcriptome. Many improperly processed transcripts are not exported from the nucleus, are restricted to the site of transcription, and are in some cases degraded, which helps to limit any possibility of aberrant RNA causing harm to cellular health. These critical quality control pathways are regulated by the highly dynamic protein–protein interaction network at the site of transcription. Recent work has further revealed the extent to which the processes of transcription and RNA processing and quality control are integrated, and how critically their coupling relies upon the dynamic protein interactions that take place co‐transcriptionally. This review focuses specifically on the intricate balance between 3′ end processing and RNA decay during transcription termination.This article is categorized under:RNA Turnover and Surveillance > Turnover/Surveillance MechanismsRNA Processing > 3' End ProcessingRNA Processing > Splicing MechanismsRNA Processing > Capping and 5' End Modifications

Protein complex detection based on flower pollination mechanism in multi-relation reconstructed dynamic protein networks

BackgroundDetecting protein complex in protein-protein interaction (PPI) networks plays a significant part in bioinformatics field. It enables us to obtain the better understanding for the structures and characteristics of biological systems.MethodsIn this study, we present a novel algorithm, named Improved Flower Pollination Algorithm (IFPA), to identify protein complexes in multi-relation reconstructed dynamic PPI networks. Specifically, we first introduce a concept called co-essentiality, which considers the protein essentiality to search essential interactions, Then, we devise the multi-relation reconstructed dynamic PPI networks (MRDPNs) and discover the potential cores of protein complexes in MRDPNs. Finally, an IFPA algorithm is put forward based on the flower pollination mechanism to generate protein complexes by simulating the process of pollen find the optimal pollination plants, namely, attach the peripheries to the corresponding cores.ResultsThe experimental results on three different datasets (DIP, MIPS and Krogan) show that our IFPA algorithm is more superior to some representative methods in the prediction of protein complexes.ConclusionsOur proposed IFPA algorithm is powerful in protein complex detection by building multi-relation reconstructed dynamic protein networks and using improved flower pollination algorithm. The experimental results indicate that our IFPA algorithm can obtain better performance than other methods.

A Novel Core-Attachment-Based Method to Identify Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks.

Cellular functions are always performed by protein complexes. At present, many approaches have been proposed to identify protein complexes from protein-protein interaction (PPI) networks. Some approaches focus on detecting local dense subgraphs in PPI networks which are regarded as protein-complex cores, then identify protein complexes by including local neighbors. However, from gene expression profiles at different time points or tissues it is known that proteins are dynamic. Therefore, identifying dynamic protein complexes should become very important and meaningful. In this study, a novel core-attachment-based method named CO-DPC to detect dynamic protein complexes is presented. First, CO-DPC selects active proteins according to gene expression profiles and the 3-sigma principle, and constructs dynamic PPI networks based on the co-expression principle and PPI networks. Second, CO-DPC detects local dense subgraphs as the cores of protein complexes and then attach close neighbors of these cores to form protein complexes. In order to evaluate the method, the method and the existing algorithms are applied to yeast PPI networks. The experimental results show that CO-DPC performs much better than the existing methods. In addition, the identified dynamic protein complexes can match very well and thus become more meaningful for future biological study.

Moth–flame optimization-based algorithm with synthetic dynamic PPI networks for discovering protein complexes

The prediction of protein complex in protein–protein interaction (PPI) networks plays such a crucial role in the understanding of biological processes. This paper presents a moth–flame optimization-based protein complex prediction algorithm, called MFOC. First of all, we build the reliable weighted dynamic PPI networks by synthesizing topological and biological information. After that, we utilize a layer-by-layer scheme to find the cores of protein complexes as the flames and let the moths fly spirally around the flames to form the complexes. To be specific, the critical proteins have priority as the hearts and cores are extended by the hearts. And then we use MFOC algorithm to make the moths converge to the flames in order to obtain the protein complexes. At last, a two-step filtration operation is executed to refine the predicted protein complexes. The proposed algorithm MFOC is applied to the reliable weighted dynamic protein interaction networks including DIP, Krogan and MIPS, and the numerous comparison results show that MFOC outperforms other classic algorithms for identifying protein complexes.

A network-based approach reveals novel invasion and Maurer's clefts-related proteins in Plasmodium falciparum.

Malaria continues to be a major concern in developing countries despite continuous efforts to find a cure for the disease. Understanding the pathogenesis mechanism is necessary to identify more effective drug targets against malaria. Many years of experimental research have generated a large amount of data for the malarial parasite, Plasmodium falciparum. These data are useful to understand the importance of certain parasite proteins, but it often remains unclear how these proteins come together, interact with other proteins and carry out their function. Identification of all proteins involved in pathogenesis is an important step towards understanding the molecular mechanism of pathogenesis. In this study, dynamic stage-specific protein-protein interaction networks were created based on gene expression data during the parasite's intra-erythrocytic stages and static protein-protein interaction data. Using previously known proteins of a biological event as seed proteins, the random walk with restart (RWR) method was used on the dynamic protein-protein interaction networks to identify novel proteins related to that event. Two screening procedures namely, permutation test and GO enrichment test were performed to increase the reliability of the RWR predictions. The proposed method was first validated on Plasmodium falciparum proteins related to invasion, where it could reproduce the existing knowledge from a small set of seed proteins. It was then used to identify novel Maurer's clefts resident proteins, where it could identify 152 parasite proteins. We show that the current approach can annotate conserved proteins with unknown function. The predicted proteins can help build a mechanistic model for disease pathogenesis, which will be useful in identifying new drug targets.

Assortativity of aging key players in the dynamical networks

Human aging is the main social and medicinal challenge where it is one of the factors lead to disease. Understanding the process will be advantageous in the drugs and medicinal fields. In this study, we analyze the process through dynamic protein-protein interaction (PPI) networks by observing the topological changes of the network across ages. The dynamic networks consist of 14 ages, they are within 80 – 99 years, where we call aging-related protein networks. In each of the network, we analyzed the property of assortativity as proposed by recent study. Assortativity in PPI network is a measure to quantify the tendency of proteins to communicate with other similar proteins in the network. However, recent study proposes local assortativity for individual nodes in the network. Consequently, we are able to analyze the characteristics of aging network; i) Aging-related protein networks are disassortative with positive non-linear local assortativity, ii) there are strong and weak assortative key-players, iii) assortative key players are not close to all proteins in aging network, and iv) assortative hubs decrease the robustness of the networks.

Analysis of dynamic molecular networks for pancreatic ductal adenocarcinoma progression

BackgroundPancreatic ductal adenocarcinoma (PDAC) is one of the deadliest solid tumors. The rapid progression of PDAC results in an advanced stage of patients when diagnosed. However, the dynamic molecular mechanism underlying PDAC progression remains far from clear.MethodsThe microarray GSE62165 containing PDAC staging samples was obtained from Gene Expression Omnibus and the differentially expressed genes (DEGs) between normal tissue and PDAC of different stages were profiled using R software, respectively. The software program Short Time-series Expression Miner was applied to cluster, compare, and visualize gene expression differences between PDAC stages. Then, function annotation and pathway enrichment of DEGs were conducted by Database for Annotation Visualization and Integrated Discovery. Further, the Cytoscape plugin DyNetViewer was applied to construct the dynamic protein–protein interaction networks and to analyze different topological variation of nodes and clusters over time. The phosphosite markers of stage-specific protein kinases were predicted by PhosphoSitePlus database. Moreover, survival analysis of candidate genes and pathways was performed by Kaplan–Meier plotter. Finally, candidate genes were validated by immunohistochemistry in PDAC tissues.ResultsCompared with normal tissues, the total DEGs number for each PDAC stage were 994 (stage I), 967 (stage IIa), 965 (stage IIb), 1027 (stage III), 925 (stage IV), respectively. The stage-course gene expression analysis showed that 30 distinct expressional models were clustered. Kyoto Encyclopedia of Genes and Genomes analysis indicated that the up-regulated DEGs were commonly enriched in five fundamental pathways throughout five stages, including pathways in cancer, small cell lung cancer, ECM-receptor interaction, amoebiasis, focal adhesion. Except for amoebiasis, these pathways were associated with poor PDAC overall survival. Meanwhile, LAMA3, LAMB3, LAMC2, COL4A1 and FN1 were commonly shared by these five pathways and were unfavorable factors for prognosis. Furthermore, by constructing the stage-course dynamic protein interaction network, 45 functional molecular modules and 19 nodes were identified as featured regulators for all PDAC stages, among which the collagen family and integrins were considered as two main regulators for facilitating aggressive progression. Additionally, the clinical relevance analysis suggested that the stage IV featured nodes MLF1IP and ITGB4 were significantly correlated with shorter overall survival. Moreover, 15 stage-specific protein kinases were identified from the dynamic network and CHEK1 was particularly activated at stage IV. Experimental validation showed that MLF1IP, LAMA3 and LAMB3 were progressively increased from tumor initiation to progression.ConclusionsOur study provided a view for a better understanding of the dynamic landscape of molecular interaction networks during PDAC progression and offered potential targets for therapeutic intervention.

Mechanism of 53BP1 activity regulation by RNA-binding TIRR and a designer protein.

Dynamic protein interaction networks such as DNA double-strand break (DSB) signaling are modulated by post-translational modifications. The DNA repair factor 53BP1 is a rare example of a protein whose post-translational modification-binding function can be switched on and off. 53BP1 is recruited to DSBs by recognizing histone lysine methylation within chromatin, an activity directly inhibited by the 53BP1-binding protein TIRR. X-ray crystal structures of TIRR and a designer protein bound to 53BP1 now reveal a unique regulatory mechanism in which an intricate binding area centered on an essential TIRR arginine residue blocks the methylated-chromatin-binding surface of 53BP1. A 53BP1 separation-of-function mutation that abolishes TIRR-mediated regulation in cells renders 53BP1 hyperactive in response to DSBs, highlighting the key inhibitory function of TIRR. This 53BP1 inhibition is relieved by TIRR-interacting RNA molecules, providing proof-of-principle of RNA-triggered 53BP1 recruitment to DSBs.

Identifying Essential Proteins in Dynamic PPI Network with Improved FOA

Identification of essential proteins plays an important role for understanding the cellular life activity and development in postgenomic era. Identification of essential proteins from the protein-protein interaction (PPI) networks has become a hot topic in recent years. In this work, fruit fly optimization algorithm (FOA) is extended for identifying essential proteins, the extended algorithm is called EPFOA, which merges FOA with topological properties and biological information for essential proteins identification. The algorithm EPFOA has the advantage of identifying multiple essential proteins simultaneously rather than completely relying on ranking score identification individually. The performance of EPFOA is analyzed on dynamic PPI networks, which are constructed by combining the gene expression data. The experimental results demonstrate that EPFOA is more efficient in detecting essential proteins than the state-of-the-art essential proteins detection methods.

A Method for Predicting Protein Complexes from Dynamic Weighted Protein-Protein Interaction Networks.

Predicting protein complexes from protein-protein interaction (PPI) network is of great significance to recognize the structure and function of cells. A protein may interact with different proteins under different time or conditions. Existing approaches only utilize static PPI network data that may lose much temporal biological information. First, this article proposed a novel method that combines gene expression data at different time points with traditional static PPI network to construct different dynamic subnetworks. Second, to further filter out the data noise, the semantic similarity based on gene ontology is regarded as the network weight together with the principal component analysis, which is introduced to deal with the weight computing by three traditional methods. Third, after building a dynamic PPI network, a predicting protein complexes algorithm based on "core-attachment" structural feature is applied to detect complexes from each dynamic subnetworks. Finally, it is revealed from the experimental results that our method proposed in this article performs well on detecting protein complexes from dynamic weighted PPI networks.

CPredictor 4.0: effectively detecting protein complexes in weighted dynamic PPI networks

CPredictor 4.0: effectively detecting protein complexes in weighted dynamic PPI networks

DyNetViewer: a Cytoscape app for dynamic network construction, analysis and visualization.

The molecular interactions in a cell are varying with time and surrounded environmental cues. The construction and analysis of dynamic molecular networks can elucidate dynamic cellular mechanisms of different biological functions and provide a chance to understand complex diseases at the systems level. Here, we develop DyNetViewer, a Cytoscape application that provides a range of functionalities for the construction, analysis and visualization of dynamic protein-protein interaction networks. The current version of DyNetViewer consists of four different dynamic network construction methods, twelve topological variation analysis methods and four clustering algorithms. Moreover, visualization of different topological variation of nodes and clusters over time enables users to quickly identify the most variations across many network states. DyNetViewer is freely available with tutorials at the Cytoscape (3.4+) App Store (http://apps.cytoscape.org/apps/dynetviewer). limin@mail.csu.edu.cn. Supplementary data are available at Bioinformatics online.

IOPTICS-GSO for identifying protein complexes from dynamic PPI networks

BackgroundIdentifying protein complexes plays an important role for understanding cellular organization and functional mechanisms. As plenty of evidences have indicated that dense sub-networks in dynamic protein-protein interaction network (DPIN) usually correspond to protein complexes, identifying protein complexes is formulated as density-based clustering.MethodsIn this paper, a new approach named iOPTICS-GSO is developed, which is the improved Ordering Points to Identify the Clustering Structure (OPTICS) algorithm with Glowworm swarm optimization algorithm (GSO) to optimize the parameters in OPTICS when finding dense sub-networks. In our iOPTICS-GSO, the concept of core node is redefined and the Euclidean distance in OPTICS is replaced with the improved similarity between the nodes in the PPI network according to their interaction strength, and dense sub-networks are considered as protein complexes.ResultsThe experiment results have shown that our iOPTICS-GSO outperforms of algorithms such as DBSCAN, CFinder, MCODE, CMC, COACH, ClusterOne MCL and OPTICS_PSO in terms of f-measure and p-value on four DPINs, which are from the DIP, Krogan, MIPS and Gavin datasets. In addition, our predicted protein complexes have a small p-value and thus are highly likely to be true protein complexes.ConclusionThe proposed iOPTICS-GSO gains optimal clustering results by adopting GSO algorithm to optimize the parameters in OPTICS, and the result on four datasets shows superior performance. What’s more, the results provided clues for biologists to verify and find new protein complexes.

Identifying protein complex by integrating characteristic of core-attachment into dynamic PPI network.

How to identify protein complex is an important and challenging task in proteomics. It would make great contribution to our knowledge of molecular mechanism in cell life activities. However, the inherent organization and dynamic characteristic of cell system have rarely been incorporated into the existing algorithms for detecting protein complexes because of the limitation of protein-protein interaction (PPI) data produced by high throughput techniques. The availability of time course gene expression profile enables us to uncover the dynamics of molecular networks and improve the detection of protein complexes. In order to achieve this goal, this paper proposes a novel algorithm DCA (Dynamic Core-Attachment). It detects protein-complex core comprising of continually expressed and highly connected proteins in dynamic PPI network, and then the protein complex is formed by including the attachments with high adhesion into the core. The integration of core-attachment feature into the dynamic PPI network is responsible for the superiority of our algorithm. DCA has been applied on two different yeast dynamic PPI networks and the experimental results show that it performs significantly better than the state-of-the-art techniques in terms of prediction accuracy, hF-measure and statistical significance in biology. In addition, the identified complexes with strong biological significance provide potential candidate complexes for biologists to validate.

Topology potential based seed-growth method to identify protein complexes on dynamic PPI data

Protein complexes are very important for investigating the characteristics of biological processes. Identifying protein complexes from protein–protein interaction (PPI) networks is one of the recent research endeavors. The critical step of the seed-growth algorithms used for identifying protein complexes from PPI networks is to detect seed nodes (proteins) from which protein complexes are growing up in PPI networks. Topology potential was proposed to understand the evolution behavior and organizational principles of complex networks such as PPI networks. Furthermore, PPI networks are inherently dynamic in nature. In this study, we proposed a new seed-growing algorithm (called TP-WDPIN) for identifying protein complexes, which employs the concept of topology potential to detect significant proteins and mine protein complexes from Weighted Dynamic PPI Networks. To investigate the performance of the method, the TP-WDPIN algorithm was applied to four PPI databases and compared the obtained results to those produced by six other competing algorithms. Experimental results have demonstrated that the proposed TP-WDPIN algorithm exhibits better performance than other methods such as MCODE, MCL, CORE, CSO, ClusterONE, COACH when experimenting with four PPI databases (DIP, Krogan, MIPS, Gavin).

An uncertain model-based approach for identifying dynamic protein complexes in uncertain protein-protein interaction networks

BackgroundRecently, researchers have tried to integrate various dynamic information with static protein-protein interaction (PPI) networks to construct dynamic PPI networks. The shift from static PPI networks to dynamic PPI networks is essential to reveal the cellular function and organization. However, it is still impossible to construct an absolutely reliable dynamic PPI networks due to the noise and incompletion of high-throughput experimental data.ResultsTo deal with uncertain data, some uncertain graph models and theories have been proposed to analyze social networks, electrical networks and biological networks. In this paper, we construct the dynamic uncertain PPI networks to integrate the dynamic information of gene expression and the topology information of high-throughput PPI data. The dynamic uncertain PPI networks can not only provide the dynamic properties of PPI, which are neglected by static PPI networks, but also distinguish the reliability of each protein and PPI by the existence probability. Then, we use the uncertain model to identify dynamic protein complexes in the dynamic uncertain PPI networks.ConclusionWe use gene expression data and different high-throughput PPI data to construct three dynamic uncertain PPI networks. Our approach can achieve the state-of-the-art performance in all three dynamic uncertain PPI networks. The experimental results show that our approach can effectively deal with the uncertain data in dynamic uncertain PPI networks, and improve the performance for protein complex identification.

Identification of Protein Complexes by Using a Spatial and Temporal Active Protein Interaction Network.

The rapid development of proteomics and high-throughput technologies has produced a large amount of Protein-Protein Interaction (PPI) data, which makes it possible for considering dynamic properties of protein interaction networks (PINs) instead of static properties. Identification of protein complexes from dynamic PINs becomes a vital scientific problem for understanding cellular life in the post genome era. Up to now, plenty of models or methods have been proposed for the construction of dynamic PINs to identify protein complexes. However, most of the constructed dynamic PINs just focus on the temporal dynamic information and thus overlook the spatial dynamic information of the complex biological systems. To address the limitation of the existing dynamic PIN analysis approaches, in this paper, we propose a new model-based scheme for the construction of the Spatial and Temporal Active Protein Interaction Network (ST-APIN) by integrating time-course gene expression data and subcellular location information. To evaluate the efficiency of ST-APIN, the commonly used classical clustering algorithm MCL is adopted to identify protein complexes from ST-APIN and the other three dynamic PINs, NF-APIN, DPIN, and TC-PIN. The experimental results show that, the performance of MCL on ST-APIN outperforms those on the other three dynamic PINs in terms of matching with known complexes, sensitivity, specificity, and f-measure. Furthermore, we evaluate the identified protein complexes by Gene Ontology (GO) function enrichment analysis. The validation shows that the identified protein complexes from ST-APIN are more biologically significant. This study provides a general paradigm for constructing the ST-APINs, which is essential for further understanding of molecular systems and the biomedical mechanism of complex diseases.

DWCOACH: Predict protein complexes from dynamic weighted PPI networks by GO semantic similarity

DWCOACH: Predict protein complexes from dynamic weighted PPI networks by GO semantic similarity