Dynamic Protein-protein Interaction Networks Research Articles

Temporal Protein Complex Identification Based on Dynamic Heterogeneous Protein Information Network Representation Learning.

Protein complexes, as the fundamental units of cellular function and regulation, play a crucial role in understanding the normal physiological functions of cells. Existing methods for protein complex identification attempt to introduce other biological information on top of the protein-protein interaction (PPI) network to assist in evaluating the degree of association between proteins. However, these methods usually treat protein interaction networks as flat homogeneous static networks. They cannot distinguish the roles and importance of different types of biological information, nor can they reflect the dynamic changes of protein complexes. In recent years, heterogeneous network representation learning has achieved great success in processing complex heterogeneous information and mining deep semantics. We thus propose a temporal protein complex identification method based on Dynamic Heterogeneous Protein information network Representation Learning, DHPRL. DHPRL naturally integrates multiple types of heterogeneous biological information in the cellular temporal dimension. It simultaneously models the temporal dynamic properties of proteins and the heterogeneity of biological information to improve the understanding of protein interactions and the accuracy of complex prediction. Firstly, we construct Dynamic Heterogeneous Protein Information Network (DHPIN) by integrating temporal gene expression information and GO attribute information. Then we design a dual-view collaborative contrast mechanism. Specifically, proposing to learn protein representations from two views of DHPIN (1-hop relation view and meta-path view) to model the consistency and specificity between nearest-neighbour bio information and deeper biological semantics. The dynamic PPI network is thereafter re-weighted based on the learned protein representations. Finally, we perform protein identification on the re-weighted dynamic PPI network. Extensive experimental results demonstrate that DHPRL can effectively model complicated biological information and achieve state-of-the-art performance in most cases.

Reconstruction of dynamic protein-protein interaction network via graph convolutional network

Traditional studies are mostly based on static protein network models. The highly averaged and idealized static protein network does not represent well this dynamic nature that encompasses different time, place and reaction conditions. Therefore, we propose PreDPPI, a method for convolving dynamic protein-protein interaction networks (PINs) using knowledge graph reconstruction of relational graphs, which achieves better performance in terms of dynamics compared to other methods. We found that the longer the length of timestamps used in reconstructing dynamic PINs, the better the prediction. In addition, we identified three hub genes (NUSAP1, SCG3, and CKAP2L) in dynamic PINs that were shown to be significantly associated with glioma prognosis. In conclusion, this work reconstructed a web-based knowledge graph convolving dynamic PINs and identified three hub genes in the GEO glioma dataset that were confirmed to be highly associated with glioma prognosis.

SRC family kinase inhibition rescues molecular and behavioral phenotypes, but not protein interaction network dynamics, in a mouse model of Fragile X syndrome.

Glutamatergic synapses encode information from extracellular inputs using dynamic protein interaction networks (PINs) that undergo widespread reorganization following synaptic activity, allowing cells to distinguish between signaling inputs and generate coordinated cellular responses. Here, we investigate how Fragile X Messenger Ribonucleoprotein (FMRP) deficiency disrupts signal transduction through a glutamatergic synapse PIN downstream of NMDA receptor or metabotropic glutamate receptor (mGluR) stimulation. In cultured cortical neurons or acute cortical slices from P7, P17 and P60 FMR1-/y mice, the unstimulated protein interaction network state resembled that of wildtype littermates stimulated with mGluR agonists, demonstrating resting state pre-activation of mGluR signaling networks. In contrast, interactions downstream of NMDAR stimulation were similar to WT. We identified the Src family kinase (SFK) Fyn as a network hub, because many interactions involving Fyn were pre-activated in FMR1-/y animals. We tested whether targeting SFKs in FMR1-/y mice could modify disease phenotypes, and found that Saracatinib (SCB), an SFK inhibitor, normalized elevated basal protein synthesis, novel object recognition memory and social behavior in FMR1-/y mice. However, SCB treatment did not normalize the PIN to a wild-type-like state in vitro or in vivo, but rather induced extensive changes to protein complexes containing Shank3, NMDARs and Fyn. We conclude that targeting abnormal nodes of a PIN can identify potential disease-modifying drugs, but behavioral rescue does not correlate with PIN normalization.

Interaction networks of Escherichia coli replication proteins under different bacterial growth conditions

In this work we analyzed protein-protein interactions (PPIs) formed by E. coli replication proteins under three disparate bacterial growth conditions. The chosen conditions corresponded to fast exponential growth, slow exponential growth and growth cessation at the stationary phase. We performed affinity purification coupled with mass spectrometry (AP-MS) of chromosomally expressed proteins (DnaA, DnaB, Hda, SeqA, DiaA, DnaG, HolD, NrdB), tagged with sequential peptide affinity (SPA) tag. Composition of protein complexes was characterized using MaxQuant software. To filter out unspecific interactions, we employed double negative control system and we proposed qualitative and quantitative data analysis strategies that can facilitate hits identification in other AP-MS datasets. Our motivation to undertake this task was still insufficient understanding of molecular mechanisms coupling DNA replication to cellular growth. Previous works suggested that such control mechanisms could involve physical interactions of replication factors with metabolic or cell envelope proteins. However, the dynamic replication protein interaction network (PIN) obtained in this study can be used to characterize links between DNA replication and various cellular processes in other contexts.

A Construction Method of Dynamic Protein Interaction Networks by Using Relevant Features of Gene Expression Data.

Essential proteins play an important role in various life activities and are considered to be a vital part of the organism. Gene expression data are an important dataset to construct dynamic protein-protein interaction networks (DPIN). The existing methods for the construction of DPINs generally utilize all features (or the features in a cycle) of the gene expression data. However, the features observed from successive time points tend to be highly correlated, and thus there are some redundant and irrelevant features in the gene expression data, which will influence the quality of the constructed network and the predictive performance of essential proteins. To address this problem, we propose a construction method of DPINs by using selected relevant features rather than continuous and periodic features. We adopt an improved unsupervised feature selection method based on Laplacian algorithm to remove irrelevant and redundant features from gene expression data, then integrate the chosen relevant features into the static protein-protein interaction network (SPIN) to construct a more concise and effective DPIN (FS-DPIN). To evaluate the effectiveness of the FS-DPIN, we apply 15 network-based centrality methods on the FS-DPIN and compare the results with those on the SPIN and the existing DPINs. Then the predictive performance of the 15 centrality methods is validated in terms of sensitivity, specificity, positive predictive value, negative predictive value, F-measure, accuracy, Jackknife and AUPRC. The experimental results show that the FS-DPIN is superior to the existing DPINs in the identification accuracy of essential proteins.

PLA-GNN: Computational inference of protein subcellular location alterations under drug treatments with deep graph neural networks

The aberrant protein sorting has been observed in many conditions, including complex diseases, drug treatments, and environmental stresses. It is important to systematically identify protein mis-localization events in a given condition. Experimental methods for finding mis-localized proteins are always costly and time consuming. Predicting protein subcellular localizations has been studied for many years. However, only a handful of existing works considered protein subcellular location alterations. We proposed a computational method for identifying alterations of protein subcellular locations under drug treatments. We took three drugs, including TSA (trichostain A), bortezomib and tacrolimus, as instances for this study. By introducing dynamic protein-protein interaction networks, graph neural network algorithms were applied to aggregate topological information under different conditions. We systematically reported potential protein mis-localization events under drug treatments. As far as we know, this is the first attempt to find protein mis-localization events computationally in drug treatment conditions. Literatures validated that a number of proteins, which are highly related to pharmacological mechanisms of these drugs, may undergo protein localization alterations. We name our method as PLA-GNN (Protein Localization Alteration by Graph Neural Networks). It can be extended to other drugs and other conditions. All datasets and codes of this study has been deposited in a GitHub repository (https://github.com/quinlanW/PLA-GNN).

A Novel Temporal Network-Embedding Algorithm for Link Prediction in Dynamic Networks.

Understanding the evolutionary patterns of real-world complex systems such as human interactions, biological interactions, transport networks, and computer networks is important for our daily lives. Predicting future links among the nodes in these dynamic networks has many practical implications. This research aims to enhance our understanding of the evolution of networks by formulating and solving the link-prediction problem for temporal networks using graph representation learning as an advanced machine learning approach. Learning useful representations of nodes in these networks provides greater predictive power with less computational complexity and facilitates the use of machine learning methods. Considering that existing models fail to consider the temporal dimensions of the networks, this research proposes a novel temporal network-embedding algorithm for graph representation learning. This algorithm generates low-dimensional features from large, high-dimensional networks to predict temporal patterns in dynamic networks. The proposed algorithm includes a new dynamic node-embedding algorithm that exploits the evolving nature of the networks by considering a simple three-layer graph neural network at each time step and extracting node orientation by using Given's angle method. Our proposed temporal network-embedding algorithm, TempNodeEmb, is validated by comparing it to seven state-of-the-art benchmark network-embedding models. These models are applied to eight dynamic protein-protein interaction networks and three other real-world networks, including dynamic email networks, online college text message networks, and human real contact datasets. To improve our model, we have considered time encoding and proposed another extension to our model, TempNodeEmb++. The results show that our proposed models outperform the state-of-the-art models in most cases based on two evaluation metrics.

Temporal-Spatial Analysis of the Essentiality of Hub Proteins in Protein-Protein Interaction Networks

Hubs are generally defined as nodes with a high degree centrality, and they are important for maintaining the stability of complex networks. Previous studies have shown that hub proteins tend to be essential in protein-protein interaction (PPI) networks, providing us with a new way to analyze the essentiality of proteins. Unfortunately, most of the existing studies leverage static PPI networks that are both incomplete and noisy and ignore the temporal and spatial characteristics of PPI networks. Benefiting from the development of high-throughput technologies, abundant multi-biological datasets have been accumulated and can be used for network analysis. To reexamine the relationship between the network centrality and protein essentiality in PPI networks, in this study, we integrated PPI networks with gene expression data and subcellular localization information to construct temporal-spatial dynamic PPI networks. Based on the constructed temporal-spatial dynamic PPI networks, we introduced the maximum degree centrality (MDC) method to evaluate the essentiality of hub proteins. Our results illustrate that the integration of gene expression data or subcellular localization information can significantly reduce noise effects and improve the identification accuracy of essential proteins through the temporal-spatial analysis with disparate sources of PPI networks. Moreover, we redefined hubs and classified them into two types: temporospatial hubs and static hubs. The results show that temporospatial hub proteins are more likely to be essential.

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.

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.

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.

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.

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.

DPPN-SVM: Computational Identification of Mis-Localized Proteins in Cancers by Integrating Differential Gene Expressions With Dynamic Protein-Protein Interaction Networks.

Eukaryotic cells contain numerous components, which are known as subcellular compartments or subcellular organelles. Proteins must be sorted to proper subcellular compartments to carry out their molecular functions. Mis-localized proteins are related to various cancers. Identifying mis-localized proteins is important in understanding the pathology of cancers and in developing therapies. However, experimental methods, which are used to determine protein subcellular locations, are always costly and time-consuming. We tried to identify cancer-related mis-localized proteins in three different cancers using computational approaches. By integrating gene expression profiles and dynamic protein-protein interaction networks, we established DPPN-SVM (Dynamic Protein-Protein Network with Support Vector Machine), a predictive model using the SVM classifier with diffusion kernels. With this predictive model, we identified a number of mis-localized proteins. Since we introduced the dynamic protein-protein network, which has never been considered in existing works, our model is capable of identifying more mis-localized proteins than existing studies. As far as we know, this is the first study to incorporate dynamic protein-protein interaction network in identifying mis-localized proteins in cancers.

Unexpected specificity within dynamic transcriptional protein–protein complexes

A key functional event in eukaryotic gene activation is the formation of dynamic protein-protein interaction networks between transcriptional activators and transcriptional coactivators. Seemingly incongruent with the tight regulation of transcription, many biochemical and biophysical studies suggest that activators use nonspecific hydrophobic and/or electrostatic interactions to bind to coactivators, with few if any specific contacts. Here a mechanistic dissection of a set of representative dynamic activator•coactivator complexes, comprised of the ETV/PEA3 family of activators and the coactivator Med25, reveals a different molecular recognition model. The data demonstrate that small sequence variations within an activator family significantly redistribute the conformational ensemble of the complex while not affecting overall affinity, and distal residues within the activator-not often considered as contributing to binding-play a key role in mediating conformational redistribution. The ETV/PEA3•Med25 ensembles are directed by specific contacts between the disordered activator and the Med25 interface, which is facilitated by structural shifts of the coactivator binding surface. Taken together, these data highlight the critical role coactivator plasticity plays in recognition of disordered activators and indicate that molecular recognition models of disordered proteins must consider the ability of the binding partners to mediate specificity.

Twadn: an efficient alignment algorithm based on time warping for pairwise dynamic networks

BackgroundNetwork alignment is an efficient computational framework in the prediction of protein function and phylogenetic relationships in systems biology. However, most of existing alignment methods focus on aligning PPIs based on static network model, which are actually dynamic in real-world systems. The dynamic characteristic of PPI networks is essential for understanding the evolution and regulation mechanism at the molecular level and there is still much room to improve the alignment quality in dynamic networks.ResultsIn this paper, we proposed a novel alignment algorithm, Twadn, to align dynamic PPI networks based on a strategy of time warping. We compare Twadn with the existing dynamic network alignment algorithm DynaMAGNA++ and DynaWAVE and use area under the receiver operating characteristic curve and area under the precision-recall curve as evaluation indicators. The experimental results show that Twadn is superior to DynaMAGNA++ and DynaWAVE. In addition, we use protein interaction network of Drosophila to compare Twadn and the static network alignment algorithm NetCoffee2 and experimental results show that Twadn is able to capture timing information compared to NetCoffee2.ConclusionsTwadn is a versatile and efficient alignment tool that can be applied to dynamic network. Hopefully, its application can benefit the research community in the fields of molecular function and evolution.

DPCT: A Dynamic Method for Detecting Protein Complexes From TAP-Aware Weighted PPI Network

Detecting protein complexes from the Protein-Protein interaction network (PPI) is the essence of discovering the rules of the cellular world. There is a large amount of PPI data available, generated from high throughput experimental data. The enormous size of the data persuaded us to use computational methods instead of experimental methods to detect protein complexes. In past years, many researchers presented their algorithms to detect protein complexes. Most of the presented algorithms use current static PPI networks. New researches proved the dynamicity of cellular systems, and so, the PPI is not static over time. In this paper, we introduce DPCT to detect protein complexes from dynamic PPI networks. In the proposed method, TAP and GO data are used to make a weighted PPI network and to reduce the noise of PPI. Gene expression data are also used to make dynamic subnetworks from PPI. A memetic algorithm is used to bicluster gene expression data and to create a dynamic subnetwork for each bicluster. Experimental results show that DPCT can detect protein complexes with better correctness than state-of-the-art detection algorithms. The source code and datasets of DPCT used can be found at https://github.com/alisn72/DPCT.

A novel graph clustering method with a greedy heuristic search algorithm for mining protein complexes from dynamic and static PPI networks

Discovering protein complexes from protein-protein interaction (PPI) networks is one of the primary tasks in bioinformatics. However, most of the state-of-the-art methods still face some challenges, such as the inability to discover overlapping protein complexes, failure to consider the inherent structure of real protein complexes, and non-utilization of biological information. Based on the above mentioned aspects, we present a novel graph clustering method with a greedy heuristic search algorithm for mining protein complexes using a new clustering model in dynamic and static weighted PPI networks (named MPC-C). First, MPC-C constructed dynamic and static weighted PPI networks by combining biological and topological information. Second, initial clusters were obtained using core and multifunctional proteins, following which we proposed a greedy heuristic search algorithm to expand each initial cluster and form candidate protein complexes in dynamic and static weighted PPI networks. Finally, unreliable and highly overlapping protein complexes were discarded. To demonstrate the performance of MPC-C, we tested this method on five PPI networks and compared it with nine other effective methods. The experimental results indicate that MPC-C outperformed the other state-of-the-art methods with respect to various computational and biologically relevant metrics.

基于动态网络的蛋白质复合物识别研究综述

基于动态网络的蛋白质复合物识别研究综述