idenPC-MIIP: identify protein complexes from weighted PPI networks using mutual important interacting partner relation

2020 ◽  
Author(s):  
Zhourun Wu ◽  
Qing Liao ◽  
Bin Liu
Keyword(s):  
High Throughput ◽  
State Of The Art ◽  
Protein Complexes ◽  
Cell System ◽  
Protein Protein Interaction ◽  
The Past ◽  
Ppi Networks ◽  
A Cell ◽  
Relationship Of ◽  
Genome Scale

Abstract Protein complexes are key units for studying a cell system. During the past decades, the genome-scale protein–protein interaction (PPI) data have been determined by high-throughput approaches, which enables the identification of protein complexes from PPI networks. However, the high-throughput approaches often produce considerable fraction of false positive and negative samples. In this study, we propose the mutual important interacting partner relation to reflect the co-complex relationship of two proteins based on their interaction neighborhoods. In addition, a new algorithm called idenPC-MIIP is developed to identify protein complexes from weighted PPI networks. The experimental results on two widely used datasets show that idenPC-MIIP outperforms 17 state-of-the-art methods, especially for identification of small protein complexes with only two or three proteins.

scholarly journals Telomerase Biogenesis and Activities from the Perspective of Its Direct Interacting Partners

Cancers ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1679 ◽  
Author(s):  
Kathryn T. T. T. Nguyen ◽  
Judy M. Y. Wong
Keyword(s):  
Protein Complexes ◽  
Detection Methods ◽  
Protein Protein Interaction ◽  
Interaction Detection ◽  
The Past ◽  
Binding Partners ◽  
A Cell ◽  
Direct Binding ◽  
Vitro Protein

Telomerase reverse transcriptase (TERT)—the catalytic subunit of telomerase—is reactivated in up to 90% of all human cancers. TERT is observed in heterogenous populations of protein complexes, which are dynamically regulated in a cell type- and cell cycle-specific manner. Over the past two decades, in vitro protein–protein interaction detection methods have discovered a number of endogenous TERT binding partners in human cells that are responsible for the biogenesis and functionalization of the telomerase holoenzyme, including the processes of TERT trafficking between subcellular compartments, assembly into telomerase, and catalytic action at telomeres. Additionally, TERT have been found to interact with protein species with no known telomeric functions, suggesting that these complexes may contribute to non-canonical activities of TERT. Here, we survey TERT direct binding partners and discuss their contributions to TERT biogenesis and functions. The goal is to review the comprehensive spectrum of TERT pro-malignant activities, both telomeric and non-telomeric, which may explain the prevalence of its upregulation in cancer.

A comprehensive review and evaluation of computational methods for identifying protein complexes from protein–protein interaction networks

2019 ◽  
Vol 21 (5) ◽  
pp. 1531-1548 ◽  
Author(s):  
Zhourun Wu ◽  
Qing Liao ◽  
Bin Liu
Keyword(s):  
Computational Methods ◽  
Protein Interaction ◽  
State Of The Art ◽  
Protein Complexes ◽  
Data Sets ◽  
Clustering Methods ◽  
Protein Protein Interaction ◽  
Advantages And Disadvantages ◽  
Ppi Networks ◽  
Important Field

Abstract Protein complexes are the fundamental units for many cellular processes. Identifying protein complexes accurately is critical for understanding the functions and organizations of cells. With the increment of genome-scale protein–protein interaction (PPI) data for different species, various computational methods focus on identifying protein complexes from PPI networks. In this article, we give a comprehensive and updated review on the state-of-the-art computational methods in the field of protein complex identification, especially focusing on the newly developed approaches. The computational methods are organized into three categories, including cluster-quality-based methods, node-affinity-based methods and ensemble clustering methods. Furthermore, the advantages and disadvantages of different methods are discussed, and then, the performance of 17 state-of-the-art methods is evaluated on two widely used benchmark data sets. Finally, the bottleneck problems and their potential solutions in this important field are discussed.

scholarly journals HKC: An Algorithm to Predict Protein Complexes in Protein-Protein Interaction Networks

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Xiaomin Wang ◽  
Zhengzhi Wang ◽  
Jun Ye
Keyword(s):  
Protein Interaction ◽  
Protein Complexes ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Systematic Analysis ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Protein Protein Interaction Networks ◽  
Genome Scale

With the availability of more and more genome-scale protein-protein interaction (PPI) networks, research interests gradually shift to Systematic Analysis on these large data sets. A key topic is to predict protein complexes in PPI networks by identifying clusters that are densely connected within themselves but sparsely connected with the rest of the network. In this paper, we present a new topology-based algorithm, HKC, to detect protein complexes in genome-scale PPI networks. HKC mainly uses the concepts of highestk-core and cohesion to predict protein complexes by identifying overlapping clusters. The experiments on two data sets and two benchmarks show that our algorithm has relatively high F-measure and exhibits better performance compared with some other methods.

scholarly journals A Cell-Based High-Throughput Assay for Gap Junction Communication Suitable for Assessing Connexin 43–Ezrin Interaction Disruptors Using IncuCyte ZOOM

2016 ◽  
Vol 22 (1) ◽  
pp. 77-85 ◽  
Author(s):  
Aleksandra R. Dukic ◽  
David W. McClymont ◽  
Kjetil Taskén
Keyword(s):  
Gap Junction ◽  
High Throughput ◽  
Connexin 43 ◽  
General Mechanism ◽  
Protein Protein Interaction ◽  
Gap Junction Communication ◽  
Anchoring Protein ◽  
A Cell ◽  
Rat Liver Epithelial Cells ◽  
High Throughput Assay

Connexin 43 (Cx43), the predominant gap junction (GJ) protein, directly interacts with the A-kinase-anchoring protein (AKAP) Ezrin in human cytotrophoblasts and a rat liver epithelial cells (IAR20). The Cx43-Ezrin–protein kinase (PKA) complex facilitates Cx43 phosphorylation by PKA, which triggers GJ opening in cytotrophoblasts and IAR20 cells and may be a general mechanism regulating GJ intercellular communication (GJIC). Considering the importance of Cx43 GJs in health and disease, they are considered potential pharmaceutical targets. The Cx43-Ezrin interaction is a protein-protein interaction that opens possibilities for targeting with peptides and small molecules. For this reason, we developed a high-throughput cell-based assay in which GJIC can be assessed and new compounds characterized. We used two pools of IAR20 cells, calcein loaded and unloaded, that were mixed and allowed to attach. Next, GJIC was monitored over time using automated imaging via the IncuCyte imager. The assay was validated using known GJ inhibitors and anchoring peptide disruptors, and we further tested new peptides that interfered with the Cx43-Ezrin binding region and reduced GJIC. Although an AlphaScreen assay can be used to screen for Cx43-Ezrin interaction inhibitors, the cell-based assay described is an ideal secondary screen for promising small-molecule hits to help identify the most potent compounds.

Nonessential-Nonhub Proteins in the Protein-Protein Interaction Network

2014 ◽  
Vol 934 ◽  
pp. 159-164
Author(s):  
Yun Yuan Dong ◽  
Xian Chun Zhang
Keyword(s):  
Protein Interaction ◽  
Interaction Network ◽  
Clustering Coefficient ◽  
Centrality Measures ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Comparison Results ◽  
A Cell ◽  
High Degree

Protein-protein interaction (PPI) networks provide a simplified overview of the web of interactions that take place inside a cell. According to the centrality-lethality rule, hub proteins (proteins with high degree) tend to be essential in the PPI network. Moreover, there are also many low degree proteins in the PPI network, but they have different lethality. Some of them are essential proteins (essential-nonhub proteins), and the others are not (nonessential-nonhub proteins). In order to explain why nonessential-nonhub proteins don’t have essentiality, we propose a new measure n-iep (the number of essential neighbors) and compare nonessential-nonhub proteins with essential-nonhub proteins from topological, evolutionary and functional view. The comparison results show that there are statistical differences between nonessential-nonhub proteins and essential-nonhub proteins in centrality measures, clustering coefficient, evolutionary rate and the number of essential neighbors. These are reasons why nonessential-nonhub proteins don’t have lethality.

A METHOD BASED ON LOCAL DENSITY AND RANDOM WALKS FOR COMPLEXES DETECTION IN PROTEIN INTERACTION NETWORKS

2010 ◽  
Vol 08 (supp01) ◽  
pp. 47-62 ◽  
Author(s):  
LIANG YU ◽  
LIN GAO ◽  
KUI LI
Keyword(s):  
Random Walks ◽  
Protein Interaction ◽  
Local Density ◽  
Protein Complexes ◽  
Biological Significance ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Comprehensive Comparison ◽  
Attachment Proteins ◽  
Two Stages

In this paper, we present a method based on local density and random walks (LDRW) for core-attachment complexes detection in protein-protein interaction (PPI) networks whether they are weighted or not. Our LDRW method consists of two stages. Firstly, it finds all the protein-complex cores based on local density of subnetwork. Then it uses random walks with restarts for finding the attachment proteins of each detected core to form complexes. We evaluate the effectiveness of our method using two different yeast PPI networks and validate the biological significance of the predicted protein complexes using known complexes in the Munich Information Center for Protein Sequence (MIPS) and Gene Ontology (GO) databases. We also perform a comprehensive comparison between our method and other existing methods. The results show that our method can find more protein complexes with high biological significance and obtains a significant improvement. Furthermore, our method is able to identify biologically significant overlapped protein complexes.

Classification Systems for Bacterial Protein-Protein Interaction Document Retrieval

2010 ◽  
Vol 1 (1) ◽  
pp. 34-44
Author(s):  
Hongfang Liu ◽  
Manabu Torii ◽  
Guixian Xu ◽  
Johannes Goll
Keyword(s):  
High Throughput ◽  
Protein Interaction ◽  
Document Retrieval ◽  
Classification Systems ◽  
Machine Learning Algorithms ◽  
Small Scale ◽  
Protein Protein Interaction ◽  
Manual Curation ◽  
Ppi Networks ◽  
Genomics And Proteomics

Protein-protein interaction (PPI) networks are essential to understand the fundamental processes governing cell biology. Recently, studying PPI networks becomes possible due to advances in experimental high-throughput genomics and proteomics technologies. Many interactions from such high-throughput studies and most interactions from small-scale studies are reported only in the scientific literature and thus are not accessible in a readily analyzable format. This has led to the birth of manual curation initiatives such as the International Molecular Exchange Consortium (IMEx). The manual curation of PPI knowledge can be accelerated by text mining systems to retrieve PPI-relevant articles (article retrieval) and extract PPI-relevant knowledge (information extraction). In this article, the authors focus on article retrieval and define the task as binary classification where PPI-relevant articles are positives and the others are negatives. In order to build such classifier, an annotated corpus is needed. It is very expensive to obtain an annotated corpus manually but a noisy and imbalanced annotated corpus can be obtained automatically, where a collection of positive documents can be retrieved from existing PPI knowledge bases and a large number of unlabeled documents (most of them are negatives) can be retrieved from PubMed. They compared the performance of several machine learning algorithms by varying the ratio of the number of positives to the number of unlabeled documents and the number of features used.

Heuristic Modularity for Complex Identification in Protein-Protein Interaction Networks

2019 ◽  
pp. 1846-1859
Author(s):  
Amenah H. H. Abdulateef ◽  
Bara'a A. Attea ◽  
Ahmed N. Rashid
Keyword(s):  
Protein Interaction ◽  
Protein Complexes ◽  
Building Blocks ◽  
Detection Accuracy ◽  
Protein Protein Interaction ◽  
Protein Levels ◽  
Cellular Processes ◽  
Ppi Networks ◽  
Protein Protein Interaction Networks ◽  
Different Levels

     Due to the significant role in understanding cellular processes, the decomposition of Protein-Protein Interaction (PPI) networks into essential building blocks, or complexes, has received much attention for functional bioinformatics research in recent years. One of the well-known bi-clustering descriptors for identifying communities and complexes in complex networks, such as PPI networks, is modularity function.   The contribution of this paper is to introduce heuristic optimization models that can collaborate with the modularity function to improve its detection ability. The definitions of the formulated heuristics are based on nodes and different levels of their neighbor properties.  The modularity function and the formulated heuristics are then injected into the mechanism of a single objective Evolutionary Algorithm (EA) tailored specifically to tackle the problem, and thus, to identify possible complexes from PPI networks. In the experiments, different overlapping scores are used to evaluate the detection accuracy in both complex and protein levels. According to the evaluation metrics, the results reveal that the introduced heuristics have the ability to harness the accuracy of the existing modularity while identifying protein complexes in the tested PPI networks.

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