Module organization and variance in protein-protein interaction networks

Lin, Chun‐Yu; Lee, Tsai Ling; Chiu, Yi Yuan; Lin, Yung-Kai; Lo, Yu Shu; Lin, Chih Ta; Yang, Jinn-Moon

doi:10.1038/srep09386

Cited by 34 publications

(18 citation statements)

References 52 publications

(88 reference statements)

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“…Lin et al . 39 have found that a topological module usually contains core and ring components and that the major biological function is exerted through core components; thus, it is necessary to consider these core components when detecting functional modules. Furthermore, we also determined that the average shortest path in the modules (i.e., 2.25 in IBGLL) was shorter than that in the pathways (i.e., 3.82 in PID), because topological modules contain only proteins exhibiting dense interaction.…”

Section: Discussionmentioning

confidence: 99%

Functional diversity of topological modules in human protein-protein interaction networks

Wang

Chu

et al. 2017

Sci Rep

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A large-scale molecular interaction network of protein-protein interactions (PPIs) enables the automatic detection of molecular functional modules through a computational approach. However, the functional modules that are typically detected by topological community detection algorithms may be diverse in functional homogeneity and are empirically considered to be default functional modules. Thus, a significant challenge that has been described but not elucidated is investigating the relationship between topological modules and functional modules. We systematically investigated this issue by initially using seven widely used community detection algorithms to partition the PPI network into communities. Four homogeneity measures were subsequently implemented to evaluate the functional homogeneity of protein community. We determined that a significant portion of topological modules with heterogeneous functionality exists and should be further investigated; moreover, these findings indicated that topologically based functional module detection approaches must be reconsidered. Furthermore, we found that the functional homogeneity of topological modules is positively correlated with their edge densities, degree of association with diseases and general Gene Ontology (GO) terms. Thus, topologically based module detection approaches should be used with caution in the identification of functional modules with high homogeneity

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Section: Discussionmentioning

confidence: 99%

Functional diversity of topological modules in human protein-protein interaction networks

Wang

Chu

et al. 2017

Sci Rep

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“…Meanwhile, certain databases, such as IntAct 15 , BioGRID 16 and DIP 17 , have been established as repositories to collect and organize the reported protein interactions of nonspecific species. Despite these improved protocols and evolved methods, the cost and time requirements of such exploratory experimental studies remain prohibitive, and thus only small to mid-sized PIN studies have been conducted 18 19 . Alternatively, proteomic studies are progressively shifting away from classical approaches that focus on a few proteins toward whole PINs to chart the complex and dynamic interactions in cellular processes.…”

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confidence: 99%

PTIR: Predicted Tomato Interactome Resource

Yue

Ban

et al. 2016

Sci Rep

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Protein-protein interactions (PPIs) are involved in almost all biological processes and form the basis of the entire interactomics systems of living organisms. Identification and characterization of these interactions are fundamental to elucidating the molecular mechanisms of signal transduction and metabolic pathways at both the cellular and systemic levels. Although a number of experimental and computational studies have been performed on model organisms, the studies exploring and investigating PPIs in tomatoes remain lacking. Here, we developed a Predicted Tomato Interactome Resource (PTIR), based on experimentally determined orthologous interactions in six model organisms. The reliability of individual PPIs was also evaluated by shared gene ontology (GO) terms, co-evolution, co-expression, co-localization and available domain-domain interactions (DDIs). Currently, the PTIR covers 357,946 non-redundant PPIs among 10,626 proteins, including 12,291 high-confidence, 226,553 medium-confidence, and 119,102 low-confidence interactions. These interactions are expected to cover 30.6% of the entire tomato proteome and possess a reasonable distribution. In addition, ten randomly selected PPIs were verified using yeast two-hybrid (Y2H) screening or a bimolecular fluorescence complementation (BiFC) assay. The PTIR was constructed and implemented as a dedicated database and is available at http://bdg.hfut.edu.cn/ptir/index.html without registration.

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“…Protein complex is a fundamental unit formed with highly connected proteins and often possesses specific biological functions [ 26 ]. In biology, protein interaction networks (PINs) are not static—they dynamically change over time and are responsive to the stimuli caused by external environment.…”

Section: Discussionmentioning

confidence: 99%

Mining Temporal Protein Complex Based on the Dynamic PIN Weighted with Connected Affinity and Gene Co-Expression

Shen

Jiang

et al. 2016

PLoS ONE

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The identification of temporal protein complexes would make great contribution to our knowledge of the dynamic organization characteristics in protein interaction networks (PINs). Recent studies have focused on integrating gene expression data into static PIN to construct dynamic PIN which reveals the dynamic evolutionary procedure of protein interactions, but they fail in practice for recognizing the active time points of proteins with low or high expression levels. We construct a Time-Evolving PIN (TEPIN) with a novel method called Deviation Degree, which is designed to identify the active time points of proteins based on the deviation degree of their own expression values. Owing to the differences between protein interactions, moreover, we weight TEPIN with connected affinity and gene co-expression to quantify the degree of these interactions. To validate the efficiencies of our methods, ClusterONE, CAMSE and MCL algorithms are applied on the TEPIN, DPIN (a dynamic PIN constructed with state-of-the-art three-sigma method) and SPIN (the original static PIN) to detect temporal protein complexes. Each algorithm on our TEPIN outperforms that on other networks in terms of match degree, sensitivity, specificity, F-measure and function enrichment etc. In conclusion, our Deviation Degree method successfully eliminates the disadvantages which exist in the previous state-of-the-art dynamic PIN construction methods. Moreover, the biological nature of protein interactions can be well described in our weighted network. Weighted TEPIN is a useful approach for detecting temporal protein complexes and revealing the dynamic protein assembly process for cellular organization.

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Module organization and variance in protein-protein interaction networks

Cited by 34 publications

References 52 publications

Functional diversity of topological modules in human protein-protein interaction networks

Functional diversity of topological modules in human protein-protein interaction networks

PTIR: Predicted Tomato Interactome Resource

Mining Temporal Protein Complex Based on the Dynamic PIN Weighted with Connected Affinity and Gene Co-Expression

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