scholarly journals A New Method for Recognizing Protein Complexes Based on Protein Interaction Networks and GO Terms

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiaoting Wang ◽  
Nan Zhang ◽  
Yulan Zhao ◽  
Juan Wang
Keyword(s):  
Protein Interaction ◽  
Protein Complexes ◽  
Protein Interaction Networks ◽  
New Method ◽  
Protein Protein Interaction ◽  
Evolution Analysis ◽  
Ppi Networks ◽  
Multiple Protein ◽  
Topology Information ◽  
Go Terms

Motivation: A protein complex is the combination of proteins which interact with each other. Protein–protein interaction (PPI) networks are composed of multiple protein complexes. It is very difficult to recognize protein complexes from PPI data due to the noise of PPI.Results: We proposed a new method, called Topology and Semantic Similarity Network (TSSN), based on topological structure characteristics and biological characteristics to construct the PPI. Experiments show that the TSSN can filter the noise of PPI data. We proposed a new algorithm, called Neighbor Nodes of Proteins (NNP), for recognizing protein complexes by considering their topology information. Experiments show that the algorithm can identify more protein complexes and more accurately. The recognition of protein complexes is vital in research on evolution analysis.Availability and implementation: https://github.com/bioinformatical-code/NNP.

scholarly journals GASOLINE: a Cytoscape app for multiple local alignment of PPI networks

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 140 ◽  
Author(s):  
Giovanni Micale ◽  
Andrea Continella ◽  
Alfredo Ferro ◽  
Rosalba Giugno ◽  
Alfredo Pulvirenti
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Local Alignment ◽  
Stochastic Algorithms ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Alignment Task ◽  
Protein Protein Interaction Networks ◽  
Go Terms

Comparing protein interaction networks can reveal interesting patterns of interactions for a specific function or process in distantly related species. In this paper we present GASOLINE, a Cytoscape app for multiple local alignments of PPI (protein-protein interaction) networks. The app is based on the homonymous greedy and stochastic algorithms. To the authors knowledge, it is the first Cytoscape app for computing and visualizing local alignments, without requiring any post-processing operations. GO terms can be easily attached to the aligned proteins for further functional analysis of alignments. GASOLINE can perform the alignment task in few minutes, even for a large number of input networks.

scholarly journals Identifying Protein Complexes from Dynamic Temporal Interval Protein-Protein Interaction Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Jinxiong Zhang ◽  
Cheng Zhong ◽  
Hai Xiang Lin ◽  
Mian Wang
Keyword(s):  
Gene Expression ◽  
Protein Interaction ◽  
Protein Complexes ◽  
Biological Data ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Temporal Interval ◽  
Protein Protein Interaction ◽  
Identification Method ◽  
Ppi 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.

scholarly journals PPInfer: a Bioconductor package for inferring functionally related proteins using protein interaction networks

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1969
Author(s):  
Dongmin Jung ◽  
Xijin Ge
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Biological Processes ◽  
Bioconductor Package ◽  
Biological Functions ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Related Proteins

Interactions between proteins occur in many, if not most, biological processes. This fact has motivated the development of a variety of experimental methods for the identification of protein-protein interaction (PPI) networks. Leveraging PPI data available STRING database, we use network-based statistical learning methods to infer the putative functions of proteins from the known functions of neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. The package is freely available at the Bioconductor web site (http://bioconductor.org/packages/PPInfer/).

scholarly journals PPInfer: a Bioconductor package for inferring functionally related proteins using protein interaction networks

F1000Research ◽  
2018 ◽  
Vol 6 ◽  
pp. 1969 ◽  
Author(s):  
Dongmin Jung ◽  
Xijin Ge
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Biological Processes ◽  
Bioconductor Package ◽  
Biological Functions ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Related Proteins

Interactions between proteins occur in many, if not most, biological processes. This fact has motivated the development of a variety of experimental methods for the identification of protein-protein interaction (PPI) networks. Leveraging PPI data available in the STRING database, we use a network-based statistical learning methods to infer the putative functions of proteins from the known functions of neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. The package is freely available at the Bioconductor web site (http://bioconductor.org/packages/PPInfer/).

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