Protein Interaction Network of Human Protein Kinase D2 Revealed by Chemical Cross-Linking/Mass Spectrometry

2016 ◽  
Vol 15 (10) ◽  
pp. 3686-3699 ◽  
Author(s):  
Björn Häupl ◽  
Christian H. Ihling ◽  
Andrea Sinz
Keyword(s):  
Mass Spectrometry ◽  
Protein Kinase ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Human Protein ◽  
Cross Linking ◽  
Human Protein Kinase ◽  
Chemical Cross Linking

scholarly journals Probing the Protein Interaction Network of Pseudomonas aeruginosa Cells by Chemical Cross-Linking Mass Spectrometry

Structure ◽  
2015 ◽  
Vol 23 (4) ◽  
pp. 762-773 ◽  
Author(s):  
Arti T. Navare ◽  
Juan D. Chavez ◽  
Chunxiang Zheng ◽  
Chad R. Weisbrod ◽  
Jimmy K. Eng ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Pseudomonas Aeruginosa ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Cross Linking ◽  
Chemical Cross Linking

scholarly journals XlinkCyNET: a Cytoscape application for visualization of protein interaction networks based on cross-linking mass-spectrometry identifications

2020 ◽  
Author(s):  
Diogo Borges Lima ◽  
Ying Zhu ◽  
Fan Liu
Keyword(s):  
Mass Spectrometry ◽  
Protein Interaction ◽  
Large Scale ◽  
Interaction Network ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Cross Linking ◽  
Protein Interaction Data ◽  
Interaction Data ◽  
Link Type

ABSTRACTSoftware tools that allow visualization and analysis of protein interaction networks are essential for studies in systems biology. One of the most popular network visualization tools in biology is Cytoscape, which offers a large selection of plugins for interpretation of protein interaction data. Chemical cross-linking coupled to mass spectrometry (XL-MS) is an increasingly important source for such interaction data, but there are currently no Cytoscape tools to analyze XL-MS results. In light of the suitability of Cytoscape platform but also to expand its toolbox, here we introduce XlinkCyNET, an open-source Cytoscape Java plugin for exploring large-scale XL-MS-based protein interaction networks. XlinkCyNET offers rapid and easy visualization of intra and intermolecular cross-links and the locations of protein domains in a rectangular bar style, allowing subdomain-level interrogation of the interaction network. XlinkCyNET is freely available from the Cytoscape app store: http://apps.cytoscape.org/apps/xlinkcynet and at https://www.theliulab.com/software/xlinkcynet.

scholarly journals Identification of COVID-19 Infection-Related Human Genes Based on a Random Walk Model in a Virus–Human Protein Interaction Network

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
YuHang Zhang ◽  
Tao Zeng ◽  
Lei Chen ◽  
ShiJian Ding ◽  
Tao Huang ◽  
...  
Keyword(s):  
Random Walk ◽  
Infectious Diseases ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Disease Outbreaks ◽  
Interaction Network ◽  
Random Walk Model ◽  
Human Protein ◽  
Human Beings ◽  
Human Protein Interaction

Coronaviruses are specific crown-shaped viruses that were first identified in the 1960s, and three typical examples of the most recent coronavirus disease outbreaks include severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and COVID-19. Particularly, COVID-19 is currently causing a worldwide pandemic, threatening the health of human beings globally. The identification of viral pathogenic mechanisms is important for further developing effective drugs and targeted clinical treatment methods. The delayed revelation of viral infectious mechanisms is currently one of the technical obstacles in the prevention and treatment of infectious diseases. In this study, we proposed a random walk model to identify the potential pathological mechanisms of COVID-19 on a virus–human protein interaction network, and we effectively identified a group of proteins that have already been determined to be potentially important for COVID-19 infection and for similar SARS infections, which help further developing drugs and targeted therapeutic methods against COVID-19. Moreover, we constructed a standard computational workflow for predicting the pathological biomarkers and related pharmacological targets of infectious diseases.

scholarly journals Identification ofInfluenza A/H7N9 Virus Infection-Related Human Genes Based on Shortest Paths in a Virus-Human Protein Interaction Network

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Ning Zhang ◽  
Min Jiang ◽  
Tao Huang ◽  
Yu-Dong Cai
Keyword(s):  
Virus Infection ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Influenza A ◽  
Shortest Paths ◽  
Interaction Network ◽  
Human Protein ◽  
H7n9 Virus ◽  
Human Protein Interaction ◽  
Human Genes

The recently emergingInfluenza A/H7N9 virus is reported to be able to infect humans and cause mortality. However, viral and host factors associated with the infection are poorly understood. It is suggested by the “guilt by association” rule that interacting proteins share the same or similar functions and hence may be involved in the same pathway. In this study, we developed a computational method to identifyInfluenza A/H7N9 virus infection-related human genes based on this rule from the shortest paths in a virus-human protein interaction network. Finally, we screened out the most significant 20 human genes, which could be the potential infection related genes, providing guidelines for further experimental validation. Analysis of the 20 genes showed that they were enriched in protein binding, saccharide or polysaccharide metabolism related pathways and oxidative phosphorylation pathways. We also compared the results with those from human rhinovirus (HRV) and respiratory syncytial virus (RSV) by the same method. It was indicated that saccharide or polysaccharide metabolism related pathways might be especially associated with the H7N9 infection. These results could shed some light on the understanding of the virus infection mechanism, providing basis for future experimental biology studies and for the development of effective strategies for H7N9 clinical therapies.

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