scholarly journals TOOLbox: Automatic validation of phosphopeptide identifications | Assessing human protein interaction maps | Faster database searches | ByOnic | Assessing phosphopeptide isolation methods | Proteomics of Huntington?s disease | Large-scale phosphoproteomics studies with ETD | A photoactive semiconductor to digest proteins | Host proteins copurify with blood flukes

2007 ◽  
Vol 6 (4) ◽  
pp. 1237-1239
Keyword(s):  
Protein Interaction ◽  
Large Scale ◽  
Human Protein ◽  
Host Proteins ◽  
Human Protein Interaction ◽  
Blood Flukes ◽  
Isolation Methods ◽  
Automatic Validation

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.

scholarly journals Epstein–Barr virus and virus human protein interaction maps

2007 ◽  
Vol 104 (18) ◽  
pp. 7606-7611 ◽  
Author(s):  
Michael A. Calderwood ◽  
Kavitha Venkatesan ◽  
Li Xing ◽  
Michael R. Chase ◽  
Alexei Vazquez ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Function ◽  
Epstein Barr Virus ◽  
Human Protein ◽  
Human Interactome ◽  
Broad Perspective ◽  
Barr Virus ◽  
Human Protein Interaction ◽  
Human Proteins ◽  
Epstein Barr

A comprehensive mapping of interactions among Epstein–Barr virus (EBV) proteins and interactions of EBV proteins with human proteins should provide specific hypotheses and a broad perspective on EBV strategies for replication and persistence. Interactions of EBV proteins with each other and with human proteins were assessed by using a stringent high-throughput yeast two-hybrid system. Overall, 43 interactions between EBV proteins and 173 interactions between EBV and human proteins were identified. EBV–EBV and EBV–human protein interaction, or “interactome” maps provided a framework for hypotheses of protein function. For example, LF2, an EBV protein of unknown function interacted with the EBV immediate early R transactivator (Rta) and was found to inhibit Rta transactivation. From a broader perspective, EBV genes can be divided into two evolutionary classes, “core” genes, which are conserved across all herpesviruses and subfamily specific, or “noncore” genes. Our EBV–EBV interactome map is enriched for interactions among proteins in the same evolutionary class. Furthermore, human proteins targeted by EBV proteins were enriched for highly connected or “hub” proteins and for proteins with relatively short paths to all other proteins in the human interactome network. Targeting of hubs might be an efficient mechanism for EBV reorganization of cellular processes.

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