scholarly journals Interactome of Human and SARS-CoV-2 Proteins to Identify Human Hub Proteins Associated with Comorbidities

Author(s):  
Nimisha Ghosh ◽  
Indrajit Saha ◽  
Nikhil Sharma
Keyword(s):  
Author(s):  
Renganayaki G. ◽  
Achuthsankar S. Nair

Sequence alignment algorithms and  database search methods use BLOSUM and PAM substitution matrices constructed from general proteins. These de facto matrices are not optimal to align sequences accurately, for the proteins with markedly different compositional bias in the amino acid.   In this work, a new amino acid substitution matrix is calculated for the disorder and low complexity rich region of Hub proteins, based on residue characteristics. Insights into the amino acid background frequencies and the substitution scores obtained from the Hubsm unveils the  residue substitution patterns which differs from commonly used scoring matrices .When comparing the Hub protein sequences for detecting homologs,  the use of this Hubsm matrix yields better results than PAM and BLOSUM matrices. Usage of Hubsm matrix can be optimal in database search and for the construction of more accurate sequence alignments of Hub proteins.


2019 ◽  
Vol 19 (2) ◽  
pp. 146-155 ◽  
Author(s):  
Renu Chaudhary ◽  
Meenakshi Balhara ◽  
Deepak Kumar Jangir ◽  
Mehak Dangi ◽  
Mrridula Dangi ◽  
...  

<P>Background: Protein-Protein interaction (PPI) network analysis of virulence proteins of Aspergillus fumigatus is a prevailing strategy to understand the mechanism behind the virulence of A. fumigatus. The identification of major hub proteins and targeting the hub protein as a new antifungal drug target will help in treating the invasive aspergillosis. </P><P> Materials & Method: In the present study, the PPI network of 96 virulence (drug target) proteins of A. fumigatus were investigated which resulted in 103 nodes and 430 edges. Topological enrichment analysis of the PPI network was also carried out by using STRING database and Network analyzer a cytoscape plugin app. The key enriched KEGG pathway and protein domains were analyzed by STRING.Conclusion:Manual curation of PPI data identified three proteins (PyrABCN-43, AroM-34, and Glt1- 34) of A. fumigatus possessing the highest interacting partners. Top 10% hub proteins were also identified from the network using cytohubba on the basis of seven algorithms, i.e. betweenness, radiality, closeness, degree, bottleneck, MCC and EPC. Homology model and the active pocket of top three hub proteins were also predicted.</P>


2006 ◽  
Vol 2 (7) ◽  
pp. e88 ◽  
Author(s):  
Nizar N Batada ◽  
Laurence D Hurst ◽  
Mike Tyers

mBio ◽  
2017 ◽  
Vol 8 (5) ◽  
Author(s):  
Olga Sarenko ◽  
Gisela Klauck ◽  
Franziska M. Wilke ◽  
Vanessa Pfiffer ◽  
Anja M. Richter ◽  
...  

ABSTRACT The bacterial second messenger bis-(3′-5′)-cyclic diguanosine monophosphate (c-di-GMP) ubiquitously promotes bacterial biofilm formation. Intracellular pools of c-di-GMP seem to be dynamically negotiated by diguanylate cyclases (DGCs, with GGDEF domains) and specific phosphodiesterases (PDEs, with EAL or HD-GYP domains). Most bacterial species possess multiple DGCs and PDEs, often with surprisingly distinct and specific output functions. One explanation for such specificity is “local” c-di-GMP signaling, which is believed to involve direct interactions between specific DGC/PDE pairs and c-di-GMP-binding effector/target systems. Here we present a systematic analysis of direct protein interactions among all 29 GGDEF/EAL domain proteins of Escherichia coli . Since the effects of interactions depend on coexpression and stoichiometries, cellular levels of all GGDEF/EAL domain proteins were also quantified and found to vary dynamically along the growth cycle. Instead of detecting specific pairs of interacting DGCs and PDEs, we discovered a tightly interconnected protein network of a specific subset or “supermodule” of DGCs and PDEs with a coregulated core of five hyperconnected hub proteins. These include the DGC/PDE proteins representing the c-di-GMP switch that turns on biofilm matrix production in E. coli . Mutants lacking these core hub proteins show drastic biofilm-related phenotypes but no changes in cellular c-di-GMP levels. Overall, our results provide the basis for a novel model of local c-di-GMP signaling in which a single strongly expressed master PDE, PdeH, dynamically eradicates global effects of several DGCs by strongly draining the global c-di-GMP pool and thereby restricting these DGCs to serving as local c-di-GMP sources that activate specific colocalized effector/target systems. IMPORTANCE c-di-GMP signaling in bacteria is believed to occur via changes in cellular c-di-GMP levels controlled by antagonistic and potentially interacting pairs of diguanylate cyclases (DGCs) and c-di-GMP phosphodiesterases (PDEs). Our systematic analysis of protein-protein interaction patterns of all 29 GGDEF/EAL domain proteins of E. coli , together with our measurements of cellular c-di-GMP levels, challenges both aspects of this current concept. Knocking out distinct DGCs and PDEs has drastic effects on E. coli biofilm formation without changing the cellular c-di-GMP level. In addition, rather than generally coming in interacting DGC/PDE pairs, a subset of DGCs and PDEs operates as central interaction hubs in a larger "supermodule," with other DGCs and PDEs behaving as “lonely players” without contacts to other c-di-GMP-related enzymes. On the basis of these data, we propose a novel concept of “local” c-di-GMP signaling in bacteria with multiple enzymes that make or break the second messenger c-di-GMP.


Protein-Protein Interactions referred as PPIs perform significant role in biological functions like cell metabolism, immune response, signal transduction etc. Hot spots are small fractions of residues in interfaces and provide substantial binding energy in PPIs. Therefore, identification of hot spots is important to discover and analyze molecular medicines and diseases. The current strategy, alanine scanning isn't pertinent to enormous scope applications since the technique is very costly and tedious. The existing computational methods are poor in classification performance as well as accuracy in prediction. They are concerned with the topological structure and gene expression of hub proteins. The proposed system focuses on hot spots of hub proteins by eliminating redundant as well as highly correlated features using Pearson Correlation Coefficient and Support Vector Machine based feature elimination. Extreme Gradient boosting and LightGBM algorithms are used to ensemble a set of weak classifiers to form a strong classifier. The proposed system shows better accuracy than the existing computational methods. The model can also be used to predict accurate molecular inhibitors for specific PPIs


2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Guanying Wang ◽  
Xiaojuan Ren ◽  
Xingping Zhang ◽  
Qingquan Wang ◽  
Tao Liu ◽  
...  

Background. Insomnia is an economic burden and public health problem. This study is aimed at exploring potential biological pathways and protein networks for insomnia characterized by wakefulness after sleep. Method. Proteomics analysis was performed in the insomnia group with wakefulness and the control group. The differentially expressed proteins (DEPs) were enriched; then, hub proteins were identified by protein-protein interaction (PPI) network and verified by parallel reaction monitoring (PRM). Results. Compared with the control group, the sleep time and efficiency of insomnia patients were decreased, and awakening time and numbers after sleep onset were significantly increased ( P < 0.001 ). The results of proteomic sequencing found 68 DEPs in serum under 1.2-fold changed standard. These DEPs were significantly enriched in humoral immune response, complement and coagulation cascades, and cholesterol metabolism. Through the PPI network, we identified 10 proteins with the highest connectivity as hub proteins. Among them, the differential expression of 9 proteins was verified by PRM. Conclusion. We identified the hub proteins and molecular mechanisms of insomnia patients characterized by wakefulness after sleep. It provided potential molecular targets for the clinical diagnosis and treatment of these patients and indicated that the immune and metabolic systems may be closely related to insomnia characterized by wakefulness after sleep.


Author(s):  
Young-Rae Cho ◽  
Aidong Zhang

High-throughput techniques involve large-scale detection of protein-protein interactions. This interaction data set from the genome-scale perspective is structured into an interactome network. Since the interaction evidence represents functional linkage, various graph-theoretic computational approaches have been applied to the interactome networks for functional characterization. However, this data is generally unreliable, and the typical genome-wide interactome networks have a complex connectivity. In this paper, the authors explore systematic analysis of protein interactome networks, and propose a $k$-round signal flow simulation algorithm to measure interaction reliability from connection patterns of the interactome networks. This algorithm quantitatively characterizes functional links between proteins by simulating the propagation of information signals through complex connections. In this regard, the algorithm efficiently estimates the strength of alternative paths for each interaction. The authors also present an algorithm for mining the complex interactome network structure. The algorithm restructures the network by hierarchical ordering of nodes, and this structure re-formatting process reveals hub proteins in the interactome networks. This paper demonstrates that two rounds of simulation accurately scores interaction reliability in terms of ontological correlation and functional consistency. Finally, the authors validate that the selected structural hubs represent functional core proteins.


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