Predicting Essential Proteins Based on Weighted Degree Centrality

2014 ◽  
Vol 11 (2) ◽  
pp. 407-418 ◽  
Author(s):  
Xiwei Tang ◽  
Jianxin Wang ◽  
Jiancheng Zhong ◽  
Yi Pan
Keyword(s):  
Degree Centrality ◽  
Weighted Degree ◽  
Essential Proteins ◽  
Weighted Degree Centrality

scholarly journals Competitive Conditions in Global Value Chain Networks: An Assessment Using Entropy and Network Analysis

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1068 ◽  
Author(s):  
Georgios Angelidis ◽  
Evangelos Ioannidis ◽  
Georgios Makris ◽  
Ioannis Antoniou ◽  
Nikos Varsakelis
Keyword(s):  
Value Chain ◽  
Value Added ◽  
Value Chains ◽  
Global Value Chain ◽  
Degree Centrality ◽  
Sector Structure ◽  
Input Output ◽  
Weighted Degree ◽  
The World ◽  
Weighted Degree Centrality

We investigated competitive conditions in global value chains (GVCs) for a period of fifteen years (2000–2014), focusing on sector structure, countries’ dominance and diversification. For this purpose, we used data from the World Input–Output Database (WIOD) and examined GVCs as weighted directed networks, where countries are the nodes and value added flows are the edges. We compared the in-and out-weighted degree centralization of the sectoral GVC networks in order to detect the most centralized, on the import or export side, respectively (oligopsonies and oligopolies). Moreover, we examined the in- and out-weighted degree centrality and the in- and out-weight entropy in order to determine whether dominant countries are also diversified. The empirical results reveal that diversification (entropy) and dominance (degree) are not correlated. Dominant countries (rich) become more dominant (richer). Diversification is not conditioned by competitiveness.

scholarly journals A New Weighted Degree Centrality Measure: The Application in an Animal Disease Epidemic

PLoS ONE ◽  
2016 ◽  
Vol 11 (11) ◽  
pp. e0165781 ◽  
Author(s):  
Luca Candeloro ◽  
Lara Savini ◽  
Annamaria Conte
Keyword(s):  
Degree Centrality ◽  
Animal Disease ◽  
Weighted Degree ◽  
Centrality Measure ◽  
Weighted Degree Centrality

scholarly journals Mining Proteome Research Reports: A Bird’s Eye View

Proteomes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 29
Author(s):  
Jagajjit Sahu
Keyword(s):  
Programming Languages ◽  
Scientific Data ◽  
Scale Analysis ◽  
Medical Subject Headings ◽  
Publication Trends ◽  
Weighted Degree ◽  
Systematic Analysis ◽  
Analytical Approaches ◽  
Weighted Degree Centrality ◽  
Unique Genes

The complexity of data has burgeoned to such an extent that scientists of every realm are encountering the incessant challenge of data management. Modern-day analytical approaches with the help of free source tools and programming languages have facilitated access to the context of the various domains as well as specific works reported. Here, with this article, an attempt has been made to provide a systematic analysis of all the available reports at PubMed on Proteome using text mining. The work is comprised of scientometrics as well as information extraction to provide the publication trends as well as frequent keywords, bioconcepts and most importantly gene–gene co-occurrence network. Out of 33,028 PMIDs collected initially, the segregation of 24,350 articles under 28 Medical Subject Headings (MeSH) was analyzed and plotted. Keyword link network and density visualizations were provided for the top 1000 frequent Mesh keywords. PubTator was used, and 322,026 bioconcepts were able to extracted under 10 classes (such as Gene, Disease, CellLine, etc.). Co-occurrence networks were constructed for PMID-bioconcept as well as bioconcept–bioconcept associations. Further, for creation of subnetwork with respect to gene–gene co-occurrence, a total of 11,100 unique genes participated with mTOR and AKT showing the highest (64) number of connections. The gene p53 was the most popular one in the network in accordance with both the degree and weighted degree centrality, which were 425 and 1414, respectively. The present piece of study is an amalgam of bibliometrics and scientific data mining methods looking deeper into the whole scale analysis of available literature on proteome.

Isotopically Labeled Desthiobiotin Azide (isoDTB) Tags Enable Global Profiling of the Bacterial Cysteinome

2019 ◽  
Author(s):  
Patrick R. A. Zanon ◽  
Lisa Lewald ◽  
Stephan M. Hacker
Keyword(s):  
Binding Sites ◽  
Bacterial Resistance ◽  
Mevalonate Pathway ◽  
Rapid Development ◽  
High Reactivity ◽  
Functional Importance ◽  
Essential Proteins ◽  
Covalent Inhibitors ◽  
Druggable Targets ◽  
Covalent Ligands

Rapid development of bacterial resistance has led to an urgent need to find new druggable targets for antibiotics. In this context, residue-specific chemoproteomic approaches enable proteome-wide identification of binding sites for covalent inhibitors. Here, we describe isotopically labeled desthiobiotin azide (isoDTB) tags that are easily synthesized, shorten the chemoproteomic workflow and allow an increased coverage of cysteines in bacterial systems. We quantify 59% of all cysteines in essential proteins in <i>Staphylococcus aureus</i> and discover 88 cysteines with high reactivity, which correlates with functional importance. Furthermore, we identify 268 cysteines that are engaged by covalent ligands. We verify inhibition of HMG-CoA synthase, which will allow addressing the bacterial mevalonate pathway through a new target. Overall, a comprehensive map of the bacterial cysteinome is obtained, which will facilitate the development of antibiotics with novel modes-of-action.

A Survey on Computational Methods for Essential Proteins and Genes Prediction

2019 ◽  
Vol 14 (3) ◽  
pp. 211-225 ◽  
Author(s):  
Ming Fang ◽  
Xiujuan Lei ◽  
Ling Guo
Keyword(s):  
Computational Methods ◽  
Drug Targets ◽  
Disease Genes ◽  
Essential Proteins ◽  
Experimental Identification ◽  
Cellular Life ◽  
Different Types ◽  
The Stability ◽  
Human Disease Genes ◽  
Biology Research

Background: Essential proteins play important roles in the survival or reproduction of an organism and support the stability of the system. Essential proteins are the minimum set of proteins absolutely required to maintain a living cell. The identification of essential proteins is a very important topic not only for a better comprehension of the minimal requirements for cellular life, but also for a more efficient discovery of the human disease genes and drug targets. Traditionally, as the experimental identification of essential proteins is complex, it usually requires great time and expense. With the cumulation of high-throughput experimental data, many computational methods that make useful complements to experimental methods have been proposed to identify essential proteins. In addition, the ability to rapidly and precisely identify essential proteins is of great significance for discovering disease genes and drug design, and has great potential for applications in basic and synthetic biology research. Objective: The aim of this paper is to provide a review on the identification of essential proteins and genes focusing on the current developments of different types of computational methods, point out some progress and limitations of existing methods, and the challenges and directions for further research are discussed.

Identification of Essential Proteins in Yeast Using Mean Weighted Average and Recursive Feature Elimination

2019 ◽  
Vol 12 (1) ◽  
pp. 5-10 ◽  
Author(s):  
Sivagnanam Rajamanickam Mani Sekhar ◽  
Siddesh Gaddadevara Matt ◽  
Sunilkumar S. Manvi ◽  
Srinivasa Krishnarajanagar Gopalalyengar
Keyword(s):  
Drug Design ◽  
Weighted Average ◽  
Living Organism ◽  
Experimental Result ◽  
Recursive Feature Elimination ◽  
Protein Interaction Data ◽  
Essential Proteins ◽  
Protein Protein Interaction ◽  
Result Show ◽  
Better Than

Background: Essential proteins are significant for drug design, cell development, and for living organism survival. A different method has been developed to predict essential proteins by using topological feature, and biological features. Objective: Still it is a challenging task to predict essential proteins effectively and timely, as the availability of protein protein interaction data depends on network correctness. Methods: In the proposed solution, two approaches Mean Weighted Average and Recursive Feature Elimination is been used to predict essential proteins and compared to select the best one. In Mean Weighted Average consecutive slot data to be taken into aggregated count, to get the nearest value which considered as prescription for the best proteins for the slot, where as in Recursive Feature Elimination method whole data is spilt into different slots and essential protein for each slot is determined. Results: The result shows that the accuracy using Recursive Feature Elimination is at-least nine percentages superior when compared to Mean Weighted Average and Betweenness centrality. Conclusion: Essential proteins are made of genes which are essential for living being survival and drug design. Different approaches have been proposed to anticipate essential proteins using either experimental or computation methods. The experimental result show that the proposed work performs better than other approaches.

scholarly journals Proteome Profiling of PMJ2-R and Primary Peritoneal Macrophages

2021 ◽  
Vol 22 (12) ◽  
pp. 6323
Author(s):  
Alexander L. Rusanov ◽  
Peter M. Kozhin ◽  
Olga V. Tikhonova ◽  
Victor G. Zgoda ◽  
Dmitry S. Loginov ◽  
...  
Keyword(s):  
Proteomic Analysis ◽  
Living Organism ◽  
Peritoneal Macrophages ◽  
In Vitro Models ◽  
Individual Characteristics ◽  
Essential Proteins ◽  
Comparative Proteomic Analysis ◽  
Immortalized Cell ◽  
The Individual

In vitro models are often used for studying macrophage functions, including the process of phagocytosis. The application of primary macrophages has limitations associated with the individual characteristics of animals, which can lead to insufficient standardization and higher variability of the obtained results. Immortalized cell lines do not have these disadvantages, but their responses to various signals can differ from those of the living organism. In the present study, a comparative proteomic analysis of immortalized PMJ2-R cell line and primary peritoneal macrophages isolated from C57BL/6 mice was performed. A total of 4005 proteins were identified, of which 797 were quantified. Obtained results indicate significant differences in the abundances of many proteins, including essential proteins associated with the process of phagocytosis, such as Elmo1, Gsn, Hspa8, Itgb1, Ncf2, Rac2, Rack1, Sirpa, Sod1, C3, and Msr1. These findings indicate that outcomes of studies utilizing PMJ2-R cells as a model of peritoneal macrophages should be carefully validated. All MS data are deposited in ProteomeXchange with the identifier PXD022133.

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