Comput Biol Chem - A local average connectivity-based method for identifying essential proteins from the network level.

Tópicos

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Resumo

Identifying essential proteins is very important for understanding the minimal requirements of cellular survival and development. Fast growth in the amount of available protein-protein interactions has produced unprecedented opportunities for detecting protein essentiality from the network level. Essential proteins have been found to be more abundant among those highly connected proteins. However, there exist a number of highly connected proteins which are not essential. By analyzing these proteins, we find that few of their neighbors interact with each other. Thus, we propose a new local method, named LAC, to determine a protein's essentiality by evaluating the relationship between a protein and its neighbors. The performance of LAC is validated based on the yeast protein interaction networks obtained from two different databases: DIP and BioGRID. The experimental results of the two networks show that the number of essential proteins predicted by LAC clearly exceeds that explored by Degree Centrality (DC). More over, LAC is also compared with other seven measures of protein centrality (Neighborhood Component (DMNC), Betweenness Centrality (BC), Closeness Centrality (CC), Bottle Neck (BN), Information Centrality (IC), Eigenvector Centrality (EC), and Subgraph Centrality (SC)) in identifying essential proteins. The comparison results based on the validations of sensitivity, specificity, F-measure, positive predictive value, negative predictive value, and accuracy consistently show that LAC outweighs these seven previous methods.

Resumo Limpo

identifi essenti protein import understand minim requir cellular surviv develop fast growth amount avail proteinprotein interact produc unpreced opportun detect protein essenti network level essenti protein found abund among high connect protein howev exist number high connect protein essenti analyz protein find neighbor interact thus propos new local method name lac determin protein essenti evalu relationship protein neighbor perform lac valid base yeast protein interact network obtain two differ databas dip biogrid experiment result two network show number essenti protein predict lac clear exceed explor degre central dc lac also compar seven measur protein central neighborhood compon dmnc between central bc close central cc bottl neck bn inform central ic eigenvector central ec subgraph central sc identifi essenti protein comparison result base valid sensit specif fmeasur posit predict valu negat predict valu accuraci consist show lac outweigh seven previous method

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