Essential Protein Identification Based on Essential Protein

Protein Interaction Prediction by Integrated Edge Weights

Yuexu Jiang, Yan Wang, Wei Pang, Liang Chen, Huiyan Sun, Yanchun Liang, Enrico Blanzieri

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Essential proteins play a crucial role in cellular survival and development process. Experimentally, essential proteins are identified by gene knockouts or RNA interference, which are expensive and often fatal to the target organisms. Regarding this, an alternative yet important approach to essential protein identification is through computational prediction. Existing computational methods predict essential proteins based on their relative densities in a protein–protein interaction (PPI) network. Degree, betweenness, and other appropriate criteria are often used to measure the relative density. However, no matter what criterion is used, a protein is actually ordered by the attributes of this protein per se. In this research, we presented a novel computational method, Integrated Edge Weights (IEW), to first rank protein–protein interactions by integrating their edge weights, and then identified sub PPI networks consisting of those highly-ranked edges, and finally regarded the nodes in these sub networks as essential proteins. We evaluated IEW on three model organisms: Saccharomyces cerevisiae (S. cerevisiae), Escherichia coli (E. coli), and Caenorhabditis elegans (C. elegans). The experimental results showed that IEW achieved better performance than the state-of-the-art methods in terms of precision–recall and Jackknife measures. We had also demonstrated that IEW is a robust and effective method, which can retrieve biologically significant modules by its highly-ranked protein–protein interactions for S. cerevisiae, E. coli, and C. elegans. We believe that, with sufficient data provided, IEW can be used to any other organisms’ essential protein identification. A website about IEW can be accessed from http://digbio.missouri.edu/IEW/index.html.
Original languageEnglish
Pages (from-to)51-62
Number of pages12
JournalMethods
Volume83
Early online date16 Apr 2015
DOIs
Publication statusPublished - 15 Jul 2015

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Weights and Measures
Proteins
Specific Gravity
Caenorhabditis elegans
Computational methods
Yeast
Escherichia coli
Saccharomyces cerevisiae
Gene Knockout Techniques
RNA Interference
Websites
Genes
RNA
Research

Keywords

  • essential protein
  • essential protein–protein interaction
  • integrated edge weights

Cite this

Essential Protein Identification Based on Essential Protein : Protein Interaction Prediction by Integrated Edge Weights. / Jiang, Yuexu; Wang, Yan; Pang, Wei; Chen, Liang; Sun, Huiyan; Liang, Yanchun; Blanzieri , Enrico.

In: Methods, Vol. 83, 15.07.2015, p. 51-62.

Research output: Contribution to journalArticle

Jiang, Yuexu ; Wang, Yan ; Pang, Wei ; Chen, Liang ; Sun, Huiyan ; Liang, Yanchun ; Blanzieri , Enrico. / Essential Protein Identification Based on Essential Protein : Protein Interaction Prediction by Integrated Edge Weights. In: Methods. 2015 ; Vol. 83. pp. 51-62.
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abstract = "Essential proteins play a crucial role in cellular survival and development process. Experimentally, essential proteins are identified by gene knockouts or RNA interference, which are expensive and often fatal to the target organisms. Regarding this, an alternative yet important approach to essential protein identification is through computational prediction. Existing computational methods predict essential proteins based on their relative densities in a protein–protein interaction (PPI) network. Degree, betweenness, and other appropriate criteria are often used to measure the relative density. However, no matter what criterion is used, a protein is actually ordered by the attributes of this protein per se. In this research, we presented a novel computational method, Integrated Edge Weights (IEW), to first rank protein–protein interactions by integrating their edge weights, and then identified sub PPI networks consisting of those highly-ranked edges, and finally regarded the nodes in these sub networks as essential proteins. We evaluated IEW on three model organisms: Saccharomyces cerevisiae (S. cerevisiae), Escherichia coli (E. coli), and Caenorhabditis elegans (C. elegans). The experimental results showed that IEW achieved better performance than the state-of-the-art methods in terms of precision–recall and Jackknife measures. We had also demonstrated that IEW is a robust and effective method, which can retrieve biologically significant modules by its highly-ranked protein–protein interactions for S. cerevisiae, E. coli, and C. elegans. We believe that, with sufficient data provided, IEW can be used to any other organisms’ essential protein identification. A website about IEW can be accessed from http://digbio.missouri.edu/IEW/index.html.",
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N1 - Acknowledgments The first author would like to thank Duolin Wang at Jilin University and Dr. Trupti at the University of Missouri for their inspiring discussion during the development of the model and the biological analysis. This work was supported by the Natural Science Foundation of China (Grant Nos. 61272207, 61402194, 61472159) and Development Project of Jilin Province of China (Grant No. 20140101180JC).

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N2 - Essential proteins play a crucial role in cellular survival and development process. Experimentally, essential proteins are identified by gene knockouts or RNA interference, which are expensive and often fatal to the target organisms. Regarding this, an alternative yet important approach to essential protein identification is through computational prediction. Existing computational methods predict essential proteins based on their relative densities in a protein–protein interaction (PPI) network. Degree, betweenness, and other appropriate criteria are often used to measure the relative density. However, no matter what criterion is used, a protein is actually ordered by the attributes of this protein per se. In this research, we presented a novel computational method, Integrated Edge Weights (IEW), to first rank protein–protein interactions by integrating their edge weights, and then identified sub PPI networks consisting of those highly-ranked edges, and finally regarded the nodes in these sub networks as essential proteins. We evaluated IEW on three model organisms: Saccharomyces cerevisiae (S. cerevisiae), Escherichia coli (E. coli), and Caenorhabditis elegans (C. elegans). The experimental results showed that IEW achieved better performance than the state-of-the-art methods in terms of precision–recall and Jackknife measures. We had also demonstrated that IEW is a robust and effective method, which can retrieve biologically significant modules by its highly-ranked protein–protein interactions for S. cerevisiae, E. coli, and C. elegans. We believe that, with sufficient data provided, IEW can be used to any other organisms’ essential protein identification. A website about IEW can be accessed from http://digbio.missouri.edu/IEW/index.html.

AB - Essential proteins play a crucial role in cellular survival and development process. Experimentally, essential proteins are identified by gene knockouts or RNA interference, which are expensive and often fatal to the target organisms. Regarding this, an alternative yet important approach to essential protein identification is through computational prediction. Existing computational methods predict essential proteins based on their relative densities in a protein–protein interaction (PPI) network. Degree, betweenness, and other appropriate criteria are often used to measure the relative density. However, no matter what criterion is used, a protein is actually ordered by the attributes of this protein per se. In this research, we presented a novel computational method, Integrated Edge Weights (IEW), to first rank protein–protein interactions by integrating their edge weights, and then identified sub PPI networks consisting of those highly-ranked edges, and finally regarded the nodes in these sub networks as essential proteins. We evaluated IEW on three model organisms: Saccharomyces cerevisiae (S. cerevisiae), Escherichia coli (E. coli), and Caenorhabditis elegans (C. elegans). The experimental results showed that IEW achieved better performance than the state-of-the-art methods in terms of precision–recall and Jackknife measures. We had also demonstrated that IEW is a robust and effective method, which can retrieve biologically significant modules by its highly-ranked protein–protein interactions for S. cerevisiae, E. coli, and C. elegans. We believe that, with sufficient data provided, IEW can be used to any other organisms’ essential protein identification. A website about IEW can be accessed from http://digbio.missouri.edu/IEW/index.html.

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