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Predicting Essential Proteins Using a New Method

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Intelligent Computing Theories and Application (ICIC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10362))

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Abstract

Essential proteins are indispensable for the survival of organisms. Computational methods for predicting essential proteins in terms of the global protein-protein interaction (PPI) networks is severely restricted due to the insufficiency of the PPI data, but fortunately the subcellular localization information helps to make up the deficiency. In the study, a new method named CNC is developed to detect essential proteins. First, the subcellular localization information is incorporated into the PPI networks, so each interaction in the networks is weighted. Meanwhile the edge clustering coefficient of each pair interacting proteins is calculated and the second weighted value of each interaction in the networks is gained. The two kinds of weighted values are integrated to build a new weighted PPI networks. The proteins in the new weighted networks are scored by the weighted degree centrality (WDC) and sorted in descending order of their scores. Six methods, i.e., CNC, CIC, DC, NC, PeC and WDC are used to prioritize the proteins in the yeast PPI networks. The results demonstrate that CNC outperforms other state-of-the-art ones. At the same time, the analysis also mean that CNC is an effective technology to identify essential proteins by integrating different biological data.

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Acknowledgment

This work is supported in part by the National Natural Science Foundation of China under Grant Nos.61472133, 61502214, 31560317, 61370172, Hunan Provincial Natural Science Foundation of China Nos. 15JJ2038, 15JJ2037, Research Foundation of Education Bureau of Hunan Province Nos. 14A027, [2015]118, [2013]532, CSC No. 201508430098, Hunan Key Laboratory no. 2015TP1017.

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Authors and Affiliations

  1. National University of Defense Technology, Changsha, 410073, China

    Xi-wei Tang

  2. Hunan First Normal University, Changsha, 410205, China

    Xi-wei Tang

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  1. Xi-wei Tang
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Correspondence to Xi-wei Tang .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Juan Carlos Figueroa-García

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Tang, Xw. (2017). Predicting Essential Proteins Using a New Method. In: Huang, DS., Jo, KH., Figueroa-García, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_27

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  • DOI: https://doi.org/10.1007/978-3-319-63312-1_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63311-4

  • Online ISBN: 978-3-319-63312-1

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