Title: Validation of the merged co-variation signal in interacting protein pairs by mirror-dendrogram

Authors: Xiaowei Song; Xingjian He; Yajun Wang; Yezhong Tang

Addresses: College of Life Sciences and Institute for Conservation and Utilization of Agro-bioresources in Dabie Mountains, Xinyang Normal University, Xinyang 464000, China; Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, China ' Foreign Language Department, Chengdu Sport University, Chengdu 610041, China ' College of Life Science, Sichuan University, Chengdu 610064, China ' Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, China

Abstract: In the post-genomic era, in silico methods have proven increasingly useful for constructing interactomes, especially protein-protein interaction networks. Here we describe a structural co-variation based method (i.e. mirror-dendrogram) for prediction of binary interacting proteins at a proteome-wide scale. The structural variation was measured in terms of physicochemical traits (i.e. Kyte-Doolittle hydrophobicity, molecular weight and molecular Van der Waals volume). We explored the performance of a series of mirror-algorithms (i.e. mirror-tree, tree of life-mirror-tree and mirror-dendrogram) in 1117 orthologous groups of 21 species in the Enterobacteriaceae family. Interestingly, sequence divergence degree of each orthologous group was found to have an important effect on the performance of these algorithms. The mirror-dendrogram is a robust way to validate the hypothesis that interacting protein pairs possess a mixed co-variation signal, which originates from background co-evolution and structural co-adaptation. We consider that mirror-dendrogram will promote the distinguishment of physically interacting proteins from functionally related ones by characterising the merged co-variation signal.

Keywords: co-evolution; mirror-tree; mirror-dendrogram; physicochemical trait; protein-protein interaction; Enterobacteriaceae.

DOI: 10.1504/IJDMB.2017.085281

International Journal of Data Mining and Bioinformatics, 2017 Vol.17 No.3, pp.238 - 254

Available online: 17 Jul 2017 *

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