Validation of the merged co-variation signal in interacting protein pairs by mirror-dendrogram
by Xiaowei Song; Xingjian He; Yajun Wang; Yezhong Tang
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 17, No. 3, 2017

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.

Online publication date: Wed, 19-Jul-2017

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