Binding Site Extraction by Similar Subgraphs Mining from Protein Molecular Surfaces and Its Application to Protein Classification

Author:

Kurumatani Natsumi1,Monji Hiroyuki1,Ohkawa Takenao1

Affiliation:

1. Graduate School of System Informatics, Kobe University, 1-1, Rokkodai, Nada, Kobe, 657-8501, Japan

Abstract

Most proteins express their functions by binding with other proteins or molecular compounds (ligands). Since the characteristics of the local portion involved in binding (binding site) often determine the function of the protein, clarifying the location of the binding site of the protein helps analyze the function of proteins. Binding sites that bind to similar ligands often have common surface structures (surface motifs). Extracting the surface motifs among several proteins with similar functions improves binding site prediction. We propose a method that predicts binding sites by extracting the surface motifs that are frequently observed in only a specific set of proteins that bind to the same ligand (group). Since most binding sites have concave structures (pockets), the pockets are compared and common structures are searched for to extract the surface motifs by applying similar graph mining to the pocket data, which are represented as graphs. Common binding sites across several groups can be predicted in such a way to integrate more than one group. We also proposed a method of protein classification, in which the surface motifs extracted using the above method are evaluated on the assumption that a protein belongs to each one of the groups.

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Artificial Intelligence

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Uncertain maximal frequent subgraph mining algorithm based on adjacency matrix and weight;International Journal of Machine Learning and Cybernetics;2017-03-18

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