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IAPR International Conference on Pattern Recognition in Bioinformatics

PRIB 2012: Pattern Recognition in Bioinformatics pp 71–81Cite as

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Protein Clustering on a Grassmann Manifold

Protein Clustering on a Grassmann Manifold

  • Chendra Hadi Suryanto23,
  • Hiroto Saigo24 &
  • Kazuhiro Fukui23 
  • Conference paper
  • 1645 Accesses

  • 4 Citations

Part of the Lecture Notes in Computer Science book series (LNBI,volume 7632)

Abstract

We propose a new method for clustering 3D protein structures. In our method, the 3D structure of a protein is represented by a linear subspace, which is generated using PCA from the set of synthesized multi-view images of the protein. The similarity of two protein structures is then defined by the canonical angles between the corresponding subspaces. The merit of this approach is that we can avoid the difficulties of protein structure alignments because this similarity measure does not rely on the precise alignment and geometry of each alpha carbon atom. In this approach, we tackle the protein structure clustering problem by considering the set of subspaces corresponding to the various proteins. The clustering of subspaces with the same dimension is equivalent to the clustering of a corresponding set of points on a Grassmann manifold. Therefore, we call our approach the Grassmannian Protein Clustering Method (GPCM). We evaluate the effectiveness of our method through experiments on the clustering of randomly selected proteins from the Protein Data Bank into four classes: alpha, beta, alpha/beta, alpha+beta (with multi-domain protein). The results show that GPCM outperforms the k-means clustering with Gauss Integrals Tuned, which is a state-of-the-art descriptor of protein structure.

Keywords

  • protein structure clustering
  • k-means
  • Mutual Subspace Method
  • Grassmann manifold
  • Gauss Integrals

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Author information

Authors and Affiliations

  1. Graduate School of Systems and Information Engineering, Department of Computer Science, University of Tsukuba, Japan

    Chendra Hadi Suryanto & Kazuhiro Fukui

  2. Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Japan

    Hiroto Saigo

Authors
  1. Chendra Hadi Suryanto
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  2. Hiroto Saigo
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  3. Kazuhiro Fukui
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Editor information

Editors and Affiliations

  1. Institute of Medical Science, University of Tokyo, 4-6-1, Shirokanedai, 108-8639, Minato-ku, Tokyo, Japan

    Tetsuo Shibuya

  2. Department of Mathematical Informatics, The University of Tokyo, 7-3-1 Hongo, 113-8654, Bunkyo-ku, Tokyo, Japan

    Hisashi Kashima

  3. Department of Comouter Science, Tokyo Institute of Technology, 2-12-1 Ookayamama, 152-8550, Meguro-ku, Tokyo, Japan

    Jun Sese

  4. Bioinformatics Project, National Institute of Biomedical Innovation, 7-6-8 Saito-Asagi, 567-0085, Suita, Osaka, Japan

    Shandar Ahmad

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© 2012 Springer-Verlag Berlin Heidelberg

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Suryanto, C.H., Saigo, H., Fukui, K. (2012). Protein Clustering on a Grassmann Manifold. In: Shibuya, T., Kashima, H., Sese, J., Ahmad, S. (eds) Pattern Recognition in Bioinformatics. PRIB 2012. Lecture Notes in Computer Science(), vol 7632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34123-6_7

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  • DOI: https://doi.org/10.1007/978-3-642-34123-6_7

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  • Print ISBN: 978-3-642-34122-9

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