Protein clustering on a Grassmann manifold

Chendra Hadi Suryanto, Hiroto Saigo, Kazuhiro Fukui

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

4 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトルPattern Recognition in Bioinformatics - 7th IAPR International Conference, PRIB 2012, Proceedings
ページ71-81
ページ数11
DOI
出版ステータス出版済み - 2012
外部発表はい
イベント7th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2012 - Tokyo, 日本
継続期間: 11 8 201211 10 2012

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
7632 LNBI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他7th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2012
Country日本
CityTokyo
Period11/8/1211/10/12

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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