Protein clustering on a Grassmann manifold

Chendra Hadi Suryanto, Hiroto Saigo, Kazuhiro Fukui

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

5 被引用数 (Scopus)


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.

ホスト出版物のタイトルPattern Recognition in Bioinformatics - 7th IAPR International Conference, PRIB 2012, Proceedings
出版ステータス出版済み - 2012
イベント7th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2012 - Tokyo, 日本
継続期間: 11月 8 201211月 10 2012


名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
7632 LNBI


その他7th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2012

!!!All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)


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