Structural Class Classification of 3D Protein Structure Based on Multi-View 2D Images
Structural Class Classification of 3D Protein Structure Based on Multi-View 2D Images
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DOI：
10.1109/tcbb.2016.2603987
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发表时间：
2018-01-01
影响因子：
4.5
通讯作者：
Fukui，Kazuhiro
中科院分区：
文献类型：
--
作者：
Suryanto，Chendra Hadi;Saigo，Hiroto;Fukui，Kazuhiro
计算蛋白质结构之间的相似性和不相似性是结构生物学中的一项重要任务。计算蛋白质结构相异性的常规方法需要蛋白质的结构比对。然而，定义一个最佳对齐是困难的，特别是当结构非常不同时。在本文中，我们提出了一种新的相似性度量蛋白质结构的比较使用一组多视图的2D图像的3D蛋白质结构。在这种方法中，每个蛋白质结构由来自图像集的子空间表示。然后，两个蛋白质结构之间的相似性的特征在于两个子空间之间的典范角。我们的方法的主要优点是不需要精确的对齐。我们采用格拉斯曼判别分析（GDA）作为分类框架中基于子空间的学习。我们应用我们的方法的分类问题的7个SCOP结构类的蛋白质三维结构。所提出的方法优于基于传统的基于插值的方法CE，FATCAT和TM-align的k-最近邻方法（k-NN）。我们的方法也被应用到分类的SCOP折叠膜蛋白，其中所提出的方法可以识别折叠HEM结合四螺旋束（F.21）比TM-Align好得多。
Computing similarity or dissimilarity between protein structures is an important task in structural biology. A conventional method to compute protein structure dissimilarity requires structural alignment of the proteins. However, defining one best alignment is difficult, especially when the structures are very different. In this paper, we propose a new similarity measure for protein structure comparisons using a set of multi-view 2D images of 3D protein structures. In this approach, each protein structure is represented by a subspace from the image set. The similarity between two protein structures is then characterized by the canonical angles between the two subspaces. The primary advantage of our method is that precise alignment is not needed. We employed Grassmann Discriminant Analysis (GDA) as the subspace-based learning in the classification framework. We applied our method for the classification problem of seven SCOP structural classes of protein 3D structures. The proposed method outperformed the k-nearest neighbor method (k-NN) based on conventional alignment-based methods CE, FATCAT, and TM-align. Our method was also applied to the classification of SCOP folds of membrane proteins, where the proposed method could recognize the fold HEM-binding four-helical bundle (f.21) much better than TM-Align.