IEEE/ACM Trans Comput Biol Bioinform. 2018 Jan-Feb;15(1):286-299. doi: 10.1109/TCBB.2016.2603987. Epub 2016 Aug 29.
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.
计算蛋白质结构之间的相似性或相异性是结构生物学中的一项重要任务。计算蛋白质结构差异的传统方法需要对蛋白质进行结构比对。然而，定义一个最佳的比对是困难的，特别是当结构非常不同时。在本文中，我们提出了一种使用一组三维蛋白质结构的多维 2D 图像来比较蛋白质结构的新相似性度量方法。在这种方法中，每个蛋白质结构都由图像集中的子空间表示。然后，通过两个子空间之间的典型角度来描述两个蛋白质结构之间的相似性。我们方法的主要优点是不需要精确的对齐。我们在分类框架中采用 Grassmann 判别分析（GDA）作为基于子空间的学习。我们将我们的方法应用于蛋白质 3D 结构的七个 SCOP 结构类别的分类问题。所提出的方法优于基于传统基于对齐的方法 CE、FATCAT 和 TM-align 的 k-最近邻方法（k-NN）。我们的方法还应用于膜蛋白 SCOP 折叠的分类，其中所提出的方法可以比 TM-Align 更好地识别 HEM 结合四螺旋束（f.21）折叠。
