Automatic classification of protein structure by using Gauss integrals
Automatic classification of protein structure by using Gauss integrals
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DOI：
10.1073/pnas.2636460100
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发表时间：
2003-01-07
影响因子：
11.1
通讯作者：
Fain, B
中科院分区：
文献类型：
--
作者：
Rogen, P;Fain, B
我们介绍了一种观察、分析和比较蛋白质结构的方法。蛋白质的拓扑结构由30个数字捕获，灵感来自瓦西里耶夫结不变量。为了说明这种拓扑方法的简单性和力量，我们构建了一个措施（缩放高斯度量，SGM）的蛋白质形状的相似性。在这个度量下，蛋白质链自然地分离成折叠簇。我们使用SGM来构造CATH 2.4数据库的自动分类过程。该方法非常快，因为它既不需要链的对齐，也不需要任何链与链的比较。它也只有一个可调参数。我们将95.51%的链分配到适当的C（类），A（架构），T（拓扑）和H（同源超家族）折叠中，找到所有新的折叠，并且没有检测到假几何阳性。使用SGM，我们显示投影到两个维度上的折叠空间的“地图”，显示主要结构类的相对位置，并“放大”蛋白质的空间以显示架构，拓扑结构和折叠簇。从链路径计算的蛋白质折叠的简单测量的存在将对自动折叠分类产生重大影响。
We introduce a method of looking at, analyzing, and comparing protein structures. The topology of a protein is captured by 30 numbers inspired by Vassiliev knot invariants. To illustrate the simplicity and power of this topological approach, we construct a measure (scaled Gauss metric, SGM) of similarity of protein shapes. Under this metric, protein chains naturally separate into fold clusters. We use SGM to construct an automatic classification procedure for the CATH2.4 database. The method is very fast because it requires neither alignment of the chains nor any chain-chain comparison. It also has only one adjustable parameter. We assign 95.51% of the chains into the proper C (class), A (architecture), T (topology), and H (homologous superfamily) fold, find all new folds, and detect no false geometric positives. Using the SGM, we display a "map" of the space of folds projected onto two dimensions, show the relative locations of the major structural classes, and "zoom into" the space of proteins to show architecture, topology, and fold clusters. The existence of a simple measure of a protein fold computed from the chain path will have a major impact on automatic fold classification.