Local feature frequency profile: A method to measure structural similarity in proteins
Local feature frequency profile: A method to measure structural similarity in proteins
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DOI：
10.1073/pnas.0308656100
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发表时间：
2004-03-16
影响因子：
11.1
通讯作者：
Kim, SH
中科院分区：
文献类型：
--
作者：
Choi, IG;Kwon, J;Kim, SH
已知蛋白质结构之间结构相似性的测量为蛋白质折叠分类和揭示蛋白质结构宇宙的全局视图提供了客观基础。在这里，我们描述了一种快速的方法来衡量结构相似性的基础上的轮廓的代表性局部特征的C。比较蛋白质结构的距离矩阵。首先，我们提取了一个有限数量的代表性的局部特征（LF）模式的距离矩阵的所有蛋白质折叠家庭的medoid分析。然后，每个C.蛋白质结构的距离矩阵是通过用最接近的代表LF模式的索引标记其所有子矩阵来编码的。最后，结构由这些指数的频率分布表示，我们称之为蛋白质的LF频率（LFF）谱。LFF图谱允许人们快速计算大量蛋白质结构之间的结构相似性分数，并且还可以容易地构建和更新蛋白质结构宇宙的“地图”。LFF图谱方法有效地将复杂的蛋白质结构映射到共同的欧几里得空间中，而无需事先分配二级结构信息或结构比对。
Measures of structural similarity between known protein structures provide an objective basis for classifying protein folds and for revealing a global view of the protein structure universe. Here, we describe a rapid method to measure structural similarity based on the profiles of representative local features of C. distance matrices of compared protein structures. We first extract a finite number of representative local feature (LF) patterns from the distance matrices of all protein fold families by medoid analysis. Then, each C. distance matrix of a protein structure is encoded by labeling all its submatrices by the index of the nearest representative LF patterns. Finally, the structure is represented by the frequency distribution of these indices, which we call the LF frequency (LFF) profile of the protein. The LFF profile allows one to calculate structural similarity scores among a large number of protein structures quickly, and also to construct and update the "map" of the protein structure universe easily. The LFF profile method efficiently maps complex protein structures into a common Euclidean space without prior assignment of secondary structure information or structural alignment.