Similarity search for local protein structures at atomic resolution by exploiting a database management system.
Similarity search for local protein structures at atomic resolution by exploiting a database management system.
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DOI：
10.2142/biophysics.3.75
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发表时间：
2007-01-01
期刊：
影响因子：
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通讯作者：
Nakamura, Haruki
中科院分区：
文献类型：
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作者：
Kinjo, Akira R;Nakamura, Haruki
提出了一种以原子分辨率搜索蛋白质局部结构相似性的方法。事实证明，通过使用传统的关系数据库管理系统和适当的几何数据索引，可以在合理的CPU时间内处理大量的结构数据。这种方法，我们称之为几何索引，可以在普通台式计算机上几个小时的 CPU 时间内从超过 160,000 个可能的候选蛋白中枚举出结构上与查询蛋白子结构相似的配体结合位点。通过几何索引搜索检测到一组高分配体结合位点后，通过迭代应用匈牙利算法构建原子分辨率的结构比对，并根据基于伽玛分布的经验模型估计最终分数的统计显着性。将此方法应用于几种蛋白质结构清楚地表明，可以在非同源和同源蛋白质的局部结构之间检测到显着的相似性。
A method to search for local structural similarities in proteins at atomic resolution is presented. It is demonstrated that a huge amount of structural data can be handled within a reasonable CPU time by using a conventional relational database management system with appropriate indexing of geometric data. This method, which we call geometric indexing, can enumerate ligand binding sites that are structurally similar to sub-structures of a query protein among more than 160,000 possible candidates within a few hours of CPU time on an ordinary desktop computer. After detecting a set of high scoring ligand binding sites by the geometric indexing search, structural alignments at atomic resolution are constructed by iteratively applying the Hungarian algorithm, and the statistical significance of the final score is estimated from an empirical model based on a gamma distribution. Applications of this method to several protein structures clearly shows that significant similarities can be detected between local structures of non-homologous as well as homologous proteins.