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使用最小二乘中位数进行柔性蛋白质的结构叠加。

Using least median of squares for structural superposition of flexible proteins.

作者信息

Liu Yu-Shen, Fang Yi, Ramani Karthik

机构信息

Purdue University, West Lafayette, IN 47907, USA.

出版信息

BMC Bioinformatics. 2009 Jan 22;10:29. doi: 10.1186/1471-2105-10-29.

Abstract

BACKGROUND

The conventional superposition methods use an ordinary least squares (LS) fit for structural comparison of two different conformations of the same protein. The main problem of the LS fit that it is sensitive to outliers, i.e. large displacements of the original structures superimposed.

RESULTS

To overcome this problem, we present a new algorithm to overlap two protein conformations by their atomic coordinates using a robust statistics technique: least median of squares (LMS). In order to effectively approximate the LMS optimization, the forward search technique is utilized. Our algorithm can automatically detect and superimpose the rigid core regions of two conformations with small or large displacements. In contrast, most existing superposition techniques strongly depend on the initial LS estimating for the entire atom sets of proteins. They may fail on structural superposition of two conformations with large displacements. The presented LMS fit can be considered as an alternative and complementary tool for structural superposition.

CONCLUSION

The proposed algorithm is robust and does not require any prior knowledge of the flexible regions. Furthermore, we show that the LMS fit can be extended to multiple level superposition between two conformations with several rigid domains. Our fit tool has produced successful superpositions when applied to proteins for which two conformations are known. The binary executable program for Windows platform, tested examples, and database are available from https://engineering.purdue.edu/PRECISE/LMSfit.

摘要

背景

传统的叠加方法使用普通最小二乘法(LS)对同一蛋白质的两种不同构象进行结构比较。LS拟合的主要问题在于它对异常值敏感,即叠加的原始结构存在大的位移。

结果

为克服这一问题,我们提出一种新算法,利用稳健统计技术:最小二乘中位数(LMS),通过原子坐标来重叠两种蛋白质构象。为有效近似LMS优化,采用了前向搜索技术。我们的算法能够自动检测并叠加两种构象的刚性核心区域,无论位移大小。相比之下,大多数现有的叠加技术严重依赖于对蛋白质整个原子集的初始LS估计。它们在处理两种位移较大的构象的结构叠加时可能会失败。所提出的LMS拟合可被视为结构叠加的一种替代和补充工具。

结论

所提出的算法具有稳健性,且不需要关于柔性区域的任何先验知识。此外,我们表明LMS拟合可扩展到具有多个刚性结构域的两种构象之间的多级叠加。当应用于已知两种构象的蛋白质时,我们的拟合工具已成功实现叠加。Windows平台的二进制可执行程序、测试示例和数据库可从https://engineering.purdue.edu/PRECISE/LMSfit获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c73/2639377/da024bb4c9ba/1471-2105-10-29-1.jpg

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