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利用互信息的行和列加权以及多维氨基酸表示法寻找共同进化的氨基酸残基。

Finding coevolving amino acid residues using row and column weighting of mutual information and multi-dimensional amino acid representation.

作者信息

Gouveia-Oliveira Rodrigo, Pedersen Anders G

机构信息

Center for Biological sequence analysis, Technical University of Denmark, Building 208, 2800 Lyngby, Denmark.

出版信息

Algorithms Mol Biol. 2007 Oct 3;2:12. doi: 10.1186/1748-7188-2-12.

Abstract

BACKGROUND

Some amino acid residues functionally interact with each other. This interaction will result in an evolutionary co-variation between these residues - coevolution. Our goal is to find these coevolving residues.

RESULTS

We present six new methods for detecting coevolving residues. Among other things, we suggest measures that are variants of Mutual Information, and measures that use a multidimensional representation of each residue in order to capture the physico-chemical similarities between amino acids. We created a benchmarking system, in silico, able to evaluate these methods through a wide range of realistic conditions. Finally, we use the combination of different methods as a way of improving performance.

CONCLUSION

Our best method (Row and Column Weighed Mutual Information) has an estimated accuracy increase of 63% over Mutual Information. Furthermore, we show that the combination of different methods is efficient, and that the methods are quite sensitive to the different conditions tested.

摘要

背景

一些氨基酸残基在功能上相互作用。这种相互作用将导致这些残基之间的进化共变——协同进化。我们的目标是找到这些协同进化的残基。

结果

我们提出了六种检测协同进化残基的新方法。其中,我们提出了互信息变体的度量方法,以及使用每个残基的多维表示来捕捉氨基酸之间物理化学相似性的度量方法。我们创建了一个计算机模拟的基准测试系统,能够在各种现实条件下评估这些方法。最后,我们将不同方法结合起来以提高性能。

结论

我们最好的方法(行和列加权互信息)估计比互信息的准确率提高了63%。此外,我们表明不同方法的结合是有效的,并且这些方法对所测试的不同条件相当敏感。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee50/2234412/78428182370c/1748-7188-2-12-1.jpg

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