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基于物理性质序列相似性的蛋白质结构预测替代方法。

Alternative approach to protein structure prediction based on sequential similarity of physical properties.

作者信息

He Yi, Rackovsky S, Yin Yanping, Scheraga Harold A

机构信息

Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853; and.

Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853; and Department of Pharmacology and Systems Therapeutics, The Icahn School of Medicine at Mount Sinai, New York, NY 10029.

出版信息

Proc Natl Acad Sci U S A. 2015 Apr 21;112(16):5029-32. doi: 10.1073/pnas.1504806112. Epub 2015 Apr 6.

Abstract

The relationship between protein sequence and structure arises entirely from amino acid physical properties. An alternative method is therefore proposed to identify homologs in which residue equivalence is based exclusively on the pairwise physical property similarities of sequences. This approach, the property factor method (PFM), is entirely different from those in current use. A comparison is made between our method and PSI BLAST. We demonstrate that traditionally defined sequence similarity can be very low for pairs of sequences (which therefore cannot be identified using PSI BLAST), but similarity of physical property distributions results in almost identical 3D structures. The performance of PFM is shown to be better than that of PSI BLAST when sequence matching is comparable, based on a comparison using targets from CASP10 (89 targets) and CASP11 (51 targets). It is also shown that PFM outperforms PSI BLAST in informatically challenging targets.

摘要

蛋白质序列与结构之间的关系完全源于氨基酸的物理性质。因此,本文提出了另一种识别同源物的方法,其中残基等价性完全基于序列的成对物理性质相似性。这种方法,即性质因子法(PFM),与目前使用的方法完全不同。我们将我们的方法与PSI BLAST进行了比较。我们证明,对于成对的序列,传统定义的序列相似性可能非常低(因此无法使用PSI BLAST识别),但物理性质分布的相似性会导致几乎相同的三维结构。基于对来自CASP10(89个目标)和CASP11(51个目标)的目标进行比较,结果表明,当序列匹配具有可比性时,PFM的性能优于PSI BLAST。研究还表明,在信息学挑战性目标方面,PFM的表现优于PSI BLAST。

相似文献

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Fast model-based protein homology detection without alignment.基于快速模型的无需比对的蛋白质同源性检测。
Bioinformatics. 2007 Jul 15;23(14):1728-36. doi: 10.1093/bioinformatics/btm247. Epub 2007 May 8.

本文引用的文献

10
Profile hidden Markov models.轮廓隐马尔可夫模型
Bioinformatics. 1998;14(9):755-63. doi: 10.1093/bioinformatics/14.9.755.

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