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一种用于多跨膜蛋白的统一疏水性标度。

A unified hydrophobicity scale for multispan membrane proteins.

作者信息

Koehler Julia, Woetzel Nils, Staritzbichler René, Sanders Charles R, Meiler Jens

机构信息

Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37232-8725, USA.

出版信息

Proteins. 2009 Jul;76(1):13-29. doi: 10.1002/prot.22315.

Abstract

The concept of hydrophobicity is critical to our understanding of the principles of membrane protein (MP) folding, structure, and function. In the last decades, several groups have derived hydrophobicity scales using both experimental and statistical methods that are optimized to mimic certain natural phenomena as closely as possible. The present work adds to this toolset the first knowledge-based scale that unifies the characteristics of both alpha-helical and beta-barrel multispan MPs. This unified hydrophobicity scale (UHS) distinguishes between amino acid preference for solution, transition, and trans-membrane states. The scale represents average hydrophobicity values of amino acids in folded proteins, irrespective of their secondary structure type. We furthermore present the first knowledge-based hydrophobicity scale for mammalian alpha-helical MPs (mammalian hydrophobicity scale--MHS). Both scales are particularly useful for computational protein structure elucidation, for example as input for machine learning techniques, such as secondary structure or trans-membrane span prediction, or as reference energies for protein structure prediction or protein design. The knowledge-based UHS shows a striking similarity to a recent experimental hydrophobicity scale introduced by Hessa and coworkers (Hessa T et al., Nature 2007;450:U1026-U1032). Convergence of two very different approaches onto similar hydrophobicity values consolidates the major differences between experimental and knowledge-based scales observed in earlier studies. Moreover, the UHS scale represents an accurate absolute free energy measure for folded, multispan MPs--a feature that is absent from many existing scales. The utility of the UHS was demonstrated by analyzing a series of diverse MPs. It is further shown that the UHS outperforms nine established hydrophobicity scales in predicting trans-membrane spans along the protein sequence. The accuracy of the present hydrophobicity scale profits from the doubling of the number of integral MPs in the PDB over the past four years. The UHS paves the way for an increased accuracy in the prediction of trans-membrane spans.

摘要

疏水性概念对于我们理解膜蛋白(MP)折叠、结构和功能的原理至关重要。在过去几十年中,多个研究团队通过实验和统计方法得出了疏水性标度,这些方法经过优化以尽可能紧密地模拟某些自然现象。本研究在这一工具集中增加了首个基于知识的标度,该标度统一了α螺旋和β桶状多跨膜MP的特征。这种统一疏水性标度(UHS)区分了氨基酸在溶液、转变和跨膜状态下的偏好。该标度表示折叠蛋白中氨基酸的平均疏水性值,而不考虑其二级结构类型。此外,我们还提出了首个基于知识的哺乳动物α螺旋MP疏水性标度(哺乳动物疏水性标度——MHS)。这两种标度对于计算蛋白质结构解析特别有用,例如作为机器学习技术(如二级结构或跨膜跨度预测)的输入,或作为蛋白质结构预测或蛋白质设计的参考能量。基于知识的UHS与Hessa及其同事最近引入的实验疏水性标度(Hessa T等人,《自然》2007年;450:U1026 - U1032)有着惊人的相似性。两种截然不同的方法得出相似的疏水性值,这巩固了早期研究中观察到的实验性标度和基于知识的标度之间的主要差异。此外,UHS标度代表了折叠多跨膜MP的准确绝对自由能度量——这是许多现有标度所缺乏的特征。通过分析一系列不同的MP证明了UHS的实用性。进一步表明,在预测沿蛋白质序列的跨膜跨度方面,UHS优于九个已确立的疏水性标度。目前疏水性标度的准确性得益于过去四年PDB中完整MP数量的翻倍。UHS为提高跨膜跨度预测的准确性铺平了道路。

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本文引用的文献

1
Molecular code for transmembrane-helix recognition by the Sec61 translocon.
Nature. 2007 Dec 13;450(7172):1026-30. doi: 10.1038/nature06387.
2
Toward high-resolution prediction and design of transmembrane helical protein structures.
Proc Natl Acad Sci U S A. 2007 Oct 2;104(40):15682-7. doi: 10.1073/pnas.0702515104. Epub 2007 Sep 28.
3
Role of aromatic side chains in the folding and thermodynamic stability of integral membrane proteins.
J Am Chem Soc. 2007 Jul 4;129(26):8320-7. doi: 10.1021/ja068849o. Epub 2007 Jun 12.
5
Knowledge-based potentials in protein design.
Curr Opin Struct Biol. 2006 Aug;16(4):508-13. doi: 10.1016/j.sbi.2006.06.013. Epub 2006 Jul 14.
6
Voltage sensor conformations in the open and closed states in ROSETTA structural models of K(+) channels.
Proc Natl Acad Sci U S A. 2006 May 9;103(19):7292-7. doi: 10.1073/pnas.0602350103. Epub 2006 Apr 28.
7
Multipass membrane protein structure prediction using Rosetta.
Proteins. 2006 Mar 1;62(4):1010-25. doi: 10.1002/prot.20817.
8
Transmembrane helices before, during, and after insertion.
Curr Opin Struct Biol. 2005 Aug;15(4):378-86. doi: 10.1016/j.sbi.2005.07.004.
9
PISCES: recent improvements to a PDB sequence culling server.
Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W94-8. doi: 10.1093/nar/gki402.

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