An amino acid has two sides: A new 2D measure provides a different view of solvent exposure

被引:125
作者
Hamelryck, T [1 ]
机构
[1] Univ Copenhagen, Bioinformat Ctr, DK-2100 Copenhagen, Denmark
关键词
protein structure; coordination number; residue depth; accessible solvent area; solvent exposure; simplifed models; protein structure prediction;
D O I
10.1002/prot.20379
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 [生物化学与分子生物学]; 081704 [应用化学];
摘要
The concept of amino acid solvent exposure is crucial for understanding and predicting various aspects of protein structure and function. The traditional measures of solvent exposure however suffer from various shortcomings, like for example the inability to distinguish exposed, partly exposed, buried, and deeply buried residues. This article introduces a new measure of solvent exposure called Half-Sphere Exposure that addresses many of the shortcomings of other methods. The new measure outperforms other measures with respect to correlation with protein stability, conservation among fold homologs, amino acid-type dependency and interpretation. The measure consists of the number of Calpha atoms in two half spheres around a residue's Calpha atom. Conceptually, one of the half spheres corresponds to the side chain's neighborhood, the other half sphere being in the opposite direction. We show here that the two half spheres correspond to two regions around an amino acid that are surprisingly distinct in terms of geometry and energy. This aspect of protein structure introduced here forms the basis of the Half-Sphere Exposure measure. The results strongly suggest that in many respects, a 2D measure is inherently much better suited to describe solvent exposure than the traditional 1D measures. Importantly, Half-Sphere Exposure can be calculated from the Calpha atom coordinates only, which abolishes the need for a full-atom model to calculate solvent exposure. Hence, the measure can be used in protein structure prediction methods that are based on various simplified models. Half-Sphere Exposure has great potential for use in protein structure prediction and analysis. (C) 2005 Wiley-Liss, Inc.
引用
收藏
页码:38 / 48
页数:11
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