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从无序-复杂性空间洞察蛋白质结构与功能

Insights into protein structure and function from disorder-complexity space.

作者信息

Weathers Edward A, Paulaitis Michael E, Woolf Thomas B, Hoh Jan H

机构信息

Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA.

出版信息

Proteins. 2007 Jan 1;66(1):16-28. doi: 10.1002/prot.21055.

Abstract

Intrinsically disordered proteins have a wide variety of important functional roles. However, the relationship between sequence and function in these proteins is significantly different than that for well-folded proteins. In a previous work, we showed that the propensity to be disordered can be recognized based on sequence composition alone. Here that analysis is furthered by examining the relationship of disorder propensity to sequence complexity, where the metrics for these two properties depend only on composition. The distributions of 40 amino acid peptides from both ordered and disordered proteins are graphed in this disorder-complexity space. An analysis of Swiss-Prot shows that most peptides have high complexity and relatively low disorder. However, there are also an appreciable number of low complexity-high disorder peptides in the database. In contrast, there are no low complexity-low disorder peptides. A similar analysis for peptides in the PDB reveals a much narrower distribution, with few peptides of low complexity and high disorder. In this case, the bounds of the disorder-complexity distribution are well defined and might be used to evaluate the likelihood that a peptide can be crystallized with current methods. The disorder-complexity distributions of individual proteins and sets of proteins grouped by function are also examined. Among individual proteins, there is an enormous variety of distributions that in some cases can be rationalized with regard to function. Groups of functionally related proteins are found to have distributions that are similar within each group but show notable differences between groups. Finally, a pattern matching algorithm is used to search for proteins with particular disorder-complexity distributions. The results suggest that this approach might be used to identify relationships between otherwise dissimilar proteins.

摘要

内在无序蛋白质具有多种重要的功能作用。然而,这些蛋白质的序列与功能之间的关系与结构良好的蛋白质有显著不同。在之前的一项工作中,我们表明仅基于序列组成就能识别无序倾向。在此,通过研究无序倾向与序列复杂性之间的关系进一步深化了该分析,其中这两种属性的度量仅取决于组成。在这个无序 - 复杂性空间中绘制了来自有序和无序蛋白质的40个氨基酸肽段的分布。对瑞士蛋白质数据库(Swiss - Prot)的分析表明,大多数肽段具有高复杂性和相对较低的无序度。然而,数据库中也有相当数量的低复杂性 - 高无序肽段。相比之下,没有低复杂性 - 低无序肽段。对蛋白质数据银行(PDB)中的肽段进行的类似分析揭示了一个更窄的分布,低复杂性和高无序的肽段很少。在这种情况下,无序 - 复杂性分布的界限定义明确,可用于评估肽段能否用当前方法结晶的可能性。还研究了单个蛋白质以及按功能分组的蛋白质组的无序 - 复杂性分布。在单个蛋白质中,存在各种各样的分布,在某些情况下可以根据功能进行合理解释。发现功能相关的蛋白质组具有在每组内相似但组间存在显著差异的分布。最后,使用一种模式匹配算法来搜索具有特定无序 - 复杂性分布的蛋白质。结果表明,这种方法可能用于识别原本不相似的蛋白质之间的关系。

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