Effective connectivity profile: A structural representation that evidences the relationship between protein structures and sequences
Effective connectivity profile: A structural representation that evidences the relationship between protein structures and sequences
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有效的连接概况：证明蛋白质结构和序列之间关系的结构表示
DOI：
10.1002/prot.22113
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
Florian Teichert
中科院分区：
文献类型：
--
作者：
U. Bastolla;A. Ortiz;M. Porto;Florian Teichert
蛋白质结构的复杂性要求其拓扑结构的简化表示。对蛋白质结构的最简单的数学描述是一维轮廓，例如，表示埋藏度或二级结构。这种表示法已被用于研究序列与结构的关系，并应用于折叠识别。在这里，我们定义了有效连接性轮廓(EC)，这是一种网络理论轮廓，它自我一致地表示蛋白质接触矩阵的网络结构。EC图谱使蛋白质结构和蛋白质序列之间的关系在数学上变得清晰，因为它允许预测具有相同结构的同源蛋白质家族的平均疏水性图谱(HP)和每个位置的氨基酸分布。在这个意义上，EC提供了统计反折叠问题的解析解，该问题在于找到与给定结构兼容的一组序列的统计特性。我们用结构保守的结构约束中性(SCN)蛋白质进化模型的模拟来测试这些预测，对于单域和多域蛋白质，以及对于广泛的突变过程，后者产生具有非常不同的疏水性轮廓的序列，发现即使只有一个家族的序列已知，基于EC的预测也是准确的。对于PDB中的序列-结构对，EC图谱也与HP非常显著地相关。EC图谱将先前引入的结构图谱的性质概括为模块化蛋白质，如多域链，并且其与序列图谱的相关性相对于先前定义的图谱显著改善，特别是对于长蛋白质。此外，EC分布具有动态解释，因为EC组分与X射线实验中测量的温度因子强烈负相关，这意味着EC组分较大的位置在平衡动力学中受到更强的约束。最后，EC配置文件允许定义与组成蛋白质的结构域的数量相关的模块化的自然测量，建议将其应用于结构域分解。最后，我们证明了结构相似的蛋白质具有相似的EC图谱，因此排列的EC图谱之间的相似性可以用作结构相似性的度量，这是我们最近应用于蛋白质结构比对的一个性质。通过向ubasolla@cbm.uam.es发出请求，可以获得计算EC配置文件的代码，本文讨论的结构配置文件可以从sloth Web服务器http://www.fkp.tu-darmstadt.de/sloth/下载。蛋白质2008。©2008 Wiley-Liss，Inc.
The complexity of protein structures calls for simplified representations of their topology. The simplest possible mathematical description of a protein structure is a one‐dimensional profile representing, for instance, buriedness or secondary structure. This kind of representation has been introduced for studying the sequence to structure relationship, with applications to fold recognition. Here we define the effective connectivity profile (EC), a network theoretical profile that self‐consistently represents the network structure of the protein contact matrix. The EC profile makes mathematically explicit the relationship between protein structure and protein sequence, because it allows predicting the average hydrophobicity profile (HP) and the distributions of amino acids at each site for families of homologous proteins sharing the same structure. In this sense, the EC provides an analytic solution to the statistical inverse folding problem, which consists in finding the statistical properties of the set of sequences compatible with a given structure. We tested these predictions with simulations of the structurally constrained neutral (SCN) model of protein evolution with structure conservation, for single‐ and multi‐domain proteins, and for a wide range of mutation processes, the latter producing sequences with very different hydrophobicity profiles, finding that the EC‐based predictions are accurate even when only one sequence of the family is known. The EC profile is very significantly correlated with the HP for sequence‐structure pairs in the PDB as well. The EC profile generalizes the properties of previously introduced structural profiles to modular proteins such as multidomain chains, and its correlation with the sequence profile is substantially improved with respect to the previously defined profiles, particularly for long proteins. Furthermore, the EC profile has a dynamic interpretation, since the EC components are strongly inversely related with the temperature factors measured in X‐ray experiments, meaning that positions with large EC component are more strongly constrained in their equilibrium dynamics. Last, the EC profile allows to define a natural measure of modularity that correlates with the number of domains composing the protein, suggesting its application for domain decomposition. Finally, we show that structurally similar proteins have similar EC profiles, so that the similarity between aligned EC profiles can be used as a structure similarity measure, a property that we have recently applied for protein structure alignment. The code for computing the EC profile is available upon request writing to ubastolla@cbm.uam.es, and the structural profiles discussed in this article can be downloaded from the SLOTH webserver http://www.fkp.tu‐darmstadt.de/SLOTH/. Proteins 2008. © 2008 Wiley‐Liss, Inc.
影响因子：
5.6
作者：
HUANG, ES;SUBBIAH, S;LEVITT, M
通讯作者：
LEVITT, M