A combinatorial approach to detect coevolved amino acid networks in protein families of variable divergence.
A combinatorial approach to detect coevolved amino acid networks in protein families of variable divergence.
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DOI：
10.1371/journal.pcbi.1000488
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发表时间：
2009-09
影响因子：
4.3
通讯作者：
Carbone A
中科院分区：
文献类型：
--
作者：
Baussand J;Carbone A
远距离位点之间的通信通常定义了蛋白质的生物学作用：氨基酸的长程相互作用在结合特异性、变构调节和构象变化中与直接接触底物的残基一样重要。远程相互作用残基的功能和结构耦合的维持需要这些残基的共同进化。共同进化的残基之间的相互作用的网络可以重建，从网络中，人们可能会得到深入了解蛋白质家族的功能机制。我们提出了一个组合的方法来映射保守网络的氨基酸相互作用的蛋白质，这是基于一组比对序列的分析，相关的距离树及其子树的组合。所有对共同进化的残基的共同进化程度的数字识别，和网络重建一个专用的聚类算法。该方法放弃了限制先前提出的统计方法的适用范围的高序列分歧的约束。我们将该方法应用于四个蛋白质家族，在那里我们展示了功能网络的准确检测和治疗可变发散的蛋白质序列集的可能性。对蛋白质序列家族的精细分析揭示了共同进化的氨基酸网络的存在。这些网络是残基的簇，它们经常彼此物理接触，并且它们将三维结构上相距很远的残基联系起来。共进化残基在蛋白质中通常发挥主要的生物学作用，并且它们的相互作用的性质可能是多重的，跨越蛋白质的结合特异性、变构调节和构象变化。最大子树方法通过仔细追踪蛋白质家族序列的系统发育树中残基进化的方式，捕获了沿着时间尺度从保守位置进化到共同进化位置的过渡，并提供了蛋白质中共同进化残基对的共同进化程度的数值评估。这种组合的方法下降的限制，限制范围内的适用性的统计方法以前提出的高序列分歧的约束，它可以应用于具有可变分歧的蛋白质序列的家庭具有高精度。
Communication between distant sites often defines the biological role of a protein: amino acid long-range interactions are as important in binding specificity, allosteric regulation and conformational change as residues directly contacting the substrate. The maintaining of functional and structural coupling of long-range interacting residues requires coevolution of these residues. Networks of interaction between coevolved residues can be reconstructed, and from the networks, one can possibly derive insights into functional mechanisms for the protein family. We propose a combinatorial method for mapping conserved networks of amino acid interactions in a protein which is based on the analysis of a set of aligned sequences, the associated distance tree and the combinatorics of its subtrees. The degree of coevolution of all pairs of coevolved residues is identified numerically, and networks are reconstructed with a dedicated clustering algorithm. The method drops the constraints on high sequence divergence limiting the range of applicability of the statistical approaches previously proposed. We apply the method to four protein families where we show an accurate detection of functional networks and the possibility to treat sets of protein sequences of variable divergence. Fine analyses of families of protein sequences reveal the existence of networks of coevolved amino acids. These networks are clusters of residues often entering in physical contact one with the other, and they relate residues which are located far apart on the three dimensional structure. Coevolved residues often play a major biological role in the protein, and the nature of their interactions might be multiple, spanning among binding specificity, allosteric regulation and conformational change of the protein. By carefully tracing the way residues evolved within the phylogenetic tree of sequences of a protein family, the Maximal SubTree Method captures the transition along the time scale evolution of a conserved position to a coevolved position, and provides a numerical evaluation of the degree of coevolution of pairs of coevolved residues in a protein. This combinatorial approach drops the constraints on high sequence divergence limiting the range of applicability of the statistical approaches previously proposed, and it can be applied with high accuracy to families of protein sequences with variable divergence.
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影响因子：
56.9
作者：
HEDSTROM, L;SZILAGYI, L;RUTTER, WJ
通讯作者：
RUTTER, WJ
影响因子：
5.6
作者：
LIDDINGTON, R;DEREWENDA, Z;DODSON, G
通讯作者：
DODSON, G
影响因子：
5.6
作者：
Lichtarge, O;Bourne, HR;Cohen, FE
通讯作者：
Cohen, FE
影响因子：
5.6
作者：
Ota, N;Agard, DA
通讯作者：
Agard, DA
影响因子：
5.6
作者：
Goh, CS;Bogan, AA;Cohen, FE
通讯作者：
Cohen, FE