Simultaneous Bayesian Estimation of Alignment and Phylogeny under a Joint Model of Protein Sequence and Structure
Simultaneous Bayesian Estimation of Alignment and Phylogeny under a Joint Model of Protein Sequence and Structure
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DOI：
10.1093/molbev/msu184
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发表时间：
2014-09-01
影响因子：
10.7
通讯作者：
Schmidler, Scott C.
中科院分区：
文献类型：
--
作者：
Herman, Joseph L.;Challis, Christopher J.;Schmidler, Scott C.
对于高度分歧的序列，通常没有足够的信息来推断准确的比对，并且系统发育不确定性可能很高。解决这个问题的一种方法是利用蛋白质结构信息，因为结构通常比序列发散得更慢。在这项工作中，我们扩展了最近开发的随机模型的成对结构进化树上的多个结构，分析集成在祖先的结构，以允许有效的可能性计算下产生的联合序列结构模型。我们观察到，包括结构信息显着降低了对齐和拓扑结构的不确定性，并减少了拓扑结构和对齐错误的情况下，真正的树和路线是已知的。在某些情况下，结构的包含导致共有拓扑结构的改变，表明结构可能包含超出可从序列获得的信息的额外信息。我们使用该模型来研究细胞球蛋白，肌红蛋白和血红蛋白的分歧的顺序，并观察系统发育推断的稳定性：虽然基于序列的推断分配显着的后验概率到几个不同的拓扑结构，结构模型强烈倾向于其中一个超过其他的，是更强大的数据集的选择。
For sequences that are highly divergent, there is often insufficient information to infer accurate alignments, and phylogenetic uncertainty may be high. One way to address this issue is to make use of protein structural information, since structures generally diverge more slowly than sequences. In this work, we extend a recently developed stochastic model of pairwise structural evolution to multiple structures on a tree, analytically integrating over ancestral structures to permit efficient likelihood computations under the resulting joint sequence-structure model. We observe that the inclusion of structural information significantly reduces alignment and topology uncertainty, and reduces the number of topology and alignment errors in cases where the true trees and alignments are known. In some cases, the inclusion of structure results in changes to the consensus topology, indicating that structure may contain additional information beyond that which can be obtained from sequences. We use the model to investigate the order of divergence of cytoglobins, myoglobins, and hemoglobins and observe a stabilization of phylogenetic inference: although a sequence-based inference assigns significant posterior probability to several different topologies, the structural model strongly favors one of these over the others and is more robust to the choice of data set.