Protein homology network families reveal step-wise diversification of Type III and Type IV secretion systems.
Protein homology network families reveal step-wise diversification of Type III and Type IV secretion systems.
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蛋白质同源网络家族揭示了III型和IV型分泌系统的逐步多样化。
DOI：
10.1371/journal.pcbi.0020173
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发表时间：
2006-12-01
影响因子：
4.3
通讯作者：
Donati, Claudio
中科院分区：
文献类型：
--
作者：
Medini, Duccio;Covacci, Antonello;Donati, Claudio
通过对存储在公共数据库中的251个原核生物基因组的分析，761,260个推导出的蛋白质被用来重建一套完整的细菌蛋白质家族。使用新的重叠算法，我们划分了蛋白质同源网络(PHN)，其中蛋白质是节点，链接代表同源关系。该算法识别PHN的密集连接区域，这些区域定义了同源蛋白家族，这里称为PHN-家族，识别网络中嵌入的系统发育关系。通过与人工精选数据集的直接比较，我们评估了该分类算法生成的数据质量类似于人类专家。然后，我们探索了识别参与III型和IV型分泌系统(T3SS和T4SS)组装的家族的网络。我们注意到，除了一个保守功能的核心(T3SS有8个蛋白质，T4SS有7个蛋白质)外，总是存在一组可变的辅助成分(T3SS有1到9个，T4SS有1到5个)。核心的每个成员对应一个PHN家族，而辅助蛋白则分布在不同的纯家族中。PHN-Family分类表明，T3SS和T4SS是通过一个逐步的、不连续的过程组装起来的，通过用非保守蛋白质的亚群补充保守的核心。这些遗传模块独立招募并可能调节到特定的效应器，有助于这些细胞器对不同微环境的功能专门化。从一个共同的祖先进化而来的蛋白质被认为是同源的，并组成了一个具有潜在相似结构、功能和相互作用的“家族”。基于氨基酸序列守恒来识别“真正的”蛋白质家族的问题一直是广泛争论的主题，因为搜索成对同源的算法可能会错过重要的关系并产生错误的匹配。大量测序基因组的出现使我们能够将全套蛋白质相似性关系映射到一个蛋白质同源网络(PHN)中，蛋白质家族自然而然地呈现为网络中密集、高度相连的区域。在这项研究中，Medini、Covaci和Donati描述了一种新的方法，该方法可以识别PHN的这些区域，并生成一组与蛋白质功能和系统发育相关的蛋白质家族(PHN家族)，其质量可与人类专家挑选的家族集相媲美。该方法是完全无监督的，可以应用于任何数量的基因组。作者检验了通过研究III型和IV型分泌系统成员获得的PHN家族的生物学相关性，表明这一分类也可用于识别导致多蛋白结构形成的进化事件。
From the analysis of 251 prokaryotic genomes stored in public databases, the 761,260 deduced proteins were used to reconstruct a complete set of bacterial proteic families. Using the new Overlap algorithm, we have partitioned the Protein Homology Network (PHN), where the proteins are the nodes and the links represent homology relationships. The algorithm identifies the densely connected regions of the PHN that define the families of homologous proteins, here called PHN-Families, recognizing the phylogenetic relationships embedded in the network. By direct comparison with a manually curated dataset, we assessed that this classification algorithm generates data of quality similar to a human expert. Then, we explored the network to identify families involved in the assembly of Type III and Type IV secretion systems (T3SS and T4SS). We noticed that, beside a core of conserved functions (eight proteins for T3SS, seven for T4SS), a variable set of accessory components is always present (one to nine for T3SS, one to five for T4SS). Each member of the core corresponds to a single PHN-Family, while accessory proteins are distributed among different pure families. The PHN-Family classification suggests that T3SS and T4SS have been assembled through a step-wise, discontinuous process, by complementing the conserved core with subgroups of nonconserved proteins. Such genetic modules, independently recruited and probably tuned on specific effectors, contribute to the functional specialization of these organelles to different microenvironments. Proteins evolved from a common ancestor are said to be homologues and to constitute a “family” with potentially similar structures, functions, and interactions. The problem of identifying “real” protein families based on amino acid sequence conservation has been the subject of extensive debate, because algorithms that search for pairwise homologies can miss important relations and produce false hits. The availability of a large number of sequenced genomes now allows us to map the full set of protein similarity relationships into a Protein Homology Network (PHN), and protein families appear naturally as dense, highly connected regions of the network. In this study, Medini, Covacci, and Donati describe a new method that identifies these regions of the PHN, and generate a set of protein families (PHN-Families) that correlate with protein function and phylogeny, with a quality comparable to family sets curated by human experts. The method is completely unsupervised and can be applied to any number of genomes. The authors test the biological relevance of the PHN-Families obtained by studying the members of Type III and Type IV secretion systems, showing that this classification can also be used to identify the evolutionary events that led to the formation of multiprotein structures.
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影响因子：
2.4
作者：
Newman, MEJ;Girvan, M
通讯作者：
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影响因子：
56.9
作者：
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DOI：
10.1073/pnas.97.16.8778
发表时间：
2000-08-01
影响因子：
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作者：
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通讯作者：
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影响因子：
14.9
作者：
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