Age-dependent evolution of the yeast protein interaction network suggests a limited role of gene duplication and divergence.
Age-dependent evolution of the yeast protein interaction network suggests a limited role of gene duplication and divergence.
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DOI：
10.1371/journal.pcbi.1000232
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发表时间：
2008-11
影响因子：
4.3
通讯作者：
Marcotte EM
中科院分区：
文献类型：
--
作者：
Kim WK;Marcotte EM
蛋白质在复杂的蛋白质-蛋白质相互作用（PPI）网络中相互作用，其拓扑特性（例如无标度拓扑、层次模块化和不分类性）提出了网络进化模型。目前优选的模型调用优先附着或基因复制和分歧来产生其拓扑与真实 PPI 观察到的网络相匹配的网络，从而支持这些作为网络进化的可能模型。在这里，我们表明，在真实的 PPI 网络中，相互作用密度和同二聚体频率高度依赖于蛋白质年龄，其方式与这些规范模型不一致。根据这些结果，我们提出了另一种随机模型，该模型以类似于溶液中蛋白质晶体生长（CG）的方式将每种蛋白质顺序添加到生长网络中。关键思想是（1）相互作用概率随着未占据的相互作用表面的可用性而增加，从而遵循反优先附着规则，（2）随着网络的增长，高度连接的子网络出现在蛋白质模块或复合物中，以及（3）一旦新的蛋白质被投入到模块中，进一步的连接往往会局限于该模块内。 CG 模型产生的 PPI 网络在拓扑和年龄分布上都与真实的 PPI 网络一致，并且得到了已知 3-D 结构的蛋白质复合物的空间排列的良好支持，这表明网络进化的合理物理机制。蛋白质共同发挥作用，形成稳定的蛋白质复合物或在各种细胞过程（例如基因调控和信号传导）中短暂相互作用。在这里，我们解决了这些相互作用蛋白质网络如何进化的基本问题。这是一个重要的问题，因为此类网络的结构是生物系统重要特征的基础，例如功能模块化、容错性和稳定性。目前尚不清楚这些网络架构是如何起源的，或者观察到的网络结构背后的驱动力是什么。在过去的十年中，人们提出了几种模型，特别是“富者愈富”模型（优先依恋）和基于基因复制和分化的模型，通常仅基于网络拓扑。在这里，我们证明真正的酵母蛋白相互作用网络在相互作用的蛋白质之间显示出独特的年龄分布，这排除了这些规范模型。根据这些结果，我们基于既定的物理原理开发了一种简单的替代模型，类似于在溶液中生长蛋白质晶体的过程。该模型更好地解释了真实 PPI 网络的许多特征，包括网络拓扑、其特征年龄分布以及蛋白质复合物内不同年龄亚基的空间分布，暗示了网络进化的合理物理机制。
Proteins interact in complex protein–protein interaction (PPI) networks whose topological properties—such as scale-free topology, hierarchical modularity, and dissortativity—have suggested models of network evolution. Currently preferred models invoke preferential attachment or gene duplication and divergence to produce networks whose topology matches that observed for real PPIs, thus supporting these as likely models for network evolution. Here, we show that the interaction density and homodimeric frequency are highly protein age–dependent in real PPI networks in a manner which does not agree with these canonical models. In light of these results, we propose an alternative stochastic model, which adds each protein sequentially to a growing network in a manner analogous to protein crystal growth (CG) in solution. The key ideas are (1) interaction probability increases with availability of unoccupied interaction surface, thus following an anti-preferential attachment rule, (2) as a network grows, highly connected sub-networks emerge into protein modules or complexes, and (3) once a new protein is committed to a module, further connections tend to be localized within that module. The CG model produces PPI networks consistent in both topology and age distributions with real PPI networks and is well supported by the spatial arrangement of protein complexes of known 3-D structure, suggesting a plausible physical mechanism for network evolution. Proteins function together forming stable protein complexes or transient interactions in various cellular processes, such as gene regulation and signaling. Here, we address the basic question of how these networks of interacting proteins evolve. This is an important problem, as the structures of such networks underlie important features of biological systems, such as functional modularity, error-tolerance, and stability. It is not yet known how these network architectures originate or what driving forces underlie the observed network structure. Several models have been proposed over the past decade—in particular, a “rich get richer” model (preferential attachment) and a model based upon gene duplication and divergence—often based only on network topologies. Here, we show that real yeast protein interaction networks show a unique age distribution among interacting proteins, which rules out these canonical models. In light of these results, we developed a simple, alternative model based on well-established physical principles, analogous to the process of growing protein crystals in solution. The model better explains many features of real PPI networks, including the network topologies, their characteristic age distributions, and the spatial distribution of subunits of differing ages within protein complexes, suggesting a plausible physical mechanism of network evolution.
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影响因子：
9.8
作者：
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