Combinatorial and graph theoretical approach to systems biology and mol. evo.

系统生物学和分子生物学的组合和图论方法。

基本信息

批准号：
7735092
负责人：
Teresa Przytycka
金额：
$ 52.59万
依托单位：
NATIONAL LIBRARY OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
资助国家：
美国
起止时间：
至
项目状态：
未结题

项目摘要

Previously, we developed several methods to predict domain-domain interactions (1,3,6). Most recently we extended one of our recently developed parsimony approach (2). The modified method allowed for preferential selection of the so-called co-occurring domains as possible mediators of interactions between proteins. It has been proposed that such co-occurring domains are significantly more likely to mediate interactions between proteins than other domain pairs. Therefore, we also examined possible enrichment of co-occurring domains and homo-domains among domain interactions mediating the interaction of proteins in the network. The corresponding study was performed by surveying domain interactions predicted by the new method as well as by using a combinatorial counting approach independent of any prediction method. Our findings indicate that, while there is a considerable propensity towards these special domain pairs among predicted domain interactions, this overrepresentation is significantly lower than in the iPfam dataset which is typically used as a gold standard test set. Consequently, we concluded this test set is not representative of genome wide protein interactions and results benchmarked using this data set must be treated with caution. Building on our previous work (4) on graph theoretical studies of biological networks we investigated the relation between topological properties of protein interaction networks end essentiality. In particular we showed that previously proposed network theoretical explanation for essentiality are incorrect. Instead, the majority of hubs are essential due to their involvement in Essential Complex Biological Modules, a group of densely connected proteins with shared biological function that are enriched in essential proteins (7). Our studies of evolutionary relationships focused on comparative analysis of evolutionary pressure in different clades within groups of orthologous proteins (5). Our results indicate that the evolutionary pressure acting on the informational ortholog groups is not uniform across different sub-groups of organisms in this study. This suggests that fine-tuning of these informational group proteins in each lineage makes them less exchangeable between lineages. In part, this differeintation might relate to them functioning as parts of multi-protein complexes with several distinct subunits conserved subunits. In contrast, the non-informational groups might not experience such lineage-specific differences in selective pressure, as they usually catalyze individual reactions in metabolic pathways with the flux of substrates mediating most functional interactions between them.

以前，我们开发了几种预测域域相互作用的方法（1,3,6）。最近，我们扩展了最近开发的简约方法（2）。修改方法允许优先选择所谓的共发生域，作为蛋白质之间相互作用的介体。已经提出，与其他结构域对相比，这种同时存在的结构域介导蛋白质之间的相互作用的可能性更大。因此，我们还研究了介导网络中蛋白质相互作用的域相互作用之间的共发生结构域和同类域的可能富集。通过测量新方法预测的域相互作用以及使用与任何预测方法无关的组合计数方法来进行相应的研究。我们的发现表明，尽管在预测的域相互作用之间对这些特殊域对有很大的倾向，但这种过度代表大大低于IPFAM数据集中，通常用作金标准测试集。因此，我们得出结论，该测试集不能代表基因组广泛的蛋白质相互作用，并且必须谨慎对待使用此数据集进行基准测试。在我们以前的生物网络图理论研究（4）的基础上，我们研究了蛋白质相互作用网络的拓扑特性最终重要性之间的关系。特别是我们表明，先前提出的网络理论解释是不正确的。取而代之的是，大多数集线器是必不可少的，这是由于它们参与必需的复杂生物学模块，这是一组具有共同生物学功能的密集连接的蛋白质，这些蛋白具有富含必需蛋白的共享生物学功能（7）。我们对进化关系的研究集中在直系同源蛋白组内不同进化枝的进化压力的比较分析（5）。我们的结果表明，在本研究中，在不同亚组的生物体中，作用于信息直系同源组的进化压力并不统一。这表明每个谱系中这些信息群蛋白的微调使它们之间的交换较低。在某种程度上，这种扩散可能与它们起作用，作为多蛋白复合物的一部分，并具有几个不同的亚基保守亚基。相比之下，非信息组可能不会在选择性压力中遇到这种谱系特异性差异，因为它们通常会催化代谢途径中的个体反应，其底物的通量介导了它们之间的大多数功能相互作用。