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。

• 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
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7735092
• 关键词: Benchmarking; Biochemical Pathway; Biological; Biological Models; Biological Process; Cell physiology; Complex; Count; Data Set; Dependency; Development; Evolution; Genes; Genome; Goals; Gold; Graph; Homo; Individual; Mathematics; Mediating; Mediator of activation protein; Metabolic Pathway; Methods; Modeling; Molecular Biology; Molecular Evolution; Organism; Orthologous Gene; Property; Proteins; Psychological Techniques; Reaction; Shapes; Standards of Weights and Measures; Structure; Surveys; System; Systems Biology; Techniques; Tertiary Protein Structure; Testing; Theoretical Studies; Theoretical model; Work; combinatorial; comparative; experience; insight; pressure; protein protein interaction; theories; tool

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，我们开发了几种方法来预测域-域相互作用（1，3，6）。 最近，我们扩展了我们最近开发的简约方法之一（2）。修改后的方法允许优先选择所谓的共同出现的结构域作为蛋白质之间相互作用的可能介质。已经提出，这样的共现结构域比其他结构域对更可能介导蛋白质之间的相互作用。因此，我们还研究了可能的富集共现域和同源域之间的域相互作用介导的网络中的蛋白质的相互作用。相应的研究进行了调查域的相互作用预测的新方法，以及通过使用组合计数方法独立于任何预测方法。我们的研究结果表明，虽然有相当大的倾向，对这些特殊的域之间的预测域的相互作用，这种过度代表性显着低于iPfam数据集，通常被用作黄金标准测试集。 因此，我们得出结论，该测试集不代表全基因组蛋白质相互作用，必须谨慎对待使用该数据集进行基准测试的结果。 在我们以前的工作（4）生物网络的图论研究的基础上，我们研究了蛋白质相互作用网络的拓扑性质和本质性之间的关系。特别是，我们表明，以前提出的网络理论解释的必要性是不正确的。相反，大多数枢纽是必不可少的，因为它们参与了必需的复杂生物模块，这是一组密集连接的蛋白质，具有共享的生物功能，富含必需蛋白质（7）。 我们对进化关系的研究集中在比较分析不同进化枝中的正向同源蛋白组内的进化压力（5）。 我们的研究结果表明，进化压力作用于信息直系同源群体是不均匀的，在本研究中的不同亚组的生物。这表明，这些信息组蛋白在每个谱系中的微调使它们在谱系之间的可交换性降低。在某种程度上，这种重新定位可能与它们作为多蛋白复合物的一部分与几个不同的亚基保守亚基有关。 相比之下，非信息群体可能不会经历这种谱系特异性的选择压力差异，因为它们通常催化代谢途径中的个体反应，底物的通量介导它们之间的大多数功能性相互作用。