On the Planarity of Validated Complexes of Model Organisms in Protein-Protein Interaction Networks
On the Planarity of Validated Complexes of Model Organisms in Protein-Protein Interaction Networks
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DOI：
10.1007/978-3-030-50371-0_48
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发表时间：
2020-05-26
期刊：
影响因子：
--
通讯作者：
Ali H
中科院分区：
文献类型：
--
作者：
Cooper K;Cornelius N;Gasper W;Bhowmick S;Ali H
利用蛋白质相互作用网络来识别蛋白质组及其共同功能是生物信息学中的一个重要问题。通过网络科学和高通量数据建模，蛋白质-蛋白质相互作用的系统级分析成为可能。从这些分析中，小的蛋白质复合物传统上以完整的图或密集的节点簇的图形表示。然而，在PPI网络中有一些图论性质尚未被广泛研究，特别是当它们与集群发现有关时，例如平面性。图的平面性已被用于反映生物信息学之外的真实世界系统的物理约束，例如映射和成像。在这里，我们研究蛋白质复合物网络模型的平面性。我们假设，复杂的PPI子图表示将趋于平面，反映了实际的物理接口和复杂的组件的限制。在对S.通过比较酿酒酵母和选定的哺乳动物PPI，我们发现大多数经验证的复合物具有这种平面性质。我们讨论了平面与非平面子图的生物学动机，观察平面子图往往有较长的蛋白质成分。平面与非平面复杂子图的功能分类揭示了这些组的注释差异，这些组与细胞组分组织，结构分子活性，催化活性和核酸结合有关。这些结果提供了一个新的定量和生物学动机的措施，真实的蛋白质复合物的网络模型，重要的是未来的复杂的PPI发现算法的发展。解释这种性质为发现新的蛋白质复合物和揭示未知或新蛋白质的功能铺平了道路。
Leveraging protein-protein interaction networks to identify groups of proteins and their common functionality is an important problem in bioinformatics. Systems-level analysis of protein-protein interactions is made possible through network science and modeling of high-throughput data. From these analyses, small protein complexes are traditionally represented graphically as complete graphs or dense clusters of nodes. However, there are certain graph theoretic properties that have not been extensively studied in PPI networks, especially as they pertain to cluster discovery, such as planarity. Planarity of graphs have been used to reflect the physical constraints of real-world systems outside of bioinformatics, in areas such as mapping and imaging. Here, we investigate the planarity property in network models of protein complexes. We hypothesize that complexes represented as PPI subgraphs will tend to be planar, reflecting the actual physical interface and limits of components in the complex. When testing the planarity of known complex subgraphs in S. cerevisiae and selected mammalian PPIs, we find that a majority of validated complexes possess this planar property. We discuss the biological motivation of planar versus nonplanar subgraphs, observing that planar subgraphs tend to have longer protein components. Functional classification of planar versus nonplanar complex subgraphs reveals differences in annotation of these groups relating to cellular component organization, structural molecule activity, catalytic activity, and nucleic acid binding. These results provide a new quantitative and biologically motivated measure of real protein complexes in the network model, important for the development of future complex-finding algorithms in PPIs. Accounting for this property paves the way to new means for discovering new protein complexes and uncovering the functionality of unknown or novel proteins.
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