Mining protein interactomes to improve their reliability and support the advancement of network medicine.
Mining protein interactomes to improve their reliability and support the advancement of network medicine.
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DOI：
10.3389/fgene.2015.00296
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发表时间：
2015
影响因子：
3.7
通讯作者：
Alanis-Lobato G
中科院分区：
文献类型：
--
作者：
Alanis-Lobato G
对蛋白质相互作用的高通量检测对我们理解活细胞背后复杂的分子机制产生了重大影响，并使构建非常大的蛋白质相互作用成为可能。目前可用的蛋白质网络是不完整的，它们相互作用的很大一部分是假阳性。幸运的是，在高质量的社会或技术网络中观察到的结构特性也存在于生物系统中。这鼓励了工具的开发，以提高蛋白质网络的可靠性，并仅基于其组件的拓扑特征来预测新的相互作用。由于疾病很少是由单一蛋白质的故障引起的，为了识别涉及疾病病因学的相互关联的蛋白质组，拥有更完整和可靠的相互作用组是至关重要的。然后，这些系统组件就可以成为目标，附带损害最小。在本文中，回顾了一些重要的网络挖掘工具，以及可以用来构建可靠的蛋白质交互作用的资源。除了综述之外，还讨论了几个有代表性的例子，说明如何将分子和临床数据整合起来，以加深我们对发病机制的理解。
High-throughput detection of protein interactions has had a major impact in our understanding of the intricate molecular machinery underlying the living cell, and has permitted the construction of very large protein interactomes. The protein networks that are currently available are incomplete and a significant percentage of their interactions are false positives. Fortunately, the structural properties observed in good quality social or technological networks are also present in biological systems. This has encouraged the development of tools, to improve the reliability of protein networks and predict new interactions based merely on the topological characteristics of their components. Since diseases are rarely caused by the malfunction of a single protein, having a more complete and reliable interactome is crucial in order to identify groups of inter-related proteins involved in disease etiology. These system components can then be targeted with minimal collateral damage. In this article, an important number of network mining tools is reviewed, together with resources from which reliable protein interactomes can be constructed. In addition to the review, a few representative examples of how molecular and clinical data can be integrated to deepen our understanding of pathogenesis are discussed.