ESEI-Escuela Superior de Ingeniería Informática, Universidad de Vigo, Edificio Politécnico, Campus Universitario As Lagoas s/n, 32004, Ourense, Spain.
Centro de Investigaciones Biomédicas (Centro Singular de Investigación de Galicia), Vigo, Spain.
Interdiscip Sci. 2019 Mar;11(1):45-56. doi: 10.1007/s12539-019-00317-y. Epub 2019 Feb 1.
Protein-protein interaction (PPI) data is essential to elucidate the complex molecular relationships in living systems, and thus understand the biological functions at cellular and systems levels. The complete map of PPIs that can occur in a living organism is called the interactome. For animals, PPI data is stored in multiple databases (e.g., BioGRID, CCSB, DroID, FlyBase, HIPPIE, HitPredict, HomoMINT, INstruct, Interactome3D, mentha, MINT, and PINA2) with different formats. This makes PPI comparisons difficult to perform, especially between species, since orthologous proteins may have different names. Moreover, there is only a partial overlap between databases, even when considering a single species. The EvoPPI ( http://evoppi.i3s.up.pt ) web application presented in this paper allows comparison of data from the different databases at the species level, or between species using a BLAST approach. We show its usefulness by performing a comparative study of the interactome of the nine polyglutamine (polyQ) disease proteins, namely androgen receptor (AR), atrophin-1 (ATN1), ataxin 1 (ATXN1), ataxin 2 (ATXN2), ataxin 3 (ATXN3), ataxin 7 (ATXN7), calcium voltage-gated channel subunit alpha1 A (CACNA1A), Huntingtin (HTT), and TATA-binding protein (TBP). Here we show that none of the human interactors of these proteins is common to all nine interactomes. Only 15 proteins are common to at least 4 of these polyQ disease proteins, and 40% of these are involved in ubiquitin protein ligase-binding function. The results obtained in this study suggest that polyQ disease proteins are involved in different functional networks. Comparisons with Mus musculus PPIs are also made for AR and TBP, using EvoPPI BLAST search approach (a unique feature of EvoPPI), with the goal of understanding why there is a significant excess of common interactors for these proteins in humans.
蛋白质-蛋白质相互作用 (PPI) 数据对于阐明活系统中复杂的分子关系至关重要,从而可以理解细胞和系统水平的生物学功能。能够在生物体中发生的完整的 PPI 图谱称为相互作用组。对于动物,PPI 数据存储在多个数据库中(例如 BioGRID、CCSB、DroID、FlyBase、HIPPIE、HitPredict、HomoMINT、INstruct、Interactome3D、mentha、MINT 和 PINA2),格式不同。这使得 PPI 之间的比较变得困难,特别是在物种之间,因为同源蛋白可能有不同的名称。此外,即使考虑单个物种,数据库之间也只有部分重叠。本文介绍的 EvoPPI(http://evoppi.i3s.up.pt)网络应用程序允许在物种水平上比较来自不同数据库的数据,或者使用 BLAST 方法在物种之间进行比较。我们通过对九种多聚谷氨酰胺(polyQ)疾病蛋白的相互作用组进行比较研究,展示了其有用性,这九种蛋白分别是雄激素受体 (AR)、atrophin-1 (ATN1)、ataxin 1 (ATXN1)、ataxin 2 (ATXN2)、ataxin 3 (ATXN3)、ataxin 7 (ATXN7)、钙电压门控通道亚基 alpha1A (CACNA1A)、亨廷顿 (HTT) 和 TATA 结合蛋白 (TBP)。我们发现这些蛋白的人类相互作用物中没有一个是所有九个相互作用组共有的。至少有 4 种这些 polyQ 疾病蛋白共有的蛋白只有 15 种,其中 40% 与泛素蛋白连接酶结合功能有关。本研究的结果表明,polyQ 疾病蛋白涉及不同的功能网络。还使用 EvoPPI BLAST 搜索方法(EvoPPI 的独特功能)对 AR 和 TBP 与 Mus musculus 的 PPI 进行了比较,目的是了解为什么这些蛋白在人类中有如此多的共同相互作用物。