National Centre for Biological Sciences, UAS-GKVK Campus, Bangalore, India.
PLoS One. 2012;7(7):e39305. doi: 10.1371/journal.pone.0039305. Epub 2012 Jul 27.
3D domain swapping is an oligomerization process in which structural elements get exchanged between subunits. This mechanism grasped interest of many researchers due to its association with neurodegenerative diseases like Alzheimer's disease, spongiform encephalopathy etc. Despite the biomedical relevance, very little is known about understanding this mechanism. The quest for ruling principles behind this curious phenomenon that could enable early prediction provided an impetus for our bioinformatics studies.
A novel method, HIDE, has been developed to find non-domain-swapped homologues and to identify hinge from domain-swapped oligomers. Non-domain-swapped homologues were identified from the protein structural databank for majority of the domain-swapped entries and hinge boundaries could be recognised automatically by means of successive superposition techniques. Different sequence and structural features in domain-swapped proteins and related proteins have also been analysed.
The HIDE algorithm was able to identify hinge region in 83% cases. Sequence and structural analyses of hinge and interfaces reveal amino acid preferences and specific conformations of residues at hinge regions, while comparing the domain-swapped and non-domain-swapped states. Interactions differ significantly between regular dimeric interfaces and interface formed at the site of domain-swapped examples. Such preferences of residues, conformations and interactions could be of predictive value.
3D 结构域交换是一种亚基之间结构元件交换的寡聚化过程。由于与阿尔茨海默病、海绵状脑病等神经退行性疾病有关,这种机制引起了许多研究人员的兴趣。尽管具有重要的生物医学意义,但对于理解这种机制的了解却很少。探索这种奇特现象背后的规律,以便能够进行早期预测,这为我们的生物信息学研究提供了动力。
我们开发了一种新方法 HIDE,用于寻找非结构域交换的同源物,并识别结构域交换寡聚体中的铰链。从蛋白质结构数据库中为大多数结构域交换条目识别了非结构域交换的同源物,并且可以通过连续的叠加技术自动识别铰链边界。还分析了结构域交换蛋白和相关蛋白中的不同序列和结构特征。
HIDE 算法能够在 83%的情况下识别铰链区域。比较结构域交换和非结构域交换状态时,对铰链区和界面的序列和结构分析揭示了氨基酸偏好和残基的特定构象,而在结构域交换和非结构域交换状态时,界面的氨基酸偏好、构象和相互作用存在显著差异。这种残基、构象和相互作用的偏好可能具有预测价值。