Raychaudhuri Swasti, Dey Sucharita, Bhattacharyya Nitai P, Mukhopadhyay Debashis
Structural Genomics Section, Saha Institute of Nuclear Physics, Kolkata, India.
PLoS One. 2009;4(5):e5566. doi: 10.1371/journal.pone.0005566. Epub 2009 May 15.
The number and importance of intrinsically disordered proteins (IUP), known to be involved in various human disorders, are growing rapidly. To test for the generalized implications of intrinsic disorders in proteins involved in Neurodegenerative diseases, disorder prediction tools have been applied to three datasets comprising of proteins involved in Huntington Disease (HD), Parkinson's disease (PD), Alzheimer's disease (AD). Results show, in general, proteins in disease datasets possess significantly enhanced intrinsic unstructuredness. Most of these disordered proteins in the disease datasets are found to be involved in neuronal activities, signal transduction, apoptosis, intracellular traffic, cell differentiation etc. Also these proteins are found to have more number of interactors and hence as the proportion of disorderedness (i.e., the length of the unfolded stretch) increased, the size of the interaction network simultaneously increased. All these observations reflect that, "Moonlighting" i.e. the contextual acquisition of different structural conformations (transient), eventually may allow these disordered proteins to act as network "hubs" and thus they may have crucial influences in the pathogenecity of neurodegenerative diseases.
已知参与各种人类疾病的内在无序蛋白质（IUP）的数量及其重要性正在迅速增加。为了测试内在无序在神经退行性疾病相关蛋白质中的普遍影响，无序预测工具已应用于三个数据集，这些数据集包含与亨廷顿舞蹈病（HD）、帕金森病（PD）、阿尔茨海默病（AD）相关的蛋白质。结果表明，一般来说，疾病数据集中的蛋白质具有显著增强的内在无序性。在疾病数据集中发现，这些无序蛋白质大多参与神经元活动、信号转导、细胞凋亡、细胞内运输、细胞分化等。此外，还发现这些蛋白质具有更多的相互作用分子，因此随着无序程度（即未折叠片段的长度）的增加，相互作用网络的规模也同时增大。所有这些观察结果都表明，“兼职”即不同结构构象（瞬时）的情境性获得，最终可能使这些无序蛋白质充当网络“枢纽”，因此它们可能在神经退行性疾病的发病机制中具有关键影响。
