Protein structural disorder and ubiquitination

描述(由申请人提供)：泛素-蛋白酶体系统的缺陷与许多人类疾病的发展有关。一些泛素化和降解的天然底物如果没有从细胞中正确去除，可能会导致恶性转化。尽管泛素化过程很重要，但准确识别泛素连接酶底物中的泛素化(Ub)位点(即连接泛素分子的受体赖氨酸残基)仍然是一项具有实验挑战性的工作。从蛋白质序列预测Ub位点的计算方法的发展为实验方法提供了一个有吸引力的替代方法。在这里，我们建议开发一种计算算法，可以高精度地预测Ub站点。首先，我们将结合使用多维蛋白质鉴定技术(MUDIT)和质谱仪来鉴定新的蛋白质Ub位点。为了增加泛素化蛋白质组的覆盖率，将引入不同的环境扰动，如热休克、氧化应激、DNA损伤和营养饥饿。其次，我们将使用新的Ub站点的数据集来开发泛素化站点预测器。将使用一种新的机器学习方法，该方法包括具有不同数据表示的两个预测器的共同训练，以及使用未标记的数据集来提高性能精度。据我们所知，这将是迄今为止开发的第一个泛素化位点预测因子。最后，我们将把预测因子应用于细胞信号和癌症相关蛋白的数据集，以预测它们之间新的泛素化位点和底物。将在相同的数据集上进行内在性无序(ID)的预测，以检验ID区内Ub位点优先出现的假设。注释的疾病相关突变将从三个公共数据库(MutDB、Swiss-Prot和OMIM)中提取，并与预测的泛素化位点相关联。在Ub位点附近发现突变，甚至那些直接影响Ub位点的突变，将为制定和测试关于它们在癌症和其他疾病中的作用的具有生物学意义的假说奠定基础。蛋白质经过广泛的修饰来调节它们的活性。这种修饰之一，泛素化，被证明与各种人类疾病有关，包括癌症、肾脏疾病(von Hippel-Lindau病、利德尔综合征、缺血性急性肾功能衰竭)、几种神经退行性疾病(阿尔茨海默病、帕金森、CAG扩张性疾病)。蛋白质中精确的泛素化位点很难检测到。我们建议开发一种计算方法，可以高精度地识别这样的地点。这将有助于开发更好的药物，要么针对泛素化的蛋白质，要么针对这些蛋白质中的特定位置。

DESCRIPTION (provided by applicant): Defects in the ubiquitin-proteasome system are implicated in the development of numerous human diseases. Some of the natural substrates for ubiquitination and degradation can induce malignant transformation if not properly removed from the cell. Despite the importance of the ubiquitination process, precise identification of ubiquitination (Ub) sites (i.e. acceptor lysine residues to which a ubiquitin molecule is attached) within substrates of ubiquitin ligases is still experimentally challenging. The development of computational approaches to predict Ub sites from a protein sequence provides an attractive alternative to the experimental methods. Here, we propose to develop a computational algorithm that could predict Ub sites with high precision. First, we will identify new protein Ub sites using a combination of multidimensional protein identification technology (MudPit) and mass spectrometry. Different environmental perturbations, such as heat shock, oxidative stress, DNA damage, and starvation for nutrients, will be introduced in order to increase the coverage of the ubiquitinated proteome. Second, we will use the dataset of new Ub sites to develop a ubiquitination sites predictor. A novel machine learning approach that includes co-training of two predictors having different data representations, and the usage of the unlabeled dataset to increase performance accuracy will be utilized. To our knowledge, this will be the first ubiquitination sites predictor developed to date. Finally, we will apply the predictor to the datasets of cell signaling and cancer-associated proteins to predict new ubiquitination sites and substrates among them. The prediction of intrinsic disorder (ID) will be carried out on the same datasets in order to test the hypothesis about preferential occurrence of Ub sites within ID regions. Annotated disease-related mutations will be extracted from three public databases (MutDB, SWISS-PROT and OMIM) and correlated with the predicted ubiquitination sites. The discovery of mutations in proximity to Ub sites or even those directly affecting Ub sites would lay the basis for formulating and testing biologically meaningful hypotheses about their role in cancer and other diseases. Proteins undergo a wide range of modifications that regulate their activity. One of such modification, ubiquitination, was shown to be involved in various human diseases including cancer, renal diseases (von Hippel-Lindau disease, Liddle syndrome, ischemic acute renal failure), several neurodegenerative diseases (Alzheimer, Parkinson, CAG- expansion disorders). The precise ubiquitination sites in proteins are difficult to detect. We propose to develop a computational approach that could identify such sites with high precision. This would help to develop better drugs that are directed either against the ubiquitinated proteins or against specific sites in these proteins.