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。一些天然底物的泛素化和降解，可以诱导恶性转化，如果不适当地从细胞中删除。尽管泛素化过程的重要性，泛素连接酶的底物内的泛素化（Ub）位点（即泛素分子所连接的受体赖氨酸残基）的精确鉴定仍然是实验上的挑战。从蛋白质序列中预测Ub位点的计算方法的发展为实验方法提供了一种有吸引力的替代方法。在这里，我们建议开发一种计算算法，可以预测Ub网站具有高精度。首先，我们将使用多维蛋白质鉴定技术（MudPit）和质谱法的组合来鉴定新的蛋白质Ub位点。不同的环境扰动，如热休克，氧化应激，DNA损伤，营养素饥饿，将被引入，以增加泛素化蛋白质组的覆盖范围。其次，我们将使用新的Ub位点数据集来开发一个泛素化位点预测器。将利用一种新的机器学习方法，包括共同训练具有不同数据表示的两个预测器，以及使用未标记的数据集来提高性能准确性。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器。最后，我们将把预测器应用于细胞信号和癌症相关蛋白的数据集，以预测其中新的泛素化位点和底物。将在相同的数据集上进行内在紊乱（ID）的预测，以检验关于ID区域内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。发现Ub位点附近的突变，甚至直接影响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生物学意义的假设奠定基础。蛋白质经过广泛的修饰来调节其活性。这种修饰之一，泛素化，被证明涉及各种人类疾病，包括癌症、肾脏疾病（von Hippel-Lindau病、Liddle综合征、缺血性急性肾衰竭）、几种神经变性疾病（阿尔茨海默病、帕金森病、CAG-扩张障碍）。蛋白质中精确的泛素化位点很难检测。我们建议开发一种计算方法，可以确定这样的网站具有高精度。这将有助于开发更好的药物，针对泛素化蛋白质或针对这些蛋白质中的特定位点。

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.