基于网络水平的关键蛋白质识别方法及其应用研究

Essential proteins play a key role in the life activities of cells. They are very important to the existence of all living things or reproduction. The identification of essential proteins has great using value in disease diagnosis, treatment, and drug design. With the development of high-throughput technology, identifying essential proteins from a network level has become a hot topic in the post-genomic era. In our Young Scientist Project of Natural Science Foundation of China( NSFC), we have acchived great progresses on the construction of dynamic protein interaction network and the detection of protein complexes and functional modules. Based on these research results, we will further study the essential protein discovery method and its applications. The relationship betwee essential protein and functional modules will be analyzed. New topological parameters will be proposed based on dynamic network model from a fresh perspective. For an essential protein may have multiple topological characters, we will use multiple-parameter model to describe it. Moreover, the multiple properties of protein related data will be thoroughly studied, and new methods based on multiple data merging for the identification of essential proteins will be developed. It is hopeful that a serial of hard problems in the network-based essential protein discovery will be attacked. Furthermore, this project will also study the methods for constructing disease-causing molecular networks and identifying disease-causing genes. Based on the above studies, the project will design related software platform with self-owned intellectual property rights. At last, these research results will be applied to the study of osteosarcoma and other complex diseases. The results of this research will provide valuable information to the diagnosis, analysis and therapy of complex diseases.

关键蛋白质在细胞的生命活动过程中起关键作用，它们与生物生存或繁殖密切相关，在疾病诊治和药物设计等方面具有重要应用价值。高通量技术的发展使得基于网络水平的关键蛋白质识别研究成为后基因组时代的新热点。本项目以青年科学基金项目在动态蛋白质网络建模及功能模块挖掘方面的研究成果为基础，探讨关键蛋白质与网络功能模块之间的关系，面向动态网络从全新的角度研究刻画关键蛋白质的新拓扑特征参数，采用多元参数方法对关键蛋白质的多个拓扑特征进行描述和建模，深入分析多元生物信息的复杂关系，研究基于多元信息融合的关键蛋白质识别方法,解决网络水平关键蛋白质识别存在的一系列难点问题。以此为基础，进一步研究致病分子网络的构建方法和致病基因识别方法，建立自主知识产权的相关软件处理平台。最后，将研究成果应用于骨肉瘤等复杂疾病的致病机理研究中，为复杂疾病的治疗与预防提供实验基础和理论依据。

在本基金的资助下，本项目重点研究了关键蛋白质的拓扑特征、基于网络拓扑特征的关键蛋白质识别方法、基于多元信息融合的关键蛋白质识别方法及疾病基因预测方法，取得的主要研究成果如下：1. 通过分析关键蛋白质与网络功能模块之间的关系，提出了系列基于网络功能模块的关键蛋白质识别方法，主要包括集成蛋白质复合物信息和网络拓扑特征的方法UC和UC-P、基于重叠关键模块的方法POEM、结合蛋白质模块性和保守性的预测方法等；2. 提出了基于拓扑势和基于先验知识的关键蛋白质识别方法，设计了基于特征选择的关键蛋白预测方法；3. 分析了蛋白质结构域信息、同源信息、亚细胞定位信息等与蛋白质的关键性之间的关系，提出了一系列基于多元信息融合的关键蛋白质识别方法；4. 提出了基于随机游走和集成多数据源信息的疾病基因预测方法；5. 开发了基于Cytoscape的关键蛋白预测、评估及可视化工具CytoNCA和web平台。