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题名	蛋白相互作用网络与疾病及药物靶标的研究
作者	于琦
学位类别	博士
答辩日期	2011-05
授予单位	中国科学院研究生院
授予地点	北京
导师	黄京飞
关键词	蛋白相互作用网络 药物靶标 药靶家族 拓扑特征 模块检测 模块通讯网络 心血管疾病 环境因子
其他题名	Protein-protein interaction network in the analysis of disease and drug targets
学位专业	遗传学
中文摘要	大多数药靶蛋白都分布在几个药靶家族内。尽管同一家族的蛋白有相似的序列和结构，并不是所有药靶家族的蛋白都能够成为药物靶标。探讨在药靶家族内的药靶蛋白和非药靶的蛋白的差别具有重要意义。我们从蛋白质相互作用网络的角度发现药靶蛋白和非药靶蛋白在拓扑特征上有显著不同，同一家族内的药靶蛋白的拓扑特征的相似度也比非药靶蛋白高，药靶家族之间的拓扑特征相似度也有差别，我们找到不同相似度区间内富集的家族，并且以两个最大的药靶家族为例分析了他们的不同。另外我们发现实验药靶蛋白的拓扑特征比撤回药靶蛋白更类似于FDA认证的药靶，这进一步证实拓扑特征的确与蛋白能否成为药物靶标相关。最后我们发现药靶蛋白对蛋白质网络的拓扑结构的影响比非药靶蛋白要大。我们的研究为新的药物靶标的筛选提供了帮助。 组织发育和很多重要的生物学过程都用模块方式发挥功能，虽然目前有很多检测模块的方法，但是检测重叠模块并通过构建模块通讯网络来研究生物学过程之间的联系的研究尚且没有。我们写了一种新算法（MOfinder）来检测蛋白质相互作用网络中的模块。该方法灵敏度比其它方法高。在人的蛋白相互作用网络中应用MOfinder共找到451个有重叠的模块，这些模块构建了一个模块通讯网络，该网络中包含两个大的群组，和若干小群组。疾病基因的注释表明大群组主要是癌症相关的蛋白，而免疫蛋白与癌症蛋白共存于一个模块内的概率较高，这为利用免疫疗法治疗癌症提供了线索。我们对于模块和通讯蛋白的研究为蛋白质网络的研究提供了一个新思路，并且加深了对生物体内各系统之间关系的理解。 心血管疾病和其他复杂疾病不能用“一个基因一种表型”来解释。对于这类复杂疾病来说，系统生物学的方法可以鉴定疾病相关的基因，揭示环境因子如何增加致病的可能性，并帮助寻找新的潜在的药物靶标。我们建立了心血管疾病网络和环境因子-基因网络。我们发现心血管疾病网络中的连接数是符合幂律分布的。环境因子相关的中心基因在疾病网络中并不具有中心特征，这表明心血管疾病网络的稳定性不会被破坏。随后我们研究了现有的药物靶标中哪些是与环境因子相互作用的，并揭示这类基因如何影响网络的拓扑特征。本研究所使用的系统生物学方法对于理解复杂疾病和寻找合适的药物靶标提供了帮助。
英文摘要	Many drug target proteins (DTPs) have been defined by previous studies, and the DTPs are mainly distributed in several druggable families. Although proteins in one druggable family are similar in sequence and structure, not all of them are DTPs. However, the difference between DTPs and non-target proteins (NTPs) in druggable families is not yet clear. We explored the difference of DTPs and NTPs from known druggable families in human protein interaction network, and they differed significantly in several topological features, such as degree. We also found the similarity of topological features, which quantified by Pearson correlation coefficient (PCC) of six features for each two proteins in druggable families, was different between DTPs and NTPs. Most PCCs in DTPs were higher than those in NTPs, but PCCs in DTPs were diverse for different druggable families. The PCCs of withdrawn DTPs were also less than those of experimental DTPs. At last we found DTPs influenced the network to a larger extent compared with NTPs, which suggest that the difference between DTPs and NTPs might caused by efficiency. Thus, the druggability of proteins might relate to protein’s position in the network, which could be helpful to find new DTP candidates. Since organism development and many critical cell biological processes are organized in a module pattern, many algorithms have been proposed to detect modules recently. But no work constructed a module-module communicating network to interpret how these processes interact with each other. A new method, MOfinder, was presented to detect overlapping modules in protein-protein interaction (PPI) network. It demonstrated that our method is not only more sensitive and simpler, but also more reliable than common algorithms. Then, MOfinder was applied to human PPI network, and 451 overlapping modules were found. Using these modules, the module-module communication network is constructed, and it comprised by two large clusters and tens of small clusters. Disease gene annotation shows that these large clusters are cancer related, and the immune related protein always include in cancer related modules which offer some clues for cancer therapy by targeting immune genes. Our study about modules and communicating proteins enables the analysis of protein–protein interaction networks in a new perspective and thus may benefit network research and improve our understanding of disease. Cardiovascular disease (CVD) and other complex diseases cannot be explained by the“one gene one phenotype” rule. The system-level analysis is required by identifying candidate genes related to complex diseases, uncovering how environment factors (ENF) increase the possibility of sick, and improve discovering novel drug target candidates. Thus the disease network, ENF-gene network had been built. The power-law distribution observed in many other biological networks has been found in the CVD network. None of the ENF associated hub genes holds the hub property in CVD network, which suggest that hub genes keep away from environment factors in order to maintain the robustness of cardiovascular system. Combine the two networks, we found several shared genes between them and test their influence to the network: they hardly changed the network topological features. Besides, the ENF associated genes which overlap with CVD network were suggested as drug targets because both ENF and disease gene can be inhibited. We thought such situation should be considered in filtering candidate drug targets for other complex disease. The network-based approach will be helpful for understanding the mechanisms of complex diseases and finding suitable drug targets.
语种	中文
公开日期	2011-12-19
内容类型	学位论文
源URL	[http://159.226.149.42:8088/handle/353002/6839]
专题	昆明动物研究所_结构生物信息学
推荐引用方式 GB/T 7714	于琦. 蛋白相互作用网络与疾病及药物靶标的研究[D]. 北京. 中国科学院研究生院. 2011.

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