论文标题
基于域蛋白质复合物和拓扑特性鉴定蛋白质的两种新方法
Two new methods for identifying proteins based on the domain protein complexes and topological properties
论文作者
论文摘要
对必需蛋白质的识别不仅可以帮助理解细胞操作的机理，而且还可以帮助研究生物进化的机制。目前，许多学者一直根据蛋白质网络和复合物的拓扑结构发现必需的蛋白质。尽管某些蛋白质仍然无法识别。在本文中，我们提出了两种新方法复杂程度中心性（CDC）和复杂的内度和中间定义（CIBD），它们整合了蛋白质复合物和拓扑特性的局部特征，以确定蛋白质的本质。首先，我们给出了复杂平均中心（CAC）和复杂杂种中心性（CHC）的定义，这些定义都描述了蛋白质复合物的特性。然后，我们根据CAC和CHC定义提出了这些新方法CDC和CIBD。为了访问这两种方法，酿酒酵母，DIP，MIPS和YMBD的不同蛋白质蛋白相互作用（PPI）网络被用作实验材料。网络中的实验结果表明，CDC和CIBD的方法可以帮助提高预测必需蛋白的精度。
The recognition of essential proteins not only can help to understand the mechanism of cell operation, but also help to study the mechanism of biological evolution. At present, many scholars have been discovering essential proteins according to the topological structure of protein network and complexes. While some proteins still can not be recognized. In this paper, we proposed two new methods complex degree centrality (CDC) and complex in-degree and betweenness definition (CIBD) which integrate the local character of protein complexes and topological properties to determine the essentiality of proteins. First, we give the definitions of complex average centrality (CAC) and complex hybrid centrality (CHC) which both describe the properties of protein complexes. Then we propose these new methods CDC and CIBD based on CAC and CHC definitions. In order to access these two methods, different Protein-Protein Interaction (PPI) networks of Saccharomyces cerevisiae, DIP, MIPS and YMBD are used as experimental materials. Experimental results in networks show that the methods of CDC and CIBD can help to improve the precision of predicting essential proteins.
