| 引用本文: | 戚添韵,万晓耕.基于序列特征网络的蛋白质结构类型研究[J].生物信息学,2022,20(3):203-217. |
| QI Tianyun,WAN Xiaogeng.A protein structural study based on sequence feature networks[J].Chinese Journal of Bioinformatics,2022,20(3):203-217. |
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| 摘要: |
| 利用复杂网络的方法来探索序列特征因素对蛋白质结构的影响。由于蛋白质的序列对结构具有重要且复杂的影响,因此将蛋白质的结构以及序列特征之间的关系模拟成一个复杂系统,通过利用互相关系数、标准化互信息和传递熵等方法来建立以序列特征为节点的加权网络,进而利用网络中心性的方法来分析不同蛋白质结构类型对应加权网络的中心性分布的差异,探索不同结构类型蛋白质的序列特征差异。发现不同的蛋白质结构类型对应的序列特征网络既有共性又有差异,文章将针对每一种结构类型的网络中心性分布,以及不同结构类型之间的共性与差异进行详细地讨论。研究结果对蛋白质序列与结构之间关系的研究,特别是结构分类研究具有重要的意义。 |
| 关键词: 蛋白质序列 结构分类 自然向量 平均属性因子 网络中心性 |
| DOI：10.12113/202104006 |
| 分类号:Q518.2 |
| 文献标识码:A |
| 基金项目: |
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| A protein structural study based on sequence feature networks |
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QI Tianyun, WAN Xiaogeng
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(Department of Mathematics,College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing 100029, China)
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| Abstract: |
| In this study, complex network approaches were used to explore the influences of protein sequence features on their structures. Since protein sequences have important and complex influences on their structures, the structure of proteins and the relations between their sequence features were simulated as a complex system, and correlation (CR), normalized mutual information (nMIR), and transfer entropy (TE) were used to construct weighted networks with protein sequence features as nodes. Then network centrality measures were used to analyze the centrality distribution for networks of different protein structures, and identify the differences between different protein structural classes in terms of protein sequence features. Results showed that the networks of different protein structures had both commonalities and differences. The centrality distribution for networks of each structural class and the commonalities and differences among the different structural classes were discussed in this study. The results are meaningful for demonstration of the relations between protein sequences and their structures, and are particularly useful for protein structural classification studies. |
| Key words: Protein sequences Structural classification Natural vectors Averaged property factors Network centrality |
