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基于蛋白质序列特征网络中心性分析的蛋白质结构研究。

A protein structural study based on the centrality analysis of protein sequence feature networks.

机构信息

College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing, China.

The Fourth Center of PLA General Hospital, Beijing, China.

出版信息

PLoS One. 2021 Mar 29;16(3):e0248861. doi: 10.1371/journal.pone.0248861. eCollection 2021.

Abstract

In this paper, we use network approaches to analyze the relations between protein sequence features for the top hierarchical classes of CATH and SCOP. We use fundamental connectivity measures such as correlation (CR), normalized mutual information rate (nMIR), and transfer entropy (TE) to analyze the pairwise-relationships between the protein sequence features, and use centrality measures to analyze weighted networks constructed from the relationship matrices. In the centrality analysis, we find both commonalities and differences between the different protein 3D structural classes. Results show that all top hierarchical classes of CATH and SCOP present strong non-deterministic interactions for the composition and arrangement features of Cystine (C), Methionine (M), Tryptophan (W), and also for the arrangement features of Histidine (H). The different protein 3D structural classes present different preferences in terms of their centrality distributions and significant features.

摘要

在本文中,我们使用网络方法分析了 CATH 和 SCOP 顶级层次类别中蛋白质序列特征之间的关系。我们使用基本的连通性度量,如相关性 (CR)、归一化互信息率 (nMIR) 和传递熵 (TE),来分析蛋白质序列特征之间的两两关系,并使用中心性度量来分析从关系矩阵构建的加权网络。在中心性分析中,我们发现不同蛋白质 3D 结构类别之间存在共性和差异。结果表明,CATH 和 SCOP 的所有顶级层次类别在胱氨酸 (C)、蛋氨酸 (M)、色氨酸 (W) 的组成和排列特征以及组氨酸 (H) 的排列特征方面均呈现出强烈的非确定性相互作用。不同的蛋白质 3D 结构类别在它们的中心性分布和显著特征方面表现出不同的偏好。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2ebf/8006989/43335e4ebcd4/pone.0248861.g001.jpg

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