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Weighted protein residue networks based on joint recurrences between residues

机译:基于残基间联合递归的加权蛋白质残基网络

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摘要

BackgroundWeighted and un-weighted protein residue networks can predict key functional residues in proteins based on the closeness centrality C and betweenness centrality B values for each residue. A static snapshot of the protein structure, and a cutoff distance, are used to define edges between the network nodes. In this work we apply the weighted network approach to study the β-Lactamase Inhibitory Protein (BLIP). Joint recurrences extracted from molecular dynamics MD trajectory positions of the protein residue carbon alpha atoms are used to define edge weights between nodes, and no cutoff distance is used. The results for B and C from our approach are compared with those extracted from an un-weighted network, and a weighted network that uses interatomic contacts to define edge weights between nodes, respectively.
机译:背景加权和未加权的蛋白质残基网络可以根据每个残基的接近中心C和中间中心B值预测蛋白质中的关键功能残基。蛋白质结构的静态快照和截止距离用于定义网络节点之间的边缘。在这项工作中,我们采用加权网络方法来研究β-内酰胺酶抑制蛋白(BLIP)。从蛋白质残基碳α原子的分子动力学MD轨迹位置提取的联合递归用于定义节点之间的边缘权重,并且不使用截止距离。将我们的方法中B和C的结果与从未加权网络以及使用原子间接触定义节点之间的边缘权重的加权网络中提取的结果进行比较。

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