Karain Wael I, Qaraeen Nael I
Department of Physics, Birzeit University, Birzeit, Palestine.
Department of Computer Science, Birzeit University, Birzeit, Palestine.
Proteins. 2017 May;85(5):917-923. doi: 10.1002/prot.25261. Epub 2017 Feb 16.
Protein residue networks PRNs are used to describe proteins. These networks are usually based on an average structure for the protein. However, proteins are dynamic entities that are affected by their surroundings. In this work, we study the effect of temperatures above and below the protein dynamical transition temperature(≈200 K), on three important network parameters gleaned from weighted PRNs for the solvated β-lactamase inhibitory protein BLIP: the betweenness centrality B, the closeness centrality C, and the clustering coefficient CC. The B and C values will be extracted for each node from PRNs at six different temperatures: 150 K, 180 K, 200 K, 220 K, 250 K, and 310 K respectively. The average value for the CC for each PRN will also be calculated at each temperature, respectively. We find that at temperatures ≤200 K, the network nodes with the most significant B and C values tend to have lower relative solvent accessibility RSA values, and to fall within the protein secondary structure elements (α helices and β sheets). At temperatures >200 K, the significant nodes in terms of B and C tend to have larger RSA values, and to fall on the connecting loops in the protein. The average CC decreases in value for the PRNs up to 200 K, and then remains basically constant above 200 K. This clearly shows that any conclusions based on static PRNs should be handled with care. The dynamic nature of proteins and its coupling to the surrounding environment should be taken into consideration when using the PRN paradigm. Proteins 2017; 85:917-923. © 2016 Wiley Periodicals, Inc.
蛋白质残基网络(PRNs)用于描述蛋白质。这些网络通常基于蛋白质的平均结构。然而,蛋白质是受其周围环境影响的动态实体。在这项工作中,我们研究了高于和低于蛋白质动力学转变温度(≈200K)的温度对从溶剂化β-内酰胺酶抑制蛋白(BLIP)的加权PRNs中提取的三个重要网络参数的影响:介数中心性B、紧密中心性C和聚类系数CC。将分别从六个不同温度(150K、180K、200K、220K、250K和310K)的PRNs中为每个节点提取B和C值。还将分别在每个温度下计算每个PRN的CC平均值。我们发现,在温度≤200K时,具有最显著B和C值的网络节点往往具有较低的相对溶剂可及性(RSA)值,并且位于蛋白质二级结构元件(α螺旋和β折叠)内。在温度>200K时,就B和C而言的显著节点往往具有较大的RSA值,并且位于蛋白质中的连接环上。对于PRNs,平均CC值在200K之前下降,然后在200K以上基本保持恒定。这清楚地表明,基于静态PRNs得出的任何结论都应谨慎对待。在使用PRN范式时,应考虑蛋白质的动态性质及其与周围环境的耦合。《蛋白质》2017年;85:917 - 923。©2016威利期刊公司。