Karabekmez Muhammed Erkan, Kirdar Betul
Bogazici University, Department of Chemical Engineering, Istanbul, Turkey.
Mol Biosyst. 2016 Feb;12(2):666-73. doi: 10.1039/c5mb00732a.
Topological centrality in protein interaction networks and its biological implications have widely been investigated in the past. In the present study, a novel metric of centrality-weighted sum of loads eigenvector centrality (WSL-EC)-based on graph spectra is defined and its performance in identifying topologically and biologically important nodes is comparatively investigated with common metrics of centrality in a human protein-protein interaction network. The metric can capture nodes from peripherals of the network differently from conventional eigenvector centrality. Different metrics were found to selectively identify hub sets that are significantly associated with different biological processes. The widely accepted metrics degree centrality, betweenness centrality, subgraph centrality and eigenvector centrality are subject to a bias towards super-hubs, whereas WSL-EC is not affected by the presence of super-hubs. WSL-EC outperforms other metrics of centrality in detecting biologically central nodes such as pathogen-interacting, cancer, ageing, HIV-1 or disease-related proteins and proteins involved in immune system processes and autoimmune diseases in the human interactome.
过去，蛋白质相互作用网络中的拓扑中心性及其生物学意义已得到广泛研究。在本研究中，定义了一种基于图谱的新型中心性指标——负载特征向量中心性加权和（WSL-EC），并在人类蛋白质-蛋白质相互作用网络中，将其在识别拓扑和生物学重要节点方面的性能与常见的中心性指标进行了比较研究。该指标能够以不同于传统特征向量中心性的方式捕获网络边缘的节点。研究发现，不同的指标能够选择性地识别与不同生物过程显著相关的枢纽集。广泛接受的度中心性、介数中心性、子图中心性和特征向量中心性指标存在偏向超级枢纽的偏差，而WSL-EC不受超级枢纽存在的影响。在检测人类相互作用组中与病原体相互作用、癌症、衰老、HIV-1或疾病相关的蛋白质以及参与免疫系统过程和自身免疫性疾病的生物学中心节点方面，WSL-EC优于其他中心性指标。
