School of Computer Science, Shaanxi Normal University, Xi'an, China.
PLoS One. 2018 Jun 12;13(6):e0198998. doi: 10.1371/journal.pone.0198998. eCollection 2018.
Essential proteins are crucial to living cells. Identification of essential proteins from protein-protein interaction (PPI) networks can be applied to pathway analysis and function prediction, furthermore, it can contribute to disease diagnosis and drug design. There have been some experimental and computational methods designed to identify essential proteins, however, the prediction precision remains to be improved. In this paper, we propose a new method for identifying essential proteins based on Participation degree of a protein in protein Complexes and Subgraph Density, named as PCSD. In order to test the performance of PCSD, four PPI datasets (DIP, Krogan, MIPS and Gavin) are used to conduct experiments. The experiment results have demonstrated that PCSD achieves a better performance for predicting essential proteins compared with some competing methods including DC, SC, EC, IC, LAC, NC, WDC, PeC, UDoNC, and compared with the most recent method LBCC, PCSD can correctly predict more essential proteins from certain numbers of top ranked proteins on the DIP dataset, which indicates that PCSD is very effective in discovering essential proteins in most case.
必需蛋白对于活细胞至关重要。从蛋白质-蛋白质相互作用（PPI）网络中鉴定必需蛋白可应用于途径分析和功能预测，此外，还可有助于疾病诊断和药物设计。已经有一些实验和计算方法被设计用于鉴定必需蛋白，但是预测精度仍有待提高。在本文中，我们提出了一种基于蛋白复合物参与度和子图密度的新方法（PCS）用于鉴定必需蛋白。为了测试 PCSD 的性能，我们使用了四个 PPI 数据集（DIP、Krogan、MIPS 和 Gavin）进行实验。实验结果表明，与包括 DC、SC、EC、IC、LAC、NC、WDC、PeC、UDoNC 在内的一些竞争方法相比，PCS 可以更好地预测必需蛋白，与最近的方法 LBCC 相比，PCS 可以从 DIP 数据集的一定数量的排名靠前的蛋白中正确预测更多的必需蛋白，这表明 PCS 在大多数情况下发现必需蛋白非常有效。
