School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong, China.
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China, and School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China.
Brief Bioinform. 2021 Mar 22;22(2):1972-1983. doi: 10.1093/bib/bbaa016.
Protein complexes are key units for studying a cell system. During the past decades, the genome-scale protein-protein interaction (PPI) data have been determined by high-throughput approaches, which enables the identification of protein complexes from PPI networks. However, the high-throughput approaches often produce considerable fraction of false positive and negative samples. In this study, we propose the mutual important interacting partner relation to reflect the co-complex relationship of two proteins based on their interaction neighborhoods. In addition, a new algorithm called idenPC-MIIP is developed to identify protein complexes from weighted PPI networks. The experimental results on two widely used datasets show that idenPC-MIIP outperforms 17 state-of-the-art methods, especially for identification of small protein complexes with only two or three proteins.
蛋白质复合物是研究细胞系统的关键单位。在过去的几十年中,通过高通量方法已经确定了基因组规模的蛋白质-蛋白质相互作用(PPI)数据,这使得能够从 PPI 网络中鉴定蛋白质复合物。然而,高通量方法通常会产生相当数量的假阳性和假阴性样本。在这项研究中,我们提出了相互重要相互作用伙伴关系,以基于它们的相互作用邻域反映两种蛋白质的共复合物关系。此外,还开发了一种称为 idenPC-MIIP 的新算法,用于从加权 PPI 网络中识别蛋白质复合物。在两个广泛使用的数据集上的实验结果表明,idenPC-MIIP 优于 17 种最先进的方法,特别是对于仅包含两个或三个蛋白质的小蛋白质复合物的识别。