School of Computer and Communication Engineering, University of Science and Technology Beijing, No. 30 Xueyuan Road, Haidian District, Beijing, 100083, China.
School of International Economics, China Foreign Affairs University, 24 Zhanlanguan Road, Xicheng District, Beijing, 100037, China.
BMC Bioinformatics. 2022 Oct 7;23(1):414. doi: 10.1186/s12859-022-04923-4.
Accurate identification of protein complexes in protein-protein interaction (PPI) networks is crucial for understanding the principles of cellular organization. Most computational methods ignore the fact that proteins in a protein complex have a functional similarity and are co-localized and co-expressed at the same place and time, respectively. Meanwhile, the parameters of the current methods are specified by users, so these methods cannot effectively deal with different input PPI networks.
To address these issues, this study proposes a new method called MP-AHSA to detect protein complexes with Multiple Properties (MP), and an Adaptation Harmony Search Algorithm is developed to optimize the parameters of the MP algorithm. First, a weighted PPI network is constructed using functional annotations, and multiple biological properties and the Markov cluster algorithm (MCL) are used to mine protein complex cores. Then, a fitness function is defined, and a protein complex forming strategy is designed to detect attachment proteins and form protein complexes. Next, a protein complex filtering strategy is formulated to filter out the protein complexes. Finally, an adaptation harmony search algorithm is developed to determine the MP algorithm's parameters automatically.
Experimental results show that the proposed MP-AHSA method outperforms 14 state-of-the-art methods for identifying protein complexes. Also, the functional enrichment analyses reveal that the protein complexes identified by the MP-AHSA algorithm have significant biological relevance.
准确识别蛋白质-蛋白质相互作用（PPI）网络中的蛋白质复合物对于理解细胞组织的原理至关重要。大多数计算方法忽略了一个事实，即蛋白质复合物中的蛋白质具有功能相似性，分别在同一地点和时间共定位和共表达。同时，当前方法的参数由用户指定，因此这些方法无法有效地处理不同的输入 PPI 网络。
为了解决这些问题，本研究提出了一种新的方法，称为 MP-AHSA，用于检测具有多种特性（MP）的蛋白质复合物，并开发了一种自适应和声搜索算法来优化 MP 算法的参数。首先，使用功能注释构建加权 PPI 网络，并使用多种生物学特性和马尔可夫聚类算法（MCL）挖掘蛋白质复合物核心。然后，定义了一个适应度函数，并设计了一种蛋白质复合物形成策略来检测附着蛋白并形成蛋白质复合物。接下来，制定了一种蛋白质复合物过滤策略来过滤蛋白质复合物。最后，开发了一种自适应和声搜索算法来自动确定 MP 算法的参数。
实验结果表明，所提出的 MP-AHSA 方法在识别蛋白质复合物方面优于 14 种最先进的方法。此外，功能富集分析表明，MP-AHSA 算法识别的蛋白质复合物具有显著的生物学相关性。
