Mukhopadhyay Anirban, Ray Sumanta, De Moumita
Department of Computer Science and Engineering, University of Kalyani, Kalyani, India.
Mol Biosyst. 2012 Nov;8(11):3036-48. doi: 10.1039/c2mb25302j. Epub 2012 Sep 18.
Protein complexes play an important role in cellular mechanism. Identification of protein complexes in protein-protein interaction (PPI) networks is the first step in understanding the organization and dynamics of cell function. Several high-throughput experimental techniques produce a large amount of protein interactions, which can be used to predict protein complexes in a PPI network. We have developed an algorithm PROCOMOSS (Protein Complex Detection using Multi-objective Evolutionary Approach based on Semantic Similarity) for partitioning the whole PPI network into clusters, which serve as predicted protein complexes. We consider both graphical properties of a PPI network as well as biological properties based on GO semantic similarity measure as objective functions. Here three different semantic similarity measures are used for grouping functionally similar proteins in the same clusters. We have applied the PROCOMOSS algorithm on two different datasets of Saccharomyces cerevisiae to find and predict protein complexes. A real-life application of the PROCOMOSS is also shown here by applying it in the human PPI network consisting of differentially expressed genes affected by gastric cancer. Gene ontology and pathway based analyses are also performed to investigate the biological importance of the extracted gene modules.
蛋白质复合物在细胞机制中发挥着重要作用。在蛋白质 - 蛋白质相互作用（PPI）网络中识别蛋白质复合物是理解细胞功能的组织和动态的第一步。几种高通量实验技术产生了大量的蛋白质相互作用，可用于预测PPI网络中的蛋白质复合物。我们开发了一种算法PROCOMOSS（基于语义相似性的多目标进化方法进行蛋白质复合物检测），用于将整个PPI网络划分为簇，这些簇可作为预测的蛋白质复合物。我们将PPI网络的图形属性以及基于GO语义相似性度量的生物学属性都视为目标函数。这里使用三种不同的语义相似性度量将功能相似的蛋白质分组到同一簇中。我们已将PROCOMOSS算法应用于酿酒酵母的两个不同数据集，以查找和预测蛋白质复合物。通过将PROCOMOSS应用于由受胃癌影响的差异表达基因组成的人类PPI网络，还展示了该算法在实际中的应用。还进行了基于基因本体和通路的分析，以研究提取的基因模块的生物学重要性。
