Mirzarezaee Mitra, Araabi Babak N, Sadeghi Mehdi
Department of Computer Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran.
BMC Syst Biol. 2010 Dec 19;4:172. doi: 10.1186/1752-0509-4-172.
It has been understood that biological networks have modular organizations which are the sources of their observed complexity. Analysis of networks and motifs has shown that two types of hubs, party hubs and date hubs, are responsible for this complexity. Party hubs are local coordinators because of their high co-expressions with their partners, whereas date hubs display low co-expressions and are assumed as global connectors. However there is no mutual agreement on these concepts in related literature with different studies reporting their results on different data sets. We investigated whether there is a relation between the biological features of Saccharomyces Cerevisiae's proteins and their roles as non-hubs, intermediately connected, party hubs, and date hubs. We propose a classifier that separates these four classes.
We extracted different biological characteristics including amino acid sequences, domain contents, repeated domains, functional categories, biological processes, cellular compartments, disordered regions, and position specific scoring matrix from various sources. Several classifiers are examined and the best feature-sets based on average correct classification rate and correlation coefficients of the results are selected. We show that fusion of five feature-sets including domains, Position Specific Scoring Matrix-400, cellular compartments level one, and composition pairs with two and one gaps provide the best discrimination with an average correct classification rate of 77%.
We study a variety of known biological feature-sets of the proteins and show that there is a relation between domains, Position Specific Scoring Matrix-400, cellular compartments level one, composition pairs with two and one gaps of Saccharomyces Cerevisiae's proteins, and their roles in the protein interaction network as non-hubs, intermediately connected, party hubs and date hubs. This study also confirms the possibility of predicting non-hubs, party hubs and date hubs based on their biological features with acceptable accuracy. If such a hypothesis is correct for other species as well, similar methods can be applied to predict the roles of proteins in those species.
人们已经认识到生物网络具有模块化组织，这是其观察到的复杂性的来源。对网络和基序的分析表明，两种类型的中心节点，即聚会型中心节点和约会型中心节点，导致了这种复杂性。聚会型中心节点是局部协调者，因为它们与其伙伴具有高度共表达，而约会型中心节点显示出低共表达，并被视为全局连接者。然而，相关文献中对于这些概念并没有达成共识，不同的研究在不同的数据集上报告了他们的结果。我们研究了酿酒酵母蛋白质的生物学特征与其作为非中心节点、中度连接节点、聚会型中心节点和约会型中心节点的作用之间是否存在关系。我们提出了一种将这四类分开的分类器。
我们从各种来源提取了不同的生物学特征，包括氨基酸序列、结构域内容、重复结构域、功能类别、生物学过程、细胞区室、无序区域和位置特异性评分矩阵。我们检查了几种分类器，并根据平均正确分类率和结果的相关系数选择了最佳特征集。我们表明，融合五个特征集，包括结构域、位置特异性评分矩阵 - 400、一级细胞区室以及带有两个和一个空位的组成对，能提供最佳的区分效果，平均正确分类率为77%。
我们研究了蛋白质的各种已知生物学特征集，并表明酿酒酵母蛋白质的结构域、位置特异性评分矩阵 - 400、一级细胞区室、带有两个和一个空位的组成对与其在蛋白质相互作用网络中作为非中心节点、中度连接节点、聚会型中心节点和约会型中心节点的作用之间存在关系。这项研究还证实了基于蛋白质的生物学特征以可接受的准确度预测非中心节点、聚会型中心节点和约会型中心节点的可能性。如果这样的假设对其他物种也正确，那么类似的方法可以应用于预测那些物种中蛋白质的作用。
