USING INDIRECT PROTEIN-PROTEIN INTERACTIONS FOR PROTEIN COMPLEX PREDICTION
USING INDIRECT PROTEIN-PROTEIN INTERACTIONS FOR PROTEIN COMPLEX PREDICTION
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DOI：
10.1142/s0219720008003497
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发表时间：
2008-06-01
影响因子：
1
通讯作者：
Wong, Limsoon
中科院分区：
文献类型：
--
作者：
Chua, Hon Nian;Ning, Kang;Wong, Limsoon
蛋白质复合物是理解细胞组织原理的基础。随着蛋白质-蛋白质相互作用（PPI）网络规模的不断增大，从PPI网络中准确、快速地预测蛋白质复合物可以指导生物实验发现新的蛋白质复合物。然而，从PPI网络中预测蛋白质复合物并不容易，特别是在PPI网络有噪声并且仍然不完整的情况下。在这里，我们研究使用间接的相互作用水平2邻居（水平2相互作用）的蛋白质复合物预测。我们从以前的工作中知道，不相互作用但共享相互作用伙伴（2级邻居）的蛋白质通常共享生物功能。我们提出了一种方法，其中所有的直接和间接的相互作用首先加权使用拓扑权重（FS权重），估计功能关联的强度。具有低权重的交互被从网络中移除，而具有高权重的2级交互被引入交互网络。现有的聚类算法，然后可以应用到这个修改后的网络。我们还提出了一种新的算法，在修改后的网络中搜索团，并合并团，形成集群使用“部分团合并”的方法。实验结果表明：（1）利用间接相互作用和拓扑权重来增强蛋白质-蛋白质相互作用可以提高现有聚类算法预测聚类的精度;（2）我们的复合物发现算法在以这种方式修改的相互作用网络上表现得非常好。由于没有其他信息，除了原始的PPI网络使用，我们的方法将是非常有用的蛋白质复合物的预测，特别是对新的蛋白质复合物的预测。
Protein complexes are fundamental for understanding principles of cellular organizations. As the sizes of protein-protein interaction (PPI) networks are increasing, accurate and fast protein complex prediction from these PPI networks can serve as a guide for biological experiments to discover novel protein complexes. However, it is not easy to predict protein complexes from PPI networks, especially in situations where the PPI network is noisy and still incomplete. Here, we study the use of indirect interactions between level-2 neighbors (level-2 interactions) for protein complex prediction. We know from previous work that proteins which do not interact but share interaction partners (level-2 neighbors) often share biological functions. We have proposed a method in which all direct and indirect interactions are first weighted using topological weight (FS-Weight), which estimates the strength of functional association. Interactions with low weight are removed from the network, while level-2 interactions with high weight are introduced into the interaction network. Existing clustering algorithms can then be applied to this modified network. We have also proposed a novel algorithm that searches for cliques in the modified network, and merge cliques to form clusters using a "partial clique merging" method. Experiments show that (1) the use of indirect interactions and topological weight to augment protein-protein interactions can be used to improve the precision of clusters predicted by various existing clustering algorithms; and (2) our complex-finding algorithm performs very well on interaction networks modified in this way. Since no other information except the original PPI network is used, our approach would be very useful for protein complex prediction, especially for prediction of novel protein complexes.