Department of Information & Communications Engineering, Korea Advanced Institute of Science and Technology, 119 Munjiro, Yuseong-gu, Daejeon, 305-714, Korea.
Bioinformatics. 2010 Feb 1;26(3):385-91. doi: 10.1093/bioinformatics/btp668. Epub 2009 Dec 4.
The increase in the amount of available protein-protein interaction (PPI) data enables us to develop computational methods for protein complex predictions. A protein complex is a group of proteins that interact with each other at the same time and place. The protein complex generally corresponds to a cluster in PPI network (PPIN). However, clusters correspond not only to protein complexes but also to sets of proteins that interact dynamically with each other. As a result, conventional graph-theoretic clustering methods that disregard interaction dynamics show high false positive rates in protein complex predictions.
In this article, a method of refining PPIN is proposed that uses the structural interface data of protein pairs for protein complex predictions. A simultaneous protein interaction network (SPIN) is introduced to specify mutually exclusive interactions (MEIs) as indicated from the overlapping interfaces and to exclude competition from MEIs that arise during the detection of protein complexes. After constructing SPINs, naive clustering algorithms are applied to the SPINs for protein complex predictions. The evaluation results show that the proposed method outperforms the simple PPIN-based method in terms of removing false positive proteins in the formation of complexes. This shows that excluding competition between MEIs can be effective for improving prediction accuracy in general computational approaches involving protein interactions.
http://code.google.com/p/simultaneous-pin/.
Supplementary data are available at Bioinformatics online.
可用蛋白质-蛋白质相互作用 (PPI) 数据量的增加使我们能够开发用于蛋白质复合物预测的计算方法。蛋白质复合物是指在同一时间和地点相互作用的一组蛋白质。蛋白质复合物通常对应于蛋白质相互作用网络 (PPIN) 中的一个簇。然而，簇不仅对应于蛋白质复合物，还对应于相互之间动态相互作用的蛋白质集合。因此，忽略相互作用动态的传统图论聚类方法在蛋白质复合物预测中显示出高的假阳性率。
在本文中，提出了一种使用蛋白质对的结构界面数据进行蛋白质复合物预测的 PPIN 细化方法。引入了同时蛋白质相互作用网络 (SPIN)，以指定相互排斥的相互作用 (MEIs)，如重叠界面所示，并排除在检测蛋白质复合物期间出现的 MEIs 的竞争。构建 SPIN 后，将简单的聚类算法应用于 SPIN 进行蛋白质复合物预测。评估结果表明，该方法在形成复合物时去除假阳性蛋白质方面优于简单的基于 PPIN 的方法。这表明，排除 MEIs 之间的竞争对于提高涉及蛋白质相互作用的一般计算方法的预测准确性可能是有效的。
http://code.google.com/p/simultaneous-pin/。
补充数据可在生物信息学在线获得。
