...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Bioinformatics and Computational Biology >USING INDIRECT PROTEIN–PROTEIN INTERACTIONS FOR PROTEIN COMPLEX PREDICTION
【24h】

USING INDIRECT PROTEIN–PROTEIN INTERACTIONS FOR PROTEIN COMPLEX PREDICTION

机译：使用间接蛋白质-蛋白质相互作用进行蛋白质复杂预测

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

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.
机译：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）网络规模的增加，从这些PPI网络进行准确而快速的蛋白质复合物预测可以作为生物学实验发现新型蛋白质复合物的指南。但是，从PPI网络预测蛋白质复合物并不容易，尤其是在PPI网络嘈杂而仍不完整的情况下。在这里，我们研究2级邻居之间的间接相互作用（2级相互作用）用于蛋白质复合物预测。从以前的工作中我们知道，不相互作用但共享相互作用伙伴（二级邻居）的蛋白质通常具有生物学功能。我们提出了一种方法，其中首先使用拓扑权重（FS-Weight）对所有直接和间接交互进行加权，该权重估计功能关联的强度。低权重的交互将从网络中删除，而高权重的2级交互被引入到交互网络中。然后可以将现有的聚类算法应用于此修改后的网络。我们还提出了一种新颖的算法，该算法可在经过修改的网络中搜索群体，并使用“部分群体合并”方法合并群体以形成集群。实验表明：（1）利用间接相互作用和拓扑权重来增强蛋白质之间的相互作用，可以用来提高由各种现有聚类算法预测的聚类的精度; （2）我们的复杂查找算法在以这种方式修改的互动网络上表现出色。由于除原始PPI网络外没有其他信息可使用，因此我们的方法对于蛋白质复合物的预测非常有用，尤其是对于新型蛋白质复合物的预测。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号