NWE: Node-weighted expansion for protein complex prediction using random walk distances.
NWE: Node-weighted expansion for protein complex prediction using random walk distances.
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DOI：
10.1186/1477-5956-9-s1-s14
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发表时间：
2011-10-14
期刊：
影响因子：
2
通讯作者：
Chihara A
中科院分区：
文献类型：
--
作者：
Maruyama O;Chihara A
蛋白质复合物是组织细胞内各种生物过程的重要实体，如信号转导、基因表达和分子传递。在大多数情况下，蛋白质与特定的相互作用伙伴一起执行其内在任务，形成蛋白质复合物。因此，细胞中蛋白质复合物的丰富目录可以加速进一步的研究，以阐明许多生物过程背后的机制。然而，已知的复合物仍然有限。因此，从蛋白质-蛋白质相互作用网络和其他全基因组数据集计算预测蛋白质复合物是一个具有挑战性的问题。Macropol等人提出了一种蛋白质复合物预测算法RRW，该算法根据随机行走的平稳向量，重复扩展当前的蛋白质簇，并重新启动具有相同权重的蛋白质簇。在团簇扩展过程中，团簇内的所有蛋白对团簇中新加入蛋白的确定具有同等的影响。在本文中，我们通过引入随机漫步来扩展RRW算法，随机漫步与一组蛋白质重新启动，每个随机漫步都由涉及蛋白质的直接物理相互作用的支持证据的强度总和加权。由此产生的算法被称为NWE（蛋白质簇的节点加权扩展）。这些交互数据来自WI-PHI数据库。我们使用CYC2008数据库中的策划复合物验证了结果的生物学意义，并将我们的方法与RRW和MCL (Markov Clustering)（一种流行的基于聚类的方法）进行了比较，发现我们的算法优于其他算法。结果表明，扩大蛋白质簇是蛋白质复合体预测的有效方法，每个蛋白质簇都由涉及蛋白质的直接物理相互作用的支持证据的强度总和加权。
Protein complexes are important entities to organize various biological processes in the cell, like signal transduction, gene expression, and molecular transmission. In most cases, proteins perform their intrinsic tasks in association with their specific interacting partners, forming protein complexes. Therefore, an enriched catalog of protein complexes in a cell could accelerate further research to elucidate the mechanisms underlying many biological processes. However, known complexes are still limited. Thus, it is a challenging problem to computationally predict protein complexes from protein-protein interaction networks, and other genome-wide data sets. Macropol et al. proposed a protein complex prediction algorithm, called RRW, which repeatedly expands a current cluster of proteins according to the stationary vector of a random walk with restarts with the cluster whose proteins are equally weighted. In the cluster expansion, all the proteins within the cluster have equal influences on determination of newly added protein to the cluster. In this paper, we extend the RRW algorithm by introducing a random walk with restarts with a cluster of proteins, each of which is weighted by the sum of the strengths of supporting evidence for the direct physical interactions involving the protein. The resulting algorithm is called NWE (Node-Weighted Expansion of clusters of proteins). Those interaction data are obtained from the WI-PHI database. We have validated the biological significance of the results using curated complexes in the CYC2008 database, and compared our method to RRW and MCL (Markov Clustering), a popular clustering-based method, and found that our algorithm outperforms the other algorithms. It turned out that it is an effective approach in protein complex prediction to expand a cluster of proteins, each of which is weighted by the sum of the strengths of supporting evidence for the direct physical interactions involving the protein.