K-core decomposition of a protein domain co-occurrence network reveals lower cancer mutation rates for interior cores.

Journal of clinical bioinformatics Pub Date : 2015-03-03 eCollection Date: 2015-01-01 DOI:10.1186/s13336-015-0016-6
Arnold I Emerson, Simeon Andrews, Ikhlak Ahmed, Thasni Ka Azis, Joel A Malek
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引用次数: 10

Abstract

Background: Network biology currently focuses primarily on metabolic pathways, gene regulatory, and protein-protein interaction networks. While these approaches have yielded critical information, alternative methods to network analysis will offer new perspectives on biological information. A little explored area is the interactions between domains that can be captured using domain co-occurrence networks (DCN). A DCN can be used to study the function and interaction of proteins by representing protein domains and their co-existence in genes and by mapping cancer mutations to the individual protein domains to identify signals.

Results: The domain co-occurrence network was constructed for the human proteome based on PFAM domains in proteins. Highly connected domains in the central cores were identified using the k-core decomposition technique. Here we show that these domains were found to be more evolutionarily conserved than the peripheral domains. The somatic mutations for ovarian, breast and prostate cancer diseases were obtained from the TCGA database. We mapped the somatic mutations to the individual protein domains and the local false discovery rate was used to identify significantly mutated domains in each cancer type. Significantly mutated domains were found to be enriched in cancer disease pathways. However, we found that the inner cores of the DCN did not contain any of the significantly mutated domains. We observed that the inner core protein domains are highly conserved and these domains co-exist in large numbers with other protein domains.

Conclusion: Mutations and domain co-occurrence networks provide a framework for understanding hierarchal designs in protein function from a network perspective. This study provides evidence that a majority of protein domains in the inner core of the DCN have a lower mutation frequency and that protein domains present in the peripheral regions of the k-core contribute more heavily to the disease. These findings may contribute further to drug development.

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蛋白质结构域共发生网络的k核心分解揭示了内部核心较低的癌症突变率。
背景:网络生物学目前主要关注代谢途径、基因调控和蛋白质-蛋白质相互作用网络。虽然这些方法已经产生了重要的信息,但网络分析的替代方法将为生物信息提供新的视角。一个很少被探索的领域是可以使用域共现网络(DCN)捕获的域之间的相互作用。DCN可用于研究蛋白质的功能和相互作用,通过表示蛋白质结构域及其在基因中的共存,并通过将癌症突变映射到单个蛋白质结构域来识别信号。结果:构建了基于PFAM结构域的人类蛋白质组结构域共现网络。利用k核分解技术确定了中心核中的高连接结构域。在这里,我们发现这些结构域比外周结构域更具有进化保守性。从TCGA数据库中获得卵巢癌、乳腺癌和前列腺癌的体细胞突变。我们将体细胞突变映射到单个蛋白质结构域,并使用局部错误发现率来识别每种癌症类型中的显著突变结构域。发现在癌症疾病途径中富集了显著突变的结构域。然而,我们发现DCN的内核不包含任何显著突变的结构域。我们观察到内核蛋白结构域是高度保守的,并且这些结构域与其他蛋白结构域大量共存。结论:突变和结构域共现网络为从网络角度理解蛋白质功能的层次设计提供了一个框架。该研究提供的证据表明,DCN内核的大多数蛋白质结构域具有较低的突变频率,而k核外周区域存在的蛋白质结构域对该疾病的贡献更大。这些发现可能有助于进一步的药物开发。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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