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首页> 外文期刊>Journal of Clinical Bioinformatics >K-core decomposition of a protein domain co-occurrence network reveals lower cancer mutation rates for interior cores
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K-core decomposition of a protein domain co-occurrence network reveals lower cancer mutation rates for interior cores

机译：蛋白质结构域共现网络的K核心分解显示内部核心的癌症突变率更低

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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.
机译：背景技术目前，网络生物学主要集中于代谢途径，基因调控和蛋白质-蛋白质相互作用网络。尽管这些方法产生了关键信息，但网络分析的替代方法将为生物信息提供新的视角。一个需要探讨的领域是可以使用域共现网络（DCN）捕获的域之间的交互。 DCN可通过代表蛋白质结构域及其在基因中的共存以及将癌症突变映射到各个蛋白质结构域来识别信号，从而研究蛋白质的功能和相互作用。结果构建了基于蛋白质中PFAM结构域的人类蛋白质组结构域共现网络。使用k-core分解技术确定了中央核中的高度连接的域。在这里，我们显示发现这些域比外围域在进化上更保守。卵巢癌，乳腺癌和前列腺癌疾病的体细胞突变是从TCGA数据库获得的。我们将体细胞突变映射到单个蛋白质结构域，并使用本地错误发现率来识别每种癌症类型中显着突变的结构域。发现显着突变的结构域在癌症疾病途径中富集。但是，我们发现DCN的内核不包含任何明显突变的域。我们观察到内核蛋白质结构域是高度保守的，并且这些结构域与其他蛋白质结构域大量共存。结论突变和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框架。这项研究提供了证据，表明DCN内核中的大多数蛋白质结构域具有较低的突变频率，并且存在于k核外围区域的蛋白质结构域对该疾病的贡献更大。这些发现可能会进一步促进药物开发。

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