Cluster analysis of networks generated through homology: automatic identification of important protein communities involved in cancer metastasis.
Cluster analysis of networks generated through homology: automatic identification of important protein communities involved in cancer metastasis.
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通过同源性生成的网络的聚类分析：自动鉴定参与癌症转移的重要蛋白质群落。
DOI：
10.1186/1471-2105-7-2
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发表时间：
2006-01-06
影响因子：
3
通讯作者：
Bates, PA
中科院分区：
文献类型：
--
作者：
Jonsson, PF;Cavanna, T;Zicha, D;Bates, PA
蛋白质-蛋白质相互作用传统上是在小规模上研究的，使用经典的生物化学方法来研究感兴趣的蛋白质。最近，大规模的方法，如双杂交筛选，已被用于调查基因组的广泛部分。目前的高通量方法具有相对较高的错误率，而深入的生物化学研究过于昂贵和耗时，对于广泛的研究来说是不实用的。因此，我们对许多关键生物网络的知识存在空白，而计算方法特别适合这些网络。我们构建了大鼠蛋白质组中假定的蛋白质-蛋白质相互作用的网络或“相互作用组”-大鼠是广泛用于癌症研究的生物体。这是通过整合来自许多物种的实验蛋白质-蛋白质相互作用数据并将该数据转化为大鼠的参考系来实现的。假定的大鼠蛋白质相互作用基于其与实验观察到相互作用的蛋白质的同源性而给出置信度分数。根据实验证据的程度进一步加权置信度得分，为更频繁观察到的相互作用赋予更高的权重。随后验证了评分函数，并围绕关键蛋白质构建了网络，这些蛋白质在具有高转移潜力的大鼠细胞系中被鉴定为高度上调或下调。使用网络上的聚类方法，我们已经确定了参与癌症转移的关键蛋白质社区。这里使用的蛋白质网络生成和随后的网络分析被证明对于突出参与转移的关键蛋白质是有用的。这种方法，结合微阵列表达数据，可以扩展到其他物种，从而提出可能的途径周围的蛋白质的兴趣。
Protein-protein interactions have traditionally been studied on a small scale, using classical biochemical methods to investigate the proteins of interest. More recently large-scale methods, such as two-hybrid screens, have been utilised to survey extensive portions of genomes. Current high-throughput approaches have a relatively high rate of errors, whereas in-depth biochemical studies are too expensive and time-consuming to be practical for extensive studies. As a result, there are gaps in our knowledge of many key biological networks, for which computational approaches are particularly suitable. We constructed networks, or 'interactomes', of putative protein-protein interactions in the rat proteome – the rat being an organism extensively used for cancer studies. This was achieved by integrating experimental protein-protein interaction data from many species and translating this data into the reference frame of the rat. The putative rat protein interactions were given confidence scores based on their homology to proteins that have been experimentally observed to interact. The confidence score was furthermore weighted according to the extent of the experimental evidence, giving a higher weight to more frequently observed interactions. The scoring function was subsequently validated and networks constructed around key proteins, identified as being highly up- or down-regulated in rat cell lines of high metastatic potential. Using clustering methods on the networks, we have identified key protein communities involved in cancer metastasis. The protein network generation and subsequent network analysis used here, were shown to be useful for highlighting key proteins involved in metastasis. This approach, in conjunction with microarray expression data, can be extended to other species, thereby suggesting possible pathways around proteins of interest.
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影响因子：
64.8
作者：
Gavin, AC;Bösche, M;Superti-Furga, G
通讯作者：
Superti-Furga, G
影响因子：
56.9
作者：
de Lichtenberg, U;Jensen, LJ;Bork, P
通讯作者：
Bork, P
影响因子：
4.8
作者：
Ali, S;Nouhi, Z;Ali, S
通讯作者：
Ali, S
DOI：
10.1023/a:1020495201615
发表时间：
2002-01-01
期刊：
Journal of Structural and Functional Genomics
影响因子：
--
作者：
Jansen, Ronald;Lan, Ning;Gerstein, Mark
通讯作者：
Gerstein, Mark
影响因子：
46.9
作者：
Bader, GD;Hogue, CWV
通讯作者：
Hogue, CWV