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首页> 外文会议>ACM symposium on Applied computing >An integrated computational proteomics method to extract protein targets for Fanconi Anemia studies
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An integrated computational proteomics method to extract protein targets for Fanconi Anemia studies

机译：用于提取Fanconi贫血的蛋白质靶标的综合计算蛋白质组学方法

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Fanconi Anemia (FA) is a rare autosomal genetic disease with multiple birth defects and severe childhood complications for its patients. The lack of sequence homology of the entire FA Complementation Group proteins in such as FANCC, FANCG, FANCA makes them extremely difficult to characterize using conventional bioinformatics methods. In this work, we describe how to use computational methods to extract protein targets for FA, using protein interaction data set collected for FANC group C protein (FANCC). We first generated an initial set of 130 FA-interacting proteins as "FANCC seed proteins" by merging an in-house experimental set of FANCC Tandem Affinity Purification (TAP) Pulldown Proteomics data identified from Mass Spectrometry methods with publicly available human FANCC-interacting proteins. Next, we expanded the FANCC seed proteins using a nearest-neighbor method to generate a FANCC protein interaction subnetwork of 948 proteins in 903 protein interactions. We show that this network is statistically significant, with high indices of aggregation and separations. We also show a visualization of the network, support the evidence that many well-connected proteins exists in the network. Further, we developed and applied an interaction network protein scoring algorithm, which allows us to calculate a ranked list of significant FA proteins. Our result has been supporting further biological investigations of disease biologists on our team. We believe our method can be generalized to other disease biology studies with similar problems.
机译：范可尼贫血（FA）是一种罕见的常染色体遗传性疾病，患者患有多种先天缺陷和严重的儿童期并发症。 FANCC，FANCG和FANCA等完整的FA互补组蛋白缺乏序列同源性，这使得使用常规生物信息学方法很难对其进行表征。在这项工作中，我们描述了如何使用计算方法为FANC C组蛋白（FANCC）收集的蛋白相互作用数据集来提取FA的蛋白靶标。我们首先将内部实验组的FANCC串联亲和纯化（TAP）下拉蛋白质组学数据与通过质谱分析方法鉴定的内部实验组与可公开获得的人FANCC相互作用蛋白进行合并，首先生成了130组与FA相互作用的蛋白，即“ FANCC种子蛋白”。 。接下来，我们使用最近邻方法扩展FANCC种子蛋白，以在903个蛋白相互作用中生成948个蛋白的FANCC蛋白相互作用子网络。我们显示该网络具有统计学意义，具有较高的聚集和分离指数。我们还显示了网络的可视化，支持网络中存在许多连接良好的蛋白质的证据。此外，我们开发并应用了一种相互作用网络蛋白评分算法，该算法使我们能够计算出重要FA蛋白的排名列表。我们的结果一直支持我们团队中疾病生物学家的进一步生物学研究。我们相信我们的方法可以推广到其他具有类似问题的疾病生物学研究中。

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