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首页> 美国卫生研究院文献>Frontiers in Physiology >Research Topic: From structural to molecular systems biology: experimental and computational approaches to unravel mechanisms of kinase activity regulation in cancer and neurodegeneration: Interpretation of the Consequences of Mutations in Protein Kinases: Combined Use of Bioinformatics and Text Mining
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Research Topic: From structural to molecular systems biology: experimental and computational approaches to unravel mechanisms of kinase activity regulation in cancer and neurodegeneration: Interpretation of the Consequences of Mutations in Protein Kinases: Combined Use of Bioinformatics and Text Mining

机译：研究主题：从结构生物学到分子系统生物学：揭示癌症和神经变性中激酶活性调节机制的实验和计算方法：蛋白质激酶突变后果的解释：生物信息学和文本挖掘的结合使用

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摘要

Protein kinases play a crucial role in a plethora of significant physiological functions and a number of mutations in this superfamily have been reported in the literature to disrupt protein structure and/or function. Computational and experimental research aims to discover the mechanistic connection between mutations in protein kinases and disease with the final aim of predicting the consequences of mutations on protein function and the subsequent phenotypic alterations. In this article, we will review the possibilities and limitations of current computational methods for the prediction of the pathogenicity of mutations in the protein kinase superfamily. In particular we will focus on the problem of benchmarking the predictions with independent gold standard datasets. We will propose a pipeline for the curation of mutations automatically extracted from the literature. Since many of these mutations are not included in the databases that are commonly used to train the computational methods to predict the pathogenicity of protein kinase mutations we propose them to build a valuable gold standard dataset in the benchmarking of a number of these predictors. Finally, we will discuss how text mining approaches constitute a powerful tool for the interpretation of the consequences of mutations in the context of disease genome analysis with particular focus on cancer.
机译：蛋白激酶在大量重要的生理功能中起着至关重要的作用，并且已有文献报道该超家族中的许多突变会破坏蛋白的结构和/或功能。计算和实验研究旨在发现蛋白激酶突变与疾病之间的机理联系，最终目的是预测突变对蛋白功能和后续表型改变的影响。在本文中，我们将回顾预测蛋白激酶超家族中突变致病性的当前计算方法的可能性和局限性。特别是，我们将专注于使用独立的黄金标准数据集对预测进行基准测试的问题。我们将提出一个管道，用于管理从文献中自动提取的突变。由于许多此类突变未包含在通常用于训练预测蛋白激酶突变致病性的计算方法的数据库中，因此我们建议在一些此类预测因子的基准测试中建立有价值的金标准数据集。最后，我们将讨论文本挖掘方法如何构成在疾病基因组分析（尤其是癌症）的背景下解释突变后果的强大工具。

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