Sequence-based prediction of pathological mutations
Sequence-based prediction of pathological mutations
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DOI：
10.1002/prot.20252
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发表时间：
2004-12-01
影响因子：
2.9
通讯作者：
de la Cruz, X
中科院分区：
文献类型：
--
作者：
Ferrer-Costa, C;Orozco, M;de la Cruz, X
随着非同义SNPs的发现，氨基酸突变对人类健康影响的评估方法的发展已成为生物医学研究的重要目标。在这种情况下，计算方法构成了一个有价值的工具，因为它们可以很容易地处理大量的突变，并提供有用的，几乎免费的，关于其病理特征的信息。在本文中，我们提出了一种计算方法来预测疾病相关的氨基酸突变，仅使用基于序列的信息（氨基酸特性，进化信息，二级结构和可访问性预测，和数据库注释）和神经网络，作为模型构建工具。预测突变是病理性的或中性的。我们的研究结果表明，该方法具有良好的整体成功率，83%，在针对特定蛋白质进行训练时可以达到95%。该方法是快速和灵活的，足以提供很好的估计的病理特征的大集合的非同义SNP，但也可以很容易地适应，以提供更精确的预测蛋白质的特殊生物医学利益。(C)2004 Wiley-Liss，Inc.
The development of methods to assess the impact of amino acid mutations on human health has become an important goal in biomedical research, due to the growing number of nonsynonymous SNPs identified. Within this context, computational methods constitute a valuable tool, because they can easily process large amounts of mutations and give useful, almost cost-free, information on their pathological character. In this paper we present a computational approach to the prediction of disease-associated amino acid mutations, using only sequence-based information (amino acid properties, evolutionary information, secondary structure and accessibility predictions, and database annotations) and neural networks, as a model building tool. Mutations are predicted to be either pathological or neutral. Our results show that the method has a good overall success rate, 83%, that can reach 95% when trained for specific proteins. The methodology is fast and flexible enough to provide good estimates of the pathological character of large sets of nonsynonymous SNPs, but can also be easily adapted to give more precise predictions for proteins of special biomedical interest. (C) 2004 Wiley-Liss, Inc.