DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach.
DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach.
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DOI：
10.1093/nar/gku411
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发表时间：
2014-07
影响因子：
14.9
通讯作者：
Blundell TL
中科院分区：
文献类型：
--
作者：
Pires DE;Ascher DB;Blundell TL
癌症基因组和其他测序计划正在产生人类和其他基因组中非同义单核苷酸多态性（nsSNPs）的大量数据。为了了解非单核苷酸多态性对蛋白质组结构和功能的影响，指导蛋白质工程，需要精确的硅方法来研究和预测其对蛋白质稳定性的影响。尽管文献中可用的计算方法多种多样，但在需要进行突变分析的所有情况下，没有一种方法被证明是准确可靠的。在这里，我们提出DUET，一个用于研究蛋白质错义突变的综合计算方法的web服务器。DUET在共识预测中整合了两种互补的方法（mCSM和SDM），通过使用支持向量机（SVM）将单独方法的结果结合在优化的预测器中获得。我们证明，与单独的任何一种方法相比，所提出的方法提高了预测的整体准确性，并且表现得与类似方法一样好或更好。DUET web服务器可以在http://structure.bioc.cam.ac.uk/duet上免费和公开地获得。
Cancer genome and other sequencing initiatives are generating extensive data on non-synonymous single nucleotide polymorphisms (nsSNPs) in human and other genomes. In order to understand the impacts of nsSNPs on the structure and function of the proteome, as well as to guide protein engineering, accurate in silicomethodologies are required to study and predict their effects on protein stability. Despite the diversity of available computational methods in the literature, none has proven accurate and dependable on its own under all scenarios where mutation analysis is required. Here we present DUET, a web server for an integrated computational approach to study missense mutations in proteins. DUET consolidates two complementary approaches (mCSM and SDM) in a consensus prediction, obtained by combining the results of the separate methods in an optimized predictor using Support Vector Machines (SVM). We demonstrate that the proposed method improves overall accuracy of the predictions in comparison with either method individually and performs as well as or better than similar methods. The DUET web server is freely and openly available at http://structure.bioc.cam.ac.uk/duet.
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影响因子：
5.8
作者：
Smith, Richard E.;Lovell, Simon C.;Blundell, Tom L.
通讯作者：
Blundell, Tom L.
影响因子：
5.6
作者：
Guerois, R;Nielsen, JE;Serrano, L
通讯作者：
Serrano, L
影响因子：
14.9
作者：
Worth CL;Preissner R;Blundell TL
通讯作者：
Blundell TL
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
5.6
作者：
LEE, B;RICHARDS, FM
通讯作者：
RICHARDS, FM