MUPRED: A tool for bridging the gap between template based methods and sequence profile based methods for protein secondary structure prediction
MUPRED: A tool for bridging the gap between template based methods and sequence profile based methods for protein secondary structure prediction
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DOI：
10.1002/prot.21177
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发表时间：
2007-02-15
影响因子：
2.9
通讯作者：
Xu, Dong
中科院分区：
文献类型：
--
作者：
Bondugula, Rajkumar;Xu, Dong
从蛋白质序列中预测二级结构是表征蛋白质结构性质的重要步骤。现有的蛋白质二级结构预测方法大致可分为基于模板的方法和基于序列轮廓的方法。我们提出了一个新的框架，弥合了这两种根本不同的方法之间的差距。我们的框架使用神经网络将来自模糊k近邻算法和特定位置评分矩阵的信息整合在一起。它结合了两种方法的优点，与现有方法相比，具有更好的利用序列和结构数据库中的信息的潜力。我们将该框架实现在一个软件系统MUPRED中。MUPRED已经实现了三态预测精度(Q(3))，范围从79.2到80.14%，取决于所使用的基准数据集。如果查询蛋白与PDB中的模板具有显著的序列同源性(~gt；25%)，则可以获得更高的Q(3)。MUPRED还比现有方法更定量地估计单个残留物水平的预测精度。MUPRED网络服务器和可执行文件可在http://digbio.missouri.edu/mupred.上免费获得蛋白质2007；66：664-670。(C)2006年Wiley-Liss，Inc.
Predicting secondary structures from a protein sequence is an important step for characterizing the structural properties of a protein. Existing methods for protein secondary structure prediction can be broadly classified into template based or sequence profile based methods. We propose a novel framework that bridges the gap between the two fundamentally different approaches. Our framework integrates the information from the fuzzy k-nearest neighbor algorithm and position-specific scoring matrices using a neural network. It combines the strengths of the two methods and has a better potential to use the information in both the sequence and structure databases than existing methods. We implemented the framework into a software system MUPRED. MUPRED has achieved three-state prediction accuracy (Q(3)) ranging from 79.2 to 80.14%, depending on which benchmark dataset is used. A higher Q(3) can be achieved if a query protein has a significant sequence identity (> 25%) to a template in PDB. MUPRED also estimates the prediction accuracy at the individual residue level more quantitatively than existing methods. The MUPRED web server and executables are freely available at http://digbio.missouri.edu/mupred. Proteins 2007; 66:664-670. (c) 2006 Wiley-Liss, Inc.